RTA_Analysis.ipynb

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lSljFFTw8PNSjRw+dPHlSgwcPprgCAEvRokU1ffp0HT58WPXr17e2//jjjypTpozTza14NMYYjRw5UrVr17aKK6RKlUoTJkzQ4cOH1blzZ8YIAeA+KVKk0Ntvv60jR45o/PjxSpkypSTp/Pnzql69usaNG2dzhAlP5JWamSsIAABcycWLFxUeHi6JdhAQE+7ABAAggbh69aqqVaumX3/9VZLk7u6ujz76SLt27VKlSpXsDQ7AE0uVKpVGjx6trVu3Km/evJIi/t3Xrl1bc+fOtTm6hGn27NmqW7eurl+/LknKly+fduzYoc8//1z+/v72Bgcg3sqbN68WLVqkWbNmKUWKFJKkgwcPqly5cjp8+LDN0SU8xhh1795dffv2tW7OqFevng4dOqTOnTsrSZIkNkcIIL6qVq2a9uzZo379+lmrmc+dO1c1a9a02neIvevXr6tmzZr65ZdfJElubm7q27ev9uzZo2rVqtkcHQBXVL16de3Zs0d9+vSxts2ZM0e1atXSjRs3bIwsYbp+/bpq1KihefPmSYrI8/3799fu3btVtWpVm6MDEF8lSZJEXbp00eHDh61ik8YY9e7dWz179qTIwmM4dOiQypUrp0OHDkmKuCnihx9+0MKFC1mJHkCM/P39NWrUKG3fvl3PP/+8JOnatWuqU6eO5syZY3N0CdOcOXNUp04dXbt2TZL0/PPPa/v27Ro1ahRjhABilCdPHv3666/68ccfrTFCR/vu4MGDNkeX8Bhj1LNnT/Xu3du6tqhTp44OHTqkLl26MEYIAA/h6empd955R4cOHVKNGjUkSeHh4XrvvffUv39/+m0eQVBQkPWYGwsBAIAroR0EPBwFFgAASABu3bql2rVra+vWrZIiJmQtXbpUn3zyiTw9PW2ODsDTVKJECe3cudNaGeHu3btq2rSpFi1aZHNkCcuCBQvUokULa8XGhg0baseOHawOBiBW3Nzc1KJFC+3YscMqehMUFKQqVaroxIkTNkeXsPTu3dtpBYVBgwZp4cKFypAhg41RAUgovLy8NGTIEC1atMhaxWrTpk2qV68eK5w/guDgYNWvX18bN26UFLFi8aJFizR06FB5eXnZHB0AV+bt7a1hw4Zp4cKFSpYsmSRpw4YNql+/vu7cuWNzdAlHcHCw6tatq82bN0uSkidPriVLluizzz4jzwOIlQwZMmjhwoX65JNPrG1jxoxR3759bYwq4Tlx4oSqVKmi8+fPS4q4mXfnzp1q3ry5VTQOAB6kWLFi2rFjh1555RVJ0r1799S8eXNrAQbEzsKFC9W8eXNrjPCVV17Rjh07VKxYMZsjA5AQuLm5qVmzZtq5c6dV9Ob8+fOqWrWqjh8/bnN0CUu/fv00ZswY6/knn3yiRYsWKWPGjDZGBQAJT6ZMmbR06VL179/f2jZ06FB9/PHH9gWVwAQGBlqPAwICbIwEAAAgbtEOAh6OAgsAAMRz4eHhatWqlVVcIV26dNq4caNVlRZA4pM8eXLNmzdP7du3lySFhYWpadOm2r17t82RJQw7duxQ8+bNFR4eLkl644039Msvv1g35QFAbOXNm1ebN2+2Jl6eP39ederU0dWrV+0NLIH4+uuv9fnnn0uKmJA2depUDRgwgJsKADyyunXrat26dUqdOrUkaePGjWrXrh0rs8SCMUbt27fXhg0bJEmpU6fWunXrrBWKASA+qF+/vtatW6dUqVJJktavX6///e9/5PlYMMaoTZs22rRpkyQpTZo0Wr9+vWrXrm1zZAASGnd3d3300UeaPHmydd0+YsQIffPNNzZHljBcuXJFtWvX1oULFyRJxYsX1+bNm5UnTx6bIwOQ0DjGCDt06CApYoywWbNm2rVrl82RJQy7du1Ss2bNFBYWJkn63//+p3nz5il58uQ2RwYgocmTJ482b96sEiVKSJIuXLigOnXq6MqVKzZHljB8++23Gj58uKSIMcLJkyfro48+krs7U9YB4HG4u7vrs88+01dffWVtGzRokKZNm2ZjVAkHKzcDAABXRTsIeDh6qwAAiOdGjhypBQsWSJJSpEihlStXqnDhwvYGBeCZS5IkiSZPnqxmzZpJiliNsFGjRrp27ZrNkcVvV65cUePGja2VLlu1aqVvv/1WHh4eNkcGIKFKmzatVqxYofz580uSjh49qvbt23Oz10Ns27ZN7777rvX8m2++sQoHAcDjKF68uJYvX26tcP7zzz9r3LhxNkcV/40bN06zZ8+WJCVLlkzLly9X8eLFbY4KAKIqUaKEU57/8ccf9cUXX9gcVfw3ZswY/fLLL5IkPz8/rVixQi+++KLNUQFIyDp06KCvv/7aet6tWzft2LHDxojiP2OM2rVrp2PHjkmSChYsqBUrVihNmjQ2RwYgofLw8NDEiRPVokULSdKdO3fUqFEjCv8+xNWrV9WoUSMFBwdLklq0aKFJkyYxRgjgsaVJk0bLly9XwYIFJUnHjh2j8G8s7NixQ++88471/KuvvrIKBwEAnsxbb72lMWPGWM/ffPNN7du3z8aIEgZWbgYAAK6KdhDwcBRYAAAgHtu7d68+/PBDSREVvWfPnq0iRYrYHBWAuOLu7q7p06erdOnSkqRTp0453ayKqLp27aozZ85IksqVK6cpU6awCgKAJ5YmTRotWbLEWjl9wYIFrATwAMHBwWrVqpXu3bsnSerZs6c6depkc1QAEoMSJUrohx9+sJ736dNHhw4dsjGi+O3QoUPq06eP9XzWrFnWimsAEB+VLFlSM2fOtJ737t1bhw8ftjGi+O3AgQPq27ev9fzHH39UsWLFbIwIQGLRpUsXde/eXZJ09+5dtWrVyrpZFVFNmTJFCxculBTRh7R48WKrDwkAHpe7u7umTp2qsmXLSpJOnz7tdLMqourWrZtOnz4tSSpTpoymTp3KGCGAJ5Y6dWotXrzYKp61cOFCTZkyxeao4i/HGOHdu3clSd27d9ebb75pc1QAkLi89957Vm4NCQlRq1atFBoaanNU8VvkGwtZuRkAALgS2kHAwzGKAABAPBUeHq4uXbpYN6b169dPtWrVsjkqAHHN29tbs2fPVooUKSRJ3333ndauXWtzVPHTypUrrRvu/P39NXv2bHl5edkcFYDEImfOnE5FFXr16qXLly/bGFH8NWTIEB0/flySVLp0aQ0dOtTmiAAkJq+88op69uwpSQoNDVWXLl1YMSwaxhh16dLFmlDVo0cPNWjQwOaoAODhGjZsqPfee09SxOTQN998kzwfDUeed9yw8P7776tevXo2RwUgMRk+fLhKlSolSTp69KiGDRtmc0Tx06VLl/T+++9bz6dPn64cOXLYFxCARMUxRpgyZUpJ0syZM7V69Wqbo4qf1qxZoxkzZkiSUqZMqZ9//lne3t42RwUgsciRI4emT59uPX///fd16dIl+wKKx4YNG6ajR49KiiikOXz4cJsjAoDEacyYMSpatKikiEK8o0aNsjegeC4oKEhSRCG79OnT2xwNAABA3HG0gyQKLAAxocACAADx1OzZs7Vt2zZJUv78+TVw4ECbIwJgl+zZs+vzzz+3nvfs2VPh4eE2RhT/hIeHWzfaSdLo0aOVJUsWGyMCkBi98soratmypSTpypUr+vTTT22OKP45d+6cdc7y9PTUtGnT5OnpaXNUABKbTz/9VHny5JEkbdy4UfPnz7c5ovhnwYIF2rhxoyTpueee45wFIEEZMmSIcuXKJUlav369tSo4/jN37lxt3rxZkpQ3b14NGjTI5ogAJDaOa/okSZJIkkaMGOG0yg0iDB48WFevXpUktW7dmmI3AJ66rFmzOt0sxRhhVOHh4erRo4f1fNSoUcqaNauNEQFIjOrVq6c2bdpIkq5evcp1eDQCAwM1cuRISVKSJEk0ffp0xggB4Bnx9vbWtGnT5O4ecSvQkCFDKP7zAI4+rQwZMsjDw8PmaAAAAOKOox1EoSkgZhRYAAAgHgoPD9fgwYOt52PHjmUVdsDFdejQQS+++KIkac+ePVq8eLHNEcUv8+fP1x9//CEpYiWEtm3b2hwRgMRq5MiRSpo0qSTpm2++0fnz522OKH4ZMWKE7ty5I0nq1q2b8ufPb3NEABIjHx8fp5sLBg0axOrmkRhj9Mknn1jPR40aJV9fXxsjAoBH4+vr65TnP/nkE/J8JMYYpxs5Ro8eLR8fHxsjApBYFShQQO+8844k6c6dO9bNUogQFBSkb7/9VpKULFkyjRgxwuaIACRW7du3V/HixSVJ+/bt06+//mpzRPHLwoULtW/fPklS8eLF1b59e5sjApBYDR8+XMmSJZMkTZw40WkVSESMoQYHB0uSunbtqgIFCtgcEQAkbkWLFlXnzp0lSTdv3tTo0aNtjih+CgsL04ULFySxajMAAHA9jr4LCk0BMaPAAgAA8dCKFSt0+PBhSdJLL72k6tWr2xwRALu5u7s73SQ1ZswYG6OJf8aOHWs9HjRokFWhGwCetoCAAL355puSpJCQEH3zzTc2RxR/XL9+XVOmTJEkJU2aVH369LE5IgCJWb169VSyZElJETcXrFu3zt6A4pF169ZZNxaULFlS9evXtzkiAHh0DRo0sG4i27NnjzZs2GBzRPHHmjVrrCKTpUuXVp06dWyOCEBi1rdvX6tY1+TJk3Xjxg2bI4o/JkyYoNDQUEnS22+/rYwZM9ocEYDEyt3d3anAVuQxMTiPmTJGCOBZypgxo95++21JUmhoqCZMmGBzRPHHjRs3NHnyZEkRhTP79etnc0QA4Bo++OADa+G2b775Rrdv37Y5ovjn4sWLCg8PlxQx3wcAAMBVhIWFWQvI0Q4CYsaIAgAA8ZDjxjRJ6tGjh9zc3GyMBkB8UbduXeXNm1dSxE1TJ06csDmi+OHo0aPatGmTpIhV1WrWrGlzRAASu3fffdeapDlt2jRrMNbVzZ49W7du3ZIktWnTRunSpbM5IgCJmZubm3r06GE9j3wd7eqmTp1qPaZPAUBCdX+ej5zbXB19xwDiUvr06dW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99VWrkMKsWbMUFRWlPHnyKDo6OllbmVbnz59X165d1a9fP23evFndu3fX+PHjXYqov/LKK/rggw/01ltvadu2bfrwww81bNgwa6w/NjZW1atX1/z58zV27Fjt3LlTn3/+uZYtW6Zq1arp6tWrkhKLprVq1UqlSpXSn3/+qZkzZ2rRokX6/fffXTLdynEdNWqUatSooZ07d+qxxx67rWMAwPPVq1dPf/75p3766Sft27dPU6dO1YIFC6znE5K6/px9ypQp6t27t2rXrq0///xTH330kfr06ZOsyMDN7lWmNlZxO252LXDmzBlVrlxZ//zzj37++Wf9+eefKlmypJ566inrwe209CWS1KNHD02ZMkUffPCBNm/erJdfftnlfqZT06ZNNWbMGL388svatm2bxo8frzlz5qh+/fouD6NLiZO7Jk2apKVLl97W7w/A+2TKlEnTp0/X4sWLNXHiREnSiRMn1LNnT7Vv315NmjS54fevWbNGv//+u3766SctXLhQR44cUevWrfXaa69p2LBh+uuvvxQREaF27dole8biZs+q9OrVS6NHj9awYcO0b98+/f7770pISFCdOnWSPdvhdKvPy7Rp00Zt2rTRhg0b9NFHH+nbb791mcg8fPhwdevWTR06dNDmzZs1YcIEfffdd2revLm1D+00gLshLi5Oy5Ytu+mYyvWFyZzSet/uVtrajz76SJcuXdL8+fO1ZMkSXb58Wc2bN9eVK1ckJU6g9Pf3V+3atVPNmzdvXgUGBlrvu3btao1fb9u2Tb/99pvi4uJUrVq1NI3l38zFixf1xhtvaMSIEdqwYYPy5cunF154Qa1atVKrVq20YcMGNW3aVAMHDrTO551uNjbvFBISok8++URz5sxJNqnyerdy/XQ7963T8qzjF198od69e+uVV17Rtm3btHbtWpUvX16NGjW6aREjwB3ExcVZRUmYjAl3lHT+0a3cU07L/YLmzZtr/vz5+vzzz7Vhwwb93//9n7p27eryOWm9Z+DUvXt3vfbaa9q0aZOioqL0yiuvqFGjRqpQoYLWr1+vPn366NNPP01xIYObSesYmZTyc0RpudZ6//33NWLECA0ZMkQ7duzQkiVLFBkZqerVq+vo0aMpzqP6999/tWvXLusZs5T4+PjovvvuS/XrM2bMUKdOndSkSRNt27ZNf/75pypXrqymTZtq2bJl1n716tVTTEyMfvnlF+3Zs0eff/655s+fb51T7N69W40aNdIjjzyiVatW6e+//9Yrr7yi/v37W3O+kmratKnWrVsnSerbt6+io6NVvnz5ZPtxrweeICYmxiooHxkZaXMawIMYAMnkzp3bSDK5c+f2qM92B23atDG+vr7Jtl+4cMFMmTLFBAYGmrZt21rbr1y5Ynbs2GHOnDnjsn+ZMmXM448/bowx5uTJk8bHx8dMnTrVGGPMtWvXTKZMmczQoUNNUFCQuXz5sjHGmFmzZhlJ5tChQylmmzRpkpFkli1b5rK9UaNGRpJZvny5lXXbtm3m0qVLLvtlz57dNG3a1Hrfvn17c30zeujQIbN//36XbWPHjjWSzPbt21PMlR7wb+bu+Oeff8ypU6dc3ksykkyTJk1sTJb+pNZ2OV27ds3s3r3bHD9+3GX7s88+a3LmzGm9f/vtt40kM3r0aJf9XnjhBePv72/Onz9vrl27ZjJnzmzq1q3rss+RI0dMQECAef31140xxixevNhIMtOmTTPGGLN//34jyQQEBJjQ0NAU/3z22WfW5+XPn99kzJjRpX1dv369S77Zs2cbSeb33393ydKjRw8zfPhwY4wxXbp0MYGBgS5/F40x5pVXXjE+Pj7m6NGjxhhj8uXLZ6pVq+ayz8aNG40kU716dWOMMUePHjW+vr6mS5cuLvv9+eefRpIZP368McaYyZMnG0mmf//+BndW/vz5jSQTERFhdxRbbNmyxcTHx1vve/ToYbWra9eutTGZ9/riiy9MRESE9f8hd+7cpkWLFuabb74xsbGxxpjk7aGzjXj33Xetzzl27JiRlKwdKlq0qHn66aet986//9euXbO27du3z0gyr732msvnO88Nu3XrZvz8/Mzhw4ddPrtBgwYmd+7cJiEhIcXvc/68Bx54wNrHGGPq1q1rJJmDBw9a2wYPHmwkmbNnzxpjEvslSWbOnDkuP7Ny5comMjLS5fPbt2/vss/y5cuNJDN58mRjzP/OH95++21rn7T+Tvnz5zfBwcHm/PnzBvdWz549rX8Xq1evtjvOPbdnzx5z4cIF672zHZBk+vXrZ2My70V7nYj2GtdbsmSJ9e+ib9++dse5506dOmX++ecfl21hYWFGkilSpIhNqXC9o0ePGknmzTffvOm+OXPmNA0aNEj1urxSpUqmUKFCLu3liRMnjL+/vylUqJC1LX/+/NZYgDGJ7WDBggVd2vXvvvvOpQ1NyzixMcnHcJxjMSEhIamOlzjPK5x9VevWrV1+Rrdu3Ywkqx1t1KiRyZs3r8vvuX37dtOiRQvzxx9/mNjYWBMWFpZsfC42NtZERESYJ5980hhjzKJFi4wka1zbyXmu5+wzP/rooxSvS7t3724CAwPNuXPnjDGJ/Ysks3fvXgP38ffff1t9Qbt27eyOc8+dO3fO7Nu3z2Vb4cKFjSSTNWtWm1Klb3eyXa9fv77Jli2b9f7hhx82Dz30kEv7d+nSJZMjRw7TokULa1v+/PmNr69vqu2uj4+PS79Qt25dExkZaV07ODVu3NiEhYVZ/UP16tVN/vz5Xfa52c9q1aqVta/z/HzhwoXWtoSEBJMpUybTuHFjY8z/+q033njD5eeMGzfOdO7c2cTHx5sZM2YYSebnn3922WfOnDlGkpk5c6YxxpjnnnvOhIaGutxPvHr1qsmRI4dLX5WW4+oc/4+KijK4ex544AEjyWTJksXuKLbYsmWLiYuLs96/+uqrVh92/f1z3DvO81nntVVCQoLZt29fsrGAV1991fj4+Fjntqm17RUqVDAFChRwuReybds243A4rLY5rfcqUxqrcJ5TBwYGpto2z50719o/LdcCI0aMMJLM7t27rX3i4uJMq1atrHPmtPQlly5dMkFBQeb555932cd5P9Q5NuO8Xzp48GCX/b7//nsjyfz2228u/28+//xzg3urb9++Vvu0ZMkSu+Pcc3v37nUZ51u1apV1PHr16mVjMu91s+dJjEl53HnAgAEmLCzMHD582DRr1sw88MADKd77cLZ1xiSe/4aFhbns16VLl2T9tfOcddOmTcaYtD2rYowx0dHRZteuXS77/PLLL0aS1X47z00HDRpkjLn152Wubzcffvhh8+ijj1qflT17dtOyZUuXfb777jvTpk0bc/bsWdppD7J69WqrferZs6fdce65mJiYZM+p5siRwzr/gfu5lTEVY27/XuSttLXXjwM4x4q3bNlijDGmTp06Jk+ePGn+HY8cOWJ8fHxM7969XbafOnXK+Pv7mxdffNEYk/p93aT9mDHJn5F2jr2sWLHC2ubskwYOHGhtc94THjlypMvPu9nYvDOHU7NmzUxERIQ5ffq0tS3p/dVbvX661fvWaX3WsUuXLiZjxowu1ysJCQlm5cqVJiYmxsD9dOrUyerDNm7caHece2737t3m4sWL1vvDhw9bx6Nhw4b2BYPXupX5R7dyT/lm9wuczyEl7cOM+d9YlfOaKK33DJzXKNOnT7f2Wbt2rZHkMl4VHx9vQkNDXa7xr7+/npq03m9J6TmitF5r1a1b1xQtWtRln8uXL5vVq1dbfev15whr1qwxkszEiRNv+js4XT8ueubMGbN582aXcc2LFy+6PI/j7L8//PBDl8/av3+/2bp1qzHmf/+/1qxZ47LPxo0brfP36899rr8ONib530vu9cATbN682aufXQBul58AJPPvv/+6/Be3Jj4+XhkyZHDZdunSJeXIkUM9e/bUoEGDrO2BgYHavXu3Xn75ZW3ZskWnT5+WMUaXL19WwYIFJUnZsmVT2bJltXjxYrVu3Vrr1q3T1atX1blzZw0aNEirVq1StWrVtHDhQhUvXjzVlROcqzI8+uijLtsrVaqk2bNnW+9DQ0P1559/qlu3btq5c6fOnTsnY4wuXbqkkydP3vB3T0hI0EcffaQFCxbo2LFjunbtmuLi4iTppt+LlF2+fFlSYoW7PHny2Jzm3rpw4YLOnj2rokWL6vnnn1eZMmXsjuS1/Pz8dOzYMb322mtav369Tp06pYSEBF29ejVZdXxJioqKcnlfqlQpTZw4Ubt375afn5/OnDmTrLJ0ZGSkSpQoofXr198wy4svvpisOqZT9uzZXd4XK1ZMYWFh1vscOXJIkk6dOiUpceUDSXr88cddvi9phca1a9fqoYceUtasWV32qVChgj7++GP99ddfKl++vA4dOqSmTZu67FOyZEllypTJer9u3TrFx8cn+91Lly6t8PBwrV+/3mUVmbJly6b4e+K/O3HihNe1qZJ0/PhxhYSE6JlnnlGDBg2sKo2wz3PPPafmzZtr6dKlWrp0qVasWKHvv/9eX331lR5++GGXVRavl7Syq7P9K126tMs+2bNnT7YCwBNPPCE/v/9dChcsWFDZsmXTvn37Uvw5q1at0sMPP6zcuXO7bK9du7bmzJmjo0eP3rDSZ+nSpeVwOFwyhYeHK1++fMnynz592qXdrFSpUrLfeenSpbpy5YqCgoJS/Zk3cyu/U7FixZKd2+Peeuqpp264gkZ6dOnSJV24cEE1atRQgwYNlC1bNrsjeT3aa9f8tNdIydixYzV9+nS7Y9xTV69e1cmTJ1WwYEG1bt06xRXkYD9n25aQkHBL+0vJr8s3b96smjVruuwTHh6uokWL6uLFizf83LJly7q069ePUaRlnPhG5s+fr4iIiBS/FhIS4vL++hUnkmbJkCGD1qxZo/Lly7v8nkWKFNGXX34pSdqyZYu1um9S/v7+Klu2rDXecqPx6BEjRljvV61apcyZMyc73rVr19bo0aO1detWPfHEE5ISx8pvtLIH7DVjxowbnhelR9euXdPx48eVJ08etWzZUg0aNKAvuMv+S7ueUvvnbIfPnz+vrVu3qmfPni7fExwcbK1untTjjz+uL774IsWf2bJlS+tzpcQx5ipVqsjf399lvwoVKuj777/X7t27VbRo0VR/hxv9rJTOgZ1tppT4+4eHh1t5/vzzT127di3ZmHjSVX3Xrl0rKfl5foUKFSRJf/zxh5o0aaLNmzerWLFiCg4OtvYJCAhQuXLl9Msvv0i69ePKmPi9cfbsWa8cHz9x4oSCgoL09NNPMz7uxhwOhy5cuKCPP/5YK1as0IkTJxQfH6/Y2FglJCTo9OnTCg0Ntfa/vt3Ys2ePoqKi5OPjY20rVqyYChcurPj4eEnS9u3b/9O9Skl677331LBhwxS/dv1qlze7FlizZo2yZs3qslKwr6+vpk2bZr1PS18SGxurK1eupHj+nZRzFd3rf3/ntc769etVs2ZNl/ywT5MmTVxWZPYGly9f1rlz51S9enU1aNCA1ebcRErPwjk1adJEVapUSbb9zTff1G+//aaaNWtq7969WrVqlUsbnpqiRYu67JfSuHrSseqkbvSsyiOPPCJ/f399/vnn+uWXX3TkyBFdvXrV6h9Se67tVp+XSem6wzmuv3fvXp04cSLZ+Xjjxo3VuHFjSbTTnmrixIk3XWU+vYmNjdWJEyeUP39+xsc9hPO6/Hb/P6X1vt2ttLU3Gqt2Zr6VvOvXr1dCQkKyc+CsWbOqaNGi+uOPP9L8WTeS9Jz7Vvqpm43NX2/EiBEqWrSo+vTpo/Hjxyf7+q1eP93qfeu0PuvYsGFDTZgwQeXKlVO7du1UuXJlFS9ePMXVr+F+atWqlexaM727ePGiLl68qNq1a6tBgwYu7dr11/TAvZLW+Ue3ek/5RvcLbnQvN6m03jNwull/4+Pjo2zZsiXrJ5csWZLqdefIkSPVvn37W7rfcv1zRGm91mrcuLFeeOEFVa1aVS1btlTlypVVuHBhlStXLsVs0n8/z5IS7/csWbJEHTt21P79+3XhwgXra85zqOzZs6tSpUoaMGCA/vnnH9WuXVuVKlVSgQIFrH0rVqyoiIgIPfPMM+rYsaOqVq2qcuXKqWTJkredjXs98BRHjhyxXtOnA2lHgQUAd5yvr682btxovXfe+IuKitKHH37osu+cOXP01FNPqXbt2po8ebLy5MkjX1/fZA9f1a1bV5MnT5Yk/f777ypfvrwyZMigqKgoLVq0SNWqVdOCBQv09NNPp5rr3LlzcjgcyR6oTTopQpLGjBmj7t27q0WLFnrnnXeUM2dO+fj4pHgzLKlLly6pUqVKunz5soYOHarSpUsrODhYs2bNUt++fW/4vUjd+fPnJSU+LOitRU+2b9+uV199VeHh4XZH8VobNmxQlSpVVKpUKY0ZM0b33Xef/P391bdvX/3444/J9s+SJYvL+4wZM0qSy2SDvn376o033nDZ78qVKypSpMgNs1z/gNGNXD/Qcv0ARkxMTIr7JXX27FmXSWVOzrbz3LlzOnfuXKqfk7SNPXv2rCSpWbNm8vX1ddnv8uXLyf6NZ86cOdVc+G/i4+O9tk09e/asJk+erClTpjB44Cb8/f1Vo0YN1ahRQ1Li/6OPP/5YgwYN0uuvv642bdqk+H1Jb4I627frH0pK6WZzSm1LhgwZkg1YO509e1b79+9P1sY5b4D/+++/N3y4LaVMKW2TXAeYHQ5HsvNUZ4bTp0//pwfqbuV3oi2234kTJ+yOYJu5c+dq7ty5qU6WxL1Fe017jRu7cOGCyw1eb7J//34NHDhQw4cP19WrV+2Og+uEh4crY8aMqRaocbpw4YJOnDjhMnn/+rbl3LlzqV7736zAws3GKNI6TpyaAgUKpHmiZFrGS5xjOSlxjm8kLWrplClTJmucJLXxkuv7jbNnz+rs2bPJ9nNOnk56/U57794uXbqkS5cu2R3DFocPH9bgwYM1atQor3sY9V77L+16au2f9L+2bezYsZowYYLLfrGxsckK/wUHB6c6Tp204IDzs1NrM6X/tZepudHPup6vr2+yn5/0esM5Jn6zdt7hcCTb5/q8586dU/78+ZN9f0pj4mk9rrTz94Y333OMjY3VF198oS+++CLZpCC4h3///VcVK1ZUjhw59OGHH6po0aIKDAzUqFGjNHr06GT7X99unDp1KsU2Lnv27Dp69Kik/7VNt3uvUpJy5sx5R+9X3qhddma+WV/iLBqSlvNvScmKqjkzcb/SvXjzQibz58/X/PnzlTNnTrujQMmfhUsqY8aMmjt3borf0717dzVv3lyVKlVKNmknNamNS6c01n79uPrNnlWpU6eOtm3bpiFDhigqKkqhoaFau3atWrVqlWqeW31eJqV2/1bPxyXaaU/jzePjBw8e1LvvvqsRI0ZY92/gnrJly6bQ0FDt3r37tr4/rfftbqWtvdm5cv78+fXbb7/pwoULaSq0frOx6ztxbuXr6+tSYP5W+qmb/b7Xy5kzp4YMGaKOHTuqdevWyQoJ3er1063et07rs461a9fWqlWrNHLkSA0cONBa+OiVV15Rz549U/zd4D6OHz9udwTb/Pzzz/r5559dnsmhwBvsktb5R7dyT/lm9wtu5V5uWu4ZON3uc1I3KjbtLEp0K/dbrr9WSuu1Vvv27ZU/f3598skn6t27t86ePasHHnhAb731llq2bJliPue9kts9z5ISxyo//vhj9ejRQyNGjFDWrFnlcDhUuHBhax+Hw6G5c+dq3Lhx+vbbbzVu3Dj5+PioTp06GjZsmAoXLqxcuXLpjz/+0PDhwzV9+nQNGDBAmTJlUsuWLTVkyJDbWryGez3wFNHR0dZr+nQg7SiwAOCuuP5m+tChQ9WhQwd98803atasmbX9iy++UIYMGTR79myXQbczZ864fH/dunX19ttv68CBA/rtt99Ur149SVLVqlX1/fff699//9XOnTut7SnJkCGDjDHJVpB03rxxmjJligoXLqzp06e7rAR0/X7XW7RokQ4dOqTp06e7XDwwcP7fZMyYUTExMfLx8fG6k7wLFy7oypUrevLJJ9WnTx9FRkam+LAe7r4vv/xScXFxmj17tssDZ6ndHHQWBrl+v0yZMlkrtLz++utq0aJFsu+9lw8gOwdczpw5k+xGu1PmzJlTnMTmbKczZ85sDTZc/wC5Mcble7NmzSopsZBN5cqVk33m9QNZuHt8fX29crLqyZMnVapUKXXq1En16tXT+++/r1GjRtkdy2vFxcXp8uXLyQaew8LCNHDgQM2ZM0d//vlnqhN2b9f1bbRzW7Zs2VLcP2vWrAoNDdX333+f4tfv1oPIxhhdvHjRZXDdmd3Znjr3Syql3+96dv1OuD3Zs2dPNgif3l2+fFnh4eFq27atGjRooBMnTqhatWp2x/JatNc3RnsNpwwZMqR4Izs9u3r1qs6ePasaNWro5ZdfVqVKlZQjRw5WwnUzPj4+qlGjhubNm5esvUrq559/VkJCgurWretS1T6pDBkypDh5/GbjpmmR1nHieyFHjhypFvSR/vdARGrjJc6vpzZecv3xypo1q7Jmzao1a9ak+POYTOM5QkJCUh1jS6+uXbummJgYVa1aVS+99JKqVaumkiVL3nTCPG7fnWzXk8qSJYscDofatm2rPn363NHMaRljvlecY+I3a+eNMckeVLw+b1r6xbt5XHH7vPGeo5Q4Pl6iRAl16tRJTz75pIYNG6ahQ4faHQvX+eGHH3Tu3Dn9+uuv1ip4ktJczC4wMDDFtinpRCrneIU73KuUEtvm1M6FndLSlzh/77Scf0vSrFmzVKhQoWSfef1D9LBXeHi4AgMD7Y5xT125ckVZs2ZVmzZt1KBBA124cMGlPYB90lpYxunMmTPq06eP6tevr3nz5mnKlClq27bt3Qn3/93oWZWtW7dq/fr1evfdd9WtWzdrn5utZn6rz8vcSFrOx2mnPZM3jo/Hxsbq9OnTqlGjhnr16qXKlSsrX758unz5st3RkApfX19Vq1ZN8+bN0/nz51Mt9jJ16lSVL18+2fa03Le73bY2NTVr1tSnn36qWbNm6bnnnktxn40bN+rAgQNq2LBhmseur5daoYO03M+829q3b69p06apY8eOyYod/dfrp5u5lWcdy5Qpo+nTp8sYo40bN2rMmDHq1auXsmTJcsfvo+POypEjh9cVDb548aIiIiLUrl071a9fX0uXLlXXrl0lsdo17JWW+Ud38p5yWu/lpvWewX+VlmLT/+V+y61cazkX4ImPj9fatWs1ePBgtWrVSnny5EmxT8yZM6eKFy+uWbNm6f33309WmMhp3LhxatSoUYrj81OmTFGNGjU0cuRIa1vSyeJOISEheuWVV/TKK6/ozJkz+uWXX9S3b1/Vrl1be/fulcPhUJ48efTxxx/r448/1qFDh/TVV19pwIABunz5srXo763gXg88RdJ7wvTpQNpRYAFIQe7cufXvv//yoPod1L59e3355Zfq1q2bqlataj0UGhsbqwwZMrhc4KxYsUK7du1yWVXnscceU44cOfTjjz9q3bp1GjZsmKTEAgv9+vXTTz/9pCxZsqQ4sOlUrFgxSdLq1atVtWpVa/uSJUtc9ouNjVW2bNlcKrN9/fXXunz5coqVUo0xcjgc1sPj4eHh1tfi4uI0depUaz/cOucgZGRkpA4fPmxzmnsrLi7Omowvyet+f3eS0r/vffv2acmSJSn+2160aJEeeugh6/2ff/6pwMBAFS5cWAEBAcqWLZt2796dbCBk+/bt97SIRokSJSRJS5cuVaNGjaztHTt2VGBgoEaPHq3HH39cEyZM0IkTJ5Q9e3Zrn6VLl8rPz0+PPvqosmTJosjISK1evdrl89evX++ygmWZMmXk7++vAwcOqH379i77bt261aXdx92VPXt2r2xTrm9XYZ9Lly4pX758Kl++vH766adkX7969apOnDihkiVL3vGfvWLFCpeqsXv37lVMTIx1rni9ChUq6PPPP1d4eLjLAPSRI0cUEBBwV4vDLFq0SA0aNLDer1u3ToUKFbIeJsycObNOnDjh8j0rVqxI8bOS9ld2/k64dT/99JPKlStnd4x76vr2+vprNtw7tNdpQ3sNSXrxxRddVk7wBnFxcfL19U22ugHcj3P89JVXXtGnn36a7OsnT57UG2+8oXLlyqlWrVqaMmVKip9TrFgxrV271hoPlaSjR49q586dKlCgwH/KmNZx4nuhRIkSWrVqlcs5yYEDB9SqVSu9/vrrqlmzpjJnzqwlS5bopZdesr7v6tWrWrNmjSpWrCjJdTw66SrA15/bVKhQQd99950k1weHTp48qatXr950NV+4j6ZNm2rSpEl2x7in4uPj5ePjQ19wj92pdj2p0NBQlSxZUtu3b082br17927lyZPntvM+/vjjWrVqVbJVfJYuXaps2bK5rD50t++lPfTQQ/Lx8dHSpUv19NNPW9vHjRunmTNnat68eXr88cclJbbXDRs2dMkrybpGL1asmObMmeOyiuSVK1dcJgnfzeOK2xcWFsb4ONxWSvckz549q1mzZkm6eTv5wAMPaN26dS7n7Bs2bHA5r37wwQdv6V7l3W6bS5QooenTp2vjxo0qVaqUtf2pp57S448/rjfeeCNNfcmlS5fk7++v1atXq3PnztY+KZ1/S4n33mvXrm1tj4+P186dO13uh8J+3333XYoP7qdn17fX19+Dh+fo3LmzAgMDNWPGDA0ePFg9evRQVFRUihNZ7pQbPauyfft2Sa59jCTrOja19v5Wn5e5kXz58ilz5sxaunSpevXqZW3/4Ycf9PHHH2vmzJm00x7qhRde0PDhw+2OcU8xPu6Z+vXrp19++UU9evTQpEmTkv3/mzdvntq1a6e+ffvqgQcecPlaWu7bpdRmSjdva1PTsGFDPfTQQ3rttddUvXr1ZM+0nz59Wq1bt9aVK1dUq1YtPfbYY/L19U02pnH8+HHt2LHDpehDUs7fJ+n9zGvXrmnt2rW3lPducDgcGj9+vEqWLKn333/f5Wv/9frpZtL6rOP8+fOVLVs2PfbYY3I4HHrkkUc0ceJEzZw5U2vWrKHAgpv77bff7srzDu7s+muOb775xnrNZEy4k5TmH93Je8pJ7+XWqVPH2n79WFJa7xncC7dyv+V6abnWio+P15w5c1SkSBEVKVJEvr6+Kl++vCZNmqTw8HCtWbPGZZwm6Rjk66+/rubNm2vgwIF65513kv38CRMmqGvXrvL19VXHjh2TfT02Nvam51CHDx/W4sWL1bJlS/n4+Chz5sxq2bKljh49qt69e+v48eOKjo7Wv//+qyeffFJS4nVov379tHLlypsWeU0N93rgKZIWJfHGQuPA7fKxOwAA7+BwODRhwgRdunRJnTp1srZXqFBBR48e1SeffKIDBw7oq6++0osvvqgGDRroyJEj2rp1q65evSqHw6HatWtr+PDhCg0N1WOPPSZJKlWqlDJkyKCPP/5YtWvXTrXamSQ1btxYmTJlUq9evbR69Wrt2LFD/fv317Zt21z2q1ChgtavX6/vv/9eBw4c0NixYzVu3DiVK1dOu3bt0p49exQfH29Vcfvhhx+0Y8cOlSlTRgEBARoxYoR27dqldevWqVatWlYxhxUrVtywAjdwPR5yuveOHj2a4h/noMKgQYN04MAB/fLLL2rYsKGaNm2qhIQELVmyxKWQwKRJk/T9999r7969mjhxoqZPn64WLVooODhYvr6+6t+/v77++msNGjRIf//9t3bu3Kn33ntPDz/8sDXAn5oLFy6kmvPo0aO3tFJoo0aNVKJECfXo0UNLlizR/v37NWTIEE2cOFFPPPGEJKlXr14KCAhQ48aNtW7dOu3Zs0cjR47UZ599pi5duliDGW3bttXy5cs1dOhQ7dmzRwsXLlTXrl1dVu3Nnj27XnzxRQ0bNkxjx47Vrl27tHXrVvXs2VOlSpXi4RDcdbSr7iMkJERvv/22fv75Zz399NNasGCBDh06pD179uinn35SzZo1dfr0ab311lt3/Gf7+Pioffv22rJli/766y917NhR/v7+atWqVYr79+zZUz4+PmrcuLFWrlypgwcPas6cOapYsaJat259x/M5+fr66oMPPtBvv/2m3bt368MPP9TKlSv1/PPPW/uUK1dOCxYs0M8//6x9+/Zp4sSJmj9/vsvnOKvnrl69Wps3b9bp06dt+52AtKK9dh+01zdHew1v5ufnx8OjHqJs2bL65JNPNHHiRNWrV0/z5s3TwYMHtXPnTn3++ecqU6aMAgMD9e23397wc9q1a6cDBw6od+/e2rlzp1avXq1nn31WERER/zljWseJU3PixIlUx0quL3JzM6+99prOnDmj5557Trt27dLGjRvVoUMH7d+/X2XLlpW/v7/69eun2bNna+DAgdq1a5f++usvNW3aVGfPnlW/fv0kJRYHLlCggAYMGKAFCxZo9+7dGjlypH777TeXn9e2bVvly5dPzz77rBYsWKCDBw9q0aJFql27turUqaP4+PhbP6DAPcJEAnvcqXb9em+//bZWrlypl156SZs2bdLevXs1btw4lSxZUqNGjbrtvK+//rpOnTql5s2ba9OmTdqxY4feeustzZkzR6+//rp8fBIfWciaNauOHj2qZcuWae/evdb3x8bG3nBM/FZWzo2IiFDHjh01btw4TZkyRQcPHtTs2bP1+uuvWwWSGzVqpOLFi6tbt276+eeftX//fs2ePVs9evRQ6dKlrYfy2rVrp4sXL6pTp07atm2b1Rc4iy3c7eMK3CrGWzyD857k4MGDtW/fPi1dulTVqlXTM888IynxYelz586l+v0tW7bU/v371bdvX+3cuVMLFy5U+/btVbBgQWuftN6rTGmswuns2bM3bJtv5Rz2hRdeUK5cudSmTRutX79ee/bs0csvv6xff/1VlSpVkpS2viRDhgxq0qSJZsyYoUmTJmnv3r2aPXu2Bg8e7PL3v2TJkmrUqJH69eun6dOna9++ffrrr7/UunVrlS1bVvv3709zduBuoL1OH6ZNm6aZM2dq8uTJCgkJ0Ztvvqn77rtPLVu2VFxc3F37uTd6VqVIkSLKli2bJkyYoK1bt2rz5s1q2rSpNYF4zZo1KY6h3OrzMjfiHFP56aefNGTIEB08eFALFixQr169FBoaqsjISNppeAzGxz1T+fLlNWLECE2dOlW1a9e2xlT++usvvfHGG2rUqJGaNGmiAQMGJPvetNy3u922NjW+vr767rvv5OPjozJlymjcuHHauXOndu/erS+//FKPP/64zp49qx9++EEhISGKiIhQhw4dNG7cOI0dO1Z79+7VqlWr9MwzzygkJMSlYHBSpUuXVkBAgEaOHKmtW7dqy5YtateunUJCQm79IN8FDz74oF577bVkhc7/6/XTzaT1WccvvvhCDRs21KxZs3TgwAHt2bNHQ4YM0fnz51W9evXb/vnA3XL9NQeTMeGuUpp/9F/vKSf14IMP6oknntDo0aP1ww8/aO/evZo6daq1uKpTWu8Z3Atpvd+SkrRca/n6+mrIkCF65pln9Ntvv+nQoUPasWOH3n33Xfn5+VnFFa6fRyVJzZo1U+/evTVw4EA1b95cS5cu1aFDh7R27Vp17dpVnTp1Us+ePVMsriAl/r+dO3euFi1apL1792rgwIFav3698ubNq82bN+vgwYM6d+6c2rVrp86dO2vjxo06fPiwVqxYoenTp6t48eLKkSOH1q1bp6efftqa03Xo0CF9//33WrFihWrUqHHbx557PfAER44csV5TNAm4BQZAMrlz5zaSTO7cuT3qs91BmzZtjK+vb6pfHzx4sJFkpk6daowx5vLly6ZDhw4mW7ZsJmPGjKZOnTpm9+7dZuXKlSZnzpwmY8aMZvPmzcYYY7766isjyTz11FMun9mwYUOXz7yR5cuXmzJlypiAgAATHh5uunbtar755hsjySxfvtwYY8zJkydNkyZNTFhYmMmcObP5v//7P3Ps2DHz/fffm7CwMJM1a1Zz8uRJs3PnTlOkSBHj7+9vZfrmm29M4cKFTVBQkClatKiZNm2aiY2NNZUrVzYBAQHmzTffvK3j6u74N3Nv/PPPP0aSkWSaNGlid5x0pU2bNtaxTelPbGys6devn4mIiDAhISEmKirKrFu3zuzatcsULFjQBAcHm19//dW8/fbbRpL566+/TM2aNU1ISIgJCwszbdu2NRcuXHD5mZMmTTKlSpUygYGBJigoyJQtW9bMmDHD+vrixYuNJDNt2jRjjDH79++/YUbnn7lz5xpjjMmfP7+pXr26y890/h16++23rW3Hjh0zbdu2NdmzZzfBwcGmZMmS5ssvv3T5vj///NPUqVPHZMqUyfj7+5sHH3zQDB482MTHx1v7xMbGmpdeesmEh4ebwMBAU7p0abNw4ULzyCOPuOSIj483Q4YMMQ8++KDx9/c3oaGhpmrVqmbBggXWPpMnT3Zpl3Hn5M+f30gyERERdkdxCz169LD+7axdu9buOF5pwYIF5tlnnzUFChQwwcHBJjAw0BQqVMi0b9/ebN++3RiTvD1MrY24vn0zxpjKlSubypUru+zTvn17M2TIEJM/f34TEBBgihUrZn766Sdrn5Q+f+vWraZhw4YmS5YsxtfX1+TNm9f07t3bpW1P6fucPy+pNm3amPz587tsc37v/v37rX18fX3NunXrzBNPPGGCgoJMeHi46dmzp7l27Zr1fXv37jU1a9Y0GTNmNGFhYaZZs2Zm/fr1RpKZPHmytV+/fv1MaGioyZQpk5UvLb9TSn0J7o2ePXta7dPq1avtjmM7ZzsgyfTr18/uOF6J9tr1e2mv4bRkyRKrferbt6/dcdxCWFiYkWSKFClidxSkYNOmTVb7FhAQYDJmzGjKlCljhgwZYi5evGjtd6Pr8kGDBplcuXIZf39/U7RoUTNjxgzz9NNPm0KFCln7XN8updTOLl++3KUdTOs48fXjz86xmBv9yZk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\n"
                },
                "metadata": {}
              }
            ]
          }
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dr-mushtaq/Research-Work/blob/main/RTA_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Table of Content**</p>"
