Logistic Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P2U4AUV_JMmP"
   },
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "FVFLkSdKo0s_"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,RepeatedStratifiedKFold,GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import timeit\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score,f1_score,precision_score,recall_score,RocCurveDisplay\n",
    "from matplotlib import pyplot\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_df=pd.read_csv('original_data_with_encoding.csv')\n",
    "x_resampled=pd.read_csv('resampled_without_encoding.csv')\n",
    "x_resampled_encoding=pd.read_csv('resampled_with_encoding.csv')\n",
    "y_resampled=pd.read_csv('y_resampled.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data without Resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  6.3756883\n",
      "[[114633      0]\n",
      " [  4410      0]]\n"
     ]
    }
   ],
   "source": [
    "X = initial_df.drop('target',axis=1).values\n",
    "Y = initial_df['target'].values\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test)\n",
    "\n",
    "lr = LogisticRegression()\n",
    "start_time = timeit.default_timer()\n",
    "lr.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "y_pred=lr.predict(test_sc)\n",
    "cnf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(cnf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "qTQyJDnN6Qga"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score:0.9630\n"
     ]
    }
   ],
   "source": [
    "print(f\"Accuracy score:{accuracy_score(y_test, y_pred):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NPk-Rx97US7F"
   },
   "source": [
    "* 1) If there is no oversampling the accuracy is 96.3.But as data is imbalanced data we don't need accuracy.Because majority of them are negative and model predicted all values as negative and so accuracy is high\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Fas3DzRnBnLR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    }
   ],
   "source": [
    "print(f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ATyt20x2UA5Q"
   },
   "source": [
    "* 2) f2 score is 0 as there are no predicted values either correctly predicted (True positives)or wrongly predicted(False positives) as positive.\n",
    "*    The accuracy is high as there are so many negatives and all values are predicted as negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "lbHAm0feSiaO"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall score : 0.0000\n",
      "Precision score : 0.0000\n"
     ]
    }
   ],
   "source": [
    "print(f\"Recall score : {recall_score(y_test, y_pred):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_pred):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VVM6D_swBLtd"
   },
   "source": [
    "\n",
    "\n",
    "* 3) Precision and recall values are 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xcRDxMojUgrd"
   },
   "source": [
    "### Most Inference\n",
    "\n",
    "* 4) All values are precited as negative as majority of values in dataset is negative.We are missing positive cases\n",
