Brain_Stroke_prediction.ipynb

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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "from imblearn.over_sampling import SMOTE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "gxvi3mEnnHhX"
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      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "4aMKLakDgLBO"
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      "source": [
        "df = pd.read_csv('brain_stroke.csv')"
      ]
    },
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            "text/plain": [
              "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
              "0    Male  67.0             0              1          Yes        Private   \n",
              "1    Male  80.0             0              1          Yes        Private   \n",
              "2  Female  49.0             0              0          Yes        Private   \n",
              "3  Female  79.0             1              0          Yes  Self-employed   \n",
              "4    Male  81.0             0              0          Yes        Private   \n",
              "\n",
              "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
              "0          Urban             228.69  36.6  formerly smoked       1  \n",
              "1          Rural             105.92  32.5     never smoked       1  \n",
              "2          Urban             171.23  34.4           smokes       1  \n",
              "3          Rural             174.12  24.0     never smoked       1  \n",
              "4          Urban             186.21  29.0  formerly smoked       1  "
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        "le = LabelEncoder()\n",
        "df.head()"
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      "execution_count": 8,
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        "df['ever_married'] = le.fit_transform(df['ever_married'])\n",
        "df['work_type'] = le.fit_transform(df['work_type'])\n",
        "df['Residence_type'] = le.fit_transform(df['Residence_type'])\n",
        "df['smoking_status'] = le.fit_transform(df['smoking_status'])\n",
        "df['gender'] = le.fit_transform(df['gender'])\n",
        "df['age'] = df['age'].astype(int)"
      ]
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      "cell_type": "code",
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      "metadata": {
