Logistic Regression Code.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "In this Machine Learning Model we are going to predict a binary outcome.\n",
    "We use the Cars Hardware Specifications dataset. We predict the cars Gear System - Automatic(1) or Manual(0) - (AM)  from different independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Importing libraries for exploratory Data Analysis & Data Visualization\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  importing Data \n",
    "cars = pd.read_csv(\"cars.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>Model</th>\n",
       "      <th>MPG</th>\n",
       "      <th>Cylinder</th>\n",
       "      <th>Disp</th>\n",
       "      <th>HP</th>\n",
       "      <th>Drat</th>\n",
       "      <th>Wt</th>\n",
       "      <th>Qsec</th>\n",
       "      <th>VS</th>\n",
       "      <th>AM</th>\n",
       "      <th>Gear</th>\n",
       "      <th>Carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model    MPG  Cylinder   Disp   HP  Drat     Wt   Qsec  VS  AM  \\\n",
       "0          Mazda RX4  21.0         6  160.0  110  3.90  2.620  16.46   0   1   \n",
       "1      Mazda RX4 Wag  21.0         6  160.0  110  3.90  2.875  17.02   0   1   \n",
       "2         Datsun 710  22.8         4  108.0   93  3.85  2.320  18.61   1   1   \n",
       "3     Hornet 4 Drive  21.4         6  258.0  110  3.08  3.215  19.44   1   0   \n",
       "4  Hornet Sportabout  18.7         8  360.0  175  3.15  3.440  17.02   0   0   \n",
       "\n",
       "   Gear  Carb  \n",
       "0     4     4  \n",
       "1     4     4  \n",
       "2     4     1  \n",
       "3     3     1  \n",
       "4     3     2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 12)\n"
     ]
    }
   ],
   "source": [
    "#checking data set shape\n",
    "print(cars.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32 entries, 0 to 31\n",
      "Data columns (total 12 columns):\n",
      "Model       32 non-null object\n",
      "MPG         32 non-null float64\n",
      "Cylinder    32 non-null int64\n",
      "Disp        32 non-null float64\n",
      "HP          32 non-null int64\n",
      "Drat        32 non-null float64\n",
      "Wt          32 non-null float64\n",
      "Qsec        32 non-null float64\n",
      "VS          32 non-null int64\n",
      "AM          32 non-null int64\n",
      "Gear        32 non-null int64\n",
      "Carb        32 non-null int64\n",
      "dtypes: float64(5), int64(6), object(1)\n",
      "memory usage: 3.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exloratory Data Analysis & Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model       0\n",
       "MPG         0\n",
       "Cylinder    0\n",
       "Disp        0\n",
       "HP          0\n",
       "Drat        0\n",
       "Wt          0\n",
       "Qsec        0\n",
       "VS          0\n",
       "AM          0\n",
       "Gear        0\n",
       "Carb        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for null values in data set\n",
    "cars.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0c7ff0eb8>"
      ]
     },
     "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": [
    "sb.countplot(x='AM',data=cars,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff0c7cf1c50>"
      ]
     },
     "execution_count": 9,
     "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": [
    "\n",
    "sb.countplot(x='AM',hue='Gear',data=cars,palette='rainbow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training & Testing Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#car has  Automatic(1) or Manual(0) Gear System  - AM \n",
    "\n",
    "# Using predictor variables/ features/ independent variabes, x1, x2, x3,... => X\n",
    "y = cars.AM\n",
    "X = cars.loc[:,['MPG','HP','Wt']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training and testing model selection\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=0.2,random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing machine learning algorithm\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ml/anaconda3/envs/py36/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    0\n",
       "7     0\n",
       "5     0\n",
       "2     1\n",
       "3     0\n",
       "21    0\n",
       "13    0\n",
       "Name: AM, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model evaluation \n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571428571428571"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.83      0.91         6\n",
      "           1       0.50      1.00      0.67         1\n",
      "\n",
      "   micro avg       0.86      0.86      0.86         7\n",
      "   macro avg       0.75      0.92      0.79         7\n",
      "weighted avg       0.93      0.86      0.87         7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 1],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "6/7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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