diff --git a/HML071 - California Housing Pricing Prediction.ipynb b/HML071 - California Housing Pricing Prediction.ipynb
index 192ef8a..774009b 100644
--- a/HML071 - California Housing Pricing Prediction.ipynb	
+++ b/HML071 - California Housing Pricing Prediction.ipynb	
@@ -2,24 +2,36 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Yashwanth Sri\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
+      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
+     ]
+    }
+   ],
    "source": [
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from sklearn.preprocessing import LabelEncoder\n",
     "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from math import sqrt\n",
     "from sklearn.metrics import r2_score, mean_squared_error\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
     "from sklearn.model_selection import cross_val_score\n",
-    "from sklearn.ensemble import RandomForestRegressor"
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "from sklearn.cross_validation import train_test_split"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -28,7 +40,7 @@
        "(20640, 10)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -447,7 +459,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -458,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {
     "scrolled": true
    },
@@ -479,6 +491,378 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "longitude               0\n",
+       "latitude                0\n",
+       "housing_median_age      0\n",
+       "total_rooms             0\n",
+       "total_bedrooms        207\n",
+       "population              0\n",
+       "households              0\n",
+       "median_income           0\n",
+       "ocean_proximity         0\n",
+       "median_house_value      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking for Null values\n",
+    "data.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Replacing the null values with mean \n",
+    "data['total_bedrooms'] = data['total_bedrooms'].fillna(data['total_bedrooms'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#One-hot encoding\n",
+    "dummy = pd.get_dummies(data['ocean_proximity'])\n",
+    "data = pd.concat([data, dummy], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Standadizing the Data\n",
+    "data['housing_median_age'] = data.housing_median_age.astype(float)\n",
+    "data['total_rooms'] = data.total_rooms.astype(float)\n",
+    "data['population'] = data.population.astype(float)\n",
+    "data['households'] = data.households.astype(float)\n",
+    "data['median_house_value'] = data.median_house_value.astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data.median_income,data.median_house_value)\n",
+    "plt.title(\"median_income vs median_house_value\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Extracting input(x) and output(y)\n",
+    "X = data.drop(['median_house_value', 'ocean_proximity'], axis=1)\n",
