Classification Car Insurance_Final (1) copy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abcee029",
   "metadata": {},
   "source": [
    "# Car Insurance Modeling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55222b94",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0fbcf46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f07f997f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load data as dataframe\n",
    "df = pd.read_csv(\"Car_insurance_clean+EDA.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45d8124d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>DRIVING_EXPERIENCE</th>\n",
       "      <th>CREDIT_SCORE</th>\n",
       "      <th>VEHICLE_OWNERSHIP</th>\n",
       "      <th>MARRIED</th>\n",
       "      <th>ANNUAL_MILEAGE</th>\n",
       "      <th>SPEEDING_VIOLATIONS</th>\n",
       "      <th>DUIS</th>\n",
       "      <th>PAST_ACCIDENTS</th>\n",
       "      <th>OUTCOME</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>VEHICLE_YEAR(before 2015)</th>\n",
       "      <th>VEHICLE_TYPE(sports car)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.629027</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
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       "      <td>0.357757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>11.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.388366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   AGE  DRIVING_EXPERIENCE  CREDIT_SCORE  VEHICLE_OWNERSHIP  MARRIED  \\\n",
       "0    3                   0      0.629027                1.0      0.0   \n",
       "1    0                   0      0.357757                0.0      0.0   \n",
       "2    0                   0      0.493146                1.0      0.0   \n",
       "3    0                   0      0.206013                1.0      0.0   \n",
       "4    1                   1      0.388366                1.0      0.0   \n",
       "\n",
       "   ANNUAL_MILEAGE  SPEEDING_VIOLATIONS  DUIS  PAST_ACCIDENTS  OUTCOME  GENDER  \\\n",
       "0            12.0                    0     0               0      0.0       0   \n",
       "1            16.0                    0     0               0      1.0       1   \n",
       "2            11.0                    0     0               0      0.0       0   \n",
       "3            11.0                    0     0               0      0.0       1   \n",
       "4            12.0                    2     0               1      1.0       1   \n",
       "\n",
       "   VEHICLE_YEAR(before 2015)  VEHICLE_TYPE(sports car)  \n",
       "0                          0                         0  \n",
       "1                          1                         0  \n",
       "2                          1                         0  \n",
       "3                          1                         0  \n",
       "4                          1                         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85c14db9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['AGE', 'DRIVING_EXPERIENCE', 'CREDIT_SCORE', 'VEHICLE_OWNERSHIP',\n",
       "       'MARRIED', 'ANNUAL_MILEAGE', 'SPEEDING_VIOLATIONS', 'DUIS',\n",
       "       'PAST_ACCIDENTS', 'OUTCOME', 'GENDER', 'VEHICLE_YEAR(before 2015)',\n",
       "       'VEHICLE_TYPE(sports car)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c2ace0",
   "metadata": {},
   "source": [
    "## Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9f320949",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take the 6 best Feauters\n",
    "from sklearn.model_selection import train_test_split\n",
    "X, y = df.drop(['OUTCOME','CREDIT_SCORE','MARRIED','ANNUAL_MILEAGE','GENDER','VEHICLE_YEAR(before 2015)', 'VEHICLE_TYPE(sports car)'],axis=1), df['OUTCOME']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b6533527",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>DRIVING_EXPERIENCE</th>\n",
       "      <th>VEHICLE_OWNERSHIP</th>\n",
       "      <th>SPEEDING_VIOLATIONS</th>\n",
       "      <th>DUIS</th>\n",
       "      <th>PAST_ACCIDENTS</th>\n",
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      "text/plain": [
       "   AGE  DRIVING_EXPERIENCE  VEHICLE_OWNERSHIP  SPEEDING_VIOLATIONS  DUIS  \\\n",
       "0    3                   0                1.0                    0     0   \n",
       "1    0                   0                0.0                    0     0   \n",
       "2    0                   0                1.0                    0     0   \n",
       "3    0                   0                1.0                    0     0   \n",
       "4    1                   1                1.0                    2     0   \n",
       "\n",
