Logistic Regression Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "needed-channel",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "north-australian",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "alleged-differential",
   "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.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "familiar-friday",
   "metadata": {},
   "source": [
    "## Loading the Processed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "assumed-square",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "enabling-bahrain",
   "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": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aquatic-stockholm",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "julian-stupid",
   "metadata": {},
   "source": [
    "## Dividing the dataset into Independent and Dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "relative-accused",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"RainTomorrow\", \"Date\"], axis=1)\n",
    "y = df[\"RainTomorrow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "roman-archives",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "approved-coating",
   "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": 7,
   "id": "alternate-spencer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43447     0\n",
       "140904    0\n",
       "54615     0\n",
       "109793    0\n",
       "74462     0\n",
       "         ..\n",
       "106567    0\n",
       "52791     1\n",
       "56339     0\n",
       "82299     1\n",
       "115175    0\n",
       "Name: RainTomorrow, Length: 116368, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjusted-exchange",
   "metadata": {},
   "source": [
    "## Handling imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "finnish-horizontal",
   "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": 8,
     "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": 9,
   "id": "optional-percentage",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 90866, 1: 25502})\n",
      "The number of classes after fit Counter({0: 90866, 1: 90866})\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": "thrown-study",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "czech-baking",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SWARNAVA\\anaconda3\\envs\\rain\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "specialized-incident",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17526  5191]\n",
      " [ 1522  4853]]\n",
      "0.7692492781520693\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.77      0.84     22717\n",
      "           1       0.48      0.76      0.59      6375\n",
      "\n",
      "    accuracy                           0.77     29092\n",
      "   macro avg       0.70      0.77      0.72     29092\n",
      "weighted avg       0.82      0.77      0.78     29092\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred2 = logreg.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred2))\n",
    "print(accuracy_score(y_test,y_pred2))\n",
    "print(classification_report(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "forward-treasury",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.766373808333916"
      ]
     },
     "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": [
    "metrics.plot_roc_curve(logreg, X_test, y_test)\n",
    "metrics.roc_auc_score(y_test, y_pred2, average=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "other-ivory",
   "metadata": {},
   "source": [
    "## Saving the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "polished-vector",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LogisticRegression.pkl']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(logreg,\"LogisticRegression.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
}