+    "y = data['median_house_value']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import model_selection\n",
+    "x_train,x_test,y_train,y_test = model_selection.train_test_split(X,y,test_size=0.2,random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Linear Regression\n",
+    "lin = LinearRegression()\n",
+    "lin.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Squared error : 70031.41991955668\n"
+     ]
+    }
+   ],
+   "source": [
+    "prediction = lin.predict(x_test)\n",
+    "rmse=(sqrt(mean_squared_error(y_test, prediction)))\n",
+    "print(\"Root Mean Squared error : \"+str(rmse))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DecisionTreeRegressor(criterion='mse', max_depth=9, max_features=None,\n",
+       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
+       "           min_impurity_split=None, min_samples_leaf=1,\n",
+       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+       "           presort=False, random_state=None, splitter='best')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Decision Tree Regression\n",
+    "dtree_reg = DecisionTreeRegressor(max_depth=9)\n",
+    "dtree_reg.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Squared error = 61875.06399619727\n",
+      "Accuracy = 70.78375066024853\n",
+      "R2 score = 0.7078375066024853\n"
+     ]
+    }
+   ],
+   "source": [
+    "dt_prediction = dtree_reg.predict(x_test)\n",
+    "print(\"Root Mean Squared error = \" + str(sqrt(mean_squared_error(y_test, dt_prediction))))\n",
+    "dt_accuracy = dtree_reg.score(x_test,y_test)\n",
+    "print(\"Accuracy = \" + str(dt_accuracy*100))\n",
+    "print(\"R2 score = \" + str(r2_score(y_test,dt_prediction)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
+       "           max_features='auto', max_leaf_nodes=None,\n",
+       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
+       "           min_samples_leaf=1, min_samples_split=2,\n",
+       "           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=1,\n",
+       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Random Forest Regression\n",
+    "rf = RandomForestRegressor(30)\n",
+    "rf.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Squared Error = 49758.60281164083\n",
+      "Accuracy = 81.10575709661028\n",
+      "R2 score = 0.8110575709661028\n"
+     ]
+    }
+   ],
+   "source": [
+    "rf_prediction = rf.predict(x_test)\n",
+    "print(\"Root Mean Squared Error = \" + str(sqrt(mean_squared_error(y_test,rf_prediction))))\n",
+    "rf_accuracy = rf.score(x_test,y_test)\n",
+    "print(\"Accuracy = \" + str(rf_accuracy*100))\n",
+    "print(\"R2 score = \" + str(r2_score(y_test,rf_prediction)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Bonus Exercise\n",
+    "x1 = data.iloc[:, 7].values\n",
+    "y1 = data.iloc[:, 9].values\n",
+    "#Spliting the dataset and Reshapping the data\n",
+    "xtrain, xtest, ytrain, ytest = train_test_split(x1, y1, test_size= 0.2, random_state=2)\n",