       "   PAST_ACCIDENTS  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "068125fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into 80% train and 20% test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77c117fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into 80% train and 20% validation\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2398bd03",
   "metadata": {
    "scrolled": true
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08af487a",
   "metadata": {},
   "source": [
    "### Handling the Imbalancing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "21e06a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4373\n",
       "1.0    2027\n",
       "Name: OUTCOME, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data befor over sampling\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5c38cb46",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_pos = np.sum(y_train == 1)\n",
    "n_neg = np.sum(y_train == 0)\n",
    "ratio = {1 : n_pos * 2, 0 : n_neg}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c19a68be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn.over_sampling\n",
    "smote = imblearn.over_sampling.SMOTE(sampling_strategy=ratio, random_state = 42)\n",
    "    \n",
    "X_tr_smote, y_tr_smote = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4821cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    4373\n",
       "1.0    4054\n",
       "Name: OUTCOME, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data after over sampling\n",
    "y_tr_smote.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "58eda13e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_tr_smote.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10b85355",
   "metadata": {},
   "source": [
    "### Standard Scaling of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc1e7862",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train_scaled = sc.fit_transform(X_tr_smote)\n",
    "X_val_scaled=sc.transform(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c8ac9c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1138x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(pd.concat([X_train, y_train], axis=1), hue='OUTCOME');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "216bc973",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0efc8702",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import sklearn.ensemble\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#import lightgbm as ltb\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import precision_score, recall_score, precision_recall_curve,f1_score, fbeta_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6181b213",
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy(actuals, preds):\n",
    "    return np.mean(actuals == preds)\n",
    "\n",
    "def precision(actuals, preds):\n",
    "    tp = np.sum((actuals == 1) & (preds == 1))\n",
    "    fp = np.sum((actuals == 0) & (preds == 1))\n",
    "    return tp / (tp + fp)\n",
    "\n",
    "def recall(actuals, preds):\n",
    "    tp = np.sum((actuals == 1) & (preds == 1))\n",
    "    fn = np.sum((actuals == 1) & (preds == 0))\n",
    "    return tp / (tp + fn)\n",
    "\n",
    "def F1(actuals, preds):\n",
    "    p, r = precision(actuals, preds), recall(actuals, preds)\n",
    "    return 2*p*r / (p + r)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67629677",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9dee47cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.8028954550848463\n",
      "Accuracy on validation set: 0.764375\n"
     ]
    }
   ],
   "source": [
    "lr_c=LogisticRegression(random_state=42, solver='lbfgs',max_iter=400 )\n",
    "lr_c.fit(X_tr_smote, y_tr_smote)\n",
    "lr_pred=lr_c.predict(X_test)\n",
    "lr_cm=confusion_matrix(y_test,lr_pred)\n",
    "lr_ac=accuracy_score(y_test, lr_pred)\n",
    "print('Accuracy on training set:',lr_c.score(X_tr_smote, y_tr_smote))\n",
    "print('Accuracy on validation set:',lr_c.score(X_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bdea9c12",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for Logistic Regression Classifier \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Confusion matrix for logistic regression model\n",
    "print(\"Confusion Matrix for Logistic Regression Classifier\",\"\\n\")\n",
    "score = round(accuracy_score(y_test, lr_pred),3) \n",
    "cm1 = confusion_matrix(y_test, lr_pred)\n",
    "sns.heatmap(cm1, annot=True, fmt=\".1f\", linewidths=.3, \n",
    "        square = True, cmap = 'PuBu')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.title('Accuracy Score: {0}'.format(score), size = 12)\n",