      ],
      "metadata": {
        "id": "-usbrFyVq-Gp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Importing and installing libraries⭐️\n",
        "*   Installing Pycaret and other libraries⭐️\n",
        "*   Import libraries⭐️\n",
        "*   Particle Swarm Optimization⭐️"
      ],
      "metadata": {
        "id": "dtGPlYffrCEo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Importing and Installing Librarie**</p>"
      ],
      "metadata": {
        "id": "EBMUCHxBJ21j"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "cZ8QQyJ1I1gt"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from prettytable import PrettyTable\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from mlxtend.plotting import plot_confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Installing Pycaret and other libraries**</p>"
      ],
      "metadata": {
        "id": "sNtPGWskrmOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# install the full version\n",
        "!python -m pip install --upgrade pip setuptools wheel\n",
        "!pip install pycaret\n",
        "\n",
        "!pip install pyyaml==5.4.1\n",
        "\n",
        "!pip install markupsafe==2.0.1\n",
        "\n",
        "!pip install Jinja2\n",
        "\n",
        "!pip install mlflow\n",
        "\n",
        "!pip install optuna"
      ],
      "metadata": {
        "id": "4RTpzyA1JeHr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "38469132-c0f6-4bf0-e919-accc6e126fa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: setuptools, pip\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 75.1.0\n",
            "    Uninstalling setuptools-75.1.0:\n",
            "      Successfully uninstalled setuptools-75.1.0\n",
            "  Attempting uninstall: pip\n",
            "    Found existing installation: pip 24.1.2\n",
            "    Uninstalling pip-24.1.2:\n",
            "      Successfully uninstalled pip-24.1.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed pip-24.3.1 setuptools-75.8.0\n",
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            "Building wheels for collected packages: pyod\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-2.0.3-py3-none-any.whl size=200521 sha256=c84aee07f2f2d7f3305d200ec348d1d0b0f23e50cf3c8f89a8b14f2c6d1b7d4e\n",
            "  Stored in directory: /root/.cache/pip/wheels/2d/60/5b/f74eccd2c9c892a2c298202ca510f10995f9940647fcc2d97f\n",
            "Successfully built pyod\n",
            "Installing collected packages: kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, Werkzeug, tsdownsample, scipy, scikit-base, schemdraw, retrying, joblib, jedi, deprecation, scikit-learn, pandas, matplotlib, Flask, sktime, scikit-plot, pyod, dash, pmdarima, plotly-resampler, category-encoders, tbats, pycaret\n",
            "  Attempting uninstall: Werkzeug\n",
            "    Found existing installation: Werkzeug 3.1.3\n",
            "    Uninstalling Werkzeug-3.1.3:\n",
            "      Successfully uninstalled Werkzeug-3.1.3\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.13.1\n",
            "    Uninstalling scipy-1.13.1:\n",
            "      Successfully uninstalled scipy-1.13.1\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.4.2\n",
            "    Uninstalling joblib-1.4.2:\n",
            "      Successfully uninstalled joblib-1.4.2\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.6.0\n",
            "    Uninstalling scikit-learn-1.6.0:\n",
            "      Successfully uninstalled scikit-learn-1.6.0\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.2.2\n",
            "    Uninstalling pandas-2.2.2:\n",
            "      Successfully uninstalled pandas-2.2.2\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.10.0\n",
            "    Uninstalling matplotlib-3.10.0:\n",
            "      Successfully uninstalled matplotlib-3.10.0\n",
            "  Attempting uninstall: Flask\n",
            "    Found existing installation: Flask 3.1.0\n",
            "    Uninstalling Flask-3.1.0:\n",
            "      Successfully uninstalled Flask-3.1.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.1.4 which is incompatible.\n",
            "mizani 0.13.1 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.\n",
            "plotnine 0.14.5 requires matplotlib>=3.8.0, but you have matplotlib 3.7.5 which is incompatible.\n",
            "plotnine 0.14.5 requires pandas>=2.2.0, but you have pandas 2.1.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Flask-3.0.3 Werkzeug-3.0.6 category-encoders-2.7.0 dash-2.18.2 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecation-2.1.0 jedi-0.19.2 joblib-1.3.2 kaleido-0.2.1 matplotlib-3.7.5 pandas-2.1.4 plotly-resampler-0.10.0 pmdarima-2.0.4 pycaret-3.3.2 pyod-2.0.3 retrying-1.3.4 schemdraw-0.15 scikit-base-0.7.8 scikit-learn-1.4.2 scikit-plot-0.3.7 scipy-1.11.4 sktime-0.26.0 tbats-1.1.3 tsdownsample-0.1.3 wurlitzer-3.1.1 xxhash-3.5.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "joblib",
                  "matplotlib",
                  "mpl_toolkits",
                  "sklearn"
                ]
              },
              "id": "0268aa442c284346b505760ab5fc7097"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyyaml==5.4.1\n",
            "  Downloading PyYAML-5.4.1.tar.gz (175 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mGetting requirements to build wheel\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m \u001b[32mGetting requirements to build wheel\u001b[0m did not run successfully.\n",
            "\u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "Collecting markupsafe==2.0.1\n",
            "  Downloading MarkupSafe-2.0.1.tar.gz (18 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: markupsafe\n",
            "  Building wheel for markupsafe (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for markupsafe: filename=MarkupSafe-2.0.1-cp311-cp311-linux_x86_64.whl size=27995 sha256=0844708c6c5012e0d6e3f556d1444ce976826759609a89e78a84faa4d0c191b2\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/18/79/6266ea508b8164a77b95aa19534c77eb805f2878612c37efca\n",
            "Successfully built markupsafe\n",
            "Installing collected packages: markupsafe\n",
            "  Attempting uninstall: markupsafe\n",
            "    Found existing installation: MarkupSafe 3.0.2\n",
            "    Uninstalling MarkupSafe-3.0.2:\n",
            "      Successfully uninstalled MarkupSafe-3.0.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "werkzeug 3.0.6 requires MarkupSafe>=2.1.1, but you have markupsafe 2.0.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed markupsafe-2.0.1\n",
            "Requirement already satisfied: Jinja2 in /usr/local/lib/python3.11/dist-packages (3.1.5)\n",
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            "Collecting mlflow\n",
            "  Downloading mlflow-2.20.0-py3-none-any.whl.metadata (30 kB)\n",
            "Collecting mlflow-skinny==2.20.0 (from mlflow)\n",
            "  Downloading mlflow_skinny-2.20.0-py3-none-any.whl.metadata (31 kB)\n",
            "Requirement already satisfied: Flask<4 in /usr/local/lib/python3.11/dist-packages (from mlflow) (3.0.3)\n",
            "Requirement already satisfied: Jinja2<4,>=2.11 in /usr/local/lib/python3.11/dist-packages (from mlflow) (3.1.5)\n",
            "Collecting alembic!=1.10.0,<2 (from mlflow)\n",
            "  Downloading alembic-1.14.1-py3-none-any.whl.metadata (7.4 kB)\n",
            "Collecting docker<8,>=4.0.0 (from mlflow)\n",
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            "\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m28.0/28.3 MB\u001b[0m \u001b[31m188.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Restart Runtime"
      ],
      "metadata": {
        "id": "fYY89DgnJyML"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Import Libraries**</p>"
      ],
      "metadata": {
        "id": "UvRsg-AesioS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pycaret\n",
        "import jinja2\n",
        "#from pycaret.regression import*\n",
        "from pycaret.classification import*"
      ],
      "metadata": {
        "id": "FwHDUdg4Jzz8"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Importing Dataset**</p>"
      ],
      "metadata": {
        "id": "xqvG5bd_KM4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import pandas as pd\n",
        "data = pd.read_csv('/content/drive/MyDrive/Virtual University /Thesis /Shujaat/Research Paper/KSILatest.csv')\n"
      ],
      "metadata": {
        "id": "sMyvkAmjKP2r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "159e3a0b-d06f-4998-8a69-601cb6ffea84"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "37LwoxTy06Xa",
        "outputId": "29520cbe-29b1-4f6c-fa09-61d10d0471b0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YEAR  Month  Date  TIME Nature of weekday       Day      ROAD_CLASS  \\\n",
              "0  2006      3    11   852           Weekend  Saturday  Major Arterial   \n",
              "1  2006      3    11   852           Weekend  Saturday  Major Arterial   \n",
              "2  2006      3    11   915           Weekend  Saturday  Major Arterial   \n",
              "3  2006      1     1   236           Weekend    Sunday  Major Arterial   \n",
              "4  2006      3    11   915           Weekend  Saturday  Major Arterial   \n",
              "\n",
              "                DISTRICT      LOCCOORD                ACCLOC  ... MOTORCYCLE  \\\n",
              "0  Toronto and East York  Intersection       At Intersection  ...         No   \n",
              "1  Toronto and East York  Intersection       At Intersection  ...         No   \n",
              "2            Scarborough  Intersection       At Intersection  ...        Yes   \n",
              "3  Toronto and East York  Intersection  Intersection Related  ...         No   \n",
              "4            Scarborough  Intersection       At Intersection  ...        Yes   \n",
              "\n",
              "  TRUCK TRSN_CITY_VEH EMERG_VEH PASSENGER SPEEDING AG_DRIV REDLIGHT ALCOHOL  \\\n",
              "0    No            No        No        No       No     Yes       No      No   \n",
              "1    No            No        No        No       No     Yes       No      No   \n",
              "2    No            No        No        No       No     Yes      Yes      No   \n",
              "3    No            No        No       Yes      Yes     Yes       No     Yes   \n",
              "4    No            No        No        No       No     Yes      Yes      No   \n",
              "\n",
              "  DISABILITY  \n",
              "0         No  \n",
              "1         No  \n",
              "2         No  \n",
              "3         No  \n",
              "4         No  \n",
              "\n",
              "[5 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8c784dd0-e6b1-400c-9338-de27d152f0a5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YEAR</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>TIME</th>\n",
              "      <th>Nature of weekday</th>\n",
              "      <th>Day</th>\n",
              "      <th>ROAD_CLASS</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>LOCCOORD</th>\n",
              "      <th>ACCLOC</th>\n",
              "      <th>...</th>\n",
              "      <th>MOTORCYCLE</th>\n",
              "      <th>TRUCK</th>\n",
              "      <th>TRSN_CITY_VEH</th>\n",
              "      <th>EMERG_VEH</th>\n",
              "      <th>PASSENGER</th>\n",
              "      <th>SPEEDING</th>\n",
              "      <th>AG_DRIV</th>\n",
              "      <th>REDLIGHT</th>\n",
              "      <th>ALCOHOL</th>\n",
              "      <th>DISABILITY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2006</td>\n",
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              "      <td>11</td>\n",
              "      <td>852</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Toronto and East York</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>852</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Toronto and East York</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>915</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>236</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Toronto and East York</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>Intersection Related</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>915</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c784dd0-e6b1-400c-9338-de27d152f0a5')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8c784dd0-e6b1-400c-9338-de27d152f0a5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8c784dd0-e6b1-400c-9338-de27d152f0a5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3b8be7b8-2cb1-4149-b48b-4c06cbd8a04d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3b8be7b8-2cb1-4149-b48b-4c06cbd8a04d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3b8be7b8-2cb1-4149-b48b-4c06cbd8a04d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "id": "xoJyhvc31HcY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data2 = data.sample(frac=0.95, random_state=786).reset_index(drop=True)\n",
        "data_unseen = data.drop(data2.index).reset_index(drop=True)\n",
        "\n",
        "print('Data for Modeling: ' + str(data2.shape))\n",
        "print('Unseen Data For Predictions: ' + str(data_unseen.shape))"
      ],
      "metadata": {
        "id": "E0mGBcEoKa77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88b642ae-f97c-4270-962e-bbd0773c21d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data for Modeling: (15749, 43)\n",
            "Unseen Data For Predictions: (829, 43)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Data Exploration**</p>"
      ],
      "metadata": {
        "id": "9pFUodn3MLvZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20, 8))\n",
        "plt.title(\"Top District with the highest number of accidents\")\n",
        "state_counts = data[\"DISTRICT\"].value_counts()\n",
        "# Pass x and y as named arguments\n",
        "sns.barplot(x=state_counts[:3].values, y=state_counts[:3].index, orient=\"h\")\n",
        "plt.xlabel(\"Number of accident\")\n",
        "plt.ylabel(\"DISTRICT\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "6aD5jvoB0c5W",
        "outputId": "00c32f06-ed58-4bab-a20a-c207a592cc4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Number of accidents for weekday**"
      ],
      "metadata": {
        "id": "yt_yOYkQ1oFC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts = data[\"Day\"].value_counts()[:15]\n",
        "plt.figure(figsize=(20, 8))\n",
        "plt.title(\"Number of accidents for weekday\")\n",
        "# Pass x and y as named arguments\n",
        "sns.barplot(x=counts.index, y=counts.values)\n",
        "plt.xlabel(\"Days\")\n",
        "plt.ylabel(\"Value\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "Ytk5Bo5y2Dmg",
        "outputId": "23e6f925-aea3-4c24-aed7-8e6c9b294b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation**"
      ],
      "metadata": {
        "id": "ggDcKZcU3nem"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Day' column to numerical representation before calculating correlation\n",
        "data['Day_Numeric'] = pd.factorize(data['Day'])[0]\n",
        "\n",
        "# Now calculate correlation and plot the heatmap using the new numerical column\n",
        "plt.figure(figsize=(20, 5))\n",
        "sns.heatmap(data.corr(numeric_only=True), annot=True)  # Specify numeric_only=True for pandas >= 2.0\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KASW-oHM3mgW",
        "outputId": "5334d959-239a-4027-e504-ea8c1ad97b35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Setting up Environment in PyCaret**</p>"
      ],
      "metadata": {
        "id": "2Tz8LFjJKi8Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.classification import *\n",
        "exp_mclf101 = setup(data, target = 'injury_type', fix_imbalance = True, session_id=123)"
      ],
      "metadata": {
        "id": "m_3pWdCLKk0S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "outputId": "e58f44b3-ea73-410d-9328-0a9b6e72b8db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c071875add0>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_16745_row11_col1, #T_16745_row17_col1 {\n",
              "  background-color: lightgreen;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_16745\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_16745_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
              "      <th id=\"T_16745_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_16745_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_16745_row0_col1\" class=\"data row0 col1\" >123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_16745_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_16745_row1_col1\" class=\"data row1 col1\" >injury_type</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_16745_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_16745_row2_col1\" class=\"data row2 col1\" >Multiclass</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_16745_row3_col0\" class=\"data row3 col0\" >Target mapping</td>\n",
              "      <td id=\"T_16745_row3_col1\" class=\"data row3 col1\" >Fatal: 0, Major: 1, Minimal: 2, Minor: 3, No injury: 4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_16745_row4_col0\" class=\"data row4 col0\" >Original data shape</td>\n",
              "      <td id=\"T_16745_row4_col1\" class=\"data row4 col1\" >(16578, 43)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_16745_row5_col0\" class=\"data row5 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_16745_row5_col1\" class=\"data row5 col1\" >(29309, 270)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_16745_row6_col0\" class=\"data row6 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_16745_row6_col1\" class=\"data row6 col1\" >(24335, 270)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_16745_row7_col0\" class=\"data row7 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_16745_row7_col1\" class=\"data row7 col1\" >(4974, 270)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_16745_row8_col0\" class=\"data row8 col0\" >Numeric features</td>\n",
              "      <td id=\"T_16745_row8_col1\" class=\"data row8 col1\" >4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_16745_row9_col0\" class=\"data row9 col0\" >Categorical features</td>\n",
              "      <td id=\"T_16745_row9_col1\" class=\"data row9 col1\" >38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_16745_row10_col0\" class=\"data row10 col0\" >Rows with missing values</td>\n",
              "      <td id=\"T_16745_row10_col1\" class=\"data row10 col1\" >100.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_16745_row11_col0\" class=\"data row11 col0\" >Preprocess</td>\n",
              "      <td id=\"T_16745_row11_col1\" class=\"data row11 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_16745_row12_col0\" class=\"data row12 col0\" >Imputation type</td>\n",
              "      <td id=\"T_16745_row12_col1\" class=\"data row12 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_16745_row13_col0\" class=\"data row13 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_16745_row13_col1\" class=\"data row13 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_16745_row14_col0\" class=\"data row14 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_16745_row14_col1\" class=\"data row14 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_16745_row15_col0\" class=\"data row15 col0\" >Maximum one-hot encoding</td>\n",
              "      <td id=\"T_16745_row15_col1\" class=\"data row15 col1\" >25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_16745_row16_col0\" class=\"data row16 col0\" >Encoding method</td>\n",
              "      <td id=\"T_16745_row16_col1\" class=\"data row16 col1\" >None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_16745_row17_col0\" class=\"data row17 col0\" >Fix imbalance</td>\n",
              "      <td id=\"T_16745_row17_col1\" class=\"data row17 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_16745_row18_col0\" class=\"data row18 col0\" >Fix imbalance method</td>\n",
