Xgboost model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "minor-luxembourg",
   "metadata": {},
   "source": [
    "# Xgboost Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "renewable-indie",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "proof-rendering",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "import scipy.stats as stats\n",
    "from sklearn.model_selection import train_test_split\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report\n",
    "from sklearn import metrics\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "likely-montreal",
   "metadata": {},
   "source": [
    "## Loading the Processed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "handed-lightweight",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "minus-material",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>MinTemp</th>\n",
       "      <th>MaxTemp</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>WindGustDir</th>\n",
       "      <th>WindGustSpeed</th>\n",
       "      <th>WindDir9am</th>\n",
       "      <th>...</th>\n",
       "      <th>Pressure9am</th>\n",
       "      <th>Pressure3pm</th>\n",
       "      <th>Cloud9am</th>\n",
       "      <th>Cloud3pm</th>\n",
       "      <th>Temp9am</th>\n",
       "      <th>Temp3pm</th>\n",
       "      <th>RainToday</th>\n",
       "      <th>RainTomorrow</th>\n",
       "      <th>Date_month</th>\n",
       "      <th>Date_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12-01</td>\n",
       "      <td>30</td>\n",
       "      <td>13.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.7</td>\n",
       "      <td>1007.1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>21.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12-02</td>\n",
       "      <td>30</td>\n",
       "      <td>7.4</td>\n",
       "      <td>25.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>1007.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12-03</td>\n",
       "      <td>30</td>\n",
       "      <td>12.9</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1007.6</td>\n",
       "      <td>1008.7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12-04</td>\n",
       "      <td>30</td>\n",
       "      <td>9.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.6</td>\n",
       "      <td>8.9</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1017.6</td>\n",
       "      <td>1012.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.1</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12-05</td>\n",
       "      <td>30</td>\n",
       "      <td>17.5</td>\n",
       "      <td>32.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1010.8</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>29.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \\\n",
       "0  2008-12-01        30     13.4     22.9       0.6          2.4       8.3   \n",
       "1  2008-12-02        30      7.4     25.1       0.0          3.6      10.0   \n",
