KNeighbors Classifier Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "structured-absorption",
   "metadata": {},
   "source": [
    "# KNeighbors Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "geological-venue",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "invisible-trigger",
   "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.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "graphic-canyon",
   "metadata": {},
   "source": [
    "## Loading the Processed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "detailed-microwave",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "another-cancer",
   "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": "selective-significance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amber-subsection",
   "metadata": {},
   "source": [
    "## Dividing the dataset into Independent and Dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "mediterranean-chile",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"RainTomorrow\", \"Date\"], axis=1)\n",
    "y = df[\"RainTomorrow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nonprofit-tuning",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "filled-physiology",
   "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": "driven-reliance",
   "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": "handy-consent",
   "metadata": {},
   "source": [
    "## Handling imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "separated-envelope",
   "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": "resident-sussex",
   "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": "shared-rates",
   "metadata": {},
   "source": [
    "## KNeighbors Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "alleged-clear",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "adaptive-chemical",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[17409  5308]\n",
      " [ 1808  4567]]\n",
      "0.7553966726247766\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.77      0.83     22717\n",
      "           1       0.46      0.72      0.56      6375\n",
      "\n",
      "    accuracy                           0.76     29092\n",
      "   macro avg       0.68      0.74      0.70     29092\n",
      "weighted avg       0.81      0.76      0.77     29092\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred4 = knn.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred4))\n",
    "print(accuracy_score(y_test,y_pred4))\n",
    "print(classification_report(y_test,y_pred4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "constitutional-haven",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.74136727181078"
      ]
     },
     "execution_count": 18,
     "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(knn, X_test, y_test)\n",
    "metrics.roc_auc_score(y_test, y_pred4, average=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rough-greece",
   "metadata": {},
   "source": [
    "## Saving the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "conscious-africa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn.pkl']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(knn, \"knn.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
}