              "      <td id=\"T_16745_row18_col1\" class=\"data row18 col1\" >SMOTE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_16745_row19_col0\" class=\"data row19 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_16745_row19_col1\" class=\"data row19 col1\" >StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_16745_row20_col0\" class=\"data row20 col0\" >Fold Number</td>\n",
              "      <td id=\"T_16745_row20_col1\" class=\"data row20 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
              "      <td id=\"T_16745_row21_col0\" class=\"data row21 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_16745_row21_col1\" class=\"data row21 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
              "      <td id=\"T_16745_row22_col0\" class=\"data row22 col0\" >Use GPU</td>\n",
              "      <td id=\"T_16745_row22_col1\" class=\"data row22 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
              "      <td id=\"T_16745_row23_col0\" class=\"data row23 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_16745_row23_col1\" class=\"data row23 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
              "      <td id=\"T_16745_row24_col0\" class=\"data row24 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_16745_row24_col1\" class=\"data row24 col1\" >clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_16745_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
              "      <td id=\"T_16745_row25_col0\" class=\"data row25 col0\" >USI</td>\n",
              "      <td id=\"T_16745_row25_col1\" class=\"data row25 col1\" >e12f</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
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        }
      ]
    },
    {
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        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Classification**</p>"
      ],
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        "id": "Su4zJpDOKswI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Compairing All Models**</p>"
      ],
      "metadata": {
        "id": "fnTErdw7K3SJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "compare_models()"
      ],
      "metadata": {
        "id": "OzoEd82OK6D0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670,
          "referenced_widgets": [
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            "2018bf97675f412e9efea0c7f5cb1dfd",
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        "outputId": "44c830a1-21f1-48b5-cc44-15ca88575688"
      },
      "execution_count": null,
      "outputs": [
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              "<table id=\"T_b7f6d\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_b7f6d_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_b7f6d_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_b7f6d_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_b7f6d_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_b7f6d_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_b7f6d_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_b7f6d_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_b7f6d_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_b7f6d_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row0\" class=\"row_heading level0 row0\" >lightgbm</th>\n",
              "      <td id=\"T_b7f6d_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_b7f6d_row0_col1\" class=\"data row0 col1\" >0.7578</td>\n",
              "      <td id=\"T_b7f6d_row0_col2\" class=\"data row0 col2\" >0.9244</td>\n",
              "      <td id=\"T_b7f6d_row0_col3\" class=\"data row0 col3\" >0.7578</td>\n",
              "      <td id=\"T_b7f6d_row0_col4\" class=\"data row0 col4\" >0.7332</td>\n",
              "      <td id=\"T_b7f6d_row0_col5\" class=\"data row0 col5\" >0.7419</td>\n",
              "      <td id=\"T_b7f6d_row0_col6\" class=\"data row0 col6\" >0.6301</td>\n",
              "      <td id=\"T_b7f6d_row0_col7\" class=\"data row0 col7\" >0.6319</td>\n",
              "      <td id=\"T_b7f6d_row0_col8\" class=\"data row0 col8\" >57.0780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row1\" class=\"row_heading level0 row1\" >xgboost</th>\n",
              "      <td id=\"T_b7f6d_row1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_b7f6d_row1_col1\" class=\"data row1 col1\" >0.7554</td>\n",
              "      <td id=\"T_b7f6d_row1_col2\" class=\"data row1 col2\" >0.9229</td>\n",
              "      <td id=\"T_b7f6d_row1_col3\" class=\"data row1 col3\" >0.7554</td>\n",
              "      <td id=\"T_b7f6d_row1_col4\" class=\"data row1 col4\" >0.7316</td>\n",
              "      <td id=\"T_b7f6d_row1_col5\" class=\"data row1 col5\" >0.7400</td>\n",
              "      <td id=\"T_b7f6d_row1_col6\" class=\"data row1 col6\" >0.6263</td>\n",
              "      <td id=\"T_b7f6d_row1_col7\" class=\"data row1 col7\" >0.6281</td>\n",
              "      <td id=\"T_b7f6d_row1_col8\" class=\"data row1 col8\" >22.4640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
              "      <td id=\"T_b7f6d_row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_b7f6d_row2_col1\" class=\"data row2 col1\" >0.7492</td>\n",
              "      <td id=\"T_b7f6d_row2_col2\" class=\"data row2 col2\" >0.9116</td>\n",
              "      <td id=\"T_b7f6d_row2_col3\" class=\"data row2 col3\" >0.7492</td>\n",
              "      <td id=\"T_b7f6d_row2_col4\" class=\"data row2 col4\" >0.7211</td>\n",
              "      <td id=\"T_b7f6d_row2_col5\" class=\"data row2 col5\" >0.7282</td>\n",
              "      <td id=\"T_b7f6d_row2_col6\" class=\"data row2 col6\" >0.6132</td>\n",
              "      <td id=\"T_b7f6d_row2_col7\" class=\"data row2 col7\" >0.6171</td>\n",
              "      <td id=\"T_b7f6d_row2_col8\" class=\"data row2 col8\" >9.2920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row3\" class=\"row_heading level0 row3\" >et</th>\n",
              "      <td id=\"T_b7f6d_row3_col0\" class=\"data row3 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_b7f6d_row3_col1\" class=\"data row3 col1\" >0.7468</td>\n",
              "      <td id=\"T_b7f6d_row3_col2\" class=\"data row3 col2\" >0.9015</td>\n",
              "      <td id=\"T_b7f6d_row3_col3\" class=\"data row3 col3\" >0.7468</td>\n",
              "      <td id=\"T_b7f6d_row3_col4\" class=\"data row3 col4\" >0.7231</td>\n",
              "      <td id=\"T_b7f6d_row3_col5\" class=\"data row3 col5\" >0.7313</td>\n",
              "      <td id=\"T_b7f6d_row3_col6\" class=\"data row3 col6\" >0.6136</td>\n",
              "      <td id=\"T_b7f6d_row3_col7\" class=\"data row3 col7\" >0.6155</td>\n",
              "      <td id=\"T_b7f6d_row3_col8\" class=\"data row3 col8\" >10.6410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row4\" class=\"row_heading level0 row4\" >gbc</th>\n",
              "      <td id=\"T_b7f6d_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_b7f6d_row4_col1\" class=\"data row4 col1\" >0.7397</td>\n",
              "      <td id=\"T_b7f6d_row4_col2\" class=\"data row4 col2\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row4_col3\" class=\"data row4 col3\" >0.7397</td>\n",
              "      <td id=\"T_b7f6d_row4_col4\" class=\"data row4 col4\" >0.7396</td>\n",
              "      <td id=\"T_b7f6d_row4_col5\" class=\"data row4 col5\" >0.7388</td>\n",
              "      <td id=\"T_b7f6d_row4_col6\" class=\"data row4 col6\" >0.6135</td>\n",
              "      <td id=\"T_b7f6d_row4_col7\" class=\"data row4 col7\" >0.6139</td>\n",
              "      <td id=\"T_b7f6d_row4_col8\" class=\"data row4 col8\" >97.7730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
              "      <td id=\"T_b7f6d_row5_col0\" class=\"data row5 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_b7f6d_row5_col1\" class=\"data row5 col1\" >0.6855</td>\n",
              "      <td id=\"T_b7f6d_row5_col2\" class=\"data row5 col2\" >0.7819</td>\n",
              "      <td id=\"T_b7f6d_row5_col3\" class=\"data row5 col3\" >0.6855</td>\n",
              "      <td id=\"T_b7f6d_row5_col4\" class=\"data row5 col4\" >0.6945</td>\n",
              "      <td id=\"T_b7f6d_row5_col5\" class=\"data row5 col5\" >0.6895</td>\n",
              "      <td id=\"T_b7f6d_row5_col6\" class=\"data row5 col6\" >0.5345</td>\n",
              "      <td id=\"T_b7f6d_row5_col7\" class=\"data row5 col7\" >0.5348</td>\n",
              "      <td id=\"T_b7f6d_row5_col8\" class=\"data row5 col8\" >3.2570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
              "      <td id=\"T_b7f6d_row6_col0\" class=\"data row6 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_b7f6d_row6_col1\" class=\"data row6 col1\" >0.6817</td>\n",
              "      <td id=\"T_b7f6d_row6_col2\" class=\"data row6 col2\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row6_col3\" class=\"data row6 col3\" >0.6817</td>\n",
              "      <td id=\"T_b7f6d_row6_col4\" class=\"data row6 col4\" >0.7797</td>\n",
              "      <td id=\"T_b7f6d_row6_col5\" class=\"data row6 col5\" >0.7166</td>\n",
              "      <td id=\"T_b7f6d_row6_col6\" class=\"data row6 col6\" >0.5591</td>\n",
              "      <td id=\"T_b7f6d_row6_col7\" class=\"data row6 col7\" >0.5689</td>\n",
              "      <td id=\"T_b7f6d_row6_col8\" class=\"data row6 col8\" >2.2760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row7\" class=\"row_heading level0 row7\" >lda</th>\n",
              "      <td id=\"T_b7f6d_row7_col0\" class=\"data row7 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_b7f6d_row7_col1\" class=\"data row7 col1\" >0.6675</td>\n",
              "      <td id=\"T_b7f6d_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row7_col3\" class=\"data row7 col3\" >0.6675</td>\n",
              "      <td id=\"T_b7f6d_row7_col4\" class=\"data row7 col4\" >0.7986</td>\n",
              "      <td id=\"T_b7f6d_row7_col5\" class=\"data row7 col5\" >0.7122</td>\n",
              "      <td id=\"T_b7f6d_row7_col6\" class=\"data row7 col6\" >0.5470</td>\n",
              "      <td id=\"T_b7f6d_row7_col7\" class=\"data row7 col7\" >0.5620</td>\n",
              "      <td id=\"T_b7f6d_row7_col8\" class=\"data row7 col8\" >2.9550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row8\" class=\"row_heading level0 row8\" >lr</th>\n",
              "      <td id=\"T_b7f6d_row8_col0\" class=\"data row8 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_b7f6d_row8_col1\" class=\"data row8 col1\" >0.6020</td>\n",
              "      <td id=\"T_b7f6d_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row8_col3\" class=\"data row8 col3\" >0.6020</td>\n",
              "      <td id=\"T_b7f6d_row8_col4\" class=\"data row8 col4\" >0.7180</td>\n",
              "      <td id=\"T_b7f6d_row8_col5\" class=\"data row8 col5\" >0.6407</td>\n",
              "      <td id=\"T_b7f6d_row8_col6\" class=\"data row8 col6\" >0.4578</td>\n",
              "      <td id=\"T_b7f6d_row8_col7\" class=\"data row8 col7\" >0.4697</td>\n",
              "      <td id=\"T_b7f6d_row8_col8\" class=\"data row8 col8\" >28.9750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row9\" class=\"row_heading level0 row9\" >nb</th>\n",
              "      <td id=\"T_b7f6d_row9_col0\" class=\"data row9 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_b7f6d_row9_col1\" class=\"data row9 col1\" >0.4993</td>\n",
              "      <td id=\"T_b7f6d_row9_col2\" class=\"data row9 col2\" >0.8325</td>\n",
              "      <td id=\"T_b7f6d_row9_col3\" class=\"data row9 col3\" >0.4993</td>\n",
              "      <td id=\"T_b7f6d_row9_col4\" class=\"data row9 col4\" >0.7374</td>\n",
              "      <td id=\"T_b7f6d_row9_col5\" class=\"data row9 col5\" >0.5669</td>\n",
              "      <td id=\"T_b7f6d_row9_col6\" class=\"data row9 col6\" >0.3491</td>\n",
              "      <td id=\"T_b7f6d_row9_col7\" class=\"data row9 col7\" >0.3805</td>\n",
              "      <td id=\"T_b7f6d_row9_col8\" class=\"data row9 col8\" >2.1120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row10\" class=\"row_heading level0 row10\" >ada</th>\n",
              "      <td id=\"T_b7f6d_row10_col0\" class=\"data row10 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_b7f6d_row10_col1\" class=\"data row10 col1\" >0.4705</td>\n",
              "      <td id=\"T_b7f6d_row10_col2\" class=\"data row10 col2\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row10_col3\" class=\"data row10 col3\" >0.4705</td>\n",
              "      <td id=\"T_b7f6d_row10_col4\" class=\"data row10 col4\" >0.6026</td>\n",
              "      <td id=\"T_b7f6d_row10_col5\" class=\"data row10 col5\" >0.5147</td>\n",
              "      <td id=\"T_b7f6d_row10_col6\" class=\"data row10 col6\" >0.2908</td>\n",
              "      <td id=\"T_b7f6d_row10_col7\" class=\"data row10 col7\" >0.3058</td>\n",
              "      <td id=\"T_b7f6d_row10_col8\" class=\"data row10 col8\" >6.3360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
              "      <td id=\"T_b7f6d_row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_b7f6d_row11_col1\" class=\"data row11 col1\" >0.2312</td>\n",
              "      <td id=\"T_b7f6d_row11_col2\" class=\"data row11 col2\" >0.5103</td>\n",
              "      <td id=\"T_b7f6d_row11_col3\" class=\"data row11 col3\" >0.2312</td>\n",
              "      <td id=\"T_b7f6d_row11_col4\" class=\"data row11 col4\" >0.3356</td>\n",
              "      <td id=\"T_b7f6d_row11_col5\" class=\"data row11 col5\" >0.2458</td>\n",
              "      <td id=\"T_b7f6d_row11_col6\" class=\"data row11 col6\" >0.0356</td>\n",
              "      <td id=\"T_b7f6d_row11_col7\" class=\"data row11 col7\" >0.0389</td>\n",
              "      <td id=\"T_b7f6d_row11_col8\" class=\"data row11 col8\" >3.0920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row12\" class=\"row_heading level0 row12\" >svm</th>\n",
              "      <td id=\"T_b7f6d_row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_b7f6d_row12_col1\" class=\"data row12 col1\" >0.1872</td>\n",
              "      <td id=\"T_b7f6d_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row12_col3\" class=\"data row12 col3\" >0.1872</td>\n",
              "      <td id=\"T_b7f6d_row12_col4\" class=\"data row12 col4\" >0.3037</td>\n",
              "      <td id=\"T_b7f6d_row12_col5\" class=\"data row12 col5\" >0.1442</td>\n",
              "      <td id=\"T_b7f6d_row12_col6\" class=\"data row12 col6\" >0.0623</td>\n",
              "      <td id=\"T_b7f6d_row12_col7\" class=\"data row12 col7\" >0.0834</td>\n",
              "      <td id=\"T_b7f6d_row12_col8\" class=\"data row12 col8\" >23.5190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row13\" class=\"row_heading level0 row13\" >qda</th>\n",
              "      <td id=\"T_b7f6d_row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_b7f6d_row13_col1\" class=\"data row13 col1\" >0.1861</td>\n",
              "      <td id=\"T_b7f6d_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row13_col3\" class=\"data row13 col3\" >0.1861</td>\n",
              "      <td id=\"T_b7f6d_row13_col4\" class=\"data row13 col4\" >0.7359</td>\n",
              "      <td id=\"T_b7f6d_row13_col5\" class=\"data row13 col5\" >0.1934</td>\n",
              "      <td id=\"T_b7f6d_row13_col6\" class=\"data row13 col6\" >0.0773</td>\n",
              "      <td id=\"T_b7f6d_row13_col7\" class=\"data row13 col7\" >0.1441</td>\n",
              "      <td id=\"T_b7f6d_row13_col8\" class=\"data row13 col8\" >2.8980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_b7f6d_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
              "      <td id=\"T_b7f6d_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_b7f6d_row14_col1\" class=\"data row14 col1\" >0.0562</td>\n",
              "      <td id=\"T_b7f6d_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
              "      <td id=\"T_b7f6d_row14_col3\" class=\"data row14 col3\" >0.0562</td>\n",
              "      <td id=\"T_b7f6d_row14_col4\" class=\"data row14 col4\" >0.0032</td>\n",
              "      <td id=\"T_b7f6d_row14_col5\" class=\"data row14 col5\" >0.0060</td>\n",
              "      <td id=\"T_b7f6d_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_b7f6d_row14_col8\" class=\"data row14 col8\" >1.9210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
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              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
              "               importance_type='split', learning_rate=0.1, max_depth=-1,\n",
              "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "               n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
              "               random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,\n",
              "               subsample_for_bin=200000, subsample_freq=0)"
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            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier(boosting_type=&#x27;gbdt&#x27;, class_weight=None, colsample_bytree=1.0,\n",
              "               importance_type=&#x27;split&#x27;, learning_rate=0.1, max_depth=-1,\n",
              "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "               n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
              "               random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,\n",
              "               subsample_for_bin=200000, subsample_freq=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LGBMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(boosting_type=&#x27;gbdt&#x27;, class_weight=None, colsample_bytree=1.0,\n",
              "               importance_type=&#x27;split&#x27;, learning_rate=0.1, max_depth=-1,\n",
              "               min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "               n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
              "               random_state=123, reg_alpha=0.0, reg_lambda=0.0, subsample=1.0,\n",
              "               subsample_for_bin=200000, subsample_freq=0)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Selecting Best Model**</p>"
      ],
      "metadata": {
        "id": "zOMfLSwoLMcP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best= compare_models(n_select = 9, sort= 'Accuracy')"
      ],
      "metadata": {
        "id": "nFwvFivMLRzS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957,
          "referenced_widgets": [
            "98235673a70c4dd984f010b0644177b5",
            "f9e208a9e2394bf499c22c2a701c3704",
            "9d89d614418e42b1808e6b447d7c9c72",
            "f5c8c1ebf21441ab880f59f79b51ca69",
            "5d927ddb409c401b934b9549f9f493cd",
            "33750d0f91504632a735b791de603ef2",
            "2546bbd869054881919466ea936e0dd2",
            "5d872900490740ff999f6ed7b2bfc203",
            "e9e73b0e288647bcb8f27a7c3ecc58fb",
            "8d7294457ae5499481a85cf9260cde74",
            "15a5ca3c5f96496cabcee725a02df570"
          ]
        },
        "outputId": "125b9deb-f6de-4ba2-b201-d3327aa2d44c"
      },
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"best= compare_models(n_select = 9, sort= 'Accuracy')\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \" \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \". . . . . . . . . . . . . . . . . .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"  \",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"07:36:26\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-930e8f72-64c0-4559-93a2-573dcab4d376\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Initiated</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>07:36:26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Status</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>Fitting 10 Folds</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Estimator</th>\n",
              "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-930e8f72-64c0-4559-93a2-573dcab4d376')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-930e8f72-64c0-4559-93a2-573dcab4d376 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-930e8f72-64c0-4559-93a2-573dcab4d376');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
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              "    }\n",