       "2  2008-12-03        30     12.9     25.7       0.0          2.6       4.4   \n",
       "3  2008-12-04        30      9.2     28.0       0.0         14.6       8.9   \n",
       "4  2008-12-05        30     17.5     32.3       1.0          5.4       3.0   \n",
       "\n",
       "   WindGustDir  WindGustSpeed  WindDir9am  ...  Pressure9am  Pressure3pm  \\\n",
       "0          4.0           44.0         5.0  ...       1007.7       1007.1   \n",
       "1          2.0           44.0         0.0  ...       1010.6       1007.8   \n",
       "2          5.0           46.0         5.0  ...       1007.6       1008.7   \n",
       "3         11.0           24.0        13.0  ...       1017.6       1012.8   \n",
       "4          4.0           41.0        12.0  ...       1010.8       1006.0   \n",
       "\n",
       "   Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  RainTomorrow  Date_month  \\\n",
       "0       8.0       7.0     16.9     21.8          0             0          12   \n",
       "1       7.0       7.0     17.2     24.3          0             0          12   \n",
       "2       7.0       2.0     21.0     23.2          0             0          12   \n",
       "3       7.0       7.0     18.1     26.5          0             0          12   \n",
       "4       7.0       8.0     17.8     29.7          0             0          12   \n",
       "\n",
       "   Date_day  \n",
       "0         1  \n",
       "1         2  \n",
       "2         3  \n",
       "3         4  \n",
       "4         5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "honey-restoration",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "copyrighted-contract",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.sample(n = 12000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "descending-choir",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 25)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cultural-clearing",
   "metadata": {},
   "source": [
    "## Dividing the dataset into Independent and Dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "genetic-philippines",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"RainTomorrow\", \"Date\"], axis=1)\n",
    "y = df[\"RainTomorrow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "matched-treat",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "living-calendar",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size =0.2, stratify = y, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "plastic-block",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26967     0\n",
       "38336     0\n",
       "72588     0\n",
       "23663     1\n",
       "80966     0\n",
       "         ..\n",
       "54768     1\n",
       "10575     0\n",
       "47064     0\n",
       "141958    0\n",
       "51312     0\n",
       "Name: RainTomorrow, Length: 9600, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distinct-consumer",
   "metadata": {},
   "source": [
