Random Forest model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "coordinated-portuguese",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occupied-hollow",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "directed-status",
   "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 sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tested-right",
   "metadata": {},
   "source": [
    "## Loading the Processed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "assured-defense",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "velvet-flexibility",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<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": "exceptional-coverage",
   "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": "blind-services",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data.sample(n=12000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "pleased-penny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 25)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "protecting-concern",
   "metadata": {},
   "source": [
    "## Dividing the dataset into Independent and Dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "lesbian-absence",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"RainTomorrow\", \"Date\"], axis=1)\n",
    "y = df[\"RainTomorrow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equivalent-technician",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "inner-program",
   "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": "average-tumor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53731     0\n",
       "100936    0\n",
       "63911     0\n",
       "12632     0\n",
       "117171    1\n",
       "         ..\n",
       "21958     1\n",
       "126913    0\n",
       "13640     0\n",
       "110460    0\n",
       "29537     0\n",
       "Name: RainTomorrow, Length: 9600, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "static-lender",
   "metadata": {},
   "source": [
    "## Handling imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "minus-passport",
   "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": "anonymous-shopping",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 7587, 1: 2013})\n",
      "The number of classes after fit Counter({0: 7587, 1: 7587})\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": "talented-serve",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "incorporate-armor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier()\n",
    "rf.fit(X_train_res,y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "outside-motor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1706  191]\n",
      " [ 221  282]]\n",
      "0.8283333333333334\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.90      0.89      1897\n",
      "           1       0.60      0.56      0.58       503\n",
      "\n",
      "    accuracy                           0.83      2400\n",
      "   macro avg       0.74      0.73      0.74      2400\n",
      "weighted avg       0.82      0.83      0.83      2400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred1 = rf.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred1))\n",
    "print(accuracy_score(y_test,y_pred1))\n",
    "print(classification_report(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "grave-booking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7299754451676865"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(rf, X_test, y_test)\n",
    "metrics.roc_auc_score(y_test, y_pred1, average=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "liable-capitol",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "revolutionary-scroll",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth': [10, 120, 230, 340, 450, 560, 670, 780, 890, 1000], 'min_samples_split': [2, 5, 10, 14], 'min_samples_leaf': [1, 2, 4, 6, 8], 'criterion': ['entropy', 'gini']}\n"
     ]
    }
   ],
   "source": [
    "# RandomizedSearchCV\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators=[int(x) for x in np.linspace(start=200,stop=2000,num=10)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features=['auto','sqrt', 'log2']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth=[int(x) for x in np.linspace(10,1000,10)]\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split=[2,5,10,14]\n",
    "\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf=[1,2,4,6,8]\n",
    "\n",
    "# Create the random grid\n",
    "random_grid={'n_estimators':n_estimators,\n",
    "            'max_features':max_features,\n",
    "             'max_depth':max_depth,\n",
    "             'min_samples_split':min_samples_split,\n",
    "             'min_samples_leaf':min_samples_leaf,\n",
    "             'criterion':['entropy','gini']}\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "japanese-gabriel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestClassifier(), n_iter=25,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['entropy', 'gini'],\n",
       "                                        'max_depth': [10, 120, 230, 340, 450,\n",
       "                                                      560, 670, 780, 890,\n",
       "                                                      1000],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': [1, 2, 4, 6, 8],\n",
       "                                        'min_samples_split': [2, 5, 10, 14],\n",
       "                                        'n_estimators': [200, 400, 600, 800,\n",
       "                                                         1000, 1200, 1400, 1600,\n",
       "                                                         1800, 2000]},\n",
       "                   random_state=100, verbose=2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier()\n",
    "rf_randomCV=RandomizedSearchCV(estimator=rf,param_distributions=random_grid,n_iter=25,cv=3,verbose=2,random_state=100,n_jobs=-1)\n",
    "\n",
    "### fit the randomized model\n",
    "rf_randomCV.fit(X_train_res,y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "activated-january",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 600,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 120,\n",
       " 'criterion': 'entropy'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_randomCV.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "buried-marine",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_random_grid=rf_randomCV.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "published-country",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1690  207]\n",
      " [ 213  290]]\n",
      "Accuracy score 0.825\n",
      "Classification report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.89      0.89      1897\n",
      "           1       0.58      0.58      0.58       503\n",
      "\n",
      "    accuracy                           0.82      2400\n",
      "   macro avg       0.74      0.73      0.73      2400\n",
      "weighted avg       0.82      0.82      0.82      2400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "y_pred = best_random_grid.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred))\n",
    "print('Accuracy score {}'.format(accuracy_score(y_test,y_pred)))\n",
    "print('Classification report {}'.format(classification_report(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "happy-insertion",
   "metadata": {},
   "source": [
    "## Saving the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "final-corrections",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rf.pkl']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(rf_randomCV, \"rf.pkl\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}