    "So oversampling is needed to overcome this problem\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data with Resampling and without OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "T9dJAe6eHDWU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  5.584927499999999\n",
      "Accuracy score: 0.8897\n",
      "F1 score : 0.8860\n",
      "Recall score : 0.8548\n",
      "Precision score : 0.9195\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test)\n",
    "\n",
    "lr = LogisticRegression()\n",
    "start_time = timeit.default_timer()\n",
    "lr.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "y_pred=lr.predict(test_sc)\n",
    "print(f\"Accuracy score: {accuracy_score(y_test, y_pred):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test, y_pred):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test, y_pred):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_pred):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'l1_ratio': None,\n",
       " 'max_iter': 100,\n",
       " 'multi_class': 'auto',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': None,\n",
       " 'solver': 'lbfgs',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,y_pred),annot=True, fmt='', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "m4m9v1x5xlLw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.92      0.89    114386\n",
      "           1       0.92      0.85      0.89    115022\n",
      "\n",
      "    accuracy                           0.89    229408\n",
      "   macro avg       0.89      0.89      0.89    229408\n",
      "weighted avg       0.89      0.89      0.89    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x166b065b190>"
      ]
     },
     "execution_count": 11,
     "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": [
    "RocCurveDisplay.from_predictions(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x166aaafddc0>"
      ]
     },
     "execution_count": 12,
     "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": [
    "RocCurveDisplay.from_estimator(lr, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking on training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.8899\n",
      "F1 score : 0.8858\n",
      "Recall score : 0.8552\n",
      "Precision score : 0.9187\n"
     ]
    }
   ],
   "source": [
    "y_pred=lr.predict(train_sc)\n",
    "print(f\"Accuracy score: {accuracy_score(y_train, y_pred):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_train, y_pred):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_train, y_pred):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_train, y_pred):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'class_weight': None,\n",
       " 'dual': False,\n",
       " 'fit_intercept': True,\n",
       " 'intercept_scaling': 1,\n",
       " 'l1_ratio': None,\n",
       " 'max_iter': 100,\n",
       " 'multi_class': 'auto',\n",
       " 'n_jobs': None,\n",
       " 'penalty': 'l2',\n",
       " 'random_state': None,\n",
       " 'solver': 'lbfgs',\n",
       " 'tol': 0.0001,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data with Resampling and with OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  28.678844999999995\n",
      "Accuracy score: 0.9026\n",
      "F1 score : 0.8997\n",