    "## Handling imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "grand-sigma",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='RainTomorrow', ylabel='count'>"
      ]
     },
     "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": [
    "sns.countplot(df[\"RainTomorrow\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "interpreted-tongue",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 7494, 1: 2106})\n",
      "The number of classes after fit Counter({0: 7494, 1: 7494})\n"
     ]
    }
   ],
   "source": [
    "sm=SMOTE(random_state=0)\n",
    "X_train_res, y_train_res = sm.fit_resample(X_train, y_train)\n",
    "print(\"The number of classes before fit {}\".format(Counter(y_train)))\n",
    "print(\"The number of classes after fit {}\".format(Counter(y_train_res)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rocky-bahrain",
   "metadata": {},
   "source": [
    "### Xgboost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "beneficial-direction",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\xgboost\\sklearn.py:1146: 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": [
      "[16:13:28] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "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)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier()\n",
    "xgb.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "changed-female",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1738  136]\n",
      " [ 236  290]]\n",
      "0.845\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.93      0.90      1874\n",
      "           1       0.68      0.55      0.61       526\n",
      "\n",
      "    accuracy                           0.84      2400\n",
      "   macro avg       0.78      0.74      0.76      2400\n",
      "weighted avg       0.84      0.84      0.84      2400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = xgb.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print(accuracy_score(y_test,y_pred))\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "comparative-newman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7393793800292983"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/zUjGLnq2bqEXjksDZlZKai0RSGoj6URJP5G0a7ruSElPAzflee+9gPkR8XZE/Bu4DxhSzX4/B64BVtU9/MblRmIzK0X5qoZ+A5wKbAXcKOkeYBRwbUTsnufYrkDu0NWL0nWVJO0BbBcRf63tjSSdLmm2pNlLly7Nc9r6c7WQmZWifFVDZcDXI2KdpPbAP4DtI+LDhp5Y0kbA9cDwfPtGxBhgDEBZWVk09Ny1cbWQmZWafCWCf0fEOoCIWAW8XYcksBjYLme5W7quwmbArsB0SQuAfYBJzdVg7GohMytV+UoEO0mal74WsH26LCAi4uu1HDsL6C2pJ0kCOAE4qWJjRCwDtq5YljQd+ElEzK7zVTQCVwuZWanKlwh2ru8bR8RaSWcDU0lGKh0bEa9IGgnMjohJ9X3vQnG1kJmVonyDzjVooLmImAJMqbLu0hr2Pagh5zIzs/rJPHm9mZm1Tk4EZmYlLnMikNRB0o6FDKa5+IkhMytlmRKBpKOAucDf0uX+klpcY299+YkhMytlWUsEl5MMGfExQETMJZmboNXwE0NmVqqyJoI16XP/uQraw9fMzJpG1vkIXpF0EtBGUm/gHODpwoVlZmZNJWuJ4Ick8xWvBn5HMhz1jwsUk5mZNaGsJYKdIuJi4OJCBmNmZk0va4ngl5JelfTzinkJzMysdciUCCJiIMnMZEuB2yW9JOmSgkbWRNyHwMxKXeYOZRHxj4i4EfgBSZ+CascMKjbuQ2BmpS5rh7KdJV0u6SXg1yRPDHUraGRNyH0IzKyUZW0sHgtMBL4VEe8VMB4zM2timRJBROxb6EDMzKx51JoIJP0+Io5Pq4RyexJnmaHMzMyKQL4SwY/Sf48sdCBmZtY8am0sjogl6cszI+LvuT