RandomForestClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split,cross_val_score,GridSearchCV,RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": {
    "id": "P2U4AUV_JMmP"
   },
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data with Resampling and without OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  382.8573328\n",
      "Accuracy score:0.9779\n",
      "F1 score : 0.9776\n",
      "Recall score : 0.9599\n",
      "Precision score : 0.9960\n"
     ]
    }
   ],
   "source": [
    "X = x_resampled.values\n",
    "Y = np.squeeze(y_resampled.values)\n",
    "X_train,X_test1,y_train,y_test1=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_test1)\n",
    "\n",
    "model=RandomForestClassifier(random_state=48)\n",
    "start_time = timeit.default_timer()\n",
    "model.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "y_pred1=model.predict(test_sc)\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\n",
    "print(f\"Accuracy score:{accuracy_score(y_test1, y_pred1):.4f}\")\n",
    "print(f\"F1 score : {f1_score(y_test1, y_pred1):.4f}\")\n",
    "print(f\"Recall score : {recall_score(y_test1, y_pred1):.4f}\")\n",
    "print(f\"Precision score : {precision_score(y_test1, y_pred1):.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "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_test1,y_pred1),annot=True,fmt='', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98    114386\n",
      "           1       1.00      0.96      0.98    115022\n",
      "\n",
      "    accuracy                           0.98    229408\n",
      "   macro avg       0.98      0.98      0.98    229408\n",
      "weighted avg       0.98      0.98      0.98    229408\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test1, y_pred1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oSZKkJgZHSZIkNTE4SpIkqYnBUZIkSU0MjpIkSWpicJQkSVITg6MkSZKaTHlwTHJqkhMn2OeaJIv6nF+Y5KYktyf5SJIMO/eGJLckWZvkb/qMcWSSFWOcuzLJzhOpWZIkabaZN9UTVtWFW2HYC4BTgBuAK4GjgC8k2Qf4A+DFVXVvkl+czOBV9dotVqkkSdIMNeEVxyQLktyW5KIka5IsT7J9knO7lb01Sc7v0//sJGd2x9ckOS/JjUm+m+SlXft2ST7VjfVpYLs+4z0T2LGqrq+qAj4JHNOd/m3gL6rqXoCq+rdxnt6OSa7onseFSZ7UzbEuyfzuud+a5K+6FcwvJRm1tiSnJFmZZCXcNc60kiRJ099kt6r3BZZW1UHAA8DpwLHA/l3bhyYw1ryqWgycAbyvazsNeKgb6xxgYZ/+uwMbhj3e0LUBPBd4bpLrktyQ5KhxalkMvAs4ENgbOG6Ua/ahF0b3B+4DXjfaQFW1tKoWVdUiePo400qSJE1/kw2O66vquu74EuAI4GFgWZLjgIcmMNZnuu9DwILu+IhuXKpqDbCmT/+M0lbd93n0gt6RwJKuvp37jHVjVf2gqjYClwIvGeWaO6pq1Sg1S5IkzWqTDY414vGj9FbrLqe3TXzVBMZ6pPu+kSe+53LkHGPZAOwx7PEewJ3Dzn2uqh6tqjuA79ALkmMZOedoNTwy7HhkzZIkSbPWZIPjXkkO746XAKuAnarqSnpbzodsZl1fA04ASHIAcNBYF1bVj4AHkxzWfZr6ROBz3enPAi/vxplPb+v6B33mXZzk2d17G98IfGMzn4ckSdKsMdngeCtwUpI1wK7AMmBF9/ha4B2bWdcFwA7deO8Bbhzn+tO6Gm4Hvg98oWv/IvDjJLcAXwXeXVU/7jPO9cC5wM3AHcAVk34GkiRJs0x6H0SeQIdkAbCiqg7YKhXNQsmigpWDLkOSpBllghFFW0iSod6He3+ed46RJElSkwl/sKOq1gHjrjYmOQs4fkTzZVV1zkTnHDbmN4FtRzS/uapumsAYBwIXj2h+pKoOnWxdkiRJc8GEt6o1cW5VS5I0cUaUwXCrWpIkSZvN4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmE75XtSZu4UJY6R0HJUnSDOeKoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpp455gpMDQEyaCrkCRp66sadAXamlxxlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUpMpD45JTk1y4gT7XJNkUZ/zC5PclOT2JB9Jkq79WUm+nGRNN8YefcY4MsmKMc5dmWTnidQsSZI020x5cKyqC6vqk1t42AuAU4B9uq+juvbzgU9W1UHAB4A/mszgVfXaqrpvC9QpSZI0Y004OCZZkOS2JBd1K3nLk2yf5Nwkt3Rt5/fpf3aSM7vja5Kcl+TGJN9N8tKufbskn+rG+jSwXZ/xngnsWFXXV1UBnwSO6U4/H/hyd/xV4Ohxnt6OSa7onseFSZ7UzbEuyfzuud+a5K+SrE3ypSSj1pbklCQrk6yEu8aZVpIkafqb7IrjvsDSbiXvAeB04Fhg/67tQxMYa15VLQbOAN7XtZ0GPNSNdQ6wsE//3YENwx5v6NoAVgOv646PBZ6WZLc+Yy0G3gUcCOwNHDfKNfsAf1FV+wP3DRv/CapqaVUtqqpF8PQ+U0qSJM0Mkw2O66vquu74EuAI4GFgWZLjgIcmMNZnuu9DwILu+IhuXKpqDbCmT/+M0lbd9zOBlyX5NvAy4J+Bx/qMdWNV/aCqNgKXAi8Z5Zo7qmrVKDVLkiTNapMNjjXi8aP0Vusup7dNfNUExnqk+74RmNdnjrFsAIZ/6GUP4E6Aqrqzqo6rql8Gzura7u8z1sg5R6vhkWHHI2uWJEmatSYbHPdKcnh3vARYBexUVVfS23I+ZDPr+hpwAkCSA4CDxrqwqn4EPJjksO7T1CcCn+v6zt/0PkXgD4CPjzPv4iTP7vq8EfjG5j0NSZKk2WOywfFW4KQka4BdgWXAiu7xtcA7NrOuC4AduvHeA9w4zvWndTXcDnwf+ELXfiTwnSTfBZ5B7/2S/VwPnAvcDNwBXDGZ4iVJkmaj9D6IPIEOyQJgRVUdsFUqmoWSRQUrB12GJElb3QRjhaahJEO9D/f+PO8cI0mSpCYT/mBHVa0Dxl1tTHIWcPyI5suqarzt4n5jfhPYdkTzm6vqpgmMcSBw8YjmR6rq0MnWJUmSNBdMeKtaE+dWtSRprjBWzHxuVUuSJGmzGRwlSZLUxOAoSZKkJgZHSZIkNTE4SpIkqYnBUZIkSU0MjpIkSWpicJQkSVITg6MkSZKaGBwlSZLUxOAoSZKkJvMGXcBcsHAhrPRW1ZIkaYZzxVGSJElNDI6SJElqYnCUJElSE4OjJEmSmhgcJUmS1MTgKEmSpCYGR0mSJDUxOEqSJKmJwVGSJElNvHPMFBgagmTQVUiSpkrVoCuQtg5XHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktRkyoNjklOTnDjBPtckWdTn/MIkNyW5PclHkqRr3yvJV5N8O8maJK/tM8aRSVaMce7KJDtPpGZJkqTZZsqDY1VdWFWf3MLDXgCcAuzTfR3Vtb8X+Nuq+mXgTcBHJzN4Vb22qu7bAnVKkiTNWBMOjkkWJLktyUXdKt7yJNsnOTfJLV3b+X36n53kzO74miTnJbkxyXeTvLRr3y7Jp7qxPg1s12e8ZwI7VtX1VVXAJ4FjutMF7Ngd7wTcOc7T2zHJFd3zuDDJk7o51iWZ3z33W5P8VZK1Sb6UZNTakpySZGWSlXDXONNKkiRNf5NdcdwXWFpVBwEPAKcDxwL7d20fmsBY86pqMXAG8L6u7TTgoW6sc4CFffrvDmwY9nhD1wZwNvAbSTYAVwK/O04ti4F3AQcCewPHjXLNPsBfVNX+wH3A60YbqKqWVtWiqloETx9nWkmSpOlvssFxfVVd1x1fAhwBPAwsS3Ic8NAExvpM930IWNAdH9GNS1WtAdb06Z9R2qr7vgT4RFXtAbwWuHjTKuIYbqyqH1TVRuBS4CWjXHNHVa0apWZJkqRZbbLBsUY8fpTeat3l9LaJr5rAWI903zcC8/rMMZYNwB7DHu/Bf2xJnwz8LUBVXQ88FZjfZ6yRc45WwyPDjkfWLEmSNGtNNjjuleTw7ngJsArYqaqupLflfMhm1vU14ASAJAcAB411YVX9CHgwyWHdp6lPBD7Xnf4h8MpunOfRC4793nC4OMmzu1XJNwLf2MznIUmSNGtMNjjeCpyUZA2wK7AMWNE9vhZ4x2bWdQGwQzfee4Abx7n+tK6G24HvA1/o2t8F/HaS1fS2nt/SfYBmLNcD5wI3A3cAV0z6GUiSJM0y6Z+jRumQLABWVNUBW6WiWShZVLBy0GVIkqbIBH+1StNKkqHeh3t/nneOkSRJUpMJf7CjqtYB4642JjkLOH5E82VVdc5E5xw25jeBbUc0v7mqbprAGAcCF49ofqSqDp1sXZIkSXPBhLeqNXFuVUvS3OKvVs1kblVLkiRpsxkcJUmS1MTgKEmSpCYGR0mSJDUxOEqSJKmJwVGSJElNDI6SJElqYnCUJElSE4OjJEmSmhgcJUmS1GTC96rWxC1cCCu946AkSZrhXHGUJElSE4OjJEmSmhgcJUmS1MTgKEmSpCYGR0mSJDUxOEqSJKmJwVGSJElNDI6SJElqYnCUJElSE+8cMwWGhiAZdBWSpC2patAVSFPPFUdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1mfLgmOTUJCdOsM81SRb1Ob8wyU1Jbk/ykSTp2v8kyaru67tJ7uszxpFJVoxx7sokO0+kZkmSpNlm3lRPWFUXboVhLwBOAW4ArgSOAr5QVe/YdEGS3wV+eTKDV9Vrt0SRkiRJM9mEVxyTLEhyW5KLkqxJsjzJ9knOTXJL13Z+n/5nJzmzO74myXlJbuxWBF/atW+X5FPdWJ8Gtusz3jOBHavq+qoq4JPAMaNcugS4dJynt2OSK7rncWGSJ3VzrEsyv3vutyb5qyRrk3wpyai1JTklycokK+GucaaVJEma/ia7Vb0vsLSqDgIeAE4HjgX279o+NIGx5lXVYuAM4H1d22nAQ91Y5wAL+/TfHdgw7PGGru1xSZ4FPBv4yji1LAbeBRwI7A0cN8o1+wB/UVX7A/cBrxttoKpaWlWLqmoRPH2caSVJkqa/yQbH9VV1XXd8CXAE8DCwLMlxwEMTGOsz3fchYEF3fEQ3LlW1BljTp39GaasRj98ELK+qjePUcmNV/aC77lLgJaNcc0dVrRqlZkmSpFltssFxZDB7lN5q3eX0tomvmsBYj3TfN/LE91yOnGMsG4A9hj3eA7hzxDVvYvxt6tHmHK2GR4Ydj6xZkiRp1ppscNwryeHd8RJgFbBTVV1Jb8v5kM2s62vACQBJDgAOGuvCqvoR8GCSw7pPU58IfG7T+ST7ArsA1zfMuzjJs7v3Nr4R+Mbkn4IkSdLsMtngeCtwUpI1wK7AMmBF9/ha4B39Oje4ANihG+89wI3jXH9aV8PtwPeBLww7twT4VPfBmfFcD5wL3AzcAVwxwbolSZJmrbTlqWEdkgXAiqo6YKtUNAsliwpWDroMSdIWNMFfn9KMkWSo9+Hen+edYyRJktRkwh/sqKp1wLirjUnOAo4f0XxZVZ0z0TmHjflNYNsRzW+uqpsmMMaBwMUjmh+pqkMnW5ckSdJcMOGtak2cW9WSNPv461OzlVvVkiRJ2mwGR0mSJDUxOEqSJKmJwVGSJElNDI6SJElqYnCUJElSE4OjJEmSmhgcJUmS1MTgKEmSpCYGR0mSJDWZ8L2qNXELF8JK7zgoSZJmOFccJUmS1MTgKEmSpCYGR0mSJDUxOEqSJKmJwVGSJElNDI6SJElqYnCUJElSE4OjJEmSmhgcJUmS1MQ7x0yBoSFIBl2FJE2NqkFXIGlrccVRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJajIngmOSdUnmT7DPtkk+neT2JN9MsqBrPyTJ9UnWJlmT5I1bpWhJkqRpZk4Ex0k6Gbi3qp4D/AlwXtf+EHBiVe0PHAX8aZKdB1OiJEnS1BlIcEyyIMltSS7qVu2WJ9k+yblJbunazu/T/xlJrkiyuvt6Udf+2SRD3WrgKWP0PbEbf3WSi/uUeTRwUXe8HHhlklTVd6vqewBVdSfwb8DTR5nnlCQrk6yEu9p+MJIkSdPYvAHOvS9wclVdl+TjwOnAscB+VVXjrOJ9BLi2qo5Nsg2wQ9f+1qq6J8l2wLeSXF5VP97UKcn+wFnAi6vq7iS79pljd2A9QFU9luR+YDfg7mHjLQaeAnx/ZOeqWgos7V23qPr+JCRJkmaAQW5Vr6+q67rjS4AjgIeBZUmOo7clPJZXABcAVNXGqrq/a39bktXADcCewD6j9FteVXd3fe/pM0dGaXs8ACZ5JnAx8JtV9bM+40iSJM0KgwyOI1fhHgUWA5cDxwBXTWSwJEcCrwIOr6qDgW8DTx152SjzjmUDvfBJknnATsA93eMdgb8H3ltVN0ykTkmSpJlqkMFxrySHd8dLgFXATlV1JXAGcEifvl8GTgNIsk0X5Hai92GWh5LsBxw2Rr83JNmt69tvq/rzwEnd8euBr3Rb6E8BrgA+WVWXjfssJUmSZolBBsdbgZOSrAF2BZYBK7rH1wLv6NP37cDLk9wEDAH701uhnNf1/yC97eonqKq1wDnAtd2W9of7zPExYLcktwPvBH6/a38DvW31tyRZ1X0d0vicJUmSZqxUTf3nNrq/ibiiqg6Y8skHoPfhmJWDLkOSpsQAfq1I2oKSDFXVotHO+XccJUmS1GQgf46nqtYB4642JjkLOH5E82VVdc6WqmUq5pAkSZoNBrJVPde4VS1pLvHXijSzuVUtSZKkzWZwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUpOB3Kt6rlm4EFZ6x0FJkjTDueIoSZKkJgZHSZIkNTE4SpIkqYnBUZIkSU0MjpIkSWpicJQkSVITg6MkSZKaGBwlSZLUxOAoSZKkJt45ZgoMDUEy6CokaeuqGnQFkrY2VxwlSZLUxOAoSZKkJgZHSZIkNTE4SpIkqYnBUZIkSU0MjpIkSWpicJQkSVITg6MkSZKaGBwlSZLUxOAoSZKkJgZHSZIkNTE4SpIkqYnBUZIkSU0MjpIkSWpicJQkSVITg6MkSZKaGBwlSZLUZE4ExyTrksyfYJ9tk3w6ye1JvplkQdf+rCRDSVYlWZvk1K1StCRJ0jQzb9AFTGMnA/dW1XOSvAk4D3gj8CPgRVX1SJIdgJuTfL6q7hxksZIkSVvbQFYckyxIcluSi5KsSbI8yfZJzk1yS9d2fp/+z0hyRZLV3deLuvbPdquBa5OcMkbfE7vxVye5uE+ZRwMXdcfLgVcmSVX976p6pGvfljF+hklOSbIyyUq4a7wfiSRJ0rQ3yBXHfYGTq+q6JB8HTgeOBfarqkqyc5++HwGurapjk2wD7NC1v7Wq7kmyHfCtJJdX1Y83dUqyP3AW8OKqujvJrn3m2B1YD1BVjyW5H9gNuDvJnsDfA88B3j3aamNVLQWW9uZdVOP/OCRJkqa3Qb7HcX1VXdcdXwIcATwMLEtyHPBQn76vAC4AqKqNVXV/1/62JKuBG4A9gX1G6be8qu7u+t7TZ46M0lZdv/VVdRC94HhSkmf0GUeSJGlWGGRwHLkK9yiwGLgcOAa4aiKDJTkSeBVweFUdDHwbeOrIy0aZdywb6IVPkswDdgKeEDS7lca1wEsnUqskSdJMNMjguFeSw7vjJcAqYKequhI4AzikT98vA6cBJNkmyY70gt29VfVQkv2Aw8bo94Yku3V9+21Vfx44qTt+PfCVbgt9j24rnCS7AC8GvjPOc5UkSZrxBhkcb6W3zbsG2BVYBqzoHl8LvKNP37cDL09yEzAE7E9vhXJe1/+D9Larn6Cq1gLnANd2W9of7jPHx4DdktwOvBP4/a79ecA3u/7XAudX1U2Nz1mSJGnGStXUf26j+5uIK6rqgCmffAB6H45ZOegyJGmrGsCvE0lbQZKhqlo02rk58QfAJUmStPkG8ud4qmodMO5qY5KzgONHNF9WVedsqVqmYg5JkqTZYCBb1XONW9WS5gJ/nUizg1vVkiRJ2mwGR0mSJDUxOEqSJKmJwVGSJElNDI6SJElqYnCUJElSE4OjJEmSmhgcJUmS1MTgKEmSpCYGR0mSJDUZyL2q55qFC2GldxyUJEkznCuOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJauKdY6bA0BAkg65CksZXNegKJE1nrjhKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpyZwIjknWJZk/wT7bJvl0ktuTfDPJgmHnrkpyX5IVW7xYSZKkaWpOBMdJOhm4t6qeA/wJcN6wc/8dePNAqpIkSRqQgQTHJAuS3JbkoiRrkixPsn2Sc5Pc0rWd36f/M5JckWR19/Wirv2zSYaSrE1yyhh9T+zGX53k4j5lHg1c1B0vB16ZJABV9WXgwXGe4ylJViZZCXf1u1SSJGlGmDfAufcFTq6q65J8HDgdOBbYr6oqyc59+n4EuLaqjk2yDbBD1/7WqronyXbAt5JcXlU/3tQpyf7AWcCLq+ruJLv2mWN3YD1AVT2W5H5gN+DulidXVUuBpb15F1VLH0mSpOlskFvV66vquu74EuAI4GFgWZLjgIf69H0FcAFAVW2sqvu79rclWQ3cAOwJ7DNKv+VVdXfX954+c2SUNgOgJEmaswYZHEeGsEeBxcDlwDHAVRMZLMmRwKuAw6vqYODbwFNHXjbKvGPZQC98kmQesBPQL2hKkiTNaoMMjnslObw7XgKsAnaqqiuBM4BD+vT9MnAaQJJtkuxIL9jdW1UPJdkPOGyMfm9IslvXt99W9eeBk7rj1wNfqSpXHCVJ0pw1yOB4K3BSkjXArsAyYEX3+FrgHX36vh14eZKbgCFgf3orlPO6/h+kt139BFW1FjgHuLbb0v5wnzk+BuyW5HbgncDvbzqR5OvAZfQ+MLMhya80PmdJkqQZK4NYROv+JuKKqjpgyicfgN6HY1YOugxJGpf7KpKSDFXVotHO+XccJUmS1GQgf46nqtYB4642JjkLOH5E82VVdc6WqmUq5pAkSZoNBrJVPde4VS1ppvBXgiS3qiVJkrTZDI6SJElqYnCUJElSE4OjJEmSmhgcJUmS1MTgKEmSpCYGR0mSJDUxOEqSJKmJwVGSJElNDI6SJElqYnCUJElSk3mDLmAuWLgQVnqrakmSNMO54ihJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQm3jlmCgwNQTLoKiSpp2rQFUiaqVxxlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUpNpFxyTrEsyf4J9jkjyj0keS/L6EefOS3Jz9/XGycyd5NQkJ06kJkmSpNlm3qAL2EJ+CLwFOHN4Y5JfBV4AHAJsC1yb5AtV9cBEBq+qC7dMmZIkSTPXuCuOSRYkuS3JRUnWJFmeZPsk5ya5pWs7v0//ZyS5Isnq7utFXftnkwwlWZvklDH6ntiNvzrJxWPNUVXrqmoN8LMRp54PXFtVj1XVT4DVwFHjPOV3J7mx+3pOV8fZSc7sjq/pVjFvTPLdJC8do/ZTkqxMshLuGmdKSZKk6a91q3pfYGlVHQQ8AJwOHAvs37V9qE/fj9ALbwfTW/1b27W/taoWAouAtyXZbXinJPsDZwGv6Pq+vbHW4VYDr+mC7nzg5cCe4/R5oKoWA38O/OkY18zrrjkDeN9oF1TV0qpaVFWL4OmTKF2SJGl6aQ2O66vquu74EuAI4GFgWZLjgIf69H0FcAFAVW2sqvu79rclWQ3cQC/M7TNKv+VVdXfX957GWh9XVV8CrgT+AbgUuB54bJxulw77fvgY13ym+z4ELJhoXZIkSTNRa3CsEY8fBRYDlwPHAFdNZNIkRwKvAg7vVhO/DTx15GWjzDthVXVOVR1SVf93N+b3xusyxvFwj3TfNzJ73icqSZLUV2tw3CvJptW3JcAqYKequpLedu0hffp+GTgNIMk2SXYEdgLuraqHkuwHHDZGvzds2sJOsmtjrY/r5tvU/yDgIOBL43R747Dv1090TkmSpNmqdbXsVuCkJH9Jb8XubGBFkqfSW8V7R5++bweWJjmZ3grdafRWKE9Nsgb4Dr3t6ieoqrVJzqH3SeiN9FYl3zLaBEleCFwB7AL8epL3V9X+wJOBryeB3nszf6Oqxtuq3jbJN+mF6iXjXCtJkjRnpKr/bnCSBcCKqjpgSiqahZJFBSsHXYYkATDOP/uS5rgkQ70P9/68afcHwCVJkjQ9jbtVXVXrgHFXG5OcBRw/ovmyqjpncqVtvTmSXAE8e0Tz71XVFzenPkmSpNls3K1qbT63qiVNJ/6zL6kft6olSZK02QyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJajLuvaq1+RYuhJXecVCSJM1wrjhKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1MThKkiSpicFRkiRJTQyOkiRJamJwlCRJUhODoyRJkpoYHCVJktTE4ChJkqQmBkdJkiQ1SVUNuoZZL8mDwHcGXYfGNR+4e9BFqImv1czg6zQz+DrNDFP5Oj2rqp4+2ol5U1TAXPedqlo06CLUX5KVvk4zg6/VzODrNDP4Os0M0+V1cqtakiRJTQyOkiRJamJwnBpLB12Amvg6zRy+VjODr9PM4Os0M0yL18kPx0iSJKmJK46SJElqYnCUJElSE4PjFpTkqCTfSXJ7kt8f5XySfKQ7vybJCwZR51zX8Dqd0L0+a5L8Q5KDB1HnXDfe6zTsuhcm2Zjk9VNZn3paXqckRyZZlWRtkmunukb1NPzbt1OSv0uyunutfnMQdc51ST6e5N+S3DzG+YFmCYPjFpJkG+AvgNcAzweWJHn+iMteA+zTfZ0CXDClRar1dboDeFlVHQR8kGnyhuS5pPF12nTdecAXp7ZCQdvrlGRn4KPAf66q/YHjp7pONf839V+BW6rqYOBI4I+TPGVKCxXAJ4Cj+pwfaJYwOG45i4Hbq+oHVfW/gU8BR4+45mjgk9VzA7BzkmdOdaFz3LivU1X9Q1Xd2z28AdhjimtU239PAL8LXA7821QWp8e1vE7/BfhMVf0QoKp8rQaj5bUq4GlJAuwA3AM8NrVlqqq+Ru9nP5aBZgmD45azO7B+2OMNXdtEr9HWNdHX4GTgC1u1Io1m3Ncpye7AscCFU1iXnqjlv6fnArskuSbJUJITp6w6DdfyWv058DzgTuAm4O1V9bOpKU8TMNAs4S0Ht5yM0jbybx21XKOtq/k1SPJyesHxJVu1Io2m5XX6U+D3qmpjb4FEA9DyOs0DFgKvBLYDrk9yQ1V9d2sXpydoea1+BVgFvALYG7g6yder6oGtXJsmZqBZwuC45WwA9hz2eA96/9c20Wu0dTW9BkkOApYBr6mqH09RbfoPLa/TIuBTXWicD7w2yWNV9dkpqVDQ/u/e3VX1E+AnSb4GHAwYHKdWy2v1m8C51fsDz7cnuQPYD7hxakpUo4FmCbeqt5xvAfskeXb3ZuI3AZ8fcc3ngRO7T0QdBtxfVT+a6kLnuHFfpyR7AZ8B3uyqyMCM+zpV1bOrakFVLQCWA79jaJxyLf/ufQ54aZJ5SbYHDgVuneI61fZa/ZDeyjBJngHsC/xgSqtUi4FmCVcct5CqeizJ6fQ+3bkN8PGqWpvk1O78hcCVwGuB24GH6P3fnaZQ4+v0h8BuwEe71azHqmrRoGqeixpfJw1Yy+tUVbcmuQpYA/wMWFZVo/6ZEW09jf9NfRD4RJKb6G2H/l5V3T2woueoJJfS+1T7/CQbgPcBT4bpkSW85aAkSZKauFUtSZKkJgZHSZIkNTE4SpIkqYnBUZIkSU0MjpIkSWpicJQ0JyXZmGRVkpuT/F2Snce5/uwkZ45zzTFJnj/s8QeSvGoL1PqJJK/f3HEmOOcZ3d9dlKTHGRwlzVU/rapDquoA4B7gv26BMY8BHg+OVfWHVfW/tsC4UyrJNsAZgMFR0hMYHCUJrgd2B0iyd5Krkgwl+XqS/UZenOS3k3wryeoklyfZPsmLgP8M/PduJXPvTSuFSV6T5G+H9T8yyd91x69Ocn2Sf0xyWZId+hWaZF2S/6/rszLJC5J8Mcn3N/0x5278ryW5IsktSS5M8qTu3JIkN3UrrecNG/ffuxXSbwJnAb8EfDXJV7vzF3TzrU3y/hH1vL+r/6ZNP68kOyT5n13bmiSvm8zzlTS9GBwlzWnd6tor+Y/bry0FfreqFgJnAh8dpdtnquqFVXUwvdvnnVxV/9CN8e5uJfP7w66/GjgsyS90j98IfDrJfOC9wKuq6gXASuCdDWWvr6rDga8DnwBeDxwGfGDYNYuBdwEHAnsDxyX5JeA84BXAIcALkxzTXf8LwM1VdWhVfYDevW9fXlUv786f1d1B6SDgZendz32Tu7v6L+h+ZgD/jd6t0A6sqoOAr2zG85U0TXjLQUlz1XZJVgELgCHg6m7160XAZd3tJgG2HaXvAUk+BOwM7EDvNm5j6m73dhXw60mWA78KvAd4Gb2t7eu6+Z5Cb/VzPJtC7k3ADlX1IPBgkoeHvVfzxqr6ATx+C7OXAI8C11TVXV37XwNHAJ8FNgKX95nzDUlOofd745ld3Wu6c5/pvg8Bx3XHr6J3P+RNP4N