      "Recall score : 0.8718\n",
      "Precision score : 0.9295\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled_encoding.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=10)\n",
    "\n",
    "scaler = StandardScaler().fit(X_train)\n",
    "train_sc = scaler.transform(X_train)\n",
    "test_sc = scaler.transform(X_test)\n",
    "\n",
    "lr1 = LogisticRegression()\n",
    "start_time = timeit.default_timer()\n",
    "lr1.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "y_pred1=lr1.predict(test_sc)\n",
    "print(f\"Accuracy score: {accuracy_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test, y_pred1):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test, y_pred1):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,y_pred1),annot=True, fmt='', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.91    114386\n",
      "           1       0.93      0.87      0.90    115022\n",
      "\n",
      "    accuracy                           0.90    229408\n",
      "   macro avg       0.90      0.90      0.90    229408\n",
      "weighted avg       0.90      0.90      0.90    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x166b0515100>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wZHhn/AJwZ2vHmFmdpCuA+UBXYK6ZLZN0abj9TjN7U9IzwOtAA3CPmZXu9tnEoLyikgP273SdmZxzrt1ErRq6g6Cd4PZweVa47qLWDjKzp4Gnm627s9nyTcBNEePoUNV19azb6glpnHOFLWpBcIiZHZi2/FdJS+MIqDPZmZDGZx11zhWuqL2G6iWNTS1I2heojyekzsPHEDjnkiDqE8FVwPOS3iXoFvpJ4PzYouokUnkIvGrIOVfIshYEYVfRzQQjhYcSFARvmVl1zLHlXGowmSekcc4VslarhiRdBCwDfgUsAUab2dIkFAKwMyFNj+6ekMY5V7iyPRF8HZhoZuvCdoEH2HUsQMFKDSZzzrlClq2xuMbM1gGY2btAcfwhdR7lFZWM8B5DzrkCl+2JYKSkW1paNrMr4wkr94KENFUc5YPJnHMFLltB0HyG0cVxBdLZVOyopbK23quGnHMFL0rO4kQq8+mnnXMJka3X0F2SJrWwrbekCyR9JZ7QcqvcB5M55xIiW9XQ7cA1kj4NlALrgB7AOKAfMJegJ1HB2flE4I3FzrnClq1qaAlwpqQ+QAkwjCAnwZtm9nb84eVOeUUlxd26MLB3Ua5Dcc65WEWaYsLMtgEL4g2lcykP8xB4QhrnXKGLOulc4vhgMudcUnhB0ILyikpvH3DOJUKbCgJJveMKpDOprqtnrSekcc4lRKSCQNLhkpYT5hOWdKCk27Mclrc+3hzMqecFgXMuCaI+EdwMTAc2AJjZUuDIuILKtVTX0ZFeEDjnEiBy1ZCZfdhsVcFmKCv3UcXOuQSJmqHsQ0mHAyapCLiSsJqoEHlCGudckkR9IrgUuBwYAawGJgOXxRRTzpVvrmRwH09I45xLhqhPBPubWZM5hSR9Dnip/UPKvbKKKs9D4JxLjKhPBL+KuK4glPtgMudcgrT6RCDpMOBwYIikb6Zt6gcUZL1JkJCmki+MH5LrUJxzrkNkqxoqAvqE+/VNW78FOCOuoHJpc2UtO2o8IY1zLjmyzT76AvCCpHvN7P0OiimnVm9K5SHwNgLnXDJEbSzeIekmYCJBPgIAzOzoWKLKIR9D4JxLmqiNxQ8AbwFjgB8Aq4CFMcWUU14QOOeSJmpBMMjMfgPUmtkLZnYBcGiMceVM+eYqirp1YZAnpHHOJUTUqqHa8PcaSScC5cDIeELKrbKKSk9I45xLlKhPBDdI6g/8H+BbwD3A17MdJGmGpLclrZQ0p5X9DpFULynnPZE8D4FzLmkiFQRm9icz22xmpWZ2lJkdDGxs7RhJXYHbgOOBCcDZkia0sN9PgPltjj4G5RWVDO/v7QPOueRotSCQ1FXS2ZK+JWlSuG6mpJeBW7O891RgpZm9a2Y1wDzglAz7fRV4FFjb9vDbV01dA2u3VjNiLy8InHPJka2N4DfAPsCrwC2S3gcOA+aY2eNZjh0BpE9dvRr4bPoOkkYApwFHA4e09EaSZgOzAUaNGpXlY3ffx1uqMPMeQ865ZMlWEJQAnzGzBkk9gPXAfmb2UYT3ztTaas2WfwF8x8zqW2ucNbO7gLsASkpKmr9Hu0klpBnhBYFzLkGyFQQ1ZtYAYGZVklZELAQgeALYJ215JEFvo3QlwLywEBgMnCCpLsLTRix8DIFzLomyFQQHSHo9fC1gbLgswMzsM60cuxAYJ2kMUAacBXw5fQczG5N6Lele4E+5KgRgZ0EwzBPSOOcSJFtB8KndfWMzq5N0BUFvoK7AXDNbJunScPudu/vecSmrqGJwnyJPSOOcS5Rsk87t0URzZvY08HSzdRkLADM7b08+qz14HgLnXBJFTl6fBD6GwDmXRF4QhMyMMn8icM4lUOSCQFJPSfvHGUwu7UxI4w3FzrlkiVQQSDoJWAI8Ey5PlvREjHF1OB9D4JxLqqhPBNcSTBlRAWBmS4DRcQSUK+UVVYCPIXDOJU/UgqDOzDbHGkmO+WAy51xSRc1HUCrpy0BXSeOAK4GX4wur