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      "source": [
        "X = df.drop('stroke', axis = 'columns')\n",
        "y = df.stroke"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
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        "scaler = StandardScaler()\n",
        "X_normalize = scaler.fit_transform(X)"
      ]
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        "oversampler = SMOTE(random_state=42)\n",
        "X_resampled, y_resampled = oversampler.fit_resample(X, y)"
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        "# Create a new balanced DataFrame\n",
        "balanced_data = pd.concat([X_resampled, y_resampled], axis=1)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "balanced_data.head()"
      ],
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            "text/plain": [
              "   gender  age  hypertension  heart_disease  ever_married  work_type  \\\n",
              "0       1   67             0              1             1          1   \n",
              "1       1   80             0              1             1          1   \n",
              "2       0   49             0              0             1          1   \n",
              "3       0   79             1              0             1          2   \n",
              "4       1   81             0              0             1          1   \n",
              "\n",
              "   Residence_type  avg_glucose_level   bmi  smoking_status  stroke  \n",
              "0               1             228.69  36.6               1       1  \n",
              "1               0             105.92  32.5               2       1  \n",
              "2               1             171.23  34.4               3       1  \n",
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      "cell_type": "code",
      "source": [
        "A = balanced_data.drop('stroke', axis = 'columns')\n",
        "B = balanced_data.stroke"
      ],
      "metadata": {
        "id": "oRonZLaCX_Xy"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "hPF_Y3-pmyd2"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(A,B, test_size = 0.3, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "YdbXQlj8nY-O"
      },
      "outputs": [],
      "source": [
        "rf = RandomForestClassifier(max_depth = 20, max_features = 'log2', n_estimators = 1000, min_samples_split = 5, min_samples_leaf = 2, criterion = 'entropy', class_weight = 'balanced', bootstrap = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCbMZaCaywNT",