7k/zaJJ+vpGnC4ChprvppVR2SZCdgBb33OH4CuK+qDhmn7yeAY6pqdZK30Luv7Hg+3c1xD/CtqnowvfR0dVUtmWDtj3TffzbseNPjTf+uj7yfbNG7//BYHq6qjaOdSPJseiuJL+wC4CeAp45Sz8Zh82eUGib7fCVNE25VS5rTqup+4G30gtFPgTuSHA+QnoNH6fY04EdJngycMKz9we7caK4BXgD8Nr0QCXAD8OIkz+nm2z7JczfvGT1ucZJnd+9tfCPwDeCb9LaZ53db9EuAa8foP/y57Aj8BLg/yTOA1zTM/yXg9E0PkuzC1n2+kqaAwVHSnFdV3wZW09taPQE4OclqYC1w9Chd/hu9EHY1cNuw9k8B707y7SR7j5hjI72Vzdd03+m2jN8CXJpkDb1g9XMfxpmk64FzgZuBO4ArqupHwB8AX6X3fP+xqj43Rv+lwBeSfLWqVgPfpvfz+DhwXcP8HwJ26T6Es5re+yW35vOVNAVSNXInQZI0kyU5Ejizqn5twKVImmVccZQkSVITVxwlSZLUxBVHSZIkNTE4SpIkqYnBUZIkSU0MjpIkSWpicJQkSVKT/wMlPlt3Iqg+EAAAAABJRU5ErkJggg==\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.columns})\n",
    "feat_imp[\"Coef\"] = model.feature_importances_\n",
    "feat_imp = feat_imp.sort_values(\"Coef\", ascending=False)\n",
    "#Scaling Weights\n",
    "feat_imp[\"FeatImp\"] = (\n",
    "        (feat_imp[\"Coef\"] - feat_imp[\"Coef\"].min())/ (feat_imp[\"Coef\"].max() - feat_imp[\"Coef\"].min())).round(4)\n",
    "\n",
    "# customized number \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": [
    "### Data with Resampling and with OneHotEncoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken to fit the model in sec is  368.69278399999996\n",
      "Accuracy score:0.9771\n",
      "F1 score : 0.9767\n",
      "Recall score : 0.9600\n",
      "Precision score : 0.9940\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",
    "model=RandomForestClassifier(random_state=48)\n",
    "start_time = timeit.default_timer()\n",
    "model.fit(train_sc,y_train)\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "y_pred1=model.predict(test_sc)\n",
    "print(\"Time taken to fit the model in sec is \",elapsed)\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': 48,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1ce80890ca0>"
      ]
     },
     "execution_count": 9,
     "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_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1ce80fd1c10>"
      ]
     },
     "execution_count": 10,
     "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(model,X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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\"] = model.feature_importances_\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",