+    "xtrain= xtrain.reshape(-1, 1)\n",
+    "ytrain= ytrain.reshape(-1, 1)\n",
+    "xtest = xtest.reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#linnear Regression\n",
+    "rg = LinearRegression()\n",
+    "rg.fit(xtrain,ytrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Root Mean Square Error = 84914.99659785254\n",
+      "Accuracy = 46.470481929345276\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred = rg.predict(xtest)\n",
+    "rms1 = sqrt(mean_squared_error(ytest,pred))\n",
+    "print(\"Root Mean Square Error = \" + str(rms1))\n",
+    "accuracy1 = rg.score(xtest,ytest)\n",
+    "print(\"Accuracy = \" + str(accuracy1*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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w2+ubRlsUh0MctX9Xhk5ZkNB3LvlV2ovAgOx+1nT/v+qaWq5+YgWAL8GRLgAkmcqqaleBAdDh449AhOuc8tt79GL0OTfFNIv4PtN9jqDt9UZ6bEd4G5O8b8PPlz35mugmw3g6l4QZeVA3Hl9a3aTvkweVJdTnK1F13+3zZ4vopst8JBqOGd1flGphki4AJBnXME9V7vrbdQx/d3Gs6ugL7uS93XskNIv2WVlVnVL7ygV7vZEeM5G3Men2hfi9JpmSjh148Z1Nrn2/+M4mTh5UlnUbblsSDgmTRvdl0ui+roEJ2aJBNa//ThtramOLjOqaWpTGxUNlVXWsnZvzVYCj9u/q2W88I1YtYu0fT2gUGDffTOUbG9jYrTyhXbwgmDp/lafAKC0ON7HX5xKVVdUMnbKAXhOfZuiUBQl/y/aGaRptTKZmgXTv+WlTXVPbJB1JvtGpY4eECeXyWcta7V75qWdE2Lu0OOXCJF4LSW6jwONLqxm8T5cmk3dUM+n6+WZev+OcWP3KruWc85Pbef2ykbGdt14aTqpndNm1xzb/Q7cyzbEOFDKmabQxXup/KluuHzvv3qXFBW0P3uqYpKITntGU6Krea3KurqnlmsoVMS3EDS+td/yx3+Suyj8kCIxjLvgzx51/O5u2E1t9Vwwsi/nRNtbUMnX+qtiq3Ov5LMvx57Y51oFCxlfuqXwi16OnUuW6AvdVWrr8Vc3JYZOPlBaH+eyrehpc0rpA+4rMSiY+eipVhJKfv1GTCKYnnoCTT44Vfz/sQu75dtOMPsXhkKv/LNXzmZxLKRfxij4stEivrEVPGdkl+uVIFg5AWhU4PnpKBGq21bk6OaMZdfOBTDKTpnKAt2eBIZAQJjt+eB/PxYOfv1FMI/jvf6Fbt1j91v36MHrsLaz7vN41YKC2rsHVDBpdlUfHmGnkYNBkGjRQ6Jim4YNrKlfEUqC7pT5PprKqml8+8WYsRFEEzjy0J4P36dIkxfrIg7rx4jub0k7y0fTQ8YICSLyP0662bgdFeRwFZGRGSIQdqk200+b4fYrDIa7/wYFU/OFyePzxWP0Ljy3g4mVfNVuLzedVebpM2IWCX03DhEYa3M60ABJ2fcdTWVXNlbOX4WFByRrhkFDXUFj/O6PlxE9mXmaqZK0sWi4rLeZPHddw6FUXNr55441w5ZVpN+WlI9WmwXygOXur8g0zT2WJaIpzt3o3oTF1/qpWFxiACQzDlfgoKTczVdTv8OI7mxInwL07wF57NXZ0wAFQVQUdOwIty6xaCPsvouk+DBMaafEy8XjVB5222DCiz6CX/yxh8lOFMWPg0Ucb6/79b+jbN6FPL7t+us2QXulN2sPKvVAxoZEGry9FcqLCKLmUssNon8Q7aL1WyJVV1bxy073c8MBvGisdU5QbXlpLc6KibN9DfmP7NNIQTXHut3788D604oblGOFQPu9bNlqTdKagZ/6+jIqDu8cExrtf78FBV8+h8qgxntekygadLlN0MrbvIb8xTSMNUb+F3+ip6Bcl0+ipqJp+1P5dY/dKpsg5lD6qzsf3ZeQXrRkiHJ18o89izBS0ZRt/ffZGjntzYaztseffzn+6lsMOYr4QL1LZ9TOx+TcnK0JrYCay5mFCwweTK/qlDLFNJt2XKx2D9+niW90v5M18BU0rSo14cw9EnpHD317Eoicmx+r+cOR5TDv05ITr0k3a2Zpkc2Hfg5nImo8JjVaiJV+weAdmdU0tIZEE9T3+gKhou401teBoIvlEe92U19r/p+jzUvr5FlZed0qs/r0u3TnuvNvY3qHpoUapJm23SfaKWct4dMk61n5am9Fz7uUfacsIq3T5uQxvTGi0Ai1ZxSSfmxG/H8Otn6hW09zNXEFSHA6xU4ciM7H5pLQ4TN+9v8ai91KeQxZBlQkzfsvolS/Fqoaffzurupa7Nk83aXslOIwfi9/n3FdUVyuTKyayfMSERivQ3FXMNZUrmLl4XWzl7TaZ1tY1cNXs5Vwxa1nCly0fnYgnDypjZp5n3wUoCRd5HlCUTUTg1ME9eW3NZlLd7ujVr3LP47+Pla8/8lzuOvSUhDalxWE67dTB96TtdzL1u1pvy30Pblp/LpjI8hUTGq1Ac1YxlVXVCQIjFVEnefzKLh/