              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    90% {\n",
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              "\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "    (() => {\n",
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              "\n",
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              "  </div>\n"
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              "                                                                            \n",
              "                                                                            \n",
              "Initiated  . . . . . . . . . . . . . . . . . .                      07:36:26\n",
              "Status     . . . . . . . . . . . . . . . . . .              Fitting 10 Folds\n",
              "Estimator  . . . . . . . . . . . . . . . . . .  Gradient Boosting Classifier"
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_8c49b_level0_row0\" class=\"row_heading level0 row0\" >rf</th>\n",
              "      <td id=\"T_8c49b_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_8c49b_row0_col1\" class=\"data row0 col1\" >0.7492</td>\n",
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              "      <td id=\"T_8c49b_row0_col3\" class=\"data row0 col3\" >0.7492</td>\n",
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              "      <td id=\"T_8c49b_row0_col5\" class=\"data row0 col5\" >0.7282</td>\n",
              "      <td id=\"T_8c49b_row0_col6\" class=\"data row0 col6\" >0.6132</td>\n",
              "      <td id=\"T_8c49b_row0_col7\" class=\"data row0 col7\" >0.6171</td>\n",
              "      <td id=\"T_8c49b_row0_col8\" class=\"data row0 col8\" >8.8060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8c49b_level0_row1\" class=\"row_heading level0 row1\" >dt</th>\n",
              "      <td id=\"T_8c49b_row1_col0\" class=\"data row1 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_8c49b_row1_col1\" class=\"data row1 col1\" >0.6855</td>\n",
              "      <td id=\"T_8c49b_row1_col2\" class=\"data row1 col2\" >0.7819</td>\n",
              "      <td id=\"T_8c49b_row1_col3\" class=\"data row1 col3\" >0.6855</td>\n",
              "      <td id=\"T_8c49b_row1_col4\" class=\"data row1 col4\" >0.6945</td>\n",
              "      <td id=\"T_8c49b_row1_col5\" class=\"data row1 col5\" >0.6895</td>\n",
              "      <td id=\"T_8c49b_row1_col6\" class=\"data row1 col6\" >0.5345</td>\n",
              "      <td id=\"T_8c49b_row1_col7\" class=\"data row1 col7\" >0.5348</td>\n",
              "      <td id=\"T_8c49b_row1_col8\" class=\"data row1 col8\" >3.1010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8c49b_level0_row2\" class=\"row_heading level0 row2\" >ridge</th>\n",
              "      <td id=\"T_8c49b_row2_col0\" class=\"data row2 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_8c49b_row2_col1\" class=\"data row2 col1\" >0.6817</td>\n",
              "      <td id=\"T_8c49b_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
              "      <td id=\"T_8c49b_row2_col3\" class=\"data row2 col3\" >0.6817</td>\n",
              "      <td id=\"T_8c49b_row2_col4\" class=\"data row2 col4\" >0.7797</td>\n",
              "      <td id=\"T_8c49b_row2_col5\" class=\"data row2 col5\" >0.7166</td>\n",
              "      <td id=\"T_8c49b_row2_col6\" class=\"data row2 col6\" >0.5591</td>\n",
              "      <td id=\"T_8c49b_row2_col7\" class=\"data row2 col7\" >0.5689</td>\n",
              "      <td id=\"T_8c49b_row2_col8\" class=\"data row2 col8\" >2.1990</td>\n",
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              "    <tr>\n",
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              "      <td id=\"T_8c49b_row3_col1\" class=\"data row3 col1\" >0.6020</td>\n",
              "      <td id=\"T_8c49b_row3_col2\" class=\"data row3 col2\" >0.0000</td>\n",
              "      <td id=\"T_8c49b_row3_col3\" class=\"data row3 col3\" >0.6020</td>\n",
              "      <td id=\"T_8c49b_row3_col4\" class=\"data row3 col4\" >0.7180</td>\n",
              "      <td id=\"T_8c49b_row3_col5\" class=\"data row3 col5\" >0.6407</td>\n",
              "      <td id=\"T_8c49b_row3_col6\" class=\"data row3 col6\" >0.4578</td>\n",
              "      <td id=\"T_8c49b_row3_col7\" class=\"data row3 col7\" >0.4697</td>\n",
              "      <td id=\"T_8c49b_row3_col8\" class=\"data row3 col8\" >28.5470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8c49b_level0_row4\" class=\"row_heading level0 row4\" >nb</th>\n",
              "      <td id=\"T_8c49b_row4_col0\" class=\"data row4 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_8c49b_row4_col1\" class=\"data row4 col1\" >0.4993</td>\n",
              "      <td id=\"T_8c49b_row4_col2\" class=\"data row4 col2\" >0.8325</td>\n",
              "      <td id=\"T_8c49b_row4_col3\" class=\"data row4 col3\" >0.4993</td>\n",
              "      <td id=\"T_8c49b_row4_col4\" class=\"data row4 col4\" >0.7374</td>\n",
              "      <td id=\"T_8c49b_row4_col5\" class=\"data row4 col5\" >0.5669</td>\n",
              "      <td id=\"T_8c49b_row4_col6\" class=\"data row4 col6\" >0.3491</td>\n",
              "      <td id=\"T_8c49b_row4_col7\" class=\"data row4 col7\" >0.3805</td>\n",
              "      <td id=\"T_8c49b_row4_col8\" class=\"data row4 col8\" >2.1260</td>\n",
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              "      <td id=\"T_8c49b_row5_col0\" class=\"data row5 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_8c49b_row5_col1\" class=\"data row5 col1\" >0.4705</td>\n",
              "      <td id=\"T_8c49b_row5_col2\" class=\"data row5 col2\" >0.0000</td>\n",
              "      <td id=\"T_8c49b_row5_col3\" class=\"data row5 col3\" >0.4705</td>\n",
              "      <td id=\"T_8c49b_row5_col4\" class=\"data row5 col4\" >0.6026</td>\n",
              "      <td id=\"T_8c49b_row5_col5\" class=\"data row5 col5\" >0.5147</td>\n",
              "      <td id=\"T_8c49b_row5_col6\" class=\"data row5 col6\" >0.2908</td>\n",
              "      <td id=\"T_8c49b_row5_col7\" class=\"data row5 col7\" >0.3058</td>\n",
              "      <td id=\"T_8c49b_row5_col8\" class=\"data row5 col8\" >6.1970</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_8c49b_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
              "      <td id=\"T_8c49b_row6_col0\" class=\"data row6 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_8c49b_row6_col1\" class=\"data row6 col1\" >0.2312</td>\n",
              "      <td id=\"T_8c49b_row6_col2\" class=\"data row6 col2\" >0.5103</td>\n",
              "      <td id=\"T_8c49b_row6_col3\" class=\"data row6 col3\" >0.2312</td>\n",
              "      <td id=\"T_8c49b_row6_col4\" class=\"data row6 col4\" >0.3356</td>\n",
              "      <td id=\"T_8c49b_row6_col5\" class=\"data row6 col5\" >0.2458</td>\n",
              "      <td id=\"T_8c49b_row6_col6\" class=\"data row6 col6\" >0.0356</td>\n",
              "      <td id=\"T_8c49b_row6_col7\" class=\"data row6 col7\" >0.0389</td>\n",
              "      <td id=\"T_8c49b_row6_col8\" class=\"data row6 col8\" >2.9720</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_8c49b_level0_row7\" class=\"row_heading level0 row7\" >svm</th>\n",
              "      <td id=\"T_8c49b_row7_col0\" class=\"data row7 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_8c49b_row7_col1\" class=\"data row7 col1\" >0.1872</td>\n",
              "      <td id=\"T_8c49b_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
              "      <td id=\"T_8c49b_row7_col3\" class=\"data row7 col3\" >0.1872</td>\n",
              "      <td id=\"T_8c49b_row7_col4\" class=\"data row7 col4\" >0.3037</td>\n",
              "      <td id=\"T_8c49b_row7_col5\" class=\"data row7 col5\" >0.1442</td>\n",
              "      <td id=\"T_8c49b_row7_col6\" class=\"data row7 col6\" >0.0623</td>\n",
              "      <td id=\"T_8c49b_row7_col7\" class=\"data row7 col7\" >0.0834</td>\n",
              "      <td id=\"T_8c49b_row7_col8\" class=\"data row7 col8\" >22.9810</td>\n",
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              "    <tr>\n",
              "      <th id=\"T_8c49b_level0_row8\" class=\"row_heading level0 row8\" >qda</th>\n",
              "      <td id=\"T_8c49b_row8_col0\" class=\"data row8 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_8c49b_row8_col1\" class=\"data row8 col1\" >0.1861</td>\n",
              "      <td id=\"T_8c49b_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_8c49b_row8_col3\" class=\"data row8 col3\" >0.1861</td>\n",
              "      <td id=\"T_8c49b_row8_col4\" class=\"data row8 col4\" >0.7359</td>\n",
              "      <td id=\"T_8c49b_row8_col5\" class=\"data row8 col5\" >0.1934</td>\n",
              "      <td id=\"T_8c49b_row8_col6\" class=\"data row8 col6\" >0.0773</td>\n",
              "      <td id=\"T_8c49b_row8_col7\" class=\"data row8 col7\" >0.1441</td>\n",
              "      <td id=\"T_8c49b_row8_col8\" class=\"data row8 col8\" >2.7320</td>\n",
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              "#T_d93e2_row0_col0, #T_d93e2_row0_col4, #T_d93e2_row1_col0, #T_d93e2_row1_col1, #T_d93e2_row1_col2, #T_d93e2_row1_col3, #T_d93e2_row1_col4, #T_d93e2_row1_col5, #T_d93e2_row1_col6, #T_d93e2_row1_col7, #T_d93e2_row2_col0, #T_d93e2_row2_col1, #T_d93e2_row2_col2, #T_d93e2_row2_col3, #T_d93e2_row2_col4, #T_d93e2_row2_col5, #T_d93e2_row2_col6, #T_d93e2_row2_col7, #T_d93e2_row3_col0, #T_d93e2_row3_col1, #T_d93e2_row3_col2, #T_d93e2_row3_col3, #T_d93e2_row3_col4, #T_d93e2_row3_col5, #T_d93e2_row3_col6, #T_d93e2_row3_col7, #T_d93e2_row4_col0, #T_d93e2_row4_col1, #T_d93e2_row4_col2, #T_d93e2_row4_col3, #T_d93e2_row4_col4, #T_d93e2_row4_col5, #T_d93e2_row4_col6, #T_d93e2_row4_col7, #T_d93e2_row5_col0, #T_d93e2_row5_col1, #T_d93e2_row5_col2, #T_d93e2_row5_col3, #T_d93e2_row5_col4, #T_d93e2_row5_col5, #T_d93e2_row5_col6, #T_d93e2_row5_col7, #T_d93e2_row6_col0, #T_d93e2_row6_col1, #T_d93e2_row6_col2, #T_d93e2_row6_col3, #T_d93e2_row6_col4, #T_d93e2_row6_col5, #T_d93e2_row6_col6, #T_d93e2_row6_col7, #T_d93e2_row7_col0, #T_d93e2_row7_col1, #T_d93e2_row7_col2, #T_d93e2_row7_col3, #T_d93e2_row7_col5, #T_d93e2_row7_col6, #T_d93e2_row7_col7, #T_d93e2_row8_col0, #T_d93e2_row8_col1, #T_d93e2_row8_col2, #T_d93e2_row8_col3, #T_d93e2_row8_col4, #T_d93e2_row8_col5, #T_d93e2_row8_col6, #T_d93e2_row8_col7, #T_d93e2_row9_col0, #T_d93e2_row9_col1, #T_d93e2_row9_col2, #T_d93e2_row9_col3, #T_d93e2_row9_col4, #T_d93e2_row9_col5, #T_d93e2_row9_col6, #T_d93e2_row9_col7, #T_d93e2_row10_col0, #T_d93e2_row10_col1, #T_d93e2_row10_col2, #T_d93e2_row10_col3, #T_d93e2_row10_col4, #T_d93e2_row10_col5, #T_d93e2_row10_col6, #T_d93e2_row10_col7, #T_d93e2_row11_col0, #T_d93e2_row11_col1, #T_d93e2_row11_col2, #T_d93e2_row11_col3, #T_d93e2_row11_col4, #T_d93e2_row11_col5, #T_d93e2_row11_col6, #T_d93e2_row11_col7, #T_d93e2_row12_col0, #T_d93e2_row12_col1, #T_d93e2_row12_col2, #T_d93e2_row12_col3, #T_d93e2_row12_col4, #T_d93e2_row12_col5, #T_d93e2_row12_col6, #T_d93e2_row12_col7, #T_d93e2_row13_col0, #T_d93e2_row13_col1, #T_d93e2_row13_col2, #T_d93e2_row13_col3, #T_d93e2_row13_col4, #T_d93e2_row13_col5, #T_d93e2_row13_col6, #T_d93e2_row13_col7, #T_d93e2_row14_col0, #T_d93e2_row14_col1, #T_d93e2_row14_col2, #T_d93e2_row14_col3, #T_d93e2_row14_col4, #T_d93e2_row14_col5, #T_d93e2_row14_col6, #T_d93e2_row14_col7 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_d93e2_row0_col1, #T_d93e2_row0_col2, #T_d93e2_row0_col3, #T_d93e2_row0_col5, #T_d93e2_row0_col6, #T_d93e2_row0_col7, #T_d93e2_row7_col4 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_d93e2_row0_col8, #T_d93e2_row1_col8, #T_d93e2_row2_col8, #T_d93e2_row3_col8, #T_d93e2_row4_col8, #T_d93e2_row5_col8, #T_d93e2_row6_col8, #T_d93e2_row7_col8, #T_d93e2_row8_col8, #T_d93e2_row9_col8, #T_d93e2_row10_col8, #T_d93e2_row11_col8, #T_d93e2_row12_col8, #T_d93e2_row13_col8 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_d93e2_row14_col8 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d93e2\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d93e2_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_d93e2_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th id=\"T_d93e2_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
              "      <th id=\"T_d93e2_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
              "      <th id=\"T_d93e2_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
              "      <th id=\"T_d93e2_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
              "      <th id=\"T_d93e2_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th id=\"T_d93e2_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
              "      <th id=\"T_d93e2_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row0\" class=\"row_heading level0 row0\" >lightgbm</th>\n",
              "      <td id=\"T_d93e2_row0_col0\" class=\"data row0 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_d93e2_row0_col1\" class=\"data row0 col1\" >0.7578</td>\n",
              "      <td id=\"T_d93e2_row0_col2\" class=\"data row0 col2\" >0.9244</td>\n",
              "      <td id=\"T_d93e2_row0_col3\" class=\"data row0 col3\" >0.7578</td>\n",
              "      <td id=\"T_d93e2_row0_col4\" class=\"data row0 col4\" >0.7332</td>\n",
              "      <td id=\"T_d93e2_row0_col5\" class=\"data row0 col5\" >0.7419</td>\n",
              "      <td id=\"T_d93e2_row0_col6\" class=\"data row0 col6\" >0.6301</td>\n",
              "      <td id=\"T_d93e2_row0_col7\" class=\"data row0 col7\" >0.6319</td>\n",
              "      <td id=\"T_d93e2_row0_col8\" class=\"data row0 col8\" >60.4480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row1\" class=\"row_heading level0 row1\" >xgboost</th>\n",
              "      <td id=\"T_d93e2_row1_col0\" class=\"data row1 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_d93e2_row1_col1\" class=\"data row1 col1\" >0.7554</td>\n",
              "      <td id=\"T_d93e2_row1_col2\" class=\"data row1 col2\" >0.9229</td>\n",
              "      <td id=\"T_d93e2_row1_col3\" class=\"data row1 col3\" >0.7554</td>\n",
              "      <td id=\"T_d93e2_row1_col4\" class=\"data row1 col4\" >0.7316</td>\n",
              "      <td id=\"T_d93e2_row1_col5\" class=\"data row1 col5\" >0.7400</td>\n",
              "      <td id=\"T_d93e2_row1_col6\" class=\"data row1 col6\" >0.6263</td>\n",
              "      <td id=\"T_d93e2_row1_col7\" class=\"data row1 col7\" >0.6281</td>\n",
              "      <td id=\"T_d93e2_row1_col8\" class=\"data row1 col8\" >22.0710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
              "      <td id=\"T_d93e2_row2_col0\" class=\"data row2 col0\" >Random Forest Classifier</td>\n",
              "      <td id=\"T_d93e2_row2_col1\" class=\"data row2 col1\" >0.7492</td>\n",
              "      <td id=\"T_d93e2_row2_col2\" class=\"data row2 col2\" >0.9116</td>\n",
              "      <td id=\"T_d93e2_row2_col3\" class=\"data row2 col3\" >0.7492</td>\n",
              "      <td id=\"T_d93e2_row2_col4\" class=\"data row2 col4\" >0.7211</td>\n",
              "      <td id=\"T_d93e2_row2_col5\" class=\"data row2 col5\" >0.7282</td>\n",
              "      <td id=\"T_d93e2_row2_col6\" class=\"data row2 col6\" >0.6132</td>\n",
              "      <td id=\"T_d93e2_row2_col7\" class=\"data row2 col7\" >0.6171</td>\n",
              "      <td id=\"T_d93e2_row2_col8\" class=\"data row2 col8\" >8.8060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row3\" class=\"row_heading level0 row3\" >et</th>\n",
              "      <td id=\"T_d93e2_row3_col0\" class=\"data row3 col0\" >Extra Trees Classifier</td>\n",
              "      <td id=\"T_d93e2_row3_col1\" class=\"data row3 col1\" >0.7468</td>\n",
              "      <td id=\"T_d93e2_row3_col2\" class=\"data row3 col2\" >0.9015</td>\n",
              "      <td id=\"T_d93e2_row3_col3\" class=\"data row3 col3\" >0.7468</td>\n",
              "      <td id=\"T_d93e2_row3_col4\" class=\"data row3 col4\" >0.7231</td>\n",
              "      <td id=\"T_d93e2_row3_col5\" class=\"data row3 col5\" >0.7313</td>\n",
              "      <td id=\"T_d93e2_row3_col6\" class=\"data row3 col6\" >0.6136</td>\n",
              "      <td id=\"T_d93e2_row3_col7\" class=\"data row3 col7\" >0.6155</td>\n",
              "      <td id=\"T_d93e2_row3_col8\" class=\"data row3 col8\" >10.9780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row4\" class=\"row_heading level0 row4\" >gbc</th>\n",
              "      <td id=\"T_d93e2_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Classifier</td>\n",
              "      <td id=\"T_d93e2_row4_col1\" class=\"data row4 col1\" >0.7397</td>\n",
              "      <td id=\"T_d93e2_row4_col2\" class=\"data row4 col2\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row4_col3\" class=\"data row4 col3\" >0.7397</td>\n",
              "      <td id=\"T_d93e2_row4_col4\" class=\"data row4 col4\" >0.7396</td>\n",
              "      <td id=\"T_d93e2_row4_col5\" class=\"data row4 col5\" >0.7388</td>\n",
              "      <td id=\"T_d93e2_row4_col6\" class=\"data row4 col6\" >0.6135</td>\n",
              "      <td id=\"T_d93e2_row4_col7\" class=\"data row4 col7\" >0.6139</td>\n",
              "      <td id=\"T_d93e2_row4_col8\" class=\"data row4 col8\" >96.7110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row5\" class=\"row_heading level0 row5\" >dt</th>\n",
              "      <td id=\"T_d93e2_row5_col0\" class=\"data row5 col0\" >Decision Tree Classifier</td>\n",
              "      <td id=\"T_d93e2_row5_col1\" class=\"data row5 col1\" >0.6855</td>\n",
              "      <td id=\"T_d93e2_row5_col2\" class=\"data row5 col2\" >0.7819</td>\n",
              "      <td id=\"T_d93e2_row5_col3\" class=\"data row5 col3\" >0.6855</td>\n",
              "      <td id=\"T_d93e2_row5_col4\" class=\"data row5 col4\" >0.6945</td>\n",
              "      <td id=\"T_d93e2_row5_col5\" class=\"data row5 col5\" >0.6895</td>\n",
              "      <td id=\"T_d93e2_row5_col6\" class=\"data row5 col6\" >0.5345</td>\n",
              "      <td id=\"T_d93e2_row5_col7\" class=\"data row5 col7\" >0.5348</td>\n",
              "      <td id=\"T_d93e2_row5_col8\" class=\"data row5 col8\" >3.1010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row6\" class=\"row_heading level0 row6\" >ridge</th>\n",
              "      <td id=\"T_d93e2_row6_col0\" class=\"data row6 col0\" >Ridge Classifier</td>\n",
              "      <td id=\"T_d93e2_row6_col1\" class=\"data row6 col1\" >0.6817</td>\n",
              "      <td id=\"T_d93e2_row6_col2\" class=\"data row6 col2\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row6_col3\" class=\"data row6 col3\" >0.6817</td>\n",
              "      <td id=\"T_d93e2_row6_col4\" class=\"data row6 col4\" >0.7797</td>\n",
              "      <td id=\"T_d93e2_row6_col5\" class=\"data row6 col5\" >0.7166</td>\n",
              "      <td id=\"T_d93e2_row6_col6\" class=\"data row6 col6\" >0.5591</td>\n",
              "      <td id=\"T_d93e2_row6_col7\" class=\"data row6 col7\" >0.5689</td>\n",
              "      <td id=\"T_d93e2_row6_col8\" class=\"data row6 col8\" >2.1990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row7\" class=\"row_heading level0 row7\" >lda</th>\n",
              "      <td id=\"T_d93e2_row7_col0\" class=\"data row7 col0\" >Linear Discriminant Analysis</td>\n",
              "      <td id=\"T_d93e2_row7_col1\" class=\"data row7 col1\" >0.6675</td>\n",
              "      <td id=\"T_d93e2_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row7_col3\" class=\"data row7 col3\" >0.6675</td>\n",
              "      <td id=\"T_d93e2_row7_col4\" class=\"data row7 col4\" >0.7986</td>\n",
              "      <td id=\"T_d93e2_row7_col5\" class=\"data row7 col5\" >0.7122</td>\n",
              "      <td id=\"T_d93e2_row7_col6\" class=\"data row7 col6\" >0.5470</td>\n",
              "      <td id=\"T_d93e2_row7_col7\" class=\"data row7 col7\" >0.5620</td>\n",
              "      <td id=\"T_d93e2_row7_col8\" class=\"data row7 col8\" >2.8970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row8\" class=\"row_heading level0 row8\" >lr</th>\n",