    "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",
    "# customized number \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": [
    "### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.61727577 0.98066319 0.97996574 0.98088986 0.98001805]\n",
      "Mean 5-Fold R Squared: 0.9077625205193278\n",
      "2639.3376154\n"
     ]
    }
   ],
   "source": [
    "rf_reg = RandomForestClassifier(random_state=48)\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "rf_reg.fit(train_sc,y_train)\n",
    "#perform k fold\n",
    "\n",
    "cv_r2_scores_rf = cross_val_score(rf_reg, X, Y, cv=5,scoring='r2')\n",
    "print(cv_r2_scores_rf)\n",
    "print(\"Mean 5-Fold R Squared: {}\".format(np.mean(cv_r2_scores_rf)))\n",
    "\n",
    "elapsed = timeit.default_timer() - start_time\n",
    "print(elapsed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see that the metrics for all folds in cross_val_score are uniform so it can be concluded that the model is able to generalise\n",
    "* As 4 subsets gave accuracy of 98 we can conside the above model result is not overfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is same accuracy with and without OneHotEncoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hyperparameter tuning can be advantageous in creating a model that is better at classification. \n",
    "* In the case of a random forest, it may not be necessary, as random forests are already very good at classification. \n",
    "* Using exhaustive grid search to choose hyperparameter values can be very time consuming as well.\n",
    "* However, in cases where there are only a few potential values for your hyperparameters or when your initial classification model isn’t very accurate, it might be a good idea to at least investigate the effect of changing some of the hyperparameter values in your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As there is high accuracy it may not necessary to do hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://towardsdatascience.com/optimizing-hyperparameters-in-random-forest-classification-ec7741f9d3f6#:~:text=Hyperparameter%20tuning%20can%20be%20advantageous%20in%20creating%20a,values%20can%20be%20very%20time%20consuming%20as%20well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}