        "outputId": "a67baa2a-bb06-401a-e242-c5701c8fbf10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9341549295774648"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "rf.fit(X_train, y_train)\n",
        "rf.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E9u5zePr7W80",
        "outputId": "d1f25bda-b08c-4705-d9e6-2ab64a1bfc8d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1291,  116],\n",
              "       [  71, 1362]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "y_preds = rf.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_preds)\n",
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "glqN5-C27xkB",
        "outputId": "0d541a0a-74df-415c-aeab-d1eacdd9f374"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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9+3YlJSVpw4YN6tv3+HeyrF69WsOHD9e+ffuUkJCghQsX6r777pPT6VT4f49QmDFjhlauXKkdO3ac9vyoQAAAYOTHFkaTX9/gclme0p49e+R0OpWamuq9FhMTo+TkZO8J0KWlpYqNjfWGB0lKTU1VSEiIysrKvGMGDRrkDQ+SlJaWpp07d+rQoUOnPR8CBAAAzSg/P18xMTE+r/z8fMvPcTqdknTSgY12u917z+l0Kj4+3ud+aGio4uLifMY09Yxv/xmng+MCAQAw8Od3YeTl5Sk3N9fnmvE05h8iAgQAAEZ+PMq6qa9vOBMOh0PS8dOfv32MQmVlpXr16uUdU1VV5fO+Y8eOqbq62vt+h8OhyspKnzEnfj4x5nTQwgAA4AcgMTFRDodDxcXF3mt1dXUqKytTSkqKJCklJUU1NTUqLy/3jlmzZo3cbreSk5O9Y0pKStTQ0OAdU1RUpEsvvVRt27Y97fkQIAAAMArQORCHDx/W5s2bvd9svWfPHm3evFkVFRWy2WyaPHmyZs+erddff11bt27VhAkTlJCQ4N2p0a1bNw0bNkwTJ07U+vXr9f777ysnJ0fjxo1TQsLxr5gfP368wsPDlZmZqW3btmn58uV66qmnTmqzmKGFAQCAUYC+TGvjxo0aPHiw9+cT/6hnZGRoyZIluueee1RfX6+srCzV1NToiiuu0OrVq9WyZUvve5YtW6acnBwNGTJEISEhGjNmjAoLC733Y2Ji9Pbbbys7O1t9+vRR+/btNWvWLJ+zIk4H50AAQYxzIICmNfc5EIdzr/Hbs1r/7nW/PSuY0MIAAACW0cIAAMDA48ddGOcqAgQAAEYECFO0MAAAgGVUIAAAMPLjSZTnKgIEAABGtDBM0cIAAACWUYEAAMCICoQpAgQAAAZBcsZiUKOFAQAALKMCAQCAES0MUwQIAACMCBCmCBAAABhwlLU51kAAAADLqEAAAGBEBcIUAQIAACNOsjZFCwMAAFhGBQIAAAMWUZojQAAAYESAMEULAwAAWEYFAgAAIxZRmiJAAABgwBoIc7QwAACAZVQgAAAwooVhigABAIABLQxzBAgAAIyoQJhiDQQAALCMCgQAAAYeKhCmCBAAABgRIEzRwgAAAJZRgQAAwIAWhjkCBAAARgQIU7QwAACAZVQgAAAwoIVhjgABAIABAcIcAQIAAAMChDnWQAAAAMuoQAAAYOSxBXoGQY8AAQCAAS0Mc7QwAACAZVQgAAAw8LhpYZghQAAAYEALwxwtDAAAYBkVCAAADDzswjBFgAAAwIAWhjlaGAAAwDICBAAABh63zW8vKxobGzVz5kwlJiYqMjJSF110kR555BF5PJ7/zc3j0axZs9SxY0dFRkYqNTVVu3bt8nlOdXW10tPTFR0drdjYWGVmZurw4cN++d2cQIAAAMDA4/Hfy4rHH39cCxcu1DPPPKPt27fr8ccfV0FBgZ5++mnvmIKCAhUWFmrRokUqKytTVFSU0tLSdOTIEe+Y9PR0bdu2TUVFRVq1apVKSkqUlZXlr1+PJMnm8Vj9eM3DtX1toKcABJ0OfTMDPQUgKNXV727W5++9LNVvz+qy6Z3THnv11VfLbrfr+eef914bM2aMIiMj9fLLL8vj8SghIUFTp07V3XffLUmqra2V3W7XkiVLNG7cOG3fvl1JSUnasGGD+vbtK0lavXq1hg8frn379ikhIcEvn4sKBAAAQeLyyy9XcXGxPvnkE0nShx9+qPfee09XXXWVJGnPnj1yOp1KTf1fwImJiVFycrJKS0slSaWlpYqNjfWGB0lKTU1VSEiIysrK/DZXdmEAAGDgz5MoXS6XXC6Xz7WIiAhFREScNHbGjBmqq6tT165d1aJFCzU2NmrOnDlKT0+XJDmdTkmS3W73eZ/dbvfeczqdio+P97kfGhqquLg47xh/oAIBAICBP9dA5OfnKyYmxueVn5/f5J/7xz/+UcuWLdMrr7yiTZs2aenSpXriiSe0dOnS7/k3YI4KBAAAzSgvL0+5ubk+15qqPkjStGnTNGPGDI0bN06S1KNHD+3du1f5+fnKyMiQw+GQJFVWVqpjx47e91VWVqpXr16SJIfDoaqqKp/nHjt2TNXV1d73+wMVCAAADPy5jTMiIkLR0dE+r1MFiK+//lohIb7/NLdo0UJu9/GTrRITE+VwOFRcXOy9X1dXp7KyMqWkpEiSUlJSVFNTo/Lycu+YNWvWyO12Kzk52W+/IyoQAAAYBOoo65EjR2rOnDnq3LmzfvrTn+pf//qXfve73+m2226TJNlsNk2ePFmzZ8/WJZdcosTERM2cOVMJCQkaNWqUJKlbt24aNmyYJk6cqEWLFqmhoUE5OTkaN26c33ZgSAQIAACCxtNPP62ZM2fqN7/5jaqqqpSQkKDbb79ds2bN8o655557VF9fr6ysLNXU1OiKK67Q6tWr1bJlS++YZcuWKScnR0OGDFFISIjGjBmjwsJCv86VcyCAIMY5EEDTmvsciE+T0vz2rIs/fstvzwomVCAAADBw822cplhECQAALKMCAQCAQaAWUf6QECAAADDw50mU5yoCBAAABsGxvSC4sQYCAABYRgUCAAADWhjmCBAAABiwjdMcLQwAAGAZFQgAAAzYxmmOAAEAgAG7MMzRwgAAAJZRgQAAwIBFlOYIEAAAGLAGwhwtDAAAYBkVCAAADFhEaY4AAQCAAWsgzAVNgIjq+ctATwEIOt/s/0egpwD8KLEGwhxrIAAAgGVBU4EAACBY0MIwR4AAAMCANZTmaGEAAADLqEAAAGBAC8McAQIAAAN2YZijhQEAACyjAgEAgIE70BP4ASBAAABg4BEtDDO0MAAAgGVUIAAAMHBzEIQpAgQAAAZuWhimCBAAABiwBsIcayAAAIBlVCAAADBgG6c5AgQAAAa0MMzRwgAAAJZRgQAAwIAWhjkCBAAABgQIc7QwAACAZVQgAAAwYBGlOQIEAAAGbvKDKVoYAADAMioQAAAY8F0Y5ggQAAAY8GWc5ggQAAAYsI3THGsgAACAZVQgAAAwcNtYA2GGAAEAgAFrIMzRwgAAIIh88cUX+uUvf6l27dopMjJSPXr00MaNG733PR6PZs2apY4dOyoyMlKpqanatWuXzzOqq6uVnp6u6OhoxcbGKjMzU4cPH/brPAkQAAAYuP34suLQoUMaOHCgwsLC9Oabb+rjjz/Wb3/7W7Vt29Y7pqCgQIWFhVq0aJHKysoUFRWltLQ0HTlyxDsmPT1d27ZtU1FRkVatWqWSkhJlZWWd0e/iVGwejycoKjWh4ecFegpA0Plm/z8CPQUgKIW1v7BZn/9qQrrfnnXT/mWnPXbGjBl6//339Y9/NP133+PxKCEhQVOnTtXdd98tSaqtrZXdbteSJUs0btw4bd++XUlJSdqwYYP69u0rSVq9erWGDx+uffv2KSEh4ew/lKhAAADQrFwul+rq6nxeLperybGvv/66+vbtq+uvv17x8fHq3bu3nnvuOe/9PXv2yOl0KjU11XstJiZGycnJKi0tlSSVlpYqNjbWGx4kKTU1VSEhISorK/Pb5yJAAABg4JbNb6/8/HzFxMT4vPLz85v8c3fv3q2FCxfqkksu0VtvvaU77rhDd955p5YuXSpJcjqdkiS73e7zPrvd7r3ndDoVHx/vcz80NFRxcXHeMf7ALgwAAAz82dvPy8tTbm6uz7WIiIgmx7rdbvXt21ePPvqoJKl379766KOPtGjRImVkZPhxVmePCgQAAM0oIiJC0dHRPq9TBYiOHTsqKSnJ51q3bt1UUVEhSXI4HJKkyspKnzGVlZXeew6HQ1VVVT73jx07purqau8YfyBAAABg4Lb572XFwIEDtXPnTp9rn3zyibp06SJJSkxMlMPhUHFxsfd+XV2dysrKlJKSIklKSUlRTU2NysvLvWPWrFkjt9ut5OTkM/yNnIwWBgAABoH6LowpU6bo8ssv16OPPqobbrhB69ev1+LFi7V48WJJks1m0+TJkzV79mxdcsklSkxM1MyZM5WQkKBRo0ZJOl6xGDZsmCZOnKhFixapoaFBOTk5GjdunN92YEgECAAAThKo8w369eunFStWKC8vTw8//LASExM1b948paf/b1vpPffco/r6emVlZammpkZXXHGFVq9erZYtW3rHLFu2TDk5ORoyZIhCQkI0ZswYFRYW+nWunAMBBDHOgQCa1tznQLxw3i/99qxbv3jZb88KJlQgAAAwsLp24ceIAAEAgEGg1kD8kLALAwAAWEYFAgAAAyoQ5ggQAAAYeFgDYYoWBgAAsIwKBAAABrQwzBEgAAAwIECYo4UBAAAsowIBAIBBUBzRHOQIEAAAGHASpTkCBAAABqyBMMcaCAAAYBkVCAAADKhAmCNAAABgwCJKc7QwAACAZVQgAAAwYBeGOQIEAAAGrIEwRwsDAABYRgUCAAADFlGaI0AAAGDgJkKYooUBAAAsowIBAIABiyjNESAAADCggWGOAAEAgAEVCHOsgQAAAJZRgQAAwICTKM0RIAAAMGAbpzlaGAAAwDIqEAAAGFB/MEeAAADAgF0Y5mhhAAAAy6hAAABgwCJKcwQIAAAMiA/maGEAAADLqEAAAGDAIkpzBAgAAAxYA2GOAAEAgAHxwRxrIAAAgGVUIAAAMGANhDkCBAAABh6aGKZoYQAAAMuoQAAAYEALwxwBAgAAA7ZxmqOFAQAALKMCAQCAAfUHc1QgfoQ+/eSfOnb0i5NehU/NkST9KjNdxUWvqfrgDh07+oViYqIDPGPg7G3cvFXZ9zygwdekq/vAq1Rc8oHP/fnPv6yRN01UvyGjdPmw6/Wru/K0ZduOk56z7oP1umniZPUZfK0uH3a97pzxsPfejl27Ne2BxzRk9M3qM/hajRyfpZf+uLK5PxqagVsev73O1GOPPSabzabJkyd7rx05ckTZ2dlq166dWrdurTFjxqiystLnfRUVFRoxYoRatWql+Ph4TZs2TceOHTvjeZwKFYgfoQGXD1eLFi28P3f/aVe9tfoP+vOfV0mSWrWK1Ftvv6u33n5Xj865N1DTBPzqm2+O6NKLL9ToEUM1+d7ZJ92/oNN5ujf3Nzo/wSGX66heXL5CWVPu09+XP6+4trGSpKK17+mBx5/SXbffouQ+PdXY2Khdu/d6n/Hxzl2Kaxurx2ZNkyO+gzZ/tF0PPV6oFiEhGj/2mu/ro+IcsGHDBj377LP62c9+5nN9ypQp+tvf/qbXXntNMTExysnJ0XXXXaf3339fktTY2KgRI0bI4XDogw8+0JdffqkJEyYoLCxMjz76qF/naPN4PEFRqQkNPy/QU/jR+u0TD2nE8CHqmnSFz/WfD0pR8Tt/UrsO3VRbWxeg2f24fbP/H4Gewjmp+8Cr9FT+TA0ZdPkpxxyur9eAoWP1/556VAP69taxY41KG5uh32TerDEj0077z5r92/na/dnn+v3Tj/lj6vivsPYXNuvzJ15wvd+e9dxnr1kaf/jwYV122WVasGCBZs+erV69emnevHmqra1Vhw4d9Morr2js2LGSpB07dqhbt24qLS3VgAED9Oabb+rqq6/W/v37ZbfbJUmLFi3S9OnTdeDAAYWHh/vtc9HC+JELCwtT+vjrtGTp8kBPBQgaDQ0Neu2vb6pN6yhdevHxf6i2f/KpKg98pZAQm8bekq1fXDNev546U7t2f/adz/rP4XrFRLf+HmYNf/L48X8ul0t1dXU+L5fLdco/Ozs7WyNGjFBqaqrP9fLycjU0NPhc79q1qzp37qzS0lJJUmlpqXr06OEND5KUlpamuro6bdu2za+/IwLEj9y11w5TbGy0lr74x0BPBQi4d98vU7/U0bps8LV6aflKLZ43R21jYyRJn+//UpK04Plluj3jJs0veEjRbVrr1pzpqq37T5PP+9fWj/VWcYnGXnPV9/YZ4B9uP77y8/MVExPj88rPz2/yz/3DH/6gTZs2NXnf6XQqPDxcsbGxPtftdrucTqd3zLfDw4n7J+75k98DxOeff67bbrvtO8c0lcaCpJPyo3PbLeO0+q21+vLLSvPBwDmu/2U99ecl8/Xyot9q4IA+untmvr46VCNJ8riP/zcqK+NG/d/BV+inXS/R7HunyGaT3lpzcqtp1+7PdOeMh3THbekamNzn+/wYCDJ5eXmqra31eeXl5Z007vPPP9ddd92lZcuWqWXLlgGYqTV+DxDV1dVaunTpd45pKo153E0neDSfzp3P05AhV+r5378S6KkAQaFVZEt1Pj9BPbt30yN5U9SiRQv95Y23JEkd2sVJki66oLN3fHh4uM5P6KgvK6t8nvPvPXuVeWeexl5zlW6/5abv7wPAb/zZwoiIiFB0dLTPKyIi4qQ/s7y