              "      <td id=\"T_d93e2_row8_col0\" class=\"data row8 col0\" >Logistic Regression</td>\n",
              "      <td id=\"T_d93e2_row8_col1\" class=\"data row8 col1\" >0.6020</td>\n",
              "      <td id=\"T_d93e2_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row8_col3\" class=\"data row8 col3\" >0.6020</td>\n",
              "      <td id=\"T_d93e2_row8_col4\" class=\"data row8 col4\" >0.7180</td>\n",
              "      <td id=\"T_d93e2_row8_col5\" class=\"data row8 col5\" >0.6407</td>\n",
              "      <td id=\"T_d93e2_row8_col6\" class=\"data row8 col6\" >0.4578</td>\n",
              "      <td id=\"T_d93e2_row8_col7\" class=\"data row8 col7\" >0.4697</td>\n",
              "      <td id=\"T_d93e2_row8_col8\" class=\"data row8 col8\" >28.5470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row9\" class=\"row_heading level0 row9\" >nb</th>\n",
              "      <td id=\"T_d93e2_row9_col0\" class=\"data row9 col0\" >Naive Bayes</td>\n",
              "      <td id=\"T_d93e2_row9_col1\" class=\"data row9 col1\" >0.4993</td>\n",
              "      <td id=\"T_d93e2_row9_col2\" class=\"data row9 col2\" >0.8325</td>\n",
              "      <td id=\"T_d93e2_row9_col3\" class=\"data row9 col3\" >0.4993</td>\n",
              "      <td id=\"T_d93e2_row9_col4\" class=\"data row9 col4\" >0.7374</td>\n",
              "      <td id=\"T_d93e2_row9_col5\" class=\"data row9 col5\" >0.5669</td>\n",
              "      <td id=\"T_d93e2_row9_col6\" class=\"data row9 col6\" >0.3491</td>\n",
              "      <td id=\"T_d93e2_row9_col7\" class=\"data row9 col7\" >0.3805</td>\n",
              "      <td id=\"T_d93e2_row9_col8\" class=\"data row9 col8\" >2.1260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row10\" class=\"row_heading level0 row10\" >ada</th>\n",
              "      <td id=\"T_d93e2_row10_col0\" class=\"data row10 col0\" >Ada Boost Classifier</td>\n",
              "      <td id=\"T_d93e2_row10_col1\" class=\"data row10 col1\" >0.4705</td>\n",
              "      <td id=\"T_d93e2_row10_col2\" class=\"data row10 col2\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row10_col3\" class=\"data row10 col3\" >0.4705</td>\n",
              "      <td id=\"T_d93e2_row10_col4\" class=\"data row10 col4\" >0.6026</td>\n",
              "      <td id=\"T_d93e2_row10_col5\" class=\"data row10 col5\" >0.5147</td>\n",
              "      <td id=\"T_d93e2_row10_col6\" class=\"data row10 col6\" >0.2908</td>\n",
              "      <td id=\"T_d93e2_row10_col7\" class=\"data row10 col7\" >0.3058</td>\n",
              "      <td id=\"T_d93e2_row10_col8\" class=\"data row10 col8\" >6.1970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row11\" class=\"row_heading level0 row11\" >knn</th>\n",
              "      <td id=\"T_d93e2_row11_col0\" class=\"data row11 col0\" >K Neighbors Classifier</td>\n",
              "      <td id=\"T_d93e2_row11_col1\" class=\"data row11 col1\" >0.2312</td>\n",
              "      <td id=\"T_d93e2_row11_col2\" class=\"data row11 col2\" >0.5103</td>\n",
              "      <td id=\"T_d93e2_row11_col3\" class=\"data row11 col3\" >0.2312</td>\n",
              "      <td id=\"T_d93e2_row11_col4\" class=\"data row11 col4\" >0.3356</td>\n",
              "      <td id=\"T_d93e2_row11_col5\" class=\"data row11 col5\" >0.2458</td>\n",
              "      <td id=\"T_d93e2_row11_col6\" class=\"data row11 col6\" >0.0356</td>\n",
              "      <td id=\"T_d93e2_row11_col7\" class=\"data row11 col7\" >0.0389</td>\n",
              "      <td id=\"T_d93e2_row11_col8\" class=\"data row11 col8\" >2.9720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row12\" class=\"row_heading level0 row12\" >svm</th>\n",
              "      <td id=\"T_d93e2_row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
              "      <td id=\"T_d93e2_row12_col1\" class=\"data row12 col1\" >0.1872</td>\n",
              "      <td id=\"T_d93e2_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row12_col3\" class=\"data row12 col3\" >0.1872</td>\n",
              "      <td id=\"T_d93e2_row12_col4\" class=\"data row12 col4\" >0.3037</td>\n",
              "      <td id=\"T_d93e2_row12_col5\" class=\"data row12 col5\" >0.1442</td>\n",
              "      <td id=\"T_d93e2_row12_col6\" class=\"data row12 col6\" >0.0623</td>\n",
              "      <td id=\"T_d93e2_row12_col7\" class=\"data row12 col7\" >0.0834</td>\n",
              "      <td id=\"T_d93e2_row12_col8\" class=\"data row12 col8\" >22.9810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row13\" class=\"row_heading level0 row13\" >qda</th>\n",
              "      <td id=\"T_d93e2_row13_col0\" class=\"data row13 col0\" >Quadratic Discriminant Analysis</td>\n",
              "      <td id=\"T_d93e2_row13_col1\" class=\"data row13 col1\" >0.1861</td>\n",
              "      <td id=\"T_d93e2_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row13_col3\" class=\"data row13 col3\" >0.1861</td>\n",
              "      <td id=\"T_d93e2_row13_col4\" class=\"data row13 col4\" >0.7359</td>\n",
              "      <td id=\"T_d93e2_row13_col5\" class=\"data row13 col5\" >0.1934</td>\n",
              "      <td id=\"T_d93e2_row13_col6\" class=\"data row13 col6\" >0.0773</td>\n",
              "      <td id=\"T_d93e2_row13_col7\" class=\"data row13 col7\" >0.1441</td>\n",
              "      <td id=\"T_d93e2_row13_col8\" class=\"data row13 col8\" >2.7320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d93e2_level0_row14\" class=\"row_heading level0 row14\" >dummy</th>\n",
              "      <td id=\"T_d93e2_row14_col0\" class=\"data row14 col0\" >Dummy Classifier</td>\n",
              "      <td id=\"T_d93e2_row14_col1\" class=\"data row14 col1\" >0.0562</td>\n",
              "      <td id=\"T_d93e2_row14_col2\" class=\"data row14 col2\" >0.5000</td>\n",
              "      <td id=\"T_d93e2_row14_col3\" class=\"data row14 col3\" >0.0562</td>\n",
              "      <td id=\"T_d93e2_row14_col4\" class=\"data row14 col4\" >0.0032</td>\n",
              "      <td id=\"T_d93e2_row14_col5\" class=\"data row14 col5\" >0.0060</td>\n",
              "      <td id=\"T_d93e2_row14_col6\" class=\"data row14 col6\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row14_col7\" class=\"data row14 col7\" >0.0000</td>\n",
              "      <td id=\"T_d93e2_row14_col8\" class=\"data row14 col8\" >1.9020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Creating Best Models**</p>"
      ],
      "metadata": {
        "id": "GxwXOvT5LtUf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gbc = create_model('gbc')\n",
        "rf = create_model('rf')\n",
        "et = create_model('et')"
      ],
      "metadata": {
        "id": "Vy6mX95_LwGY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "59a2ffed64d34bd5a90311d329909598",
            "ad36e5f54df84ef880941ee58e8d0c82",
            "e433471d17244c0993ec4b4b2e196751",
            "23a5d0ba1a2443da9fd290c8bb1964db",
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            "638ca36af48043f6b5ab9fdec6314466",
            "1c441394c06e4ecc8199c960ac0c6ec8",
            "b9a02351ae0346948a8b95dfb9e549ac",
            "abc2dde29e724a4aae899029ec388b09",
            "5f9c206834a049ee89c394a347c59882",
            "814a42375dd14d6bac8c845e7ce8b2b7",
            "aadc10eb0e204e919e830a6108bca8b2",
            "9890523d74c848a58854c0680ae7e1ea",
            "f597a1f8344f4e26a921ecc09906f9bb",
            "26ef21e6a94142b4bb256371e4ee81e2",
            "c0d8a23eba49427fa8ca2a07a52302a4",
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            "8917164875574f79b4ae179bec1e2bee",
            "3fb6f6548d014ca28dc0d2727384726d",
            "83f400066b8a4b19bd92ac5a6466a771",
            "8376edcab9304947bb06b80c6187fa57",
            "7cac1bf139e14c2ba808c6e45fca360c",
            "3cc805de18244bb0be2fb03b8b5470a4",
            "79df23b1f5244b66af65308603bf91a2",
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            "d10728dba54742549b6d191876aacaa5",
            "3ae4a9e9653145ad94a84632dea56702"
          ]
        },
        "outputId": "5a505683-8532-4eba-b1ab-064ea042e68e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c0714e11490>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_a1ebd_row10_col0, #T_a1ebd_row10_col1, #T_a1ebd_row10_col2, #T_a1ebd_row10_col3, #T_a1ebd_row10_col4, #T_a1ebd_row10_col5, #T_a1ebd_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_a1ebd\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_a1ebd_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_a1ebd_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_a1ebd_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_a1ebd_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_a1ebd_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_a1ebd_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_a1ebd_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_a1ebd_row0_col0\" class=\"data row0 col0\" >0.7485</td>\n",
              "      <td id=\"T_a1ebd_row0_col1\" class=\"data row0 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row0_col2\" class=\"data row0 col2\" >0.7485</td>\n",
              "      <td id=\"T_a1ebd_row0_col3\" class=\"data row0 col3\" >0.7457</td>\n",
              "      <td id=\"T_a1ebd_row0_col4\" class=\"data row0 col4\" >0.7466</td>\n",
              "      <td id=\"T_a1ebd_row0_col5\" class=\"data row0 col5\" >0.6262</td>\n",
              "      <td id=\"T_a1ebd_row0_col6\" class=\"data row0 col6\" >0.6264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_a1ebd_row1_col0\" class=\"data row1 col0\" >0.7459</td>\n",
              "      <td id=\"T_a1ebd_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row1_col2\" class=\"data row1 col2\" >0.7459</td>\n",
              "      <td id=\"T_a1ebd_row1_col3\" class=\"data row1 col3\" >0.7415</td>\n",
              "      <td id=\"T_a1ebd_row1_col4\" class=\"data row1 col4\" >0.7429</td>\n",
              "      <td id=\"T_a1ebd_row1_col5\" class=\"data row1 col5\" >0.6228</td>\n",
              "      <td id=\"T_a1ebd_row1_col6\" class=\"data row1 col6\" >0.6230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_a1ebd_row2_col0\" class=\"data row2 col0\" >0.7313</td>\n",
              "      <td id=\"T_a1ebd_row2_col1\" class=\"data row2 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row2_col2\" class=\"data row2 col2\" >0.7313</td>\n",
              "      <td id=\"T_a1ebd_row2_col3\" class=\"data row2 col3\" >0.7387</td>\n",
              "      <td id=\"T_a1ebd_row2_col4\" class=\"data row2 col4\" >0.7345</td>\n",
              "      <td id=\"T_a1ebd_row2_col5\" class=\"data row2 col5\" >0.6030</td>\n",
              "      <td id=\"T_a1ebd_row2_col6\" class=\"data row2 col6\" >0.6033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_a1ebd_row3_col0\" class=\"data row3 col0\" >0.7425</td>\n",
              "      <td id=\"T_a1ebd_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row3_col2\" class=\"data row3 col2\" >0.7425</td>\n",
              "      <td id=\"T_a1ebd_row3_col3\" class=\"data row3 col3\" >0.7388</td>\n",
              "      <td id=\"T_a1ebd_row3_col4\" class=\"data row3 col4\" >0.7397</td>\n",
              "      <td id=\"T_a1ebd_row3_col5\" class=\"data row3 col5\" >0.6162</td>\n",
              "      <td id=\"T_a1ebd_row3_col6\" class=\"data row3 col6\" >0.6167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_a1ebd_row4_col0\" class=\"data row4 col0\" >0.7233</td>\n",
              "      <td id=\"T_a1ebd_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row4_col2\" class=\"data row4 col2\" >0.7233</td>\n",
              "      <td id=\"T_a1ebd_row4_col3\" class=\"data row4 col3\" >0.7248</td>\n",
              "      <td id=\"T_a1ebd_row4_col4\" class=\"data row4 col4\" >0.7227</td>\n",
              "      <td id=\"T_a1ebd_row4_col5\" class=\"data row4 col5\" >0.5900</td>\n",
              "      <td id=\"T_a1ebd_row4_col6\" class=\"data row4 col6\" >0.5907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_a1ebd_row5_col0\" class=\"data row5 col0\" >0.7276</td>\n",
              "      <td id=\"T_a1ebd_row5_col1\" class=\"data row5 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row5_col2\" class=\"data row5 col2\" >0.7276</td>\n",
              "      <td id=\"T_a1ebd_row5_col3\" class=\"data row5 col3\" >0.7311</td>\n",
              "      <td id=\"T_a1ebd_row5_col4\" class=\"data row5 col4\" >0.7278</td>\n",
              "      <td id=\"T_a1ebd_row5_col5\" class=\"data row5 col5\" >0.5957</td>\n",
              "      <td id=\"T_a1ebd_row5_col6\" class=\"data row5 col6\" >0.5966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_a1ebd_row6_col0\" class=\"data row6 col0\" >0.7422</td>\n",
              "      <td id=\"T_a1ebd_row6_col1\" class=\"data row6 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row6_col2\" class=\"data row6 col2\" >0.7422</td>\n",
              "      <td id=\"T_a1ebd_row6_col3\" class=\"data row6 col3\" >0.7433</td>\n",
              "      <td id=\"T_a1ebd_row6_col4\" class=\"data row6 col4\" >0.7424</td>\n",
              "      <td id=\"T_a1ebd_row6_col5\" class=\"data row6 col5\" >0.6159</td>\n",
              "      <td id=\"T_a1ebd_row6_col6\" class=\"data row6 col6\" >0.6161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_a1ebd_row7_col0\" class=\"data row7 col0\" >0.7509</td>\n",
              "      <td id=\"T_a1ebd_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row7_col2\" class=\"data row7 col2\" >0.7509</td>\n",
              "      <td id=\"T_a1ebd_row7_col3\" class=\"data row7 col3\" >0.7505</td>\n",
              "      <td id=\"T_a1ebd_row7_col4\" class=\"data row7 col4\" >0.7503</td>\n",
              "      <td id=\"T_a1ebd_row7_col5\" class=\"data row7 col5\" >0.6301</td>\n",
              "      <td id=\"T_a1ebd_row7_col6\" class=\"data row7 col6\" >0.6303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_a1ebd_row8_col0\" class=\"data row8 col0\" >0.7336</td>\n",
              "      <td id=\"T_a1ebd_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row8_col2\" class=\"data row8 col2\" >0.7336</td>\n",
              "      <td id=\"T_a1ebd_row8_col3\" class=\"data row8 col3\" >0.7275</td>\n",
              "      <td id=\"T_a1ebd_row8_col4\" class=\"data row8 col4\" >0.7291</td>\n",
              "      <td id=\"T_a1ebd_row8_col5\" class=\"data row8 col5\" >0.6029</td>\n",
              "      <td id=\"T_a1ebd_row8_col6\" class=\"data row8 col6\" >0.6035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_a1ebd_row9_col0\" class=\"data row9 col0\" >0.7517</td>\n",
              "      <td id=\"T_a1ebd_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row9_col2\" class=\"data row9 col2\" >0.7517</td>\n",
              "      <td id=\"T_a1ebd_row9_col3\" class=\"data row9 col3\" >0.7541</td>\n",
              "      <td id=\"T_a1ebd_row9_col4\" class=\"data row9 col4\" >0.7523</td>\n",
              "      <td id=\"T_a1ebd_row9_col5\" class=\"data row9 col5\" >0.6324</td>\n",
              "      <td id=\"T_a1ebd_row9_col6\" class=\"data row9 col6\" >0.6327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_a1ebd_row10_col0\" class=\"data row10 col0\" >0.7397</td>\n",
              "      <td id=\"T_a1ebd_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row10_col2\" class=\"data row10 col2\" >0.7397</td>\n",
              "      <td id=\"T_a1ebd_row10_col3\" class=\"data row10 col3\" >0.7396</td>\n",
              "      <td id=\"T_a1ebd_row10_col4\" class=\"data row10 col4\" >0.7388</td>\n",
              "      <td id=\"T_a1ebd_row10_col5\" class=\"data row10 col5\" >0.6135</td>\n",
              "      <td id=\"T_a1ebd_row10_col6\" class=\"data row10 col6\" >0.6139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_a1ebd_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_a1ebd_row11_col0\" class=\"data row11 col0\" >0.0096</td>\n",
              "      <td id=\"T_a1ebd_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
              "      <td id=\"T_a1ebd_row11_col2\" class=\"data row11 col2\" >0.0096</td>\n",
              "      <td id=\"T_a1ebd_row11_col3\" class=\"data row11 col3\" >0.0091</td>\n",
              "      <td id=\"T_a1ebd_row11_col4\" class=\"data row11 col4\" >0.0095</td>\n",
              "      <td id=\"T_a1ebd_row11_col5\" class=\"data row11 col5\" >0.0141</td>\n",
              "      <td id=\"T_a1ebd_row11_col6\" class=\"data row11 col6\" >0.0139</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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        },
        {
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          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": []
          },
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7c07187e1b50>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_4cfbc_row10_col0, #T_4cfbc_row10_col1, #T_4cfbc_row10_col2, #T_4cfbc_row10_col3, #T_4cfbc_row10_col4, #T_4cfbc_row10_col5, #T_4cfbc_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_4cfbc\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_4cfbc_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_4cfbc_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_4cfbc_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_4cfbc_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_4cfbc_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_4cfbc_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_4cfbc_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_4cfbc_row0_col0\" class=\"data row0 col0\" >0.7571</td>\n",
              "      <td id=\"T_4cfbc_row0_col1\" class=\"data row0 col1\" >0.9199</td>\n",
              "      <td id=\"T_4cfbc_row0_col2\" class=\"data row0 col2\" >0.7571</td>\n",
              "      <td id=\"T_4cfbc_row0_col3\" class=\"data row0 col3\" >0.7311</td>\n",
              "      <td id=\"T_4cfbc_row0_col4\" class=\"data row0 col4\" >0.7370</td>\n",
              "      <td id=\"T_4cfbc_row0_col5\" class=\"data row0 col5\" >0.6269</td>\n",
              "      <td id=\"T_4cfbc_row0_col6\" class=\"data row0 col6\" >0.6306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_4cfbc_row1_col0\" class=\"data row1 col0\" >0.7666</td>\n",
              "      <td id=\"T_4cfbc_row1_col1\" class=\"data row1 col1\" >0.9196</td>\n",
              "      <td id=\"T_4cfbc_row1_col2\" class=\"data row1 col2\" >0.7666</td>\n",
              "      <td id=\"T_4cfbc_row1_col3\" class=\"data row1 col3\" >0.7348</td>\n",
              "      <td id=\"T_4cfbc_row1_col4\" class=\"data row1 col4\" >0.7430</td>\n",
              "      <td id=\"T_4cfbc_row1_col5\" class=\"data row1 col5\" >0.6396</td>\n",
              "      <td id=\"T_4cfbc_row1_col6\" class=\"data row1 col6\" >0.6445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_4cfbc_row2_col0\" class=\"data row2 col0\" >0.7476</td>\n",
              "      <td id=\"T_4cfbc_row2_col1\" class=\"data row2 col1\" >0.9022</td>\n",
              "      <td id=\"T_4cfbc_row2_col2\" class=\"data row2 col2\" >0.7476</td>\n",
              "      <td id=\"T_4cfbc_row2_col3\" class=\"data row2 col3\" >0.7177</td>\n",
              "      <td id=\"T_4cfbc_row2_col4\" class=\"data row2 col4\" >0.7267</td>\n",
              "      <td id=\"T_4cfbc_row2_col5\" class=\"data row2 col5\" >0.6099</td>\n",
              "      <td id=\"T_4cfbc_row2_col6\" class=\"data row2 col6\" >0.6135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_4cfbc_row3_col0\" class=\"data row3 col0\" >0.7442</td>\n",
              "      <td id=\"T_4cfbc_row3_col1\" class=\"data row3 col1\" >0.9065</td>\n",
              "      <td id=\"T_4cfbc_row3_col2\" class=\"data row3 col2\" >0.7442</td>\n",
              "      <td id=\"T_4cfbc_row3_col3\" class=\"data row3 col3\" >0.7163</td>\n",
              "      <td id=\"T_4cfbc_row3_col4\" class=\"data row3 col4\" >0.7236</td>\n",
              "      <td id=\"T_4cfbc_row3_col5\" class=\"data row3 col5\" >0.6061</td>\n",
              "      <td id=\"T_4cfbc_row3_col6\" class=\"data row3 col6\" >0.6096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_4cfbc_row4_col0\" class=\"data row4 col0\" >0.7328</td>\n",
              "      <td id=\"T_4cfbc_row4_col1\" class=\"data row4 col1\" >0.9067</td>\n",