    "plt.show()\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee6fb84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression validation metrics: \n",
      " Accuracy: 0.7825 \n",
      " Precision: 0.6157 \n",
      " Recall: 0.8325 \n",
      " F1: 0.7079\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression validation metrics: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, lr_c.predict(X_test)), \n",
    "         precision(y_test, lr_c.predict(X_test)), \n",
    "         recall(y_test, lr_c.predict(X_test)),\n",
    "         F1(y_test, lr_c.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860e29cc",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d10d2818",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.8204580515011274\n",
      "Accuracy on validation set: 0.75125\n"
     ]
    }
   ],
   "source": [
    "rf_clf = RandomForestClassifier(n_estimators = 10, random_state = 42)\n",
    "rf_clf.fit(X_tr_smote, y_tr_smote)\n",
    "rf_pred=rf_clf.predict(X_test)\n",
    "rf_ac=accuracy_score(y_test, rf_pred)\n",
    "print('Accuracy on training set:',rf_clf.score(X_tr_smote, y_tr_smote))\n",
    "print('Accuracy on validation set:',rf_clf.score(X_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "28d66cbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Classifier: \n",
      " Accuracy: 0.7725 \n",
      " Precision: 0.6032 \n",
      " Recall: 0.8215 \n",
      " F1: 0.6957\n"
     ]
    }
   ],
   "source": [
    "print('Random Forest Classifier: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, rf_clf.predict(X_test)), \n",
    "         precision(y_test, rf_clf.predict(X_test)), \n",
    "         recall(y_test, rf_clf.predict(X_test)),\n",
    "         F1(y_test, rf_clf.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e45c0a",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5a5a735e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.8210513824611368\n",
      "Accuracy on validation set: 0.75125\n"
     ]
    }
   ],
   "source": [
    "# Create Decision Tree classifer object\n",
    "clf = DecisionTreeClassifier()\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "clf = clf.fit(X_tr_smote, y_tr_smote)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "clf_pred = clf.predict(X_test)\n",
    "clf_ac=accuracy_score(y_test, clf_pred)\n",
    "print('Accuracy on training set:',clf.score(X_tr_smote, y_tr_smote))\n",
    "print('Accuracy on validation set:',clf.score(X_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c57adff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree: \n",
      " Accuracy: 0.7740 \n",
      " Precision: 0.6051 \n",
      " Recall: 0.8231 \n",
      " F1: 0.6975\n"
     ]
    }
   ],
   "source": [
    "print('Decision Tree: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, clf.predict(X_test)), \n",
    "         precision(y_test, clf.predict(X_test)), \n",
    "         recall(y_test, clf.predict(X_test)),\n",
    "         F1(y_test, clf.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b577a25",
   "metadata": {},
   "source": [
    "### Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b924d9a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of training set:  0.7705\n",
      "Accuracy of training set:  0.75125\n"
     ]
    }
   ],
   "source": [
    "svc_r=SVC(kernel='rbf', random_state=0, probability=True)\n",
    "svc_r.fit(X_tr_smote, y_tr_smote)\n",
    "svr_pred=svc_r.predict(X_test)\n",
    "svr_ac=accuracy_score(y_test, svr_pred)\n",
    "svr_val=svc_r.score(X_val,y_val)\n",
    "pred_prob1 = svc_r.predict_proba(X_test)\n",
    "print('Accuracy of training set: ', svr_ac)\n",
    "print('Accuracy of training set: ', svr_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ea49fd68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Classifier: \n",
      " Accuracy: 0.7705 \n",
      " Precision: 0.5971 \n",
      " Recall: 0.8452 \n",
      " F1: 0.6998\n"
     ]
    }
   ],
   "source": [
    "print('Support Vector Classifier: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, svc_r.predict(X_test)), \n",
    "         precision(y_test, svc_r.predict(X_test)), \n",
    "         recall(y_test, svc_r.predict(X_test)),\n",
    "         F1(y_test, svc_r.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22812f82",
   "metadata": {},
   "source": [
    "### K-nearest nieghbours\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a6bc82fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.7305090779636881\n",
      "Accuracy on validation set: 0.7675\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_tr_smote, y_tr_smote)\n",
    "knn_pred = knn.predict(X_test)\n",
    "knn_val=clf.score(X_val,y_val)\n",