45VXVHpCGudcIkV9IvgqQb7iauBBYDMR8hHkk7KKSob370GXLp6QxjmXLFGfCPY3s6uBq+MMJpfKKyp9+mnnXCJFfSL4uaS3JF0vaWKsEeVIeUWVDyZzziVS1AxlRwHTgHXAXZLekPTdOAPrSLX1DXy8tcobip1ziRR5QJmZfWRmtwCXEowpuCauoDraR5uDhDQ+hsA5l0RRB5R9StK1kkoJUlS+TJBfoCB411HnXJJFbSz+f8BDwHFm1jy5TN4r35wqCHx6Cedc8kQqCMzs0LgDySUfVeycS7JWCwJJD5vZmZLeoGm+4SgZyvJGWUUlg3p7QhrnXDJleyL4Wvh7ZtyB5FLZJp9+2jmXXK02FpvZmvDlZWb2fvoPcFn84XWMIDOZtw8455IpavfRL2ZYd3x7BpIrZuYpKp1ziZatjeDfCe7895X0etqmvsBLcQbWUbZU1rG9pt7HEDjnEitbG8GDwJ+BHwNz0tZvNbONsUXVgcp8DIFzLuGyFQRmZqskXd58g6SBhVAY+GAy51zSRXkimAksJug+mj5HswH7xhRXh/HBZM65pGu1IDCzmeHvMR0TTscrCxPSDO5dnOtQnHMuJ6LONfQ5Sb3D1+dI+rmkUfGG1jGC6ac9IY1zLrmidh+9A9gh6UDg28D7wO9ii6oDeddR51zStSV5vQGnAL80s18SdCHNe14QOOeSLurso1sl/V9gFnCEpK5A9/jC6hi19Q18vMUT0jjnki3qE8GXCBLXX2BmHwEjgJtii6qDfLyligaDEd5jyDmXYFFTVX4EPAD0lzQTqDKz38YaWQfw6aedcy56r6EzgVeBfwPOBP5H0hkRjpsh6W1JKyXNybD9K5JeD39eDhujO4wPJnPOuehtBFcDh5jZWgBJQ4DngEdaOiBsR7iNYMK61cBCSU+Y2fK03d4DvmBmmyQdD9wFfLbtp7F7GqeX6O8FgXMuuaK2EXRJFQKhDRGOnQqsNLN3zawGmEfQ66iRmb1sZpvCxVfo4DzIZRWVDOxdRM8iT0jjnEuuqE8Ez0iaT5C3GILG46ezHDMC+DBteTWt3+1fSDDB3S4kzQZmA4wa1X7j2DwPgXPORc9ZfJWk/wV8nmC+obvM7LEsh2UaqmsZ1iHpKIKC4PMtfP5dBNVGlJSUZHyP3VFeUcnoQb3b6+2ccy4vZctHMA74GTAWeAP4lpmVRXzv1cA+acsjgfIMn/EZ4B7geDPbEPG995iZUbapksPHDu6oj3TOuU4pWz3/XOBPwOkEM5D+qg3vvRAYJ2mMpCLgLOCJ9B3C+Yr+AMwysxVteO89tqXKE9I45xxkrxrqa2Z3h6/flvRa1Dc2szpJVwDzga7AXDNbJunScPudwDXAIOB2SRBMZVHS1pPYHamuoyP28oLAOZds2QqCHpIOYmd9f8/0ZTNrtWAws6dp1qgcFgCp1xcBF7U16PbgYwiccy6QrSBYA/w8bfmjtGUDjo4jqI6wsyDwXkPOuWTLlpjmqI4KpKOVVVRR1NUT0jjnXNQBZQWnvKKSYQM8IY1zziW6IPCpJZxzLukFgTcUO+dc5NlHFeYqviZcHiVparyhxaeuvoGPtlR5HgLnnCP6E8HtwGHA2eHyVoKZRfPSx1uraTDvOuqccxB90rnPmtkUSf8ECKeNLooxrliVbfIxBM45lxL1iaA2zC9g0JiPoCG2qGLmg8mcc26nqAXBLcBjwFBJPwT+DvwotqhiVuaDyZxzrlHUaagfkLQYOIZgeolTzezNWCOLUXlFJXv16k6voqg1Y845V7giXQnDWUJ3AE+mrzOzD+IKLE7eddQ553aKekv8FEH7gIAewBjgbWBiTHHFqryiilGDeuU6DOec6xSiVg19On1Z0hTgklgi6gDlFZUcNnZQrsNwzrlOYbdGFofTTx/SzrF0iC1VtWytrvOENM45F4raRvDNtMUuwBRgXSwRxcy7jjrnXFNR2wj6pr2uI2gzeLT9w4mf5yFwzrmmshYE4UCyPmZ2VQfEE7uyiioArxpyzrlQq20EkrqZWT1BVVBBKK+opHtXMbiPJ6RxzjnI/kTwKkEhsETSE8Dvge2pjWb2hxhji0V5RSXD+vf0hDTOOReK2kYwENhAkKM4NZ7AgLwsCLx9wDnndspWEAwNewyVsrMASLHYoopReUUVn913YK7DcI7a2lpWr15NVVVVrkNxBaRHjx6MHDmS7t27Rz4mW0HQFehD0wIgJe8Kgp0Jabyh2OXe6tWr6du3L6NHj0byqkq358yMDRs2sHr1asaMGRP5uGwFwRozu27PQus8Pt5aTX2D+RgC1ylUVVV5IeDalSQGDRrEunVtG+aVbWRxQf0L9cFkrrPxQsC1t935N5WtIDhm90LpnFIFgecqds65nVotCMxsY0cF0hFSCWmG9fcnAucAPvroI8466yzGjh3LhAkTOOGEE1ixYgWrVq1i0qRJ7fY511xzDc899xwAf/vb35g4cSKTJ0+mrKyMM844Y4/e28w4+uij2bJlS+O6xx57DEm89dZbjesWLFjAzJkzmxx73nnn8cgjjwBB4/2cOXMYN24ckyZNYurUqfz5z3/eo9gAfvzjH7Pffvux//77M3/+/Iz7LF26lMMOO4xPf/rTnHTSSU3OpaXjjz32WDZt2rTH8cFuTjqXr8orKhnQqzu9iz0hjXNmxmmnnca0adP417/+xfLly/nRj37Exx9/3O6fdd1113HssccC8MADD/Ctb32LJUuWMGLEiMYLcRT19fW7rHv66ac58MAD6devX+O6hx56iM9//vPMmzcv8nt/73vfY82aNZSWllJaWsqTTz7J1q1bIx+fyfLly5k3bx7Lli3jmWee4bLLLst4DhdddBE33ngjb7zxBqeddho33XRT1uNnzZrF7bffvkfxpSTqilhe4T2GXOf0gyeXsbx8S/Yd22DC8H58/6SWU4Y8//zzdO/enUsvvbRx3eTJkwFYtWpV47pVq1Yxa9Ystm8PxpLeeuutHH744axZs4YvfelLbNmyhbq6Ou644w4OP/xwLrzwQhYtWoQkLrjgAr7xjW9w3nnnMXPmTCoqKnj44YeZP38+zz33HD/84Q+ZOXMmpaWl1NfXM2fOHBYsWEB1dTWXX345l1xyCQsWLOAHP/gBw4YNY8mSJSxfvrzJeTzwwAPMnj27cXnbtm289NJLPP/885x88slce+21Wb+rHTt2cPfdd/Pee+9RXBzMOrD33ntz5plnZj22NX/84x8566yzKC4uZsyYMey33368+uqrHHbYYU32e/vttznyyCMB+OIXv8j06dO5/vrrWz3+5JNP5ogjjuDqq6/eoxghcQVBJfsM9IQ0zgGUlpZy8MEHZ91v6NChPPvss/To0YN33nmHs88+m0WLFvHggw8yffp0rr76aurr69mxYwdLliyhrKyM0tJSACoqKpq810UXXcTf//53Zs6cyRlnnNGkwPnNb35D//79WbhwIdXV1Xzuc5/juOOOA+DVV1+ltLQ0Y5fIl156iV//+teNy48//jgzZsxg/PjxDBw4kNdee40pU1qfJWflypWMGjWqyVNFS77xjW/w/PPP77L+rLPOYs6cOU3WlZWVceihhzYujxw5krKysl2OnTRpEk888QSnnHIKv//97/nwww+zHr/XXntRXV3Nhg0bGDRoz/KrJKogKKuo5NB9PSGN63xau3PPtdraWq644gqWLFlC165dWbFiBQCHHHIIF1xwAbW1tZx66qlMnjyZfffdl3fffZevfvWrnHjiiY0X8ij+8pe/8PrrrzdWFW3evJl33nmHoqIipk6d2mK/+I0bN9K3784Jkh966CG+/vWvA8HF+aGHHmLKlCkt9qZpay+bm2++OfK+ZrsOt8r0eXPnzuXKK6/kuuuu4+STT6aoqCjS8UOHDqW8vLxzFwSSZgC/JBiYdo+Z3dhsu8LtJxDkRD4vTHrT7rZU1bK1qs6nl3AuNHHixEj18zfffDN77703S5cupaGhgR49gv9DRx55JC+++CJPPfUUs2bN4qqrruLcc89l6dKlzJ8/n9tuu42HH36YuXPnRorHzPjVr37F9OnTm6xfsGABvXv3bvG4bt260dDQQJcuXdiwYQN//etfKS0tRRL19fVI4qc//SmDBg3apXF148aNDB48mP32248PPviArVu3NilUMmnLE8HIkSMb7+4hGEQ4fPjwXY494IAD+Mtf/gLAihUreOqppyIdX1VVRc+ee17dHVtjcTh99W3A8cAE4GxJE5rtdjwwLvyZDdwRVzxrwumnfQyBc4Gjjz6a6upq7r777sZ1Cxcu5IUXXmiy3+bNmxk2bBhdunThd7/7XWNj5fvvv8/QoUO5+OKLufDCC3nttddYv349DQ0NnH766Vx//fW89lr0+7rp06dzxx13UFtbCwQXxFS7RGv2339/3n33XQAeeeQRzj33XN5//31WrVrFhx9+yJgxY/j73//OuHHjKC8v580332yMf+nSpUyePJlevXpx4YUXcuWVV1JTUwPAmjVruP/++3f5vJtvvpklS5bs8tO8EAA4+eSTmTdvHtXV1bz33nu88847TJ06dZf91q5dC0BDQwM33HBDY7tNa8ebGR999BGjR4/O+h1lE2evoanASjN718xqgHnAKc32OQX4rQVeAQZIGhZHMD6YzLmmJPHYY4/x7LPPMnbsWCZOnMi11167yx3rZZddxn333cehhx7KihUrGu/OFyxYwOTJkznooIN49NFH+drXvkZZWRnTpk1j8uTJnHfeefz4xz+OHM9FF13EhAkTmDJlCpMmTeKSSy6hrq4u63EnnngiCxYsAIJqodNOO63J9tNPP50HH3yQ4uJi7r//fs4//3wmT57MGWecwT333EP//v0BuOGGGxgyZAgTJkxg0qRJnHrqqQwZMiRy/JlMnDiRM888kwkTJjBjxgxuu+02unbt2ni+ixYtaox7/PjxHHDAAQwfPpzzzz8/6/GLFy/m0EMPpVu3dqjYMbNYfoAzCKqDUsuzgFub7fMn4PNpy/8NlGR4r9nAImDRqFGjbHcsfG+Dzf7tQlu7pWq3jneuvS1fvjzXIRSE8vJyO/bYY3MdRoe78sor7bnnnsu4LdO/LWCRtXC9jrONIMpEdZEmszOzu4C7AEpKSnZrsruS0QMpGe2zjjpXaIYNG8bFF1/Mli1bIvX6KRSTJk3imGPaZ/KHOAuC1cA+acsjgfLd2Mc551q1p/3989HFF1/cbu8VZxvBQmCcpDGSioCzgCea7fMEcK4ChwKbzWxNjDE516lYhu6Bzu2J3fk3FdsTgZnVSboCmE/QfXSumS2TdGm4/U7gaYKuoysJuo+eH1c8znU2PXr0aBwM5LOQuvZgYT6CVBffqJRvdyQlJSWWaml3Lp95hjIXh5YylElabGYlmY5J1Mhi5zqT7t27tymLlHNxSdTso84553blBYFzziWcFwTOOZdweddYLGkd8P5uHj4YWN+O4eQDP+dk8HNOhj0550+aWcY5M/KuINgTkha11GpeqPyck8HPORniOmevGnLOuYTzgsA55xIuaQXBXbkOIAf8nJPBzzkZYjnnRLUROOec21XSngicc8414wWBc84lXEEWBJJmSHpb0kpJuyQSDae9viXc/rqkKbmIsz1FOOevhOf6uqSXJR2YizjbU7ZzTtvvEEn1ks7oyPjiEOWcJU2TtETSMkkvZNonn0T4t91f0pOSlobnnNezGEuaK2mtpNIWtrf/9aul1GX5+kMw5fW/gH2BImApMKHZPicAfybIkHYo8D+5jrsDzvlwYK/w9fFJOOe0/f5KMOX5GbmOuwP+zgOA5cCocHloruPugHP+D+An4eshwEagKNex78E5HwlMAUpb2N7u169CfCKYCqw0s3fNrAaYB5zSbJ9TgN9a4BVggKRhHR1oO8p6zmb2spltChdfIcgGl8+i/J0Bvgo8CqztyOBiEuWcvwz8wcw+ADCzfD/vKOdsQF8FSR36EBQE2bPed1Jm9iLBObSk3a9fhVgQjAA+TFteHa5r6z75pK3ncyHBHUU+y3rOkkYApwF3dmBccYrydx4P7CVpgaTFks7tsOjiEeWcbwU+RZDm9g3ga2bW0DHh5US7X78KMR9BplRPzfvIRtknn0Q+H0lHERQEn481ovhFOedfAN8xs/oCyQAW5Zy7AQcDxwA9gX9IesXMVsQdXEyinPN0YAlwNDAWeFbS38xsS8yx5Uq7X78KsSBYDeyTtjyS4E6hrfvkk0jnI+kzwD3A8Wa2oYNii0uUcy4B5oWFwGDgBEl1ZvZ4h0TY/qL+215vZtuB7ZJeBA4E8rUgiHLO5wM3WlCBvlLSe8ABwKsdE2KHa/frVyFWDS0ExkkaI6kIOAt4otk+TwDnhq3vhwKbzWxNRwfajrKes6RRwB+AWXl8d5gu6zmb2RgzG21mo4FHgMvyuBCAaP+2/wgcIambpF7AZ4E3OzjO9hTlnD8geAJC0t7A/sC7HRplx2r361fBPRGYWZ2kK4D5BD0O5prZMkmXhtvvJOhBcgKwEthBcEeRtyKe8zXAIOD28A65zvJ45saI51xQopyzmb0p6RngdaABuMfMMnZDzAcR/87XA/dKeoOg2uQ7Zpa301NLegiYBgyWtBr4PtAd4rt++RQTzjmXcIVYNeScc64NvCBwzrmE84LAOecSzgsC55xLOC8InHMu4bwgSIBw5s0laT+jW9l3Wzt83r2S3gs/6zVJh+3Ge9wjaUL4+j+abXt5T2MM3yf1vZSGs1cOyLL/ZEkn7MbnDJP0p/D1NEmbJf1T0puSvr8b73dyahZOSaemvqdw+TpJx7b1PTN8xr3KMltrOI1F5C7I4bn/KcJ+GWfflPQzSUdH/TwXnRcEyVBpZpPTflZ1wGdeZWaTgTnAr9t6sJldZGbLw8X/aLbt8D0PD9j5vUwimOTr8iz7Tybov91W3wTuTlv+m5kdRDDy+RxJB7flzczsCTO7MVw8FZiQtu0aM3tuN2LsTO4FZmRY/yuCf0+unXlBkECS+kj67/Bu/Q1Ju8zaGd7Fvph2x3xEuP44Sf8Ij/29pD5ZPu5FYL/w2G+G71Uq6evhut6SnlIwl3yppC+F6xdIKpF0I9AzjOOBcNu28Pd/pd+hh3exp0vqKukmSQsVzNd+SYSv5R+EE3dJmqogZ8M/w9/7h6NarwO+FMbypTD2ueHn/DPT9xg6HXim+cpwGojFwNjwaeOVMN7HJO0VxnKlpOXh+nnhuvMk3SrpcOBk4KYwprGpO3lJx0t6OO27mSbpyfB1m/6Gkq4Jz7FU0l1Sk4mbzgm/o1JJU8P9o34vGbU0+6aZvQ8MkvSJtryfi6Cj5tj2n9z9APUEk3ItAR4jGFHeL9w2mGCEYmpw4bbw9/8Brg5fdwX6hvu+CPQO138HuCbD591LOPc/8G/A/xBMhPYG0JtgquBlwEEEF8m7047tH/5eAJSkx5S2TyrG04D7wtdFBDMy9gRmA98N1xcDi4AxGeLclnZ+vwdmhMv9gG7h62OBR8PX5wG3ph3/I+Cc8PUAgvl8ejf7jDHA4rTlacCfwteDgFXARIKRwF8I118H/CJ8XQ4Upz6jeRzp33X6cvg3/iDtb3UHcM5u/g0Hpq3/HXBS2t/o7vD1kYTz57f0vTQ79xKCUc8t/ZsdTYb5+AmerE7P9f+pQvspuCkmXEaVFlTTACCpO/AjSUcSTEMwAtgb+CjtmIXA3HDfx81siaQvEFRDvBTeFBYR3ElncpOk7wLrCGY7PQZ4zIK7YCT9ATiC4E75Z5J+QnCR+FsbzuvPwC2SigmqEl40s0pJxwGfSavj7g+MA95rdnxPSUsILjqLgWfT9r9P0jiCWR27t/D5xwEnS/pWuNwDGEXTuX2Ghd9BuiMk/ZPgu7+RYBKxAWaWyiZ2H0HBBEEB8YCkx4HHW4hjFxZMzfAMcJKkR4ATgW8Dbfkbphwl6dtAL2AgQSH+ZLjtofDzXpTUT0E7S0vfS3p8i4CLop5PmrXA8N04zrXCC4Jk+gpBJqeDzaxW0iqC/6yNwv/YRxJcQH4n6SZgE/CsmZ0d4TOuMrNHUgtqoQHTzFaEdeQnAD+W9Bczuy7KSZhZlaQFBNMQf4nwokQw38xXzWx+lreoNLPJkvoDfyJoI7iFYO6a583sNAUN6wtaOF4Ed6dvt/YZNPtuCdoIZja+SfD5LTmR4G77ZOB7kia2sm9z/0VwThuBhWa2NazWifo3RFIP4HaCp7MPJV1L0/NpPkeN0cL3omBCuD3Vg+A7de3I2wiSqT+wNiwEjgI+2XwHSZ8M97kb+A1B6rxXgM9JStX595I0PuJnvgicGh7Tm6Ba52+ShgM7zOx+4Gfh5zRXGz6ZZDKPYNKtIwgmJiP8/e+pYySNDz8zIzPbDFwJfCs8pj9QFm4+L23XrQRVZCnzga+m6swlHZTh7VcQPHG0KPz8TQrbYYBZwAuSugD7mNnzBHfzAwiq1dI1jyndAoLv82KCQgHa/jdMXfTXh20JzXsSpdp0Pk8wC+Zmon0vu2s8kLeT6HVWXhAk0wNAiaRFBE8Hb2XYZxqwJKzCOB34pZmtI7gwPiTpdYKLygFRPtDMXiOod36VoM3gHjP7J/Bp4NWwiuZq4IYMh98FvK6wsbiZvxDcMT9nQSpDCHIuLAdeU9AF8ddkefoNY1lKMM3xTwmeTl4iaD9IeR6YkGosJnhy6B7GVhouN3/f7cC/UhfeVvxvguq01wl6J10Xfvb9CmbV/Cdws5lVNDtuHnBV2Cg7ttln1xM86Rwf/qatf8Pw8+4maN95nKDKMN0mBd157ySoAoQI34uCjgD3ZPpMBbNv/gPYX9JqSReG67sTdDxY1FK8bvf47KPOxUzSaQTVcN/NdSz5LPwep5jZ93IdS6HxNgLnYmZmj0kalOs4CkA34D9zHUQh8icC55xLOG8jcM65hPOCwDnnEs4LAuecSzgvCJxzLuG8IHDOuYT7/xZPGCJ0/37OAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "RocCurveDisplay.from_predictions(y_test, y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x166b03e3d00>"
      ]
     },
     "execution_count": 19,
     "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": [
    "RocCurveDisplay.from_estimator(lr1, test_sc, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking accuracy on training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.9031\n",
      "F1 score : 0.9000\n",
      "Recall score : 0.8720\n",
      "Precision score : 0.9298\n"
     ]
    }
   ],
   "source": [
    "y_pred1=lr1.predict(train_sc)\n",