/AmYUPz8zMCi3r46OHVLPusMYMxMzMmke+NoIzSL7595I0L2fTZsBThQzMzMyaRr42gt8BDwL/C1yUs35FRBR9d9yKXsV799yyuUMxM2s2+RJBRMQCSWdV3SBpy2JPBu5VbGaWrURwJDCH5PFR5WwLoFeB4moy7lVsZqWu1kQQEUem/7aqaSnNzOwLWcca2l9Sx/T1yZKul+Sv0WZmrUDWx0dvBVZK6gecD7wF/LZgUZmZWZPJmgjWRkQAQ4CbIuJmkkdIzcysyGVNBCsk/RQ4BfirpI2AdoULq/A8IY2ZWSJrIhhGMnH9f0XEP0jmIriuYFE1AT86amaWyDpV5T+Ae4HNJR0JrIqIuwsaWRPwo6NmZtmfGjoeeA74D+B44FlJQzMcN1jS65LmS7qomu3nSSqXNE/So5K+VtcLMDOzhsk6Q9nFwJ4R8T6ApC7AI8AfajpAUhvgZpIB6xYBsyRNiojynN1eAMoiYmU6rtG1JNVQZmbWRLK2EWxUkQRSH2Y4di9gfkS8HRH/Bu4jeeqoUkRMi4iV6eJMWtE8yGZmxSJrieBvkqYCE9LlYcCUPMd0BRbmLC8C9q5l/xEkA9xtQNLpwOkA3bu7Tt/MrDFlnbP4AknHAv8vXTUmIh5orCAknQyUAQfWcP4xwBiAsrKyqG4fMzOrn3zzEfQGRgHbAy8BP4mIxRnfezGwXc5yt3Rd1XMcTNIGcWBErM743g3i4afNzL6Qr55/LDAZOI5kBNJf1+G9ZwG9JfWUtDFwAjApdwdJuwO3A0dXaYMoKPchMDP7Qr6qoc0i4o709euSns/6xhGxVtLZwFSgDTA2Il6RNBKYHRGTSDqldQLulwTwbkQcXeerqAf3ITAzS+RLBO3Tb+0V8xB0yF2OiFoTQ0RMoUqjckRcmvP64DpHbGZmjSpfIlgCXJ+z/I+c5QC+WYigzMys6eSbmGZgUwViZmbNI2uHMjMza6WcCMzMSlzJJQLPQ2Bmtr6so48qnav40nS5u6S9ChtaYbgPgZnZ+rKWCG4B9gVOTJdXkIwsWpTch8DM7AtZB53bOyL2kPQCQER8lPYWNjOzIpe1RLAmnV8goHI+gnUFi8rMzJpM1kRwI/AAsI2kXwBPAlcVLCozM2syWYehvlfSHGAQyfAS346IVwsamZmZNYmsTw11B1YCfyEZQfTTdF1R8aOjZmYbytpY/FeS9gEB7YGewOvALgWKqyD86KiZ2YayVg3tlrssaQ/gzIJEVGB+dNTMbH316lmcDj9d2/zDZmZWJDKVCCSdl7O4EbAH8F5BIjIzsyaVtY1gs5zXa0naDP7Y+OGYmVlTy5sI0o5km0XET5ogHjMza2K1thFIahsRnwP7N1E8ZmbWxPKVCJ4jaQ+YK2kScD/wacXGiPi/AsZmZmZNIGsbQXvgQ5I5iiv6EwTgRGBmVuTyJYJt0ieGXuaLBFAhChaVWQuzZs0aFi1axKpVq5o7FLNatW/fnm7dutGuXbvMx+RLBG2ATqyfACoUVSKoGF5i755bNncoVoQWLVrEZpttRo8ePZCq++9g1vwigg8//JBFixbRs2fPzMflSwRLImJkw0JrGTy8hDXEqlWrnASsxZPEVlttxdKlS+t0XL6exa3qr97DS1hDOAlYMajP32m+RDCofqGYmVmxqDURRITHbDZrARYuXEjPnj3517+S/5IfffQRPXv2ZMGCBQC8+eabHHnkkWy//fYMGDCAgQMHMmPGDADGjx9Ply5d6N+/P7vssgtDhw5l5cqVle89atQodtppJ/r378+ee+7J3XffDcBBBx3E7NmzGyX+2bNnc8455wCwevVqDj74YPr378/EiRM59dRTKS8vb9D7jx49ujJugLVr19KlSxcuuuii9fbr0aMHH3zwQeXy9OnTOfLIIyuXH3zwQcrKyujbty+77747559/foPiApgzZw677bYbO+ywA+eccw4RGzavLlu2jKOOOop+/fqxyy67MG7cuMpt7777Loceeig777wzffv2rfydn3DCCbz55psNjg9IGheK6WfAgAFRH8ff9nQcf9vT9TrWrLy8vLlDiGuuuSZOO+20iIg4/fTT46qrroqIiM8++yx69+4df/7znyv3femll2LcuHERETFu3Lg466yzKredeOKJMXbs2IiIuPXWW+PQQw+NZcuWRUTEsmXLYvz48RERceCBB8asWbMa/TqeeeaZGDRoUL2PX7t27XrLa9asid122y3WrFlTuW7KlCmx3377Ra9evWLdunWV67/2ta/F0qVLK5enTZsWRxxxREQkn1mvXr3i1VdfrTzPLbfcUu84K+y5557xzDPPxLp162Lw4MExZcqUDfb5xS9+ERdeeGFERLz//vvxpS99KVavXh0Rye/hoYceioiIFStWxKeffhoREdOnT49TTz212nNW9/cKzI4a7qtZ+xGYWeqKv7xC+XvLG/U9+361M5cdVfv0Hueeey4DBgxg9OjRPPnkk9x0000A3Hvvvey7774cffTRlfvuuuuu7Lrrrhu8x9q1a/n000/50pe+BMBVV13F9OnT6dy5MwCdO3fmu9/97gbHnXHGGcyaNYvPPvuMoUOHcsUVVwBw0UUXMWnSJNq2bcuhhx7KqFGjuP/++7niiito06YNm2++OTNmzGD69OmMGjWKsWPHcvLJJ7N06VL69+/PH//4R0aMGMGoUaMoKyvjoYce4rLLLmP16tVsv/32jBs3jk6dOtGjRw+GDRvGww8/zIUXXsgJJ5xQGdtjjz3GHnvsQdu2X9zOJkyYwI9+9CNuvfVWnnnmGfbbb7+8v4Nrr72Wiy++mJ122gmANm3acMYZZ+Q9rjZLlixh+fLl7LPPPgB85zvf4U9/+hOHHXbYevtJYsWKFUQEn3zyCVtuuSVt27alvLyctWvXcsghhwDQqVOnymMOOOAAhg8fztq1a9e79vpwIjArEu3ateO6665j8ODBPPTQQ5XPib/yyivssccetR47ceJEnnzySZYsWUKfPn046qijWL58OStWrKBXr155