SVC  Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "sticky-karaoke",
   "metadata": {},
   "source": [
    "# SVC "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "boxed-reply",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "alike-fourth",
   "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.svm import SVC\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intimate-passing",
   "metadata": {},
   "source": [
    "## Loading the Processed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "advanced-pierre",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"preprocessed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "incorporate-dealer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    .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": "exotic-arrangement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(145460, 25)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "quick-twins",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=data.sample(n=12000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "administrative-episode",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12000, 25)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wireless-certification",
   "metadata": {},
   "source": [
    "## Dividing the dataset into Independent and Dependent features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "chubby-belgium",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop([\"RainTomorrow\", \"Date\"], axis=1)\n",
    "y = df[\"RainTomorrow\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "failing-given",
   "metadata": {},
   "source": [
    "### Train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "certified-cover",
   "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": 20,
   "id": "racial-strap",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48101     0\n",
       "57033     0\n",
       "110437    0\n",
       "126312    1\n",
       "5265      0\n",
       "         ..\n",
       "112622    1\n",
       "4127      0\n",
       "49573     0\n",
       "16551     0\n",
       "111153    0\n",
       "Name: RainTomorrow, Length: 9600, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quantitative-government",
   "metadata": {},
   "source": [
    "## Handling imbalanced dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "boolean-huntington",
   "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": 21,
     "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": 22,
   "id": "polar-bowling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of classes before fit Counter({0: 7542, 1: 2058})\n",
      "The number of classes after fit Counter({0: 7542, 1: 7542})\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": "failing-luxembourg",
   "metadata": {},
   "source": [
    "## SVC "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "uniform-madison",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train_res, y_train_res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "handmade-status",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1417  469]\n",
      " [ 135  379]]\n",
      "0.7483333333333333\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.75      0.82      1886\n",
      "           1       0.45      0.74      0.56       514\n",
      "\n",
      "    accuracy                           0.75      2400\n",
      "   macro avg       0.68      0.74      0.69      2400\n",
      "weighted avg       0.81      0.75      0.77      2400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred5 = svc.predict(X_test)\n",
    "print(confusion_matrix(y_test,y_pred5))\n",
    "print(accuracy_score(y_test,y_pred5))\n",
    "print(classification_report(y_test,y_pred5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "higher-principle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7443398211684705"
      ]
     },
     "execution_count": 25,
     "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(svc, X_test, y_test)\n",
    "metrics.roc_auc_score(y_test, y_pred5, average=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "capital-metadata",
   "metadata": {},
   "source": [
    "## Saving the model to reuse it again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "entertaining-congo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svc.pkl']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(svc, \"svc.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
}