    "print(f\"Accuracy score: {accuracy_score(y_train, y_pred1):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_train, y_pred1):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_train, y_pred1):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_train, y_pred1):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding improved accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 5) The accuracy score is 0.9024 and f1_score is 0.90 for both standard scaling and minmax scaling\n",
    "* 6) f1_score is improved as data is balanced data due to oversampling\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.902927 using {'C': 100, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.902927 (0.000957) with: {'C': 100, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.900980 (0.001195) with: {'C': 100, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.902924 (0.000954) with: {'C': 100, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.902917 (0.000955) with: {'C': 100, 'penalty': 'l2', 'solver': 'sag'}\n",
      "0.902901 (0.000955) with: {'C': 100, 'penalty': 'l2', 'solver': 'saga'}\n",
      "0.902923 (0.000958) with: {'C': 10, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.900970 (0.001065) with: {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.902923 (0.000956) with: {'C': 10, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.902915 (0.000955) with: {'C': 10, 'penalty': 'l2', 'solver': 'sag'}\n",
      "0.902902 (0.000955) with: {'C': 10, 'penalty': 'l2', 'solver': 'saga'}\n",
      "0.902914 (0.000948) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.901069 (0.001078) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.902910 (0.000953) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.902906 (0.000953) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'sag'}\n",
      "0.902897 (0.000954) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'saga'}\n",
      "0.902815 (0.000953) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.900946 (0.001066) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.902794 (0.000948) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.902806 (0.000947) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'sag'}\n",
      "0.902803 (0.000949) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'saga'}\n",
      "0.902158 (0.000964) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.900618 (0.001132) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.902132 (0.000980) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.902161 (0.000965) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'sag'}\n",
      "0.902173 (0.000986) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'saga'}\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear','sag','saga']\n",
    "penalty = ['l2']\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
    "# define grid search\n",
    "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
    "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=None, cv=cv, scoring='accuracy',error_score=0)\n",
    "grid_result = grid_search.fit(X, Y)\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
    "means = grid_result.cv_results_['mean_test_score']\n",