z/2LX/yCLbfcks8//5xBgwYxb948unbtygMPPMBrr72GJD7++GMARo4cydSpU+natWvlugrbbLMNd955J6NGjWLy5Mnrbfvggw+48soreeSRR+jYsSPXXHMN119/PZdeeikAW221Fc8///wGsT311FMMGDCgcnnVqlU88sgj3H777Xz88cdMmDAhUyJ4+eWXM1UFTZs2jXPPPXeD9ZtuuilPP/30eusWL15Mt27dKpe7devG4sWLNzj27LPP5uijj+arX/0qK1asYOLEiWy00Ua88cYbbLHFFhx77LG88847HHzwwVx99dW0adOGjTbaiB122IEXX3xxveuvDycCszrK9829kB588EG23XZbXn755cpviVUdc8wxvPnmm/Tp04f/+7+k8/+wYcO46aabiAjOOussrrvuOs48M/vcUr///e8ZM2YMa9euZcmSJZSXl9O3b1/at2/PiBEjOPLIIyvr2vfff3+GDx/O8ccfz7HHHpv5HDNnzqS8vJz990+GNvv3v//NvvvuW7l92LBh1R63ZMkSdt5558rlyZMnM3DgQDp06MBxxx3Hz3/+c0aPHk2bNm2qfaKmrk/ZDBw4kLlz59bpmHymTp1K//79eeyxx3jrrbc45JBDOOCAA1i7di1PPPEEL7zwAt27d2fYsGGMHz+eESNGAElife+99xqcCOo1MU1WkgZLel3SfEkXVbN9E0kT0+3PSupRyHjMitncuXN5+OGHmTlzJjfccANLliwBYJdddlnvm/IDDzzA+PHjKxuWc0niqKOOYsaMGXTu3JlOnTrx9ttv13red955h1GjRvHoo48yb948jjjiCFatWkXbtm157rnnGDp0KJMnT2bw4MEA3HbbbVx55ZUsXLiQAQMG8OGHH2a6vojgkEMOYe7cucydO5fy8nJ+85vfVG7v2LFjtcd16NBhvR7fEyZM4JFHHqFHjx6V53/ssceApFTx0UcfVe77r3/9i6233hpIPsc5c+bkjXPatGn0799/g5/qSh1du3Zl0aJFlcuLFi2ia9cN+zKNGzeOY489FknssMMO9OzZk9dee41u3brRv39/evXqRdu2bfn2t7+93u961apVdOjQIW/M+RQsEaTDV98MHAb0BU6U1LfKbiOAjyJiB+AG4JpCxWNWzCKCM844g9GjR9O9e3cuuOACfvKTZGT4k046iaeeeopJkyZV7p/7VFBVTz75JNtvvz0AP/3pTznrrLNYvjxp8/jkk0/We/oGYPny5XTs2JHNN9+cf/7znzz44IOV+y5btozDDz+cG264gRdffBGAt956i7333puRI0fSpUsXFi5cmOka99lnH5566inmz58PwKeffsobb7yR97idd9658pjly5fzxBNP8O6777JgwQIWLFjAzTffzIQJE4DkSajf/va3AHz++efcc889DBw4EIALLriAq666qvKc69at47bbbtvgfBUlgqo/VauFALbddls6d+7MzJkziQjuvvtuhgwZssF+3bt359FHHwXgn//8J6+//jq9evVizz335OOPP67sIPbYY4/Rt+8Xt9E33nij2raguipk1dBewPyIeBtA0n3AECD3ObEhwOXp6z8AN0lS2sJtZqk77riD7t27V1YHnXnmmYwbN47HH3+cAw88kMmTJ3Peeefx4x//mC9/+ctsttlmXHLJJZXHV7QRrFu3jm7dujF+/HggaQT+5JNP2HPPPWnXrh3t2rXboJ68X79+7L777uy0005st912lVU3K1asYMiQIaxatYqI4PrrrweSG+qbb75JRDBo0CD69evH448/nvcau3Tpwvjx4znxxBNZvXo1AFdeeSV9+vSp9bjDDjuMU045BUhKQ9/85jfZZJNNKrcPGTKECy+8kNWrV/Ozn/2MM844g379+hERDB48mJNPPhmAr3/964wePZoTTzyRlStXImm9R0vr65ZbbmH48OF89tlnHHbYYZUNxRVJ5gc/+AE/+9nPGD58OLvtthsRwTXXXFNZUhk1ahSDBg2qeGqS0047DUgSRocOHfjKV77S4BhVqHuupKHA4Ig4NV0+Bdg7Is7O2efldJ9F6fJb6T4fVHmv04HTAbp37z7g73//e53jueIvrwDNW79rxevVV19drx7aWpZjjjmGa6+9lt69ezd3KE3mhhtuoHPnzpXtBbmq+3uVNCciyqp7r6JoLI6IMcAYgLKysnplLicAs9br6quvZsmSJSWVCLbYYovKklBDFTIRLAa2y1nulq6rbp9FktoCm5PMe2BmltmOO+7Ijjvu2NxhNKnvfe97jfZehXxqaBbQW1JPSRsDJwCTquwzCajovTIUeMztA9ZS+U/TikF9/k4LlggiYi1wNjAVeBX4fUS8ImmkpIoukL8BtpI0HzgP2OARU7OWoH379nz44YdOBtaiRTofQfv27et0XMEaiwulrKwsGmsgLLOsPEOZFYuaZigr+sZis+bWrl27Os34ZFZMCtqz2MzMWj4nAjOzEudEYGZW4oqusVjSUqDuXYsTWwMf5N2rdfE1lwZfc2loyDV/LSK6VLeh6BJBQ0iaXVOreWvlay4NvubSUKhrdtWQmVmJcyIwMytxpZYIxjR3AM3A11wafM2loSDXXFJtBGZmtqFSKxGYmVkVTgRmZiWuVSYCSYMlvS5pvqQNRjSVtImkien2ZyX1aIYwG1WGaz5PUrmkeZIelfS15oizMeW75pz9jpMUkor+UcMs1yzp+PR3/Yqk3zV1jI0tw992d0nTJL2Q/n0f3hxxNhZJYyW9n87gWN12Sbox/TzmSdqjwSeNiFb1A7QB3gJ6ARsDLwJ9q+xzJnBb+voEYGJzx90E1zwQ2DR9fUYpXHO632bADGAmUNbccTfB77k38ALwpXR5m+aOuwmueQxwRvq6L7CgueNu4DV/A9gDeLmG7YcDDwIC9gGebeg5W2OJYC9gfkS8HRH/Bu4DhlTZZwhwV/r6D8AgSWrCGBtb3muOiGkRsTJdnEkyY1wxy/J7Bvg5cA3QGsaPznLNpwE3R8RHABHxfhPH2NiyXHMAndPXmwPvNWF8jS4iZgD/qmWXIcDdkZgJbCFp24acszUmgq7AwpzlRem6aveJZAKdZcBWTRJdYWS55lwjSL5RFLO815wWmbeLiL82ZWAFlOX33AfoI+kpSTMlDW6y6AojyzVfDpwsaREwBfhh04TWbOr6/z0vz0dQYiSdDJQBBzZ3LIUkaSPgemB4M4fS1NqSVA8dRFLqmyFpt4j4uDmDKrATgfER8UtJ+wK/lbRrRKxr7sCKRWssESwGtstZ7pauq3YfSW1JipMfNkl0hZHlmpF0MHAxcHRErG6i2Aol3zVvBuwKTJe0gKQudVKRNxhn+T0vAiZFxJqIeAd4gyQxFKss1zwC+D1ARDwDtCcZnK21yvT/vS5aYyKYBfSW1FPSxiSNwZOq7DMJ+G76eijwWKStMEUq7zVL2h24nSQJFHu9MeS55ohYFhFbR0SPiOhB0i5ydEQU8zynWf62/0RSGkDS1iRVRW83YYyNLcs1vwsMApC0M0kiWNqkUTatScB30qeH9gGWRcSShrxhq6saioi1ks4GppI8cTA2Il6RNBKYHRGTgN+QFB/nkzTKnNB8ETdcxmu+DugE3J+2i78bEUc3W9ANlPGaW5WM1zwVOFRSOfA5cEFEFG1pN+M1nw/cIelckobj4cX8xU7SBJJkvnXa7nEZ0A4gIm4jaQc5HJgPrAS+1+BzFvHnZWZmjaA1Vg2ZmVkdOBGYmZU4JwIzsxLnRGBmVuKcCMzMSpwTQQmQ9LmkuTk/PWrZ95NGON94Se+k53o+7e1Z1/e4U1Lf9PX/VNn2dENjTN+n4nN5WdJfJG2RZ//+9RnZUtK2kianrw+StCw976uSLqvH+x1dMQqnpG9XfE7p8si042CDpL/DoXn2mV6XDnrptU/OsF+1o29KGiXpm1nPZ9k5EZSGzyKif87PgiY45wUR0R+4iKQjW51ExKkRUZ4u/k+Vbfs1PDzgi89lV5L+JGfl2b8/yfPbdXUecEfO8hPpZ1NGMkZOnYYRjohJEXF1uvhtkhE3K7ZdGhGP1CPGlmQ8UN0YSb8m+XuyRuZEUIIkdVIyJ8Hzkl6StMGonem32Bk535gPSNcfKumZ9Nj7JXXKc7oZwA7pseel7/WypB+n6zpK+qukF9P1w9L10yWVSboa6JDGcW+67ZP03/skHZET83hJQyW1kXSdpFlKxmv/foaP5RnSgbsk7ZVe4wuSnpa0Y9qrdSQwLI1lWBr7WEnPpftWN/opwHHA36qujIhPgTnADmlpY2Ya7wOSvpTGco6+mEfivnTdcEk3SdoPOBq4Lo1p+5zPYLCk+3M+m8pv43X9HUq6NP0sX5Y0RlpvpN5Tcv5G9kr3z/q5VKum0Tcj4u/AVpK+Upf3swyaY7xt/zTtD0kP07npzwMkPco7p9u2JumhWNG58JP03/OBi9PXbUjG7tma5MbeMV3/38Cl1ZxvPDA0ff0fwLPAAOAloCNJD+dXgN1JbpJ35By7efrvdNL5AypiytmnIsZjgLvS1xuTjMjYATgduCRdvwkwG+hZTZyf5Fzf/cDgdLkz0DZ9fTDwx/T1cOCmnOOvAk5OX29BMq5Pxyrn6AnMyVk+CJicvt4KWADsAswDDkzXjwRGp6/fAzapOEfVOHI/69zl9Hf8bs7v6lbg5Hr+DrfMWf9b4Kic39Ed6etvkI6fX9PnUuXay4A7a/mb7UE14/GTlKyOa+7/U63tp9UNMWHV+iySqggAJLUDrpL0DWAdyTfhLwP/yDlmFjA23fdPETFX0oEk1RBPpV8KNyb5Jl2d6yRdQjLmywiSsWAeiORbMJL+DziA5JvyLyVdQ3KTeKIO1/Ug8CtJm5BUJcyIiM8kHQp8PaeOe3OSgdfeqXJ8B0lz0+t/FXg4Z/+7JPUmGbKgXQ3nPxQ4WtJP0uX2QPf0vSpsy4bj3hwg6QWSz/5qkoHitoiIx9Ptd5EkJkgSxL2S/kQyjlAmkQzN8DfgKEl/AI4ALiQZdTbr77DCQEkXApsCW5Ik8b+k2yak55shqbOSdpaaPpfc+GYDp2a9nhzvA1+tx3FWCyeC0vSfQBdgQESsUTI6Z/vcHdL/2N8guYGMl3Q98BHwcEScmOEcF0TEHyoWJA2qbqeIeCOtIz8cuFLSoxExMstFRMQqSdOBbwHDSCYtgWTmph9GxNQ8b/FZRPSXtCnJWDZnATeSTGYzLSKOUdKwPr2G40Xy7fT12s5Blc+WpI3gyMo3kTav5fgjSL5tHwVcLGm3Wvat6j7gbJJqltkRsSKt1sn6O0RSe+AWktLZQkmXs/71VB2jJqjhc5H05TrEXpP2JJ+pNSK3EZSmzYH30yQwENhg/mIlcxr/MyLuAO4kmTpvJrC/pIo6/46S+mQ85xPAtyVtKqkjSbXOE5K+CqyMiHtIBsarruF0TVoyqc5EkkG3KkoXkNzUz6g4RlKf9JzVimTmtnOA8/XFsOQVw/oOz9l1BUkVWYWpwA8r6syVjPBa1Rsk1Rw1iohlwEdK22GAU4DHlcypsF1ETCOpwtmcpFotV9WYcj1O8nmexhdJsq6/w4qb/gdpW0LVJ4kq2nT+H8komMvI9rnUVx+g2rl8rf6cCErTvUCZpJeA7wCvVbPPQcCLaRXGMOBXEbGU5MY4QdI8kiqFnbKcMCKeJ6l3fo6kzeDOiHgB2A14Lq2iuQy4sprDxwDzlDYWV/EQSXXHI5FMZQhJ4ioHnlfyCOLt5Cn9prHMI5nk5Frgf9Nrzz1uGtC3orGYpOTQLo3tlXS56vt+CrxVceOtxXdJqtPmkTydNJKk7eKe9Pf0AnBjbDjBzH3ABWmj7PZVzv05MBk4LP2Xuv4O0/PdQXLznUpSZZhrVfo53UZSBQgZPhclDwLcWd05lYy++Qywo6RFkkak69uRPHhQzEOJt0gefdSswCQdQ1INd0lzx1LM0s9xj4j4WXPH0tq4jcCswCLiAUnFPCd2S9EW+GVzB9EauURgZlbi3EZgZlbinAjMzEqcE4GZWYlzIjAzK3FOBGZmJe7/A27SUhN0iH9CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(xgb, X_test, y_test)\n",