              "      <td id=\"T_4cfbc_row4_col2\" class=\"data row4 col2\" >0.7328</td>\n",
              "      <td id=\"T_4cfbc_row4_col3\" class=\"data row4 col3\" >0.7087</td>\n",
              "      <td id=\"T_4cfbc_row4_col4\" class=\"data row4 col4\" >0.7176</td>\n",
              "      <td id=\"T_4cfbc_row4_col5\" class=\"data row4 col5\" >0.5922</td>\n",
              "      <td id=\"T_4cfbc_row4_col6\" class=\"data row4 col6\" >0.5941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_4cfbc_row5_col0\" class=\"data row5 col0\" >0.7431</td>\n",
              "      <td id=\"T_4cfbc_row5_col1\" class=\"data row5 col1\" >0.9029</td>\n",
              "      <td id=\"T_4cfbc_row5_col2\" class=\"data row5 col2\" >0.7431</td>\n",
              "      <td id=\"T_4cfbc_row5_col3\" class=\"data row5 col3\" >0.7151</td>\n",
              "      <td id=\"T_4cfbc_row5_col4\" class=\"data row5 col4\" >0.7221</td>\n",
              "      <td id=\"T_4cfbc_row5_col5\" class=\"data row5 col5\" >0.6023</td>\n",
              "      <td id=\"T_4cfbc_row5_col6\" class=\"data row5 col6\" >0.6068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_4cfbc_row6_col0\" class=\"data row6 col0\" >0.7500</td>\n",
              "      <td id=\"T_4cfbc_row6_col1\" class=\"data row6 col1\" >0.9083</td>\n",
              "      <td id=\"T_4cfbc_row6_col2\" class=\"data row6 col2\" >0.7500</td>\n",
              "      <td id=\"T_4cfbc_row6_col3\" class=\"data row6 col3\" >0.7254</td>\n",
              "      <td id=\"T_4cfbc_row6_col4\" class=\"data row6 col4\" >0.7293</td>\n",
              "      <td id=\"T_4cfbc_row6_col5\" class=\"data row6 col5\" >0.6141</td>\n",
              "      <td id=\"T_4cfbc_row6_col6\" class=\"data row6 col6\" >0.6175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_4cfbc_row7_col0\" class=\"data row7 col0\" >0.7534</td>\n",
              "      <td id=\"T_4cfbc_row7_col1\" class=\"data row7 col1\" >0.9204</td>\n",
              "      <td id=\"T_4cfbc_row7_col2\" class=\"data row7 col2\" >0.7534</td>\n",
              "      <td id=\"T_4cfbc_row7_col3\" class=\"data row7 col3\" >0.7219</td>\n",
              "      <td id=\"T_4cfbc_row7_col4\" class=\"data row7 col4\" >0.7319</td>\n",
              "      <td id=\"T_4cfbc_row7_col5\" class=\"data row7 col5\" >0.6182</td>\n",
              "      <td id=\"T_4cfbc_row7_col6\" class=\"data row7 col6\" >0.6222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_4cfbc_row8_col0\" class=\"data row8 col0\" >0.7466</td>\n",
              "      <td id=\"T_4cfbc_row8_col1\" class=\"data row8 col1\" >0.9151</td>\n",
              "      <td id=\"T_4cfbc_row8_col2\" class=\"data row8 col2\" >0.7466</td>\n",
              "      <td id=\"T_4cfbc_row8_col3\" class=\"data row8 col3\" >0.7227</td>\n",
              "      <td id=\"T_4cfbc_row8_col4\" class=\"data row8 col4\" >0.7251</td>\n",
              "      <td id=\"T_4cfbc_row8_col5\" class=\"data row8 col5\" >0.6083</td>\n",
              "      <td id=\"T_4cfbc_row8_col6\" class=\"data row8 col6\" >0.6131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_4cfbc_row9_col0\" class=\"data row9 col0\" >0.7509</td>\n",
              "      <td id=\"T_4cfbc_row9_col1\" class=\"data row9 col1\" >0.9144</td>\n",
              "      <td id=\"T_4cfbc_row9_col2\" class=\"data row9 col2\" >0.7509</td>\n",
              "      <td id=\"T_4cfbc_row9_col3\" class=\"data row9 col3\" >0.7171</td>\n",
              "      <td id=\"T_4cfbc_row9_col4\" class=\"data row9 col4\" >0.7260</td>\n",
              "      <td id=\"T_4cfbc_row9_col5\" class=\"data row9 col5\" >0.6145</td>\n",
              "      <td id=\"T_4cfbc_row9_col6\" class=\"data row9 col6\" >0.6188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_4cfbc_row10_col0\" class=\"data row10 col0\" >0.7492</td>\n",
              "      <td id=\"T_4cfbc_row10_col1\" class=\"data row10 col1\" >0.9116</td>\n",
              "      <td id=\"T_4cfbc_row10_col2\" class=\"data row10 col2\" >0.7492</td>\n",
              "      <td id=\"T_4cfbc_row10_col3\" class=\"data row10 col3\" >0.7211</td>\n",
              "      <td id=\"T_4cfbc_row10_col4\" class=\"data row10 col4\" >0.7282</td>\n",
              "      <td id=\"T_4cfbc_row10_col5\" class=\"data row10 col5\" >0.6132</td>\n",
              "      <td id=\"T_4cfbc_row10_col6\" class=\"data row10 col6\" >0.6171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_4cfbc_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_4cfbc_row11_col0\" class=\"data row11 col0\" >0.0085</td>\n",
              "      <td id=\"T_4cfbc_row11_col1\" class=\"data row11 col1\" >0.0067</td>\n",
              "      <td id=\"T_4cfbc_row11_col2\" class=\"data row11 col2\" >0.0085</td>\n",
              "      <td id=\"T_4cfbc_row11_col3\" class=\"data row11 col3\" >0.0074</td>\n",
              "      <td id=\"T_4cfbc_row11_col4\" class=\"data row11 col4\" >0.0070</td>\n",
              "      <td id=\"T_4cfbc_row11_col5\" class=\"data row11 col5\" >0.0125</td>\n",
              "      <td id=\"T_4cfbc_row11_col6\" class=\"data row11 col6\" >0.0130</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
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        {
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          "data": {
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              "<pandas.io.formats.style.Styler at 0x7c0715e3c750>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_224d1_row10_col0, #T_224d1_row10_col1, #T_224d1_row10_col2, #T_224d1_row10_col3, #T_224d1_row10_col4, #T_224d1_row10_col5, #T_224d1_row10_col6 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_224d1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_224d1_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
              "      <th id=\"T_224d1_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
              "      <th id=\"T_224d1_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
              "      <th id=\"T_224d1_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
              "      <th id=\"T_224d1_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
              "      <th id=\"T_224d1_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
              "      <th id=\"T_224d1_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "      <th class=\"blank col6\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_224d1_row0_col0\" class=\"data row0 col0\" >0.7606</td>\n",
              "      <td id=\"T_224d1_row0_col1\" class=\"data row0 col1\" >0.9121</td>\n",
              "      <td id=\"T_224d1_row0_col2\" class=\"data row0 col2\" >0.7606</td>\n",
              "      <td id=\"T_224d1_row0_col3\" class=\"data row0 col3\" >0.7380</td>\n",
              "      <td id=\"T_224d1_row0_col4\" class=\"data row0 col4\" >0.7450</td>\n",
              "      <td id=\"T_224d1_row0_col5\" class=\"data row0 col5\" >0.6351</td>\n",
              "      <td id=\"T_224d1_row0_col6\" class=\"data row0 col6\" >0.6372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_224d1_row1_col0\" class=\"data row1 col0\" >0.7545</td>\n",
              "      <td id=\"T_224d1_row1_col1\" class=\"data row1 col1\" >0.9076</td>\n",
              "      <td id=\"T_224d1_row1_col2\" class=\"data row1 col2\" >0.7545</td>\n",
              "      <td id=\"T_224d1_row1_col3\" class=\"data row1 col3\" >0.7290</td>\n",
              "      <td id=\"T_224d1_row1_col4\" class=\"data row1 col4\" >0.7374</td>\n",
              "      <td id=\"T_224d1_row1_col5\" class=\"data row1 col5\" >0.6248</td>\n",
              "      <td id=\"T_224d1_row1_col6\" class=\"data row1 col6\" >0.6270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_224d1_row2_col0\" class=\"data row2 col0\" >0.7425</td>\n",
              "      <td id=\"T_224d1_row2_col1\" class=\"data row2 col1\" >0.8949</td>\n",
              "      <td id=\"T_224d1_row2_col2\" class=\"data row2 col2\" >0.7425</td>\n",
              "      <td id=\"T_224d1_row2_col3\" class=\"data row2 col3\" >0.7175</td>\n",
              "      <td id=\"T_224d1_row2_col4\" class=\"data row2 col4\" >0.7276</td>\n",
              "      <td id=\"T_224d1_row2_col5\" class=\"data row2 col5\" >0.6073</td>\n",
              "      <td id=\"T_224d1_row2_col6\" class=\"data row2 col6\" >0.6089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_224d1_row3_col0\" class=\"data row3 col0\" >0.7330</td>\n",
              "      <td id=\"T_224d1_row3_col1\" class=\"data row3 col1\" >0.8916</td>\n",
              "      <td id=\"T_224d1_row3_col2\" class=\"data row3 col2\" >0.7330</td>\n",
              "      <td id=\"T_224d1_row3_col3\" class=\"data row3 col3\" >0.7096</td>\n",
              "      <td id=\"T_224d1_row3_col4\" class=\"data row3 col4\" >0.7177</td>\n",
              "      <td id=\"T_224d1_row3_col5\" class=\"data row3 col5\" >0.5925</td>\n",
              "      <td id=\"T_224d1_row3_col6\" class=\"data row3 col6\" >0.5944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_224d1_row4_col0\" class=\"data row4 col0\" >0.7302</td>\n",
              "      <td id=\"T_224d1_row4_col1\" class=\"data row4 col1\" >0.8950</td>\n",
              "      <td id=\"T_224d1_row4_col2\" class=\"data row4 col2\" >0.7302</td>\n",
              "      <td id=\"T_224d1_row4_col3\" class=\"data row4 col3\" >0.7135</td>\n",
              "      <td id=\"T_224d1_row4_col4\" class=\"data row4 col4\" >0.7206</td>\n",
              "      <td id=\"T_224d1_row4_col5\" class=\"data row4 col5\" >0.5932</td>\n",
              "      <td id=\"T_224d1_row4_col6\" class=\"data row4 col6\" >0.5938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_224d1_row5_col0\" class=\"data row5 col0\" >0.7500</td>\n",
              "      <td id=\"T_224d1_row5_col1\" class=\"data row5 col1\" >0.8947</td>\n",
              "      <td id=\"T_224d1_row5_col2\" class=\"data row5 col2\" >0.7500</td>\n",
              "      <td id=\"T_224d1_row5_col3\" class=\"data row5 col3\" >0.7249</td>\n",
              "      <td id=\"T_224d1_row5_col4\" class=\"data row5 col4\" >0.7336</td>\n",
              "      <td id=\"T_224d1_row5_col5\" class=\"data row5 col5\" >0.6169</td>\n",
              "      <td id=\"T_224d1_row5_col6\" class=\"data row5 col6\" >0.6194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_224d1_row6_col0\" class=\"data row6 col0\" >0.7483</td>\n",
              "      <td id=\"T_224d1_row6_col1\" class=\"data row6 col1\" >0.8967</td>\n",
              "      <td id=\"T_224d1_row6_col2\" class=\"data row6 col2\" >0.7483</td>\n",
              "      <td id=\"T_224d1_row6_col3\" class=\"data row6 col3\" >0.7266</td>\n",
              "      <td id=\"T_224d1_row6_col4\" class=\"data row6 col4\" >0.7334</td>\n",
              "      <td id=\"T_224d1_row6_col5\" class=\"data row6 col5\" >0.6149</td>\n",
              "      <td id=\"T_224d1_row6_col6\" class=\"data row6 col6\" >0.6166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_224d1_row7_col0\" class=\"data row7 col0\" >0.7543</td>\n",
              "      <td id=\"T_224d1_row7_col1\" class=\"data row7 col1\" >0.9093</td>\n",
              "      <td id=\"T_224d1_row7_col2\" class=\"data row7 col2\" >0.7543</td>\n",
              "      <td id=\"T_224d1_row7_col3\" class=\"data row7 col3\" >0.7274</td>\n",
              "      <td id=\"T_224d1_row7_col4\" class=\"data row7 col4\" >0.7369</td>\n",
              "      <td id=\"T_224d1_row7_col5\" class=\"data row7 col5\" >0.6221</td>\n",
              "      <td id=\"T_224d1_row7_col6\" class=\"data row7 col6\" >0.6245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_224d1_row8_col0\" class=\"data row8 col0\" >0.7517</td>\n",
              "      <td id=\"T_224d1_row8_col1\" class=\"data row8 col1\" >0.9101</td>\n",
              "      <td id=\"T_224d1_row8_col2\" class=\"data row8 col2\" >0.7517</td>\n",
              "      <td id=\"T_224d1_row8_col3\" class=\"data row8 col3\" >0.7318</td>\n",
              "      <td id=\"T_224d1_row8_col4\" class=\"data row8 col4\" >0.7370</td>\n",
              "      <td id=\"T_224d1_row8_col5\" class=\"data row8 col5\" >0.6223</td>\n",
              "      <td id=\"T_224d1_row8_col6\" class=\"data row8 col6\" >0.6245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_224d1_row9_col0\" class=\"data row9 col0\" >0.7431</td>\n",
              "      <td id=\"T_224d1_row9_col1\" class=\"data row9 col1\" >0.9025</td>\n",
              "      <td id=\"T_224d1_row9_col2\" class=\"data row9 col2\" >0.7431</td>\n",
              "      <td id=\"T_224d1_row9_col3\" class=\"data row9 col3\" >0.7129</td>\n",
              "      <td id=\"T_224d1_row9_col4\" class=\"data row9 col4\" >0.7238</td>\n",
              "      <td id=\"T_224d1_row9_col5\" class=\"data row9 col5\" >0.6064</td>\n",
              "      <td id=\"T_224d1_row9_col6\" class=\"data row9 col6\" >0.6086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_224d1_row10_col0\" class=\"data row10 col0\" >0.7468</td>\n",
              "      <td id=\"T_224d1_row10_col1\" class=\"data row10 col1\" >0.9015</td>\n",
              "      <td id=\"T_224d1_row10_col2\" class=\"data row10 col2\" >0.7468</td>\n",
              "      <td id=\"T_224d1_row10_col3\" class=\"data row10 col3\" >0.7231</td>\n",
              "      <td id=\"T_224d1_row10_col4\" class=\"data row10 col4\" >0.7313</td>\n",
              "      <td id=\"T_224d1_row10_col5\" class=\"data row10 col5\" >0.6136</td>\n",
              "      <td id=\"T_224d1_row10_col6\" class=\"data row10 col6\" >0.6155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_224d1_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_224d1_row11_col0\" class=\"data row11 col0\" >0.0092</td>\n",
              "      <td id=\"T_224d1_row11_col1\" class=\"data row11 col1\" >0.0073</td>\n",
              "      <td id=\"T_224d1_row11_col2\" class=\"data row11 col2\" >0.0092</td>\n",
              "      <td id=\"T_224d1_row11_col3\" class=\"data row11 col3\" >0.0088</td>\n",
              "      <td id=\"T_224d1_row11_col4\" class=\"data row11 col4\" >0.0082</td>\n",
              "      <td id=\"T_224d1_row11_col5\" class=\"data row11 col5\" >0.0130</td>\n",
              "      <td id=\"T_224d1_row11_col6\" class=\"data row11 col6\" >0.0134</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
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      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Analyzing Models**</p>"
      ],
      "metadata": {
        "id": "cxjbbrCDL2io"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(gbc)\n",
        "evaluate_model(rf)\n",
        "evaluate_model(et)"
      ],
      "metadata": {
        "id": "vs1xrvD3L5Jv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507,
          "referenced_widgets": [
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            "d17854917f384cb8bf00542d05f18e90",
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            "bdb4fde044ad4ccb919b4e405a540fb4",
            "2ce126c1fd2048088a339a468a1a9b47",
            "e5cbccc060a34637bd8e2f6921ba8f94",
            "dbf7f9a8f1fb405d91b95c0af6a03830",
            "79c8ae1ccae448d6a7090653f69a5c3f",
            "ae82af72eae246f586c0e6819b2bd74d",
            "2c05a05c3c22411ea16832f47e5b354c",
            "8f4e17912fc14d42a74c42c12306cb4c",
            "355181fa598445e48fb6bce0dc1bdc5a",
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            "396bcf7e772741619b51e8912317b9d6",
            "9cfa414dcc164cc0be06881b464cf2be"
          ]
        },
        "outputId": "343e9cad-5169-49b3-be62-e0fc0e0141e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
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              "version_major": 2,
              "version_minor": 0,
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              "colab": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
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        {
          "output_type": "display_data",
          "data": {
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              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84653eb4824e46f1bcc8f59b402de39b"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Association Rule Mining**</p>"
      ],
      "metadata": {
        "id": "SklBAQjsMPLF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Installing Apriori**</p>"
      ],
      "metadata": {
        "id": "qoenGG9IMUBW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install apyori"
      ],
      "metadata": {
        "id": "1VlUma0_Mg80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cffb571f-cea7-4f30-f59a-2db88b81bf4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting apyori\n",
            "  Downloading apyori-1.1.2.tar.gz (8.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: apyori\n",
            "  Building wheel for apyori (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for apyori: filename=apyori-1.1.2-py3-none-any.whl size=5993 sha256=b11beb09770b83502a22396e76c7e5d2ccf0a94763796fc1aa528e04cffabaac\n",
            "  Stored in directory: /root/.cache/pip/wheels/77/3d/a6/d317a6fb32be58a602b1e8c6b5d6f31f79322da554cad2a5ea\n",
            "Successfully built apyori\n",
            "Installing collected packages: apyori\n",
            "Successfully installed apyori-1.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Finding Associations**</p>"
      ],
      "metadata": {
        "id": "lIMN9cDPM2jt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import pandas as pd\n",
        "from apyori import apriori\n",
        "\n",
        "# Mount Google Drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "# Assuming your CSV file is named 'accident_data.csv'\n",
        "file_path = '/content/drive/MyDrive/Virtual University /Thesis /Shujaat/Research Paper/RTA Data 2020 to July 2023.xlsx'\n",
        "\n",
        "\n",
        "df = pd.read_csv(file_path)\n",
        "\n",
        "# Display the first few rows of the DataFrame\n",
        "df.head()\n",
        "\n",
        "# Convert the DataFrame to a list of lists\n",
        "transactions = df.values.tolist()\n",
        "\n",
        "# Set your desired parameters for the Apriori algorithm\n",
        "min_support = 0.04  # Adjust as needed\n",
        "min_confidence = 0.5  # Adjust as needed\n",
        "\n",
        "# Convert all items in transactions to strings\n",
        "transactions = [[str(item) for item in transaction] for transaction in transactions]\n",
        "\n",
        "# Apply Apriori algorithm\n",
        "rules = apriori(transactions, min_support=min_support, min_confidence=min_confidence)\n",
        "\n",
        "# Convert the rules to a list for easier handling\n",
        "rules_list = list(rules)\n",
        "\n",
        "# Display the discovered association rules\n",
        "for rule in rules_list:\n",
        "    print(rule)\n"
      ],
      "metadata": {
        "id": "oMIhDz4JM4a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "a3adf451-f734-46de-cecf-cf8d335a82b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "UnicodeDecodeError",
          "evalue": "'utf-8' codec can't decode byte 0xa6 in position 17: invalid start byte",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-34d9e4f0c288>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# Display the first few rows of the DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    946\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 948\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    950\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 611\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    613\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1447\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1448\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1450\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1722\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1723\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1724\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1725\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     91\u001b[0m             \u001b[0;31m# Fail here loudly instead of in cython after reading\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0mimport_optional_dependency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"pyarrow\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mparsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mparsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._get_header\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mparsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mparsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._check_tokenize_status\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mparsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xa6 in position 17: invalid start byte"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Converting rules into table**</p>"
