pima-india-diabetes-prediction-logistic-regressio.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike linear regression, logistic regression is used to classify data. Classifying means when your output or target variable is categorical value. It is used specially in case of binary ctegorical value, which means categorical values with two kinds of values like Yes-No, 1-0, True-False.\n",
    "\n",
    "In case of linear regression we get straight line, but in case of logistic regression we get a sigmoid curve. Logistic regression is a variation of linear regression, which convert your -infinity to +infinity values to 0 or 1. Win case means 1 and 0 case means. \n",
    "It set a threshold value. If your value is greater than or equal to threshold value, then output will be 1 otherwise 0. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['diabetes.csv']\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../input/diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have data for different patient and we need to calculate the outcome which is whether our patient has diabetes or not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Type of glass\n",
    "df.Outcome.value_counts() # There are 500 patient with no diabetes(0) and 268 with diabetes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets do it with Logistic Regressor Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Data into Train & Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into test and train\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['Pregnancies', 'Glucose', 'BloodPressure',  'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependent Variable\n",
    "X = df[col_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Independent Variable\n",
    "y = df.Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into training and test set.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "#sc = StandardScaler()\n",
    "#X_train = sc.fit_transform(X_train)\n",
    "#X_test = sc.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Logistic Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Your Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/logistic.py:432: 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, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict The Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred # predicted values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation - Accuacy & Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnf_matrix = confusion_matrix(y_pred, y_test) # Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[117,  29],\n",
       "       [ 13,  33]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnf_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 510.88, 'Predicted label')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names=[0,1] # name  of classes\n",
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "tick_marks = np.arange(len(class_names))\n",
    "plt.xticks(tick_marks, class_names)\n",
    "plt.yticks(tick_marks, class_names)\n",
    "# create heatmap\n",
    "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion matrix', y=1.1)\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.78125\n",
      "Precision: 0.717391304347826\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))\n",
    "print(\"Precision:\",precision_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Thank You"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}