    "knn_ac=accuracy_score(y_test, knn_pred)\n",
    "print('Accuracy on training set:',knn.score(X_tr_smote, y_tr_smote))\n",
    "print('Accuracy on validation set:',knn.score(X_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "764f71c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-nearest nieghbours: \n",
      " Accuracy: 0.7545 \n",
      " Precision: 0.6398 \n",
      " Recall: 0.5134 \n",
      " F1: 0.5697\n"
     ]
    }
   ],
   "source": [
    "print('K-nearest nieghbours: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, knn.predict(X_test)), \n",
    "         precision(y_test, knn.predict(X_test)), \n",
    "         recall(y_test, knn.predict(X_test)),\n",
    "         F1(y_test, knn.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6360e2fd",
   "metadata": {},
   "source": [
    "### Ada Boost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c6591cca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.8014714607808235\n",
      "Accuracy on validation set: 0.763125\n"
     ]
    }
   ],
   "source": [
    "abc = AdaBoostClassifier(n_estimators=50,learning_rate=1)\n",
    "# Train Adaboost Classifer\n",
    "abc.fit(X_tr_smote, y_tr_smote)\n",
    "\n",
    "#Predict the response for test dataset\n",
    "abc_pred = abc.predict(X_test)\n",
    "abc_ac=accuracy_score(y_test, abc_pred)\n",
    "print('Accuracy on training set:',abc.score(X_tr_smote, y_tr_smote))\n",
    "print('Accuracy on validation set:',abc.score(X_val,y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "efa24602",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ada Boost Classifier: \n",
      " Accuracy: 0.7825 \n",
      " Precision: 0.6162 \n",
      " Recall: 0.8294 \n",
      " F1: 0.7071\n"
     ]
    }
   ],
   "source": [
    "print('Ada Boost Classifier: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, abc.predict(X_test)), \n",
    "         precision(y_test, abc.predict(X_test)), \n",
    "         recall(y_test, abc.predict(X_test)),\n",
    "         F1(y_test, abc.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e93053",
   "metadata": {},
   "source": [
    "### Gradient Boosting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3bf12232",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score (training): 0.8146434080930343\n",
      "Accuracy score (validation): 0.755625\n"
     ]
    }
   ],
   "source": [
    "gb_clf = GradientBoostingClassifier(n_estimators=100, max_depth=3,\n",
    "                               learning_rate=.71)\n",
    "gb_clf.fit(X_tr_smote, y_tr_smote)\n",
    "gb_clf_pred = gb_clf.predict(X_test)\n",
    "gb_clf_ac=accuracy_score(y_test, gb_clf_pred)\n",
    "print(\"Accuracy score (training):\" ,gb_clf.score(X_tr_smote, y_tr_smote))\n",
    "print(\"Accuracy score (validation):\",(gb_clf.score(X_val, y_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b5cad2ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier: \n",
      " Accuracy: 0.7750 \n",
      " Precision: 0.6065 \n",
      " Recall: 0.8231 \n",
      " F1: 0.6984\n"
     ]
    }
   ],
   "source": [
    "print('Gradient Boosting Classifier: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, gb_clf.predict(X_test)), \n",
    "         precision(y_test, gb_clf.predict(X_test)), \n",
    "         recall(y_test, gb_clf.predict(X_test)),\n",
    "         F1(y_test, gb_clf.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc266975",
   "metadata": {},
   "source": [
    "### XG Boost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "05036d93",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "96425be4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/AAHMT/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:22:05] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "Accuracy score (training): 0.8171353981250742\n",
      "Accuracy score (validation): 0.7525\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "xgb_clf = XGBClassifier()\n",
    "xgb_clf.fit(X_tr_smote, y_tr_smote)\n",
    "xgb_clf_pred = xgb_clf.predict(X_test)\n",
    "xgb_clf_ac=accuracy_score(y_test, xgb_clf_pred)\n",
    "print(\"Accuracy score (training):\" ,xgb_clf.score(X_tr_smote, y_tr_smote))\n",
    "print(\"Accuracy score (validation):\",(xgb_clf.score(X_val, y_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3bcb5af8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XG Boost Classifier: \n",
      " Accuracy: 0.7760 \n",
      " Precision: 0.6077 \n",
      " Recall: 0.8246 \n",
      " F1: 0.6997\n"
     ]
    }
   ],
   "source": [
    "print('XG Boost Classifier: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, xgb_clf.predict(X_test)), \n",