    "metrics.roc_auc_score(y_test, y_pred, average=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ready-onion",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "corporate-confusion",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomized Search CV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1200, num = 12)]\n",
    "# Various learning rate parameters\n",
    "learning_rate = ['0.05','0.1', '0.2','0.3','0.5','0.6']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(5, 30, num = 6)]\n",
    "# max_depth.append(None)\n",
    "#Subssample parameter values\n",
    "subsample=[0.7,0.6,0.8]\n",
    "# Minimum child weight parameters\n",
    "min_child_weight=[3,4,5,6,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "polar-interim",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200], 'learning_rate': ['0.05', '0.1', '0.2', '0.3', '0.5', '0.6'], 'max_depth': [5, 10, 15, 20, 25, 30], 'subsample': [0.7, 0.6, 0.8], 'min_child_weight': [3, 4, 5, 6, 7]}\n"
     ]
    }
   ],
   "source": [
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'learning_rate': learning_rate,\n",
    "               'max_depth': max_depth,\n",
    "               'subsample': subsample,\n",
    "               'min_child_weight': min_child_weight}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "related-italy",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "classifier = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "brazilian-mechanics",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations\n",
    "xg_random = RandomizedSearchCV(estimator = classifier, param_distributions = random_grid, n_iter = 25, cv = 3, verbose=2, random_state=100, n_jobs = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "hawaiian-worthy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\xgboost\\sklearn.py:1146: 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": [
      "[16:36:57] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: 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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3,\n",
       "                   estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                           colsample_bylevel=None,\n",
       "                                           colsample_bynode=None,\n",
       "                                           colsample_bytree=None, gamma=None,\n",
       "                                           gpu_id=None, importance_type='gain',\n",
       "                                           interaction_constraints=None,\n",
       "                                           learning_rate=None,\n",
       "                                           max_delta_step=None, max_depth=None,\n",
       "                                           min_child_weight=None, missing=nan,\n",
       "                                           monotone_constraints=None,\n",