      ],
      "metadata": {
        "id": "kOuy0KfXM5dl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tabulate"
      ],
      "metadata": {
        "id": "YU3sRumDNHOk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tabulate import tabulate\n",
        "\n",
        "# Assuming you have already run the Apriori algorithm and obtained the rules_list\n",
        "\n",
        "# Define a function to convert the output to a table format\n",
        "def display_rules(rules_list):\n",
        "    table_headers = [\"Antecedent\", \"Consequent\", \"Support\", \"Confidence\", \"Lift\"]\n",
        "\n",
        "    # Initialize an empty list to store rows of the table\n",
        "    table_rows = []\n",
        "\n",
        "    for rule in rules_list:\n",
        "        antecedent = ', '.join(rule[0]) if rule[0] else 'None'  # Extract antecedent\n",
        "\n",
        "        # Extract consequent information from ordered statistics\n",
        "        consequent_info = rule[2][0]\n",
        "        consequent = ', '.join(consequent_info.items_add) if consequent_info.items_add else 'None'\n",
        "\n",
        "        support = rule[1]\n",
        "        confidence = consequent_info.confidence\n",
        "        lift = consequent_info.lift\n",
        "\n",
        "        # Append the current rule to the table_rows\n",
        "        table_rows.append([antecedent, consequent, support, confidence, lift])\n",
        "\n",
        "    # Display the table using the tabulate library\n",
        "    print(tabulate(table_rows, headers=table_headers, floatfmt=\".4f\"))\n",
        "\n",
        "# Display the rules in a table format\n",
        "display_rules(rules_list)\n"
      ],
      "metadata": {
        "id": "-GX_joCmNIEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Downloading Rules into Excel file**</p>"
      ],
      "metadata": {
        "id": "U4XwgBudNlGE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#excel file\n",
        "import pandas as pd\n",
        "from tabulate import tabulate\n",
        "\n",
        "# Assuming you have already run the Apriori algorithm and obtained the rules_list\n",
        "\n",
        "# Define a function to convert the output to a table format\n",
        "def display_rules(rules_list):\n",
        "    table_headers = [\"Antecedent\", \"Consequent\", \"Support\", \"Confidence\", \"Lift\"]\n",
        "\n",
        "    # Initialize an empty list to store rows of the table\n",
        "    table_rows = []\n",
        "\n",
        "    for rule in rules_list:\n",
        "        antecedent = ', '.join(rule[0]) if rule[0] else 'None'  # Extract antecedent\n",
        "\n",
        "        # Extract consequent information from ordered statistics\n",
        "        consequent_info = rule[2][0]\n",
        "        consequent = ', '.join(consequent_info.items_add) if consequent_info.items_add else 'None'\n",
        "\n",
        "        support = rule[1]\n",
        "        confidence = consequent_info.confidence\n",
        "        lift = consequent_info.lift\n",
        "\n",
        "        # Append the current rule to the table_rows\n",
        "        table_rows.append([antecedent, consequent, support, confidence, lift])\n",
        "\n",
        "    # Convert the table data to a DataFrame\n",
        "    df = pd.DataFrame(table_rows, columns=table_headers)\n",
        "\n",
        "    # Save the DataFrame to an Excel file\n",
        "    excel_file_path = '/content/drive/MyDrive/Dataset/Association Rule 13I.xlsx'\n",
        "    df.to_excel(excel_file_path, index=False)\n",
        "    print(f\"Association rules saved to {excel_file_path}\")\n",
        "\n",
        "# Display the rules in a table format\n",
        "display_rules(rules_list)\n"
      ],
      "metadata": {
        "id": "BkB3aZpONnYd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Applied Latest ML Methods**</p>"
      ],
      "metadata": {
        "id": "STatdwMZL-Tj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Data Processing**</p>"
      ],
      "metadata": {
        "id": "9XTGNObiNkag"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "76a4fhvhGx2W",
        "outputId": "cabe4987-da0f-4eb3-c074-ce85f1d852b7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YEAR  Month  Date  TIME Nature of weekday       Day      ROAD_CLASS  \\\n",
              "0  2006      3    11   852           Weekend  Saturday  Major Arterial   \n",
              "1  2006      3    11   852           Weekend  Saturday  Major Arterial   \n",
              "2  2006      3    11   915           Weekend  Saturday  Major Arterial   \n",
              "3  2006      1     1   236           Weekend    Sunday  Major Arterial   \n",
              "4  2006      3    11   915           Weekend  Saturday  Major Arterial   \n",
              "\n",
              "                DISTRICT      LOCCOORD                ACCLOC  ... MOTORCYCLE  \\\n",
              "0  Toronto and East York  Intersection       At Intersection  ...         No   \n",
              "1  Toronto and East York  Intersection       At Intersection  ...         No   \n",
              "2            Scarborough  Intersection       At Intersection  ...        Yes   \n",
              "3  Toronto and East York  Intersection  Intersection Related  ...         No   \n",
              "4            Scarborough  Intersection       At Intersection  ...        Yes   \n",
              "\n",
              "  TRUCK TRSN_CITY_VEH EMERG_VEH PASSENGER SPEEDING AG_DRIV REDLIGHT ALCOHOL  \\\n",
              "0    No            No        No        No       No     Yes       No      No   \n",
              "1    No            No        No        No       No     Yes       No      No   \n",
              "2    No            No        No        No       No     Yes      Yes      No   \n",
              "3    No            No        No       Yes      Yes     Yes       No     Yes   \n",
              "4    No            No        No        No       No     Yes      Yes      No   \n",
              "\n",
              "  DISABILITY  \n",
              "0         No  \n",
              "1         No  \n",
              "2         No  \n",
              "3         No  \n",
              "4         No  \n",
              "\n",
              "[5 rows x 43 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ce7f7b81-31f2-4b45-9778-89e213e0fbd1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YEAR</th>\n",
              "      <th>Month</th>\n",
              "      <th>Date</th>\n",
              "      <th>TIME</th>\n",
              "      <th>Nature of weekday</th>\n",
              "      <th>Day</th>\n",
              "      <th>ROAD_CLASS</th>\n",
              "      <th>DISTRICT</th>\n",
              "      <th>LOCCOORD</th>\n",
              "      <th>ACCLOC</th>\n",
              "      <th>...</th>\n",
              "      <th>MOTORCYCLE</th>\n",
              "      <th>TRUCK</th>\n",
              "      <th>TRSN_CITY_VEH</th>\n",
              "      <th>EMERG_VEH</th>\n",
              "      <th>PASSENGER</th>\n",
              "      <th>SPEEDING</th>\n",
              "      <th>AG_DRIV</th>\n",
              "      <th>REDLIGHT</th>\n",
              "      <th>ALCOHOL</th>\n",
              "      <th>DISABILITY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>852</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Toronto and East York</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
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              "      <th>1</th>\n",
              "      <td>2006</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
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              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Toronto and East York</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>915</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>236</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Toronto and East York</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>Intersection Related</td>\n",
              "      <td>...</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>915</td>\n",
              "      <td>Weekend</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>Major Arterial</td>\n",
              "      <td>Scarborough</td>\n",
              "      <td>Intersection</td>\n",
              "      <td>At Intersection</td>\n",
              "      <td>...</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ce7f7b81-31f2-4b45-9778-89e213e0fbd1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ce7f7b81-31f2-4b45-9778-89e213e0fbd1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d2075ebb-3d39-43d6-8d90-5e90cafef806 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1- Convert target variable into binary Form**"
      ],
      "metadata": {
        "id": "3qsjpP1GOGVo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = data['injury_type'].unique()\n",
        "print(f\"Names of unique classes: {class_names}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "naLPxy0aMFvA",
        "outputId": "cd6af43c-09af-4de3-bfbe-e4d42ab962e3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Names of unique classes: ['No injury' 'Fatal' 'Major' 'Minor' 'Minimal']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['injury_type'].replace({'No injury': 0, 'Fatal': 1,'Major': 2,'Minor': 3,'Minimal': 4},inplace = True)\n"
      ],
      "metadata": {
        "id": "Cj2PR8cOMgz3"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2- Hot Encoding**"
      ],
      "metadata": {
        "id": "RpcPsvhZOPTX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder  # Import LabelEncoder\n",
        "\n",
        "categorical_columns = data.select_dtypes(include=['object']).columns\n",
        "\n",
        "# Apply Label Encoding to each categorical column\n",
        "label_encoders = {}\n",
        "for col in categorical_columns:\n",
        "    le = LabelEncoder()\n",
        "    data[col] = le.fit_transform(data[col])  # Use data instead of df\n",
        "    label_encoders[col] = le  # Store the encoder for future use if needed\n",
        "\n",
        "print(\"DataFrame after Label Encoding:\")\n",
        "print(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwmjsIVuQHH6",
        "outputId": "60f1932f-9580-4724-ed0a-e3cf3c2eb328"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame after Label Encoding:\n",
            "       YEAR  Month  Date  TIME  Nature of weekday  Day  ROAD_CLASS  DISTRICT  \\\n",
            "0      2006      3    11   852                  0    2           5         4   \n",
            "1      2006      3    11   852                  0    2           5         4   \n",
            "2      2006      3    11   915                  0    2           5         2   \n",
            "3      2006      1     1   236                  0    3           5         4   \n",
            "4      2006      3    11   915                  0    2           5         2   \n",
            "...     ...    ...   ...   ...                ...  ...         ...       ...   \n",
            "16573  2022     12    21  1750                  1    6           4         5   \n",
            "16574  2022     12    21  1750                  1    6           4         5   \n",
            "16575  2022     12    21  1750                  1    6           4         5   \n",
            "16576  2022     12    30  2234                  1    0           5         4   \n",
            "16577  2022     12    30  2234                  1    0           5         4   \n",
            "\n",
            "       LOCCOORD  ACCLOC  ...  MOTORCYCLE  TRUCK  TRSN_CITY_VEH  EMERG_VEH  \\\n",
            "0             3       0  ...           0      0              0          0   \n",
            "1             3       0  ...           0      0              0          0   \n",
            "2             3       0  ...           1      0              0          0   \n",
            "3             3       2  ...           0      0              0          0   \n",
            "4             3       0  ...           1      0              0          0   \n",
            "...         ...     ...  ...         ...    ...            ...        ...   \n",
            "16573         3       0  ...           0      0              0          0   \n",
            "16574         3       0  ...           0      0              0          0   \n",
            "16575         3       0  ...           0      0              0          0   \n",
            "16576         4       4  ...           0      0              0          0   \n",
            "16577         4       4  ...           0      0              0          0   \n",
            "\n",
            "       PASSENGER  SPEEDING  AG_DRIV  REDLIGHT  ALCOHOL  DISABILITY  \n",
            "0              0         0        1         0        0           0  \n",
            "1              0         0        1         0        0           0  \n",
            "2              0         0        1         1        0           0  \n",
            "3              1         1        1         0        1           0  \n",
            "4              0         0        1         1        0           0  \n",
            "...          ...       ...      ...       ...      ...         ...  \n",
            "16573          0         0        1         0        0           0  \n",
            "16574          0         0        1         0        0           0  \n",
            "16575          0         0        1         0        0           0  \n",
            "16576          0         0        0         0        0           0  \n",
            "16577          0         0        0         0        0           0  \n",
            "\n",
            "[16578 rows x 43 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3- Checking missing value**"
      ],
      "metadata": {
        "id": "6nYQdLXwQu5x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().any()"
      ],
      "metadata": {
        "id": "6V_f01QBQpTp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data spliting and Feature Scaling**"
      ],
      "metadata": {
        "id": "2lH6b-nGRD4I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "X=data.drop('injury_type',axis=1)"
      ],
      "metadata": {
        "id": "5y02PpyURUZw"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "y=data[['injury_type']]"
      ],
      "metadata": {
        "id": "dUzAfEMoRiJm"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import numpy as np\n",
        "scaler = StandardScaler()\n",
        "X= scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "uCv2t5DMRvU9"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=100)"
      ],
      "metadata": {
        "id": "VpACrvLbR00F"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**Classification Model Training and Evaluation**</p>"
      ],
      "metadata": {
        "id": "_qnc8PMBR7Yb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**LB LSTM**"
      ],
      "metadata": {
        "id": "R9ANs1qUYK09"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**LB LSTM**</p>"
      ],
      "metadata": {
        "id": "5GDtvjUWYRtG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Split the data into training and testing sets\n",
        "train_size = int(len(data) * 0.8)\n",
        "train_data = data.iloc[:train_size]\n",
        "test_data = data.iloc[train_size:]\n",
        "\n",
        "# Step 6: Prepare the data for LSTM input\n",
        "# Separate features and target variable\n",
        "X_train = train_data.drop('injury_type', axis=1).values\n",
        "y_train = train_data['injury_type'].values\n",
        "X_test = test_data.drop('injury_type', axis=1).values\n",
        "y_test = test_data['injury_type'].values"
      ],
      "metadata": {
        "id": "ju-UDDFsVrUX"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape input to be 3D [samples, timesteps, features] for LSTM\n",
        "X_train = X_train.reshape((X_train.shape[0], 1, X_train.shape[1]))\n",
        "X_test = X_test.reshape((X_test.shape[0], 1, X_test.shape[1]))\n",
        "\n",
        "# Step 7: Build the LB-LSTM model\n",
        "model = Sequential()\n",
        "model.add(LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(64, input_shape=(X_train.shape[1], X_train.shape[2]), return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(32, return_sequences=False))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(1))"
      ],
      "metadata": {
        "id": "kkBYXlIsV-PU",
        "outputId": "54273abc-4e6e-42c1-a48c-f8cc4923ef12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.callbacks import EarlyStopping # Import EarlyStopping\n",
        "\n",
        "# ... (rest of your code) ...\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "# Define early stopping\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "\n",
        "# Step 8: Train the model\n",
        "history = model.fit(X_train, y_train, epochs=200, batch_size=512,\n",
        "                    validation_data=(X_test, y_test),\n",
        "                    callbacks=[early_stopping],  # Pass early_stopping to fit\n",
        "                    verbose=1)"
      ],
      "metadata": {
        "id": "Z__ZnXUdWRxM",
        "outputId": "92eee07a-e17d-4f95-b8f8-68c81b12b202",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.11/dist-packages (2.17.1)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.11/dist-packages (from tensorflow) (1.4.0)\n",
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            "Epoch 1/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 51ms/step - loss: 3.2339 - val_loss: 1.9371\n",
            "Epoch 2/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 1.9006 - val_loss: 1.5592\n",
            "Epoch 3/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7097 - val_loss: 1.5716\n",
            "Epoch 4/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7066 - val_loss: 1.5607\n",
            "Epoch 5/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6953 - val_loss: 1.5544\n",
            "Epoch 6/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6831 - val_loss: 1.5570\n",
            "Epoch 7/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7159 - val_loss: 1.5536\n",
            "Epoch 8/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7102 - val_loss: 1.5567\n",
            "Epoch 9/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.6642 - val_loss: 1.5534\n",
            "Epoch 10/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 1.6837 - val_loss: 1.5551\n",
            "Epoch 11/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 1.7006 - val_loss: 1.5583\n",
            "Epoch 12/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 1.6738 - val_loss: 1.5623\n",
            "Epoch 13/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 1.6405 - val_loss: 1.5622\n",
            "Epoch 14/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 1.6927 - val_loss: 1.5651\n",
            "Epoch 15/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6646 - val_loss: 1.5576\n",
            "Epoch 16/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6519 - val_loss: 1.5542\n",
            "Epoch 17/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6567 - val_loss: 1.5542\n",
            "Epoch 18/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7009 - val_loss: 1.5522\n",
            "Epoch 19/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6669 - val_loss: 1.5615\n",