    "         precision(y_test, xgb_clf.predict(X_test)), \n",
    "         recall(y_test, xgb_clf.predict(X_test)),\n",
    "         F1(y_test, xgb_clf.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7d6c70",
   "metadata": {},
   "source": [
    "### Cat Boost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d1be11a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "465fe336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.025597\n",
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      "994:\tlearn: 0.3951857\ttotal: 2.29s\tremaining: 11.5ms\n",
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      "999:\tlearn: 0.3951181\ttotal: 2.3s\tremaining: 0us\n",
      "Accuracy score (training): 0.8158300700130533\n",
      "Accuracy score (validation): 0.76\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "cbr = CatBoostClassifier()\n",
    "# Fit model\n",
    "cbr.fit(X_tr_smote, y_tr_smote)\n",
    "# Get predictions\n",
    "cbr_pred = cbr.predict(X_test)\n",
    "cbr_ac=accuracy_score(y_test, cbr_pred)\n",
    "\n",
    "\n",
    "print(\"Accuracy score (training):\" ,cbr.score(X_tr_smote, y_tr_smote))\n",
    "print(\"Accuracy score (validation):\",(cbr.score(X_val, y_val)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a5566504",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cat Boost Classifier: \n",
      " Accuracy: 0.7800 \n",
      " Precision: 0.6137 \n",
      " Recall: 0.8231 \n",
      " F1: 0.7031\n"
     ]
    }
   ],
   "source": [
    "print('Cat Boost Classifier: \\n Accuracy: %.4f \\n Precision: %.4f \\n Recall: %.4f \\n F1: %.4f' %\n",
    "        (accuracy(y_test, cbr.predict(X_test)), \n",
    "         precision(y_test, cbr.predict(X_test)), \n",
    "         recall(y_test, cbr.predict(X_test)),\n",
    "         F1(y_test, cbr.predict(X_test))\n",
    "        )\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19a7e4c",
   "metadata": {},
   "source": [
    "## Voting Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ad65702c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lr_c', LogisticRegression(max_iter=400, random_state=42)),\n",
       " ('rf_clf', RandomForestClassifier(n_estimators=10, random_state=42)),\n",
       " ('clf', DecisionTreeClassifier()),\n",
       " ('svc_r', SVC(probability=True, random_state=0)),\n",
       " ('knn', KNeighborsClassifier()),\n",
       " ('abc', AdaBoostClassifier(learning_rate=1)),\n",
       " ('gb_clf', GradientBoostingClassifier(learning_rate=0.71)),\n",
       " ('xgb_clf',\n",
       "  XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "                colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "                importance_type='gain', interaction_constraints='',\n",
       "                learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "                min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "                n_estimators=100, n_jobs=4, num_parallel_tree=1, random_state=0,\n",
       "                reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "                tree_method='exact', validate_parameters=1, verbosity=None)),\n",
       " ('cbr', <catboost.core.CatBoostClassifier at 0x7fae22154ca0>)]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_names = [\"lr_c\", \"rf_clf\", 'clf',\"svc_r\", \"knn\",\"abc\",\"gb_clf\",\"xgb_clf\",\"cbr\"]\n",
    "model_var = [eval(n) for n in model_names]\n",
    "model_list = list(zip(model_names, model_var))\n",
    "model_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4d3a37fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr_c score: 0.7825\n",
      "rf_clf score: 0.7725\n",
      "clf score: 0.774\n",
      "svc_r score: 0.7705\n",
      "knn score: 0.7545\n",
      "abc score: 0.7825\n",
      "gb_clf score: 0.775\n",
      "xgb_clf score: 0.776\n",
      "cbr score: 0.78\n"
     ]
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "    curr_model = eval(model_name)\n",
    "    print(f'{model_name} score: {curr_model.score(X_test, y_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e7b2aa57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.025597\n",
      "0:\tlearn: 0.6774142\ttotal: 59.2ms\tremaining: 59.1s\n",
      "1:\tlearn: 0.6626013\ttotal: 61.9ms\tremaining: 30.9s\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('lr_c',\n",
       "                              LogisticRegression(max_iter=400,\n",
       "                                                 random_state=42)),\n",
       "                             ('rf_clf',\n",
       "                              RandomForestClassifier(n_estimators=10,\n",
       "                                                     random_state=42)),\n",
