Logistic Regression Bank.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libabry\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read file\n",
    "election_data = pd.read_csv(\"~/Downloads/Data Science/data set/election_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Election-id</th>\n",
       "      <th>Result</th>\n",
       "      <th>Year</th>\n",
       "      <th>Amount Spent</th>\n",
       "      <th>Popularity Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>1</td>\n",
       "      <td>48</td>\n",
       "      <td>6.32</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>3.67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>965</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>2.93</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>410</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Election-id  Result  Year  Amount Spent  Popularity Rank\n",
       "0          122       0    32          3.81                3\n",
       "1          315       1    48          6.32                2\n",
       "2          201       1    51          3.67                1\n",
       "3          965       0    40          2.93                4\n",
       "4          410       1    52          3.60                1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read to 5 data\n",
    "election_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Election-id      10 non-null     int64  \n",
      " 1   Result           10 non-null     int64  \n",
      " 2   Year             10 non-null     int64  \n",
      " 3   Amount Spent     10 non-null     float64\n",
      " 4   Popularity Rank  10 non-null     int64  \n",
      "dtypes: float64(1), int64(4)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "election_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Election-id        0\n",
       "Result             0\n",
       "Year               0\n",
       "Amount Spent       0\n",
       "Popularity Rank    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null value check\n",
    "election_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#set depend and independent variable\n",
    "y = election_data.Result\n",
    "election_data.drop(['Result'], axis=1, inplace=True)\n",
    "x = election_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "5    0\n",
       "6    1\n",
       "7    1\n",
       "8    1\n",
       "9    0\n",
       "Name: Result, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create Logistic Regression model\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict on test data\n",
    "predictions = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         1\n",
      "           1       1.00      1.00      1.00         1\n",
      "\n",
      "    accuracy                           1.00         2\n",
      "   macro avg       1.00      1.00      1.00         2\n",
      "weighted avg       1.00      1.00      1.00         2\n",
      "\n",
      "[[1 0]\n",
      " [0 1]]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "#cnfusion matrix, accurarcy\n",
    "\n",
    "print(classification_report(y_test, predictions))\n",
    "print(confusion_matrix(y_test, predictions))\n",
    "print(accuracy_score(y_test, predictions))"
   ]
  }
 ],
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