            "Epoch 20/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6673 - val_loss: 1.5639\n",
            "Epoch 21/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6611 - val_loss: 1.5581\n",
            "Epoch 22/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6751 - val_loss: 1.5547\n",
            "Epoch 23/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6634 - val_loss: 1.5532\n",
            "Epoch 24/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6892 - val_loss: 1.5501\n",
            "Epoch 25/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6819 - val_loss: 1.5537\n",
            "Epoch 26/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6631 - val_loss: 1.5562\n",
            "Epoch 27/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7004 - val_loss: 1.5560\n",
            "Epoch 28/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6387 - val_loss: 1.5603\n",
            "Epoch 29/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6774 - val_loss: 1.5537\n",
            "Epoch 30/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6575 - val_loss: 1.5502\n",
            "Epoch 31/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6916 - val_loss: 1.5523\n",
            "Epoch 32/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6695 - val_loss: 1.5578\n",
            "Epoch 33/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6772 - val_loss: 1.5562\n",
            "Epoch 34/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6735 - val_loss: 1.5478\n",
            "Epoch 35/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6577 - val_loss: 1.5495\n",
            "Epoch 36/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6431 - val_loss: 1.5475\n",
            "Epoch 37/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6679 - val_loss: 1.5437\n",
            "Epoch 38/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6620 - val_loss: 1.5369\n",
            "Epoch 39/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6618 - val_loss: 1.5375\n",
            "Epoch 40/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6519 - val_loss: 1.5471\n",
            "Epoch 41/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6564 - val_loss: 1.5442\n",
            "Epoch 42/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6236 - val_loss: 1.5565\n",
            "Epoch 43/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6704 - val_loss: 1.5383\n",
            "Epoch 44/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6456 - val_loss: 1.5306\n",
            "Epoch 45/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6760 - val_loss: 1.5249\n",
            "Epoch 46/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6770 - val_loss: 1.5358\n",
            "Epoch 47/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6448 - val_loss: 1.5378\n",
            "Epoch 48/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6704 - val_loss: 1.5312\n",
            "Epoch 49/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6387 - val_loss: 1.5181\n",
            "Epoch 50/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 1.6228 - val_loss: 1.5014\n",
            "Epoch 51/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.6356 - val_loss: 1.5120\n",
            "Epoch 52/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 1.6065 - val_loss: 1.5112\n",
            "Epoch 53/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 1.6353 - val_loss: 1.5050\n",
            "Epoch 54/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.6351 - val_loss: 1.5143\n",
            "Epoch 55/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.6136 - val_loss: 1.5281\n",
            "Epoch 56/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - loss: 1.6429 - val_loss: 1.5322\n",
            "Epoch 57/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.6151 - val_loss: 1.4833\n",
            "Epoch 58/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.6044 - val_loss: 1.4988\n",
            "Epoch 59/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.5780 - val_loss: 1.5235\n",
            "Epoch 60/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5839 - val_loss: 1.4969\n",
            "Epoch 61/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6095 - val_loss: 1.4855\n",
            "Epoch 62/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5751 - val_loss: 1.4823\n",
            "Epoch 63/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5882 - val_loss: 1.4937\n",
            "Epoch 64/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6399 - val_loss: 1.4892\n",
            "Epoch 65/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6015 - val_loss: 1.5027\n",
            "Epoch 66/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - loss: 1.5977 - val_loss: 1.5400\n",
            "Epoch 67/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5986 - val_loss: 1.4719\n",
            "Epoch 68/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5918 - val_loss: 1.4802\n",
            "Epoch 69/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - loss: 1.6337 - val_loss: 1.4896\n",
            "Epoch 70/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6144 - val_loss: 1.4680\n",
            "Epoch 71/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5886 - val_loss: 1.4683\n",
            "Epoch 72/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5812 - val_loss: 1.4533\n",
            "Epoch 73/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5966 - val_loss: 1.4991\n",
            "Epoch 74/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5639 - val_loss: 1.4878\n",
            "Epoch 75/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6044 - val_loss: 1.4756\n",
            "Epoch 76/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5618 - val_loss: 1.4764\n",
            "Epoch 77/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 1.5746 - val_loss: 1.4780\n",
            "Epoch 78/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5670 - val_loss: 1.4550\n",
            "Epoch 79/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5679 - val_loss: 1.4326\n",
            "Epoch 80/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5638 - val_loss: 1.4284\n",
            "Epoch 81/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5546 - val_loss: 1.4310\n",
            "Epoch 82/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5574 - val_loss: 1.4109\n",
            "Epoch 83/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 1.5226 - val_loss: 1.4843\n",
            "Epoch 84/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 1.5606 - val_loss: 1.4057\n",
            "Epoch 85/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 1.5557 - val_loss: 1.3950\n",
            "Epoch 86/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5361 - val_loss: 1.3861\n",
            "Epoch 87/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - loss: 1.5200 - val_loss: 1.3847\n",
            "Epoch 88/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 1.5209 - val_loss: 1.4274\n",
            "Epoch 89/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.4860 - val_loss: 1.4278\n",
            "Epoch 90/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5040 - val_loss: 1.4355\n",
            "Epoch 91/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4908 - val_loss: 1.3690\n",
            "Epoch 92/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4935 - val_loss: 1.3905\n",
            "Epoch 93/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4854 - val_loss: 1.3885\n",
            "Epoch 94/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4637 - val_loss: 1.3682\n",
            "Epoch 95/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4689 - val_loss: 1.4061\n",
            "Epoch 96/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4723 - val_loss: 1.3595\n",
            "Epoch 97/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4508 - val_loss: 1.3606\n",
            "Epoch 98/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4436 - val_loss: 1.3440\n",
            "Epoch 99/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4468 - val_loss: 1.3476\n",
            "Epoch 100/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4234 - val_loss: 1.3351\n",
            "Epoch 101/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4660 - val_loss: 1.3796\n",
            "Epoch 102/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4341 - val_loss: 1.3522\n",
            "Epoch 103/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 1.4323 - val_loss: 1.3208\n",
            "Epoch 104/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4420 - val_loss: 1.4593\n",
            "Epoch 105/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4528 - val_loss: 1.3249\n",
            "Epoch 106/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4759 - val_loss: 1.3417\n",
            "Epoch 107/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4069 - val_loss: 1.3987\n",
            "Epoch 108/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4405 - val_loss: 1.3079\n",
            "Epoch 109/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4275 - val_loss: 1.3387\n",
            "Epoch 110/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4033 - val_loss: 1.3129\n",
            "Epoch 111/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4108 - val_loss: 1.3624\n",
            "Epoch 112/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4151 - val_loss: 1.3330\n",
            "Epoch 113/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4243 - val_loss: 1.2932\n",
            "Epoch 114/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3946 - val_loss: 1.3265\n",
            "Epoch 115/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3997 - val_loss: 1.2978\n",
            "Epoch 116/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3867 - val_loss: 1.3045\n",
            "Epoch 117/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3932 - val_loss: 1.2926\n",
            "Epoch 118/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4016 - val_loss: 1.3005\n",
            "Epoch 119/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 1.4004 - val_loss: 1.3171\n",
            "Epoch 120/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4030 - val_loss: 1.2841\n",
            "Epoch 121/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3906 - val_loss: 1.3941\n",
            "Epoch 122/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 1.4223 - val_loss: 1.2864\n",
            "Epoch 123/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step - loss: 1.3728 - val_loss: 1.2831\n",
            "Epoch 124/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 1.3844 - val_loss: 1.3014\n",
            "Epoch 125/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - loss: 1.3723 - val_loss: 1.2699\n",
            "Epoch 126/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 15ms/step - loss: 1.3833 - val_loss: 1.2860\n",
            "Epoch 127/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4129 - val_loss: 1.2722\n",
            "Epoch 128/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3891 - val_loss: 1.2592\n",
            "Epoch 129/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3969 - val_loss: 1.2853\n",
            "Epoch 130/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3749 - val_loss: 1.2566\n",
            "Epoch 131/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3592 - val_loss: 1.2679\n",
            "Epoch 132/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3742 - val_loss: 1.2506\n",
            "Epoch 133/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3646 - val_loss: 1.2539\n",
            "Epoch 134/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3658 - val_loss: 1.2825\n",
            "Epoch 135/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3938 - val_loss: 1.2787\n",
            "Epoch 136/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3922 - val_loss: 1.3094\n",
            "Epoch 137/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3810 - val_loss: 1.2413\n",
            "Epoch 138/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3795 - val_loss: 1.2638\n",
            "Epoch 139/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3491 - val_loss: 1.2389\n",
            "Epoch 140/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3184 - val_loss: 1.2528\n",
            "Epoch 141/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3346 - val_loss: 1.2419\n",
            "Epoch 142/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3537 - val_loss: 1.2434\n",
            "Epoch 143/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3671 - val_loss: 1.2231\n",
            "Epoch 144/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3474 - val_loss: 1.2358\n",
            "Epoch 145/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3363 - val_loss: 1.2586\n",
            "Epoch 146/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3697 - val_loss: 1.3263\n",
            "Epoch 147/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3824 - val_loss: 1.2543\n",
            "Epoch 148/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3777 - val_loss: 1.2159\n",
            "Epoch 149/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3448 - val_loss: 1.2667\n",
            "Epoch 150/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3457 - val_loss: 1.2374\n",
            "Epoch 151/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3180 - val_loss: 1.2461\n",
            "Epoch 152/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3185 - val_loss: 1.2381\n",
            "Epoch 153/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3259 - val_loss: 1.2258\n",
            "Epoch 154/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3133 - val_loss: 1.2401\n",
            "Epoch 155/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3365 - val_loss: 1.2556\n",
            "Epoch 156/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3379 - val_loss: 1.2513\n",
            "Epoch 157/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3093 - val_loss: 1.2590\n",
            "Epoch 158/200\n",
            "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3297 - val_loss: 1.2337\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Accuracy**"
      ],
      "metadata": {
        "id": "lolDwG0QYl4d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9: Make predictions\n",
        "y_pred_LSTM = model.predict(X_test)"
      ],
      "metadata": {
        "id": "cx4URorqWyyh",
        "outputId": "38d10846-dbb5-4814-ae15-b235ebcc892f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m104/104\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you are working with a classification problem and want to calculate accuracy:\n",
        "# 1. Evaluate the model to get the loss\n",
        "loss = model.evaluate(X_test, y_test, verbose=0)\n",
        "\n",
        "# 2. Calculate accuracy using sklearn.metrics\n",
        "from sklearn.metrics import accuracy_score\n",
        "y_pred_classes = [round(i[0]) for i in y_pred_LSTM]\n",
        "accuracy = accuracy_score(y_test, y_pred_classes)\n",
        "\n",
        "# 3. Print the accuracy and loss\n",
        "print('Loss: {:.4f}'.format(loss))\n",
        "print('Accuracy score= {:.4f}'.format(accuracy))"
      ],
      "metadata": {
        "id": "gyXyD5-6W9Zp",
        "outputId": "cff6664a-cea4-46be-bedc-c0ef0abc932a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loss: 1.2159\n",
            "Accuracy score= 0.5145\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix**\n",
        "\n"
      ],
      "metadata": {
        "id": "TspSGzAJYzis"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# ... (your existing code for model training and prediction) ...\n",
        "\n",
        "# Assuming y_test and y_pred_classes are defined from previous steps\n",
        "# Convert y_pred_classes to NumPy array if it's not already\n",
        "y_pred_classes = np.array(y_pred_classes)\n",
        "\n",
        "# Generate confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred_classes)\n",
        "\n",
        "# Display confusion matrix using seaborn heatmap\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()\n",
        "\n",
        "# Print classification report for more detailed metrics\n",
        "print(classification_report(y_test, y_pred_classes, target_names=class_names))"
      ],
      "metadata": {
        "id": "IkPChdEPX_im",
        "outputId": "27098e3c-6b74-4637-f9d6-4e8c52441503",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        }
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "   No injury       0.89      0.45      0.60      1435\n",
            "       Fatal       0.05      0.14      0.07       239\n",
            "       Major       0.53      0.85      0.65      1201\n",
            "       Minor       0.00      0.00      0.00       249\n",
            "     Minimal       0.00      0.00      0.00       192\n",
            "\n",
            "    accuracy                           0.51      3316\n",
            "   macro avg       0.29      0.29      0.27      3316\n",
            "weighted avg       0.58      0.51      0.50      3316\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Precision, Recall, F1**"
      ],
      "metadata": {
        "id": "u1oW40-UY9K0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print('\\n')\n",
        "print(\"Precision, Recall, F1\")\n",
        "print('\\n')\n",
        "CR=classification_report(y_test, y_pred_LSTM)\n",
        "print(CR)\n",
        "print('\\n')"
      ],
      "metadata": {
        "id": "4BiQz8cSYvs7",
        "outputId": "59c43231-4fb3-46ce-eaaf-d6bfe83efd94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        }
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Precision, Recall, F1\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Classification metrics can't handle a mix of multiclass and continuous targets",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-50217408f1ce>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Precision, Recall, F1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mCR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclassification_report\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_LSTM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/sklearn/utils/_param_validation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m                     )\n\u001b[1;32m    212\u001b[0m                 ):\n\u001b[0;32m--> 213\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInvalidParameterError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                 \u001b[0;31m# When the function is just a wrapper around an estimator, we allow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mclassification_report\u001b[0;34m(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)\u001b[0m\n\u001b[1;32m   2602\u001b[0m     \"\"\"\n\u001b[1;32m   2603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2604\u001b[0;31m     \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2606\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m     95\u001b[0m             \"Classification metrics can't handle a mix of {0} and {1} targets\".format(\n\u001b[1;32m     96\u001b[0m                 \u001b[0mtype_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype_pred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Classification metrics can't handle a mix of multiclass and continuous targets"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#<p style=\"font-family:JetBrains Mono; font-weight:normal; letter-spacing: 1px; color:#207d06; font-size:100%; text-align:left;padding: 0px; border-bottom: 3px solid #207d06;\">**References**</p>"
      ],
      "metadata": {
        "id": "PFizdCthpSv_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "[1-Road-Accident-Severity-Prediction(Projects)](https://github.com/JohnRaghul/Road-Accident-Severity-Prediction/blob/main/Road-Accident-Severity-Prediction.ipynb)\n",
        "\n"
      ],
      "metadata": {
        "id": "kWBEQnpbpXMN"
      }
    }
  ]
}