    "stds = grid_result.cv_results_['std_test_score']\n",
    "params = grid_result.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_imp = pd.DataFrame({\"Col\": x_resampled_encoding.columns})\n",
    "feat_imp[\"Coef\"] = lr1.coef_[0]\n",
    "\n",
    "#Group OneHotEncoded features\n",
    "feat_imp[\"Col\"] = feat_imp[\"Col\"].apply(lambda x: (x.split('cat')[0]+'cat') if 'cat' in x else x)\n",
    "feat_imp = feat_imp.groupby(\"Col\",as_index=False).sum().sort_values(\"Coef\", ascending=False)\n",
    "#Scale Weights\n",
    "feat_imp[\"FeatImp\"] = (\n",
    "        (feat_imp[\"Coef\"] - feat_imp[\"Coef\"].min())\n",
    "        / (feat_imp[\"Coef\"].max() - feat_imp[\"Coef\"].min())\n",
    "    ).round(4)\n",
    "\n",
    "num_features = 10 \n",
    "pyplot.figure(figsize=(10,10))\n",
    "pyplot.title('Feature Importances')\n",
    "# only plot the customized number of features\n",
    "pyplot.barh(range(num_features), feat_imp['FeatImp'][:num_features][::-1], color='b', align='center')\n",
    "pyplot.yticks(range(num_features), feat_imp['Col'][:num_features][::-1])\n",
    "pyplot.xlabel('Relative Importance')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Logistic Regression takes importances of 48 columns out of 52 columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performing Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  352.82618309999816\n",
      "Time taken to perform cv for the model in sec is  1713.2629760999698\n",
      "Cv2 scores: [0.36417213 0.6693039  0.67169267 0.67212857 0.67413374]\n",
      "Mean 5-Fold R Squared: 0.6102862038404651\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='sag',max_iter=200)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "lr.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "#perform k fold\n",
    "cv_r2_scores_rf = cross_val_score(lr, X, Y, cv=5,scoring='r2')\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to perform cv for the model in sec is \",elapsed)\n",
    "\n",
    "print(\"Cv2 scores:\",cv_r2_scores_rf)\n",
    "print(\"Mean 5-Fold R Squared: {}\".format(np.mean(cv_r2_scores_rf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  345.28897619998315\n",
      "Time taken to perform cv for the model in sec is  3877.1906133000157\n",
      "Cv2 scores: [0.054645   0.67146743 0.67310643 0.66679453 0.67376901 0.67219975\n",
      " 0.67292922 0.67306871 0.67707907 0.67355693]\n",
      "Mean 5-Fold R Squared: 0.6108616060665297\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='sag',max_iter=200)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "lr.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "#perform k fold\n",
    "cv_r2_scores_rf = cross_val_score(lr, X, Y, cv=10,scoring='r2')\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(\"Time taken to perform cv for the model in sec is \",elapsed)\n",
    "\n",
    "print(\"Cv2 scores:\",cv_r2_scores_rf)\n",
    "print(\"Mean 5-Fold R Squared: {}\".format(np.mean(cv_r2_scores_rf)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see that the metrics for folds in cross_val_score are uniform so it can be concluded that the model is able to generalise"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}