       "                             ('clf', DecisionTreeClassifier()),\n",
       "                             ('svc_r', SVC(probability=True, random_state=0)),\n",
       "                             ('knn', KNeighborsClassifier()),\n",
       "                             ('abc', AdaBoostClassifier(learning_rate=1)),\n",
       "                             ('gb_clf',\n",
       "                              GradientBoostingClassifier(lear...\n",
       "                                            learning_rate=0.300000012,\n",
       "                                            max_delta_step=0, max_depth=6,\n",
       "                                            min_child_weight=1, missing=nan,\n",
       "                                            monotone_constraints='()',\n",
       "                                            n_estimators=100, n_jobs=4,\n",
       "                                            num_parallel_tree=1, random_state=0,\n",
       "                                            reg_alpha=0, reg_lambda=1,\n",
       "                                            scale_pos_weight=1, subsample=1,\n",
       "                                            tree_method='exact',\n",
       "                                            validate_parameters=1,\n",
       "                                            verbosity=None)),\n",
       "                             ('cbr',\n",
       "                              <catboost.core.CatBoostClassifier object at 0x7fae22154ca0>)],\n",
       "                 n_jobs=-1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import VotingClassifier\n",
    "voting_classifer = VotingClassifier(estimators=model_list,\n",
    "                                    voting='hard', #<-- sklearn calls this hard voting\n",
    "                                    n_jobs=-1)\n",
    "voting_classifer.fit(X_tr_smote, y_tr_smote)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "04d9b5eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7795"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1 = voting_classifer.predict(X_test)\n",
    "accuracy_score(y_test, y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "cfa6ebf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Model Accuracy')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot of accuracy of all models\n",
    "%matplotlib inline\n",
    "model_accuracy = pd.Series(data=[lr_ac,rf_ac, clf_ac,svr_ac, knn_ac,abc_ac,gb_clf_ac,xgb_clf_ac,cbr_ac], \n",
    "        index=['Logistic Regression','Random Forest Classifier','Decision Tree Classifier','SVC','K Neighbors Classifier','Ada Boost Classifier','Gradient Boosting Classifier','XGB Classifier','Cat Boost Classifier'])\n",
    "fig= plt.figure(figsize=(8,8))\n",
    "model_accuracy.sort_values().plot.barh()\n",
    "plt.title('Model Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ba761b9",
   "metadata": {},
   "source": [
    "## Plot the ROC Curve\n",
    "\n",
    "Next, we’ll calculate the true positive rate and the false positive rate and create a ROC curve for for all the models by using the Matplotlib data visualization package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3347ef0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:22:13] WARNING: /opt/concourse/worker/volumes/live/7a2b9f41-3287-451b-6691-43e9a6c0910f/volume/xgboost-split_1619728204606/work/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/AAHMT/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:888: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "\n",
    "#create ROC curve for all the models (stands for: “receiver operating characteristic” curve)\n",
    "\n",
    "disp =  RocCurveDisplay.from_estimator(lr_c, X_test, y_test)\n",
    "\n",
    "RocCurveDisplay.from_estimator(rf_clf, X_tr_smote, y_tr_smote, ax=disp.ax_);\n",
    "RocCurveDisplay.from_estimator(clf, X_tr_smote, y_tr_smote, ax=disp.ax_);\n",
    "RocCurveDisplay.from_estimator(svc_r, X_tr_smote, y_tr_smote, ax=disp.ax_);\n",
    "RocCurveDisplay.from_estimator(knn, X_tr_smote, y_tr_smote, ax=disp.ax_);\n",
    "RocCurveDisplay.from_estimator(abc, X_tr_smote, y_tr_smote, ax=disp.ax_);\n",
    "RocCurveDisplay.from_estimator(gb_clf, X_tr_smote, y_tr_smote, ax=disp.ax_);\n",
    "RocCurveDisplay.from_estimator(xgb_clf, X_tr_smote, y_tr_smote, ax=disp.ax_);\n",
    "RocCurveDisplay.from_estimator(cbr, X_tr_smote, y_tr_smote, ax=disp.ax_);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d41f958",
   "metadata": {},
   "source": [
    "### As we can see  Logistic Regression, Ada Boost and CatBoost are our best models.\n"
   ]
  }
 ],
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