DfP/2RnVBpDtpC4EBsGVbHeMfW+4pMLps28obt50ZK6/pvDfDz7+jiSmqOBxi0ui+CWnSr5i1jKnzV3kKj0xCyXNpte6l9XslVsz3TYhtgYXctgLNSX/+26feapZtv7auIe/MUlG+2N6Q9wKjrXHNBKDKzU9NTRAYI867jaPGTWsiMESIBVRcU7mCK2YtS3lYU5Txw/v4Ppwql1brXlr/i+9syihM2GjENI1WIFNHX2VVNVu2mV3fyJxhq1/j3sd/FytPOeJc/jLkFM/2qsQ0DDfN1su8VDGwjCUfbE6rDefaaj2V1m+pQZqHCY1WIFNHXzp/ROeSMDXb6grClGNkh6amqG6MOP8OvurQ0df1qY5e9ZpoJ1f0Y/A+XRKe66P279o0j1UOTcTmu8g+voWGiISAJUC1qo4SkV7AI0AX4A3gbFXdLiI7ATOAQcCnwBhVXev0cTVwAdAAXKqq8536EcAtQAi4W1WnOPWu92jxp84SqcJqs7HZCWDovl2Y+aPDYvezfRntHFVumnsjP3h7YazquPNuZeUe3/B1eWlxxFyV6pkr6Rhi6JQFLX6uc4FcCO8tNDLxaVwGrIwr3wDcpKq9gS1EhAHO7y2quh9wk9MOETkAOB3oC4wA/iwiIUcY3QEcBxwAnOG0TXWPwIlO4H7swelItep5bc2WWJ/RdA1eea+Mwuao915n7R9PiAmMG44YS/mEuZ4CI/kpCRcJk0ZHEhGmeua+2N7Q5Lm+pnIFQ6csoNfEp2NHu+YDmaY4MdLj6zwNEekOTAeuA64ETgA2AXupar2IHAZMUtXhIjLfef2KiHQA/gt0BSYCqOr1Tp/zgUnOLSap6nCn/mqnborXPVKNta2Oe/U6X6A55wZUVlVzxaxlnuaC5D4rq6oZ/+hy6toiB7uREaEi8TyOtrkkm6I+KN2LYy/4c1pTVLhI2GXnDq4nPGa6ryd5E2YhHkLU3vF7noZfTeNm4Bc0nsz5daBGVeud8gYg+vSUAesBnPe3Ou1j9UnXeNWnukcCIjJORJaIyJJNmzb5/EgtI5ubgyoGlqV0Lib3WTGwjKmn9o+ZGozcIasCQ5Ub596YIDCOP/dWjvjx3TGBERJh6L5dXLXPuh1KSccOrJkykkUThyVM8BUDyzJ6frwc5m1FZVV1Xmo6hUhan4aIjAI+VtWlInJktNqlqaZ5z6veTXClat+0UnUaMA0imoZbm2yTbQdbWYo4eLc+k23LlVXVXDV7uTnKC4Qj31vC/Y9NipWnfvds7vhO0yy0Dar8673NGTu1AUb17+Z6KqVf2mo/huWJyi38aBpDgdEispaIU3oYEc2j1DE/AXQHNjqvNwA9AJz3dwM2x9cnXeNV/0mKewTO+OF9KA6HEupa4mAbP7yPa7rzcJG49pm88gK48bT+TcZk5Bedt21l7Q2jYgJj3W570ueqJ1wFRpRUy4RUi5gX33HXypOfQi8PWnzfrakJWCr13CKt0FDVq1W1u6qWE3FkL1DVM4EXgWhA+FjgSef1HKeM8/4CjThO5gCni8hOTlRUb+A14HWgt4j0EpGOzj3mONd43SNwsulgi0Zh1TVowlkcpcVhpp7a3/XUMzcnPEQ2bpmbPA9R5can/0RVkinqez+5x3cYbTLpFjFemkL0vPDoc/2dfbu4tiv/ejFDpyygfOLTaTcJtkSoWJ6o3KIl+zQmAI+IyGSgCrjHqb8HeEBEVhPRME4HUNW3RGQ28DZQD1ykqg0AInIxMJ9IyO29qvpWmnvkBNkIP0xWvXdoeidjqpXX+OF96BCX5NDIffyaojLB65jVeLxMrMmBF1FNNpl4s1iqTYItNS/ZXovcIiOhoaoLgYXO6/eBQ1zafAmc6nH9dUQisJLr5wHzXOpd79EaZPNAluS+vDZAefkhokkJoemXqrKq2tP3UV1Tm7cpRdojpbX/Y9mtP4yVN+y6B0dfeCdfhXdqUb9+I/j87mHwet7SLUuimkBL05DbXovcwnaEk11Hm1tf8c7GaN9LPtjM40urPR3XDapNxhDt28hzVPnjM7dw2oq/x6pGnnsLb+25r6/Li8NF1HpkLcxkMvWbuSDUzEwEUU2gpealXEilbjRiQoPsHsji1lcytXUNnke6phqDn76N3OaI95cy/dFrY+UbDz+T24ae4etaAdZMGem5RygkwvUnRU6Y9NrRnYwfE2tzBEa88MqGeSnfdqIXMiY0yK6jze81fr+I1TW1sQnAvBT5S7IpqvprXRn2o79kZIraLU0KkOgzle3wVK9w8OQNf9Fysj/FzEuFhQkNsuto83vugF+VX8jPszIMBzdT1NibeWuv/TLuqqa2jvKJT6d8dibNeSvrx5h6TfonDyrzlazQzEuFhQkNsrsScusrmegXLvkQmGTa6/nZhcJ317zBA7N/Eyv/6fAzudWnKSoVqRYbXkfnptOA0yXfhJZN+mZeKhxMaJDdlZBbX17RU4P36ZIy2skERn6yW+1nLL+1UTh8uMvXOXLctBZHRbWEVFqzn0AQm/SNKCY0UlBZVc2kOW8lrN6S7bYQERDVNbUxs0GZIyjmLv/QNXrq8lnLuPqJN7n+pINS3t80jTxDlSnP3sbpbz4Xqxo19mb+3QxTVEsoDocyOgDMK+zby6SVzfB0o+W09f/DV5bbfKI5WW7dzqkIh4SGBiXV6c/hkIDS7GyzRUCoCM8zn4384fA1VTw4+9ex8s1Dz+Dmw89McUXrUFocZtLovr4mkXTns0SjtdJdYxlvgyOb/w+/WW5N08A9lNXPjuqW7rreQeT4TSN/cTNFHTXuLr4M7xzIeCaN7uvblJQuhNvNpJXN8HSj5QTx/zChQbA5bExo5C9/ePY2frh8fqx8wjk3saJb78DG07kknNFEkeq59zJpWR6o3CKI/0cmJ/cVLJbDxsiEoWuXsfaGUTGBcct3zqB8wtxABQZAzTb3yCkvvJ776