8XFVVVbrssssUGhqq0NBQrVu3ToWFhQoNDZXdbtfRo0dVU1Pj877Kyko5HA5JksPhOGlXxomfT4zxF8u7MF5//fXvvL97927TZ+Tl5Sk3N9fnWtt2Xa1OBWfplowbVVV1UH//e3GgpwIEJbfbraMNDZKkpK4XKzw8THsqvtBlPbtLkhqOHdMXX1YqwRHvfc+nu/fqtjtn6NqrUnXX7bcEYtrwg0AcZT1kyBBt3brV59qtt96qrl27avr06erUqZPCwsJUXFysMWPGSJJ27typiooKpaSkSJJSUlI0Z84cVVVVKT7++P8vi4qKFB0draSkJL/O13KAGDVqlGw223e2HGw223c+IyIi4qT0ZfYe+JfNZlPGhBv10suvqbGx0eee3d5BDke8LrroAklSj+5d9Z/D9aqo+EKH/lvOBX5ovv76G1Xs2+/9+Yv9ldrxyb8VE91GMTHRWrz0Dxp8RbI6tI/ToZo6vfqXN1R18CulDb5SktQ6Kko3XDtcC55/SY749kpw2PXCK3+SJA3975hduz9T5qQZujy5jzLGjdbBr6olSSEhId6toMCptGnTRt27d/e5FhUVpXbt2nmvZ2ZmKjc3V3FxcYqOjtakSZOUkpKiAQMGSJKGDh2qpKQk3XzzzSooKJDT6dT999+v7OzsJqseZ8NygOjYsaMWLFiga6+9tsn7mzdvVp8+9PuCXeqQK9Wly/l6YcnJuy9uz7pZs2ZO9f787toVkqTbMqfoxZdYbIkfpo927NJtk6Z7fy54erEk6dqrUjVr2iTt2fu5Xn/zHR2qrVVsdLS6d/uJli6Yq4sv7OJ9z9ScX6lFaAvlPfKEXC6XeiR11e8LH1NMdBtJ0ttr31N1Ta1WvbVGq95a431fgiNeb//5u1u7CC7uIF2X9+STTyokJERjxoyRy+VSWlqaFixY4L3fokULrVq1SnfccYdSUlIUFRWljIwMPfzww9/x1DNj+RyIa665Rr169TrlZD788EP17t1bbre1AhDnQAAn4xwIoGnNfQ7EL7tc57dnvbz3L357VjCxXIGYNm2a6uvrT3n/4osv1tq1a89qUgAAILhZDhBXXnnld96PiorSz3/+8zOeEAAAgcbXeZvjuzAAADDwECBMcRIlAACwjAoEAAAGgTgH4oeGAAEAgAFrIMwRIAAAMGANhDnWQAAAAMuoQAAAYMAaCHMECAAADCwe0vyjRAsDAABYRgUCAAADdmGYI0AAAGDAGghztDAAAIBlVCAAADDgHAhzBAgAAAxYA2GOFgYAALCMCgQAAAacA2GOAAEAgAG7MMwRIAAAMGARpTnWQAAAAMuoQAAAYMAuDHMECAAADFhEaY4WBgAAsIwKBAAABrQwzBEgAAAwYBeGOVoYAADAMioQAAAYuFlEaYoAAQCAAfHBHC0MAABgGRUIAAAM2IVhjgABAIABAcIcAQIAAANOojTHGggAAGAZFQgAAAxoYZgjQAAAYMBJlOZoYQAAAMuoQAAAYMAiSnMECAAADFgDYY4WBgAAsIwKBAAABrQwzBEgAAAwoIVhjhYGAACwjAoEAAAGnANhjgABAICBmzUQpggQAAAYUIEwxxoIAACCRH5+vvr166c2bdooPj5eo0aN0s6dO33GHDlyRNnZ2WrXrp1at26tMWPGqLKy0mdMRUWFRowYoVatWik+Pl7Tpk3TsWPH/DpXAgQAAAZuj8dvLyvWrVun7Oxs/fOf/1RRUZEaGho0dOhQ1dfXe8dMmTJFb7zxhl577TWtW7dO+/fv13XXXee939jYqBEjRujo0aP64IMPtHTpUi1ZskSzZs3y2+9HkmyeINnsGhp+XqCnAASdb/b/I9BTAIJSWPsLm/X5XeP7+e1ZO6o2nPF7Dxw4oPj4eK1bt06DBg1SbW2tOnTooFdeeUVjx449/vwdO9StWzeVlpZqwIABevPNN3X11Vdr//79stvtkqRFixZp+vTpOnDggMLDw/3yuahAAADQjFwul+rq6nxeLpfrtN5bW1srSYqLi5MklZeXq6GhQampqd4xXbt2VefOnVVaWipJKi0tVY8ePbzhQZLS0tJUV1enbdu2+etjESAAADDyZwsjPz9fMTExPq/8/HzzObjdmjx5sgYOHKju3btLkpxOp8LDwxUbG+sz1m63y+l0esd8OzycuH/inr+wCwMAAAN/7sLIy8tTbm6uz7WIiAjT92VnZ+ujjz7Se++957e5+BMBAgCAZhQREXFageHbcnJytGrVKpWUlOj888/3Xnc4HDp69Khqamp8qhCVlZVyOBzeMevXr/d53oldGifG+AMtDAAADAK1C8Pj8SgnJ0crVqzQmjVrlJiY6HO/T58+CgsLU3Fxsffazp07VVFRoZSUFElSSkqKtm7dqqqqKu+YoqIiRUdHKykp6Sx+K76oQAAAYBCog6Sys7P1yiuv6K9//avatGnjXbMQExOjyMhIxcTEKDMzU7m5uYqLi1N0dLQmTZqklJQUDRgwQJI0dOhQJSUl6eabb1ZBQYGcTqfuv/9+ZWdnW66EfBe2cQJBjG2cQNOaexvnhe17++1Zuw/+67TH2my2Jq+/8MILuuWWWyQdP0hq6tSpevXVV+VyuZSWlqYFCxb4tCf27t2rO+64Q++++66ioqKUkZGhxx57TKGh/qsbECCAIEaAAJrW3AEisV1Pvz1rz1cf+u1ZwYQWBgAABm6+C8MUAQIAAIMgKc4HNXZhAAAAy6hAAABgQAvDHAECAAADWhjmaGEAAADLqEAAAGBg9QTJHyMCBAAABoE6ifKHhBYGAACwjAoEAAAGLKI0R4AAAMCAbZzmaGEAAADLqEAAAGBAC8McAQIAAAO2cZojQAAAYEAFwhxrIAAAgGVUIAAAMGAXhjkCBAAABrQwzNHCAAAAllGBAADAgF0Y5ggQAAAY8GVa5mhhAAAAy6hAAABgQAvDHAECAAADdmGYo4UBAAAsowIBAIABiyjNESAAADCghWGOAAEAgAEBwhxrIAAAgGVUIAAAMKD+YM7moU6Db3G5XMrPz1deXp4iIiICPR0gKPD3AjgZAQI+6urqFBMTo9raWkVHRwd6OkBQ4O8FcDLWQAAAAMsIEAAAwDICBAAAsIwAAR8RERF64IEHWCgGfAt/L4CTsYgSAABYRgUCAABYRoAAAACWESAAAIBlBAgAAGAZAQJe8+fP1wUXXKCWLVsqOTlZ69evD/SUgIAqKSnRyJEjlZCQIJvNppUrVwZ6SkDQIEBAkrR8+XLl5ubqgQce0KZNm9SzZ0+lpaWpqqoq0FMDAqa+vl49e/bU/PnzAz0VIOiwjROSpOTkZPXr10/PPPOMJMntdqtTp06aNGmSZsyYEeDZAYFns9m0YsUKjRo1KtBTAYICFQjo6NGjKi8vV2pqqvdaSEiIUlNTVVpaGsCZAQCCFQECOnjwoBobG2W3232u2+12OZ3OAM0KABDMCBAAAMAyAgTUvn17tWjRQpWVlT7XKysr5XA4AjQrAEAwI0BA4eHh6tOnj4qLi73X3G63iouLlZKSEsCZAQCCVWigJ4DgkJubq4yMDPXt21f9+/fXvHnzVF9fr1tvvTXQUwMC5vDhw/r000+9P+/Zs0ebN29WXFycOnfuHMCZAYHHNk54PfPMM5o7d66cTqd69eqlwsJCJScnB3paQMC8++67Gjx48EnXMzIytGTJku9/QkAQIUAAAADLWAMBAAAsI0AAAADLCBAAAMAyAgQAALCMAAEAACwjQAAAAMsIEAAAwDICBAAAsIwAAQAALCNAAAAAywgQAADAMgIEAACw7P8DKEOcSz4KUUgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.heatmap(cm, annot = True, fmt = 'g');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ad9c6iRz8afL",
        "outputId": "4d0e69e8-897e-46ab-9a56-59c93aa7c0d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.92      0.93      1407\n",
            "           1       0.92      0.95      0.94      1433\n",
            "\n",
            "    accuracy                           0.93      2840\n",
            "   macro avg       0.93      0.93      0.93      2840\n",
            "weighted avg       0.93      0.93      0.93      2840\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report = classification_report(y_test, y_preds)\n",
        "print(report)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "balanced_data.to_csv(\"Ready for ml.csv\")"
      ],
      "metadata": {
        "id": "PlvuXb4veDxO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(rf, open('model.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "wu6ws9xFey1s"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}