       "                                           n_estimators=100,...\n",
       "                                           subsample=None, tree_method=None,\n",
       "                                           validate_parameters=None,\n",
       "                                           verbosity=None),\n",
       "                   n_iter=25, n_jobs=-1,\n",
       "                   param_distributions={'learning_rate': ['0.05', '0.1', '0.2',\n",
       "                                                          '0.3', '0.5', '0.6'],\n",
       "                                        'max_depth': [5, 10, 15, 20, 25, 30],\n",
       "                                        'min_child_weight': [3, 4, 5, 6, 7],\n",
       "                                        'n_estimators': [100, 200, 300, 400,\n",
       "                                                         500, 600, 700, 800,\n",
       "                                                         900, 1000, 1100,\n",
       "                                                         1200],\n",
       "                                        'subsample': [0.7, 0.6, 0.8]},\n",
       "                   random_state=100, verbose=2)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_random.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "paperback-seventh",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'subsample': 0.6,\n",
       " 'n_estimators': 400,\n",
       " 'min_child_weight': 5,\n",
       " 'max_depth': 25,\n",
       " 'learning_rate': '0.05'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "scheduled-couple",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8415398985855352"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_random.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "permanent-dividend",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1745  129]\n",
      " [ 250  276]]\n",
      "Accuracy score 0.8420833333333333\n",
      "Classification report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.93      0.90      1874\n",
      "           1       0.68      0.52      0.59       526\n",
      "\n",
      "    accuracy                           0.84      2400\n",
      "   macro avg       0.78      0.73      0.75      2400\n",
      "weighted avg       0.83      0.84      0.83      2400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_predict = xg_random.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_predict))\n",
    "print('Accuracy score {}'.format(accuracy_score(y_test,y_predict)))\n",
    "print('Classification report {}'.format(classification_report(y_test,y_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "supreme-centre",
   "metadata": {},
   "source": [
    "## Saving the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "consistent-activity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['xg_random.pkl']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(xgb, \"xg_random.pkl\")"
   ]
  }
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