CZBNwHkdY19h4IhiP+HCQ3c05yHQ5L2jxMOCeEiyynbXtj1y89Ze8MoZs66BoCPdunC/lc+xk3fbXvfhRuZThRe6f1vPK1pZuV019hGvWAI4v9h5im8Q24BX9FTyW0AigR26uCdI8jIL66bfztnLns2Vh59zp94s9s3AxxRIkWQ8UTRnFBz26iXWwTx/7DoqSyQ6rzw8cP7xP6hfv7SpcVhPvuy3k7gyxG+s3YZDzmaBcAt3zmdm757VpvdX4CbxgwA3BcnUcIhYeop3hqCYaTDoqeyhJ8Y6FSpyq+YtSyjvRb/+7KObB4zbTSPXb/8nGW3nEGR89/b1KmU7427m9qObRsVpURWkYsmDmPq/FWeQqOuQS2CyWgTTGikwM9O2XQnkGU6/5vACJ7J8+/grGXPxMonnn0jy/cOzmYfjYRJFxFjEUxGW2BCIwV+YqDtnOLC4bAPlvPwI7+KlW87bAw3fu/sAEcUIergTpcM0yKYjLbAhEYK/MRA2+ou/9n1y89549Yf0kEjQQuflOzGd398T5ubotwQIg7uyqpqvviqPmW7aBp9c0wbrYkJjRT4SZnuNxW6kZv87rk7Oafq6Vi54uwbWRagKSoeAc4c0hPANd1HkUTMmfE5yrJxfkZrY7mr8hvbp5ECPzHQ44f3sb0aecipbz7H2htGxQTGHUNOpXzC3JwRGKXFYW4aM4DJFf08033sunOYstLiJn6zqAk1F4n6CaudaMKokEvnGzRyB9M0UuAnBjr6+orZyywlSB7Q9fMtvH5Ho5+itsNOHHzJzEBMUQJ0CIlrDrMvtjeaorxMoDW1dc0+PyMoLHdV/pNWaIjIzsBLwE5O+8dU9VoR6QU8AnQB3gDOVtXtIrITMAMYBHwKjFHVtU5fVwMXAA3Apao636kfAdwChIC7VXWKU+96jyx9dl+4JX9zU69bO4e5pUlvOS/95QJ6bv0oVv7piRN5Zv/DAxuPAh2K3IVGfAhtKhOo1yl+ueoUt9xV+Y8f89RXwDBV7Q8MAEaIyBDgBuAf0IVxAAAd/0lEQVQmVe0NbCEiDHB+b1HV/YCbnHaIyAHA6UBfYATwZxEJiUgIuAM4DjgAOMNpS4p7BIaXel1aEm7V+5rAaD6nrPg7a28YFRMYy/fqTfmEuYEKjCipMgZEJ9JUO70bVPMqrYflrsp/0goNjfC5Uww7PwoMAx5z6qcDFc7rE50yzvtHi4g49Y+o6lequgZYDRzi/KxW1fcdLeIR4ETnGq97BIaXeq3qnK9h5Ay7f7GFtTeM4v/m3Ryr63/pw5w49qYAR+Wf6ERaMbCMzh6LkrLSYq4/qR9lpcVIXNlNOx46ZQG9Jj7N0CkLAvMhWO6q/MeXT8PRBpYC+xHRCt4DalQ1anjdAESf0jJgPYCq1ovIVuDrTv3iuG7jr1mfVH+oc43XPZLHNw4YB9CzZ08/H6nZeKnRW2vruGnMAH771FtscbKNlhaHPW3ORuuy8K4fUV7zYaz8sxMnMi8HNAu/hEOSMJFee0Jfz3Ps052f4WeTalthuavyH19CQ1UbgAEiUgr8DfiWWzPnt9tyW1PUu2k7qdq7jW8aMA0iuafc2mSLVGG4bl9er7xURutw8ooXuHFeoyaxYs99OeHcWwIcUeZ0Lglz7Ql9gcjzE51cTx5U5nrWfDpyzfls543nNxlFT6lqjYgsBIYApSLSwdEEugMbnWYbgB7ABhHpAOwGbI6rjxJ/jVv9JynuERjjh/fxXPH5bW9kn69/UcPS2xMTCfa/9GG2Fn8toBFlTvwxnW7aweNLq5t1jKc5n41sktanISJdHQ0DESkGjgFWAi8CpzjNxgJPOq/nOGWc9xdoJJXuHOB0EdnJiYrqDbwGvA70FpFeItKRiLN8jnON1z0Co2JgmS8bcnL7kJi/o7V44a8/ThAYF4/+BeUT5uaVwIDE/RWptINMMedzIrni38lX/Gga3YDpjl+jCJitqnNF5G3gERGZDFQB9zjt7wEeEJHVRDSM0wFU9S0RmQ28DdQDFzlmL0TkYmA+kZDbe1X1LaevCR73CJRM1euKgWVcMWtZK46ofXLSv1/gT083mqLe3qMXx593W4AjajnpkhNW19RSWVWd0fOXqXZcyOSSfydfSSs0VPVNYKBL/ftEIp+S678ETvXo6zrgOpf6ecA8v/doDbxSG8TX71YcRiRyrGb8QU3R90tLwqhGNl3F76soEguZzSZdtm3ljdsST8sbcOlD1BTvGtCIsoef5ISZTnLmfG4k1/w7+YgdwkTT1QdEVmInDyrj8aXVnv6IcCgiDeosn3mb8fe//oT9Nm+IlS85YTxPHXBEgCPKHuEiYeqp/V19GsmUlRazaOKwNh5h/tNr4tOuCzgB1kwZ2dbDySnsEKYM8Fp9PPzq+pQn6Lnt5DVah4q3XuTmuTfGyiu7lnPc+bcHOKLss8vOHWKr3ejvyz3MmubEbh5+kpAaqTGhgfcX0I5cDZ7O27ZSVaCmqGRqtiXu6akYWMbU+atskssi5t9pOZblFu8voEU8Bctzd/8sQWBcesLPKZ8wtyAFBrg/h7aDOrtkGv1oNMU0DbxXH+l8GtB4poGRPUa/vZBbn/q/WHnV7j0ZfsGfAxxR6+MlCMyJnX1sc2HLMKFB6i/m4H26xOp3Dhc1TTCnJjiyhZspauAlM9lSsltAI2pdQiLsUE0rCGySM3IJi57KgExSghSHQ7YLPAOeufdivrVpbax82aireLLvUcENqJWJ3/0NdpqdETwWPdUKZBKxYgLDHye8/Q9ue2pqrPzu13vw/QvvDHBErYtAE6FgG86MfMKERgbYeeDZo7T2fyy79YcJdQdfMpPNBWqKAujUMURpSUc21tTG0oFEI6QKacOZaU2FjQmNDLDkg9lh3n2XcMDHa2LlK0Zeyd8OLOyNaqEiYXv9jtiiI16byPWEgpkIAdOaCh8TGhkQ7zA3jSNzRq18idvn/DFWfq9LGUf/6K4AR9Q2lDgBFMlLjag2kc0NZ9le5WcqBApNazKaYvs0MqRiYBmLJg7j5jEDXA/8MJqyW+1nrL1hVILAOPiSmQUvMDqXhDlrSE8U8cw9trGmNmt7MbyOIm5JFtdMs+3mutZktBwTGs2kYmBZxkkIRSI/7Ymn7r+M5beeEStfOfIKyifMLRjfheC9CbSkYwdefGdTSnNm9PCubGw4y2Y69SiZCgFLw174mHmqBZRl6BgvsOjmlIxc+TJ3zLkhVn6/894MGzctwBFln5AIN57W3zPtfbrVdbw20Zy9GMmmKK9nsSWr/ExNZ5amo/CxfRoZEP8lLS0J8/mXdSTv9Wvv7Fb7WYJmATDo4gf5tFNpQCNqXSJmJW266ZPIGfGddurgOukKUFoSTkizn+6c73gBcdT+XZtkK4hPxx9PSzLiumXbDRcJu+zcwXPsFj2Vn/jdp2FCwyfpUlUb8OT0K+j/33dj5Z8ffzmP9TsmwBG1DV4ZATqXhBl5UDceXLwu7TXJm/3icXv2vAREcn2qfv2SfKbMF9vrEzI8Z+MeRvD4FRrm0/CJm73YiHDcO/9k7Q2jYgLjg9K9KJ8wt10IDPBOIVOzrY4X39nk65pUvge3Z89rqaeQ9WR80eCPNVNG0mmnDk2OBGip38TIL8yn4ROL/mhK8fYvWXnTKQl1gy9+gE86dQ5oRLnF3qXFGT032Yg8Com0qlnIoqMM0zR8YtEfifziH/cnCIzxx11G+YS5JjAcos7fTJ6bTCOP3GK2GlSzFm6byVjs+9F+MKHhE7dY+vbIgI2rWHvDKH62+DEAZgwcSfmEuTx60PcDHlnrEC6SJuXOJeGU1whw8qBINJTbcxMOSZN+U0UYee3jOHNIz5gpyi3stzXMRna+h2HmKZ8kp08v6Rjii+3tx8dRvP1LFv3lfLrU/g+AuqIQgy6Zyf923iXgkbUyEomC2lqbGCmUKuOxQsyX4ZV2360uVWr0dO17TXza9dpsm43sfA8jbfSUiPQAZgB7ATuAaap6i4h0AWYB5cBa4DRV3SIiAtwCHA9sA85V1TecvsYC1zhdT1bV6U79IOB+oBiYB1ymqup1j1TjzVb0VKqwwfYWSfXzl2Zw8SuzY+UzTr+OV/bpH+CI2ha3kNV0z4AAa6aMbIPRRfASYi0JtzXaF9mMnqoHrlLVbwFDgItE5ABgIvCCqvYGXnDKAMcBvZ2fccCdzoC6ANcChwKHANeKSNQAfqfTNnrdCKfe6x6tSrp0DO0lkipqiooKjAcHHEf5hLntSmCA+2o9uovbazd4W9v4zWxktBVpzVOq+iHwofP6MxFZCZQBJwJHOs2mAwuBCU79DI2oMItFpFREujltn1fVzQAi8jwwQkQWAruq6itO/QygAngmxT1alXRJ1wo9UqR4+5e8fNcF7L5tKwD1UsTBlz5U8KYor70PSmQln2yGib5OtwO6sqqa3z71Flu21QERc9ek0X0992Q0x/RjZiOjrcjIpyEi5cBA4FVgT0egoKofisgeTrMyYH3cZRuculT1G1zqSXGP5HGNI6Kp0LNnz0w+kivpwgoL+VyNK196gEtfmRUr/3DMZP5VPiDAEbUdqQy1Xtld003WlVXVjH9secLehpraOsY/urxJXy1NK27HwhptgW+hISK7AI8Dl6vq/8Q7857bG9qMet+o6jRgGkR8Gplc64aXUFCg3MPhmO/037iKJx+4KlaeOWAEvxp+cYAjyj28UnynmqwnzXmryWY4gLod2qQvLw33qtlNBYxhBIUvoSEiYSICY6aqPuFUfyQi3RwNoBvwsVO/AegRd3l3YKNTf2RS/UKnvrtL+1T3aFXGD+/D+EeXU+e11beA2LnuS16+60K6flEDwA6EAZc9XNCmqHBI6NSxAzW1dRlfm4lpsrKqOuU9kvvy6rtB1Q4yMnKGtI5wJxrqHmClqv4p7q05wFjn9Vjgybj6cyTCEGCrY2KaDxwrIp0dB/ixwHznvc9EZIhzr3OS+nK7R6tSMbCMXXYu/GjkK15+kHf+dEpMYJw5ZjLfmPBUQQsMgPoGZVT/bs26NhMHd7o9Esl9peq7tVN1VFZVM3TKAnpNfJqhUxZkfVOgUTj4mRmHAmcDK0QkmgP6l8AUYLaIXACsA0513ptHJNx2NZGQ2/MAVHWziPweeN1p97uoUxz4KY0ht884P6S4R6tTsy3zVWi+0O/Dd3lqxhWx8kP9R/DLEe3HFKXArNfWp22XTKbRSKm0knCRNOkr3XHCrRWAYUe0GpngJ3rqn7j7HQCOdmmvwEUefd0L3OtSvwQ40KX+U7d7tAWF6Ozeue5L/jFtHHt+vjlW1//Sh9la/LUARxUMmZgeBRIc3H4jnLyeIQGmntrf1TcCcNXs5TS47J9qrTBeO6LVyITCt8E0k/HD+3C5x+E6+cgVL8/ksn89HCufddrv+WevgQGOKD8oLQ6z7NpjY2W3VfkVs5bx6JJ1rP20NkGQjB/ep0nkFECHIs8gEt9hvNnEkhAamWC5p1JQCEez9vvwXdbeMComMB7qP5zyCXNNYPjki+31CfZ9rzTli97b3GQzKECnjk3XZdHIKS+ydfyrXywJoZEJdgiTC4WQJmTnui9ZOG0ce5kpqsWERNihmrHJssxJje51WFJbphlJhdvzbgcrtT/8phEx85TDNZUrePjV9a625Hzjsn8+xBWLHoqVzz7td7zc6+AAR5TfRJ+J6ppaz13jbqQ6uzuXVvG2m9zIBBMaRASG25Gc+caB/13N3OmXx8qPHHQsE0dcUhh2thwhkyVFdPJtS/9Ec7Hd5IZfTGgAD7+aefhlLrFT3Ve8+Ncfs/dnn8TqzBTVuqTTOKKCwVbxRqFhQgPy2iR16aKHufKfM2Plc079LS99Y1CAIyp8ypyJf9Kct1x3fCcnJLRVvFFImNAg4ujMN8HR97+reTrOFDW73zH84rjLzBTVTEIi7FrcIZaJ1ot4DWLq/FWuQqPTTh1MSBgFiwkN4IxDe+SNT2Onuq9Y8NefUPbZpljdgEsfoqZ41wBHld9EI4Wg6f6IcJGwy84dqNlW18S0ZPsbjPaICQ1gckVkwohGT4VECBXBdpfspEFy8b8e4ecvPxgrjz31t/zDTFEtonNJmGtP6Nsk26wf/0M+REYZRrYxoeEwuaJfTHhAJHb9ilnLMsvR3kr0/eg9nr7/slj50QOPYfzxZorKBl/W7UgoZ+J/yJfIqGzT3IOijMLAhIYL0S9F0AJjp/rtvPDXn9D9f40Z4QdeMpMtJbsFOKrCoiU5ltpjZJQlNzRMaCSRK7vBL/rXLMa//ECsfO4pk1i4b9rNmkYzaIkPor1FRllyw9yjrTU/ExpJuH0p2pIDPnqfefdfGis/duDR/Pz4y80U5YNoNtpMsxObD8I/5vzPLYLQ/ExoJBHUw79T/Xaev/un9Nz6UazOTFGZ4bUDG6BTxxA7VKlN8mG01AfR3uz75vzPLYLQ/CzLbRJBPPw/e2U2q248KSYwzj3lWsonzDWBkSHRCTuaIRYi+y8ASks6cv1JB3HzmAFZyx4bXeUlZ7ct5FPvxg/vQ3E4lFDXHpz/uUoQmp9pGkmkOz0tm3zr4/d55r5GU9TjfY/iqpFXmimqmcTvwAZc1fbrT+rHoonDsnK/9mjfb4/O/1wmCM3PhEYSbl+KzV981cSs0RJ2qt/Oc/f8jH1q/hurO/iSmWw2zaLZdC4JJ5S9JvSrZi8HsmPvba/2/fbm/M9lggj7NqFBxMzw26feiqWQEAHViGkj20e+/nTxo0z4x/RY+bxTruXFfb+d1Xu0R7Zsq2PolAWxL4vX/61BNWuOwvZq3y8EP04hfAYIRvNr94cwVVZVux7JmW32/3gNz953Saz8twOO5IpRV5kpKiDKSotbbKZqj4cXFcJnLoTP0BrYIUw+mTp/VasKjI71dTx770V8Y8vGWJ2ZooInGyak9mjfLwQ/TiF8hiBJKzRE5F5gFPCxqh7o1HUBZgHlwFrgNFXdIiIC3AIcD2wDzlXVN5xrxgLXON1OVtXpTv0g4H6gGJgHXKaq6nWPFn/iJFrT/vyTxY8x8R/3x8oXnPxrXtjv0Fa7n+Gf3YrD6Rv5oL3Z9wvBj1MInyFI/ITc3g+MSKqbCLygqr2BF5wywHFAb+dnHHAnxITMtcChwCHAtSLS2bnmTqdt9LoRae6RVVrD/txn01rW3jAqJjAqDziC8l88ZQIjhzCrYPPw+r7kkx+nED5DkKQVGqr6ErA5qfpEIOrNnQ5UxNXP0AiLgVIR6QYMB55X1c2OtvA8MMJ5b1dVfUUjzpUZSX253SOrjB/eh3AoOzNIx/o6Fkwbx/x7L47VDbr4QS4/YbzNUjlGTZpzMwx3CmGfRiF8hiBprk9jT1X9EEBVPxSRPZz6MiD+7NQNTl2q+g0u9anu0QQRGUdEW6Fnz54ZfZCKgWUs+WAzMxeva5KgMJPDmca9+ji/XHhfrHzhSb/m771Ns8gWQsSk9MX2+gQfVDgkhIuEbRmGRNuqsnkUgh+nED5DkGTbEe62nNZm1GeEqk4DpkEkeiqTayurqpn12nrXm/oRGN/ctJbn4jSLOd/6HpeaZpFVQiK8d/3xAFxTuSLh3JMx3+7B5Ip+CSGUIrAjxb/OVpUtoxD8OIXwGYKiuULjIxHp5mgA3YBo7u4NQI+4dt2BjU79kUn1C5367i7tU90jq0ydv4q6VDOMBx3r63jmvkvYd3OjojTo4gf5tFNpNodn0Ci8K6uqeXxpdazcoMrjS6sZvE+XhEkgVRh1ma0qDaNFNFdozAHGAlOc30/G1V8sIo8QcXpvdSb9+cAf4pzfxwJXq+pmEflMRIYArwLnALeluUdWaU7ExIWvPcE1L97bWDZTVKsSzSPlN1TSzA+G0Xr4Cbl9mIiWsLuIbCASBTUFmC0iFwDrgFOd5vOIhNuuJhJyex6AIxx+D7zutPudqkad6z+lMeT2GeeHFPfIKrsVh6mp9ecUTTZFPbX/d7lk9C/MFNWKxJuSMgmVNPODYbQOaYWGqp7h8dbRLm0VuMijn3uBe13qlwAHutR/6naPbONnvg831DHvvkvp/WmjL3/wxQ/wSafOKa4ymktIhB2qTTSE9pq2wzByiXa/I3xLmtDLC177G79+8Z5YedwPfsVz3zystYfVbgkXCVNP7Z/gnxg6ZQEba2opLQkTLpIEH5Q5tQ2jbWn3QsMrrLb3pg94/t5GpWlun8O5+MQJZorKEp1LwnxZ15CQPbi0OMyk0X0TBEZ8jqDkhJK57NQulIR4hpFMuxcayQIj3FDH3Psvo88n62J1377oATbtYqaobHDWkJ5Mrujnq63X0buqjRpGLk7EQRzBaRhtRbs/uS8Upzlc8Hol7/7fD2IC48c/+CXlE+aawMgiL76zyXfbVJFt0aipXCRVlJdh5DumaTiaxsiVL/PrBXcDMO+b3+FnFVebKaoVyCTE2cvx3Zy+2hJLiGcUMu1e04ie+LZir/14/MBhfPuiB/jZD35pAqOVyCTSyS1HUHP7akssIZ5RyLR7ofGlY0ZY17kbV4280kxRaSgJ+39kksVuppFOFQPLuP6kfpS6pDHP5agpS4hnFDLt3jyVzbO/C5lQkVAEvhIDhkQ449AeDN6nS4siiKIRSFtr6+hcEkYVttbW5Xw0ku1INwqZdi80UpFJlttCpqy0mC++qnfdOe+1ES9KcydKt3Db4nCIm8YMyIvJ13akG4VKuxcanUvCrhv8BH9Zbgud0uIwiyYOo9fEp13f36HKmikjs35fO5LTMHKTdu/TuPaEvoSKmjq9TVxE+GJ7PZVV1W3u3LUIJMPITdq90KgYWMbXdmr3CpcndQ3K1Pmr2ty5axFIhpGbtHuhARHnar5RJG33z9tYU0vFwDJOHlQW2wwZEuHkQa1nt7cIJMPITUxokJ+r1113DvOnMQMoziAENh0hj70pe5cWex6AVFlVnbX7xxMNty0rLUaIOOOvP6mf+TMMI2DMLkNkVRsfqZMPNGpHmW9CLC0O81X9joTPWxwOcfKgMh5fWt2kfvzwPoE4pi0CyTByD9M0aLqqLS0Ox3aK5yp7lxYzac5bnoLurCE9uXnMAFcTz6TRfV1X8ZMr+nmu7s0xbRgGmKYRI3lVm7xPoDmcNaQnD7+63nfobkm4yNfmueJwiKP278qDi9d5tonPJOu1ycxtFe+1urcDkAzDABMaMZLPP/jiq/oWCYyy0mImV/Rj8D5dfAmfcJFw0qDuPPTqOnakkDEhEa4/qV/KjKnxvolsmXjcTHjmmDaM9oeZp2jUKqpralEi5x/4PTfcjfjJNBp1lMrzUFocZswhPXh8aXVKgVEcDnHjaf1TmougdTYlmmPaMAwwTQPwPuzHC8F785/baXIvvrPJtX1ZaTGLJg4DYOiUBSnHkNxvqrThZa1kMjLHtGEYJjTIzJnbuSTMyIO6uUYZea28/TiRvdoIuKbpGD+8D+MfW05dQ9LJg0ViJiPDMFqNnDdPicgIEVklIqtFZGJr3MPLmdu5JJxgjrl5zACqfnNsyiijTPqPr890B3TFwDKmntI/IcqrtDjM1FP7mzZgGEarIZrDSflEJAT8B/g+sAF4HThDVd/2umbw4MG6ZMmSjO7jFimVSnPIFD/9t/YYDMMwUiEiS1V1cLp2uW6eOgRYrarvA4jII8CJgKfQaA6tff6Bn/7tDAbDMPKBXNc0TgFGqOqFTvls4FBVvTip3ThgHEDPnj0HffDBB20+VsMwjHzGr6aR6z4Nt0jVJlJOVaep6mBVHdy1a9c2GJZhGEb7JNeFxgagR1y5O7AxoLEYhmG0e3JdaLwO9BaRXiLSETgdmBPwmAzDMNotOe0IV9V6EbkYmA+EgHtV9a2Ah2UYhtFuyWmhAaCq84B5QY/DMAzDyPHoqeYgIpuA5PCp3YFPAhhOptg4s0s+jDMfxgg2zmyTi+PcR1XTRhIVnNBwQ0SW+AklCxobZ3bJh3HmwxjBxplt8mWcbuS6I9wwDMPIIUxoGIZhGL5pL0JjWtAD8ImNM7vkwzjzYYxg48w2+TLOJrQLn4ZhGIaRHdqLpmEYhmFkgYIWGm1xFkdLEZEeIvKiiKwUkbdE5LKgx5QKEQmJSJWIzA16LF6ISKmIPCYi7zh/18OCHpMbInKF8z//t4g8LCI7Bz0mABG5V0Q+FpF/x9V1EZHnReRd53fnIMfojMltnFOd//ubIvI3ESkNcozOmJqMM+69n4uIisjuQYytORSs0HDO4rgDOA44ADhDRA4IdlSu1ANXqeq3gCHARTk6ziiXASuDHkQabgGeVdX9gf7k4HhFpAy4FBisqgcSyXhwerCjinE/MCKpbiLwgqr2Bl5wykFzP03H+TxwoKoeROQsnqvbelAu3E/TcSIiPYicFbSurQfUEgpWaBB3FoeqbgeiZ3HkFKr6oaq+4bz+jMgEl5OHaIhId2AkcHfQY/FCRHYFvgfcA6Cq21W1JthRedIBKBaRDkAJOZKMU1VfAjYnVZ8ITHdeTwcq2nRQLriNU1WfU9V6p7iYSJLTQPH4ewLcBPwCl8zduUwhC40yYH1ceQM5OhlHEZFyYCDwarAj8eRmIg/5jqAHkoJvAJuA+xwz2t0i0inoQSWjqtXA/xFZZX4IbFXV54IdVUr2VNUPIbLQAfYIeDx+OB94JuhBuCEio4FqVV0e9FgypZCFhq+zOHIFEdkFeBy4XFX/F/R4khGRUcDHqro06LGkoQNwMHCnqg4EviA3TCkJOD6BE4FewN5AJxE5K9hRFQ4i8isipt+ZQY8lGREpAX4F/CbosTSHQhYaeXMWh4iEiQiMmar6RNDj8WAoMFpE1hIx9Q0TkQeDHZIrG4ANqhrV1h4jIkRyjWOANaq6SVXrgCeA7wQ8plR8JCLdAJzfHwc8Hk9EZCwwCjhTc3NPwb5EFgvLne9Td+ANEdkr0FH5pJCFRl6cxSEiQsT+vlJV/xT0eLxQ1atVtbuqlhP5Wy5Q1ZxbGavqf4H1ItLHqTqaLJ8pnyXWAUNEpMR5Bo4mBx32ccwBxjqvxwJPBjgWT0RkBDABGK2q24IejxuqukJV91DVcuf7tAE42Hl2c56CFRqOMyx6FsdKYHaOnsUxFDibyMp9mfNzfNCDynMuAWaKyJvAAOAPAY+nCY4m9BjwBrCCyHcxJ3YJi8jDwCtAHxHZICIXAFOA74vIu0QifqYEOUbwHOftwNeA553v0l8CHSSe48xbbEe4YRiG4ZuC1TQMwzCM7GNCwzAMw/CNCQ3DMAzDNyY0DMMwDN+Y0DAMwzB8Y0LDMAzD8I0JDcMwDMM3JjQMwzAM3/w/P+65EmrcVu8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plot\n",
+    "plt.scatter(xtrain, ytrain)\n",
+    "plt.plot(xtest, pred, color='red')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'Predicted median_house_value')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(ytest, pred)\n",
+    "plt.scatter(ytrain, rg.predict(xtrain))\n",
+    "plt.xlabel(\"Actual median_house_value\")\n",
+    "plt.ylabel(\"Predicted median_house_value\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,