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The given problem was solved using logistic regression.Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for given set of features(or inputs), X. Contrary to popular belief, logistic regression IS a regression model.the taken for sloving is
Data Preprocessing step.
- Data Preprocessing step.
Fitting Logistic Regression to the Training set.
- Fitting Logistic Regression to the Training set.
Predicting the test result.
- Predicting the test result.
Test accuracy of the result(Creation of Confusion matrix).
- Test accuracy of the result(Creation of Confusion matrix).
{
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"colab": {
"name": "minor project machine learning.ipynb",
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"kernelspec": {
"name": "python3",
"display_name": "Python 3"
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"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "10EybBlVTXqQ"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"df=pd.read_csv(\"/content/diabetes.csv\")"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 414
},
"id": "WFIzfTO4URby",
"outputId": "7f1ea221-f4d4-4de5-c0b8-f060c8ae555f"
},
"source": [
"df"
],
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"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>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",
" </tr>\n",
" <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",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>763</th>\n",
" <td>10</td>\n",
" <td>101</td>\n",
" <td>76</td>\n",
" <td>48</td>\n",
" <td>180</td>\n",
" <td>32.9</td>\n",
" <td>0.171</td>\n",
" <td>63</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>764</th>\n",
" <td>2</td>\n",
" <td>122</td>\n",
" <td>70</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>36.8</td>\n",
" <td>0.340</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>765</th>\n",
" <td>5</td>\n",
" <td>121</td>\n",
" <td>72</td>\n",
" <td>23</td>\n",
" <td>112</td>\n",
" <td>26.2</td>\n",
" <td>0.245</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>766</th>\n",
" <td>1</td>\n",
" <td>126</td>\n",
" <td>60</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30.1</td>\n",
" <td>0.349</td>\n",
" <td>47</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>767</th>\n",
" <td>1</td>\n",
" <td>93</td>\n",
" <td>70</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>30.4</td>\n",
" <td>0.315</td>\n",
" <td>23</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>768 rows 9 columns</p>\n",
"</div>"
],
"text/plain": [
" Pregnancies Glucose ... Age Outcome\n",
"0 6 148 ... 50 1\n",
"1 1 85 ... 31 0\n",
"2 8 183 ... 32 1\n",
"3 1 89 ... 21 0\n",
"4 0 137 ... 33 1\n",
".. ... ... ... ... ...\n",
"763 10 101 ... 63 0\n",
"764 2 122 ... 27 0\n",
"765 5 121 ... 30 0\n",
"766 1 126 ... 47 1\n",
"767 1 93 ... 23 0\n",
"\n",
"[768 rows x 9 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 2
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "V0_3rIykUaD0"
},
"source": [
"df.corr()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dhILAYklUlV1"
},
"source": [
"x= df.iloc[ : ,0:8].values\n",
"y= df['Outcome'].values"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Q0clXlMNWyOC"
},
"source": [
"#training and testing\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "R6ZT2-bpVE1p"
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"x_train, x_test, y_train, y_test =train_test_split(x,y , test_size=0.20 ,random_state=0)"
],
"execution_count": 82,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "weWXqu3LW8Mz"
},
"source": [
"#standardisizing the features"
]
},
{
"cell_type": "code",
"metadata": {
"id": "koWr_yRvWvfi"
},
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"sc= StandardScaler()\n",
"x_train =sc.fit_transform(x_train)\n",
"x_test = sc.fit_transform(x_test) "
],
"execution_count": 83,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "lSl7woyTXou6"
},
"source": [
"#using logistic regression predicting the values"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pth5GUZ1XiCF",
"outputId": "db0c2de3-79e9-4d31-8895-229eb7b980ba"
},
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"log= LogisticRegression()\n",
"log.fit(x_train,y_train)"
],
"execution_count": 84,
"outputs": [
{
"output_type": "execute_result",
"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)"
]
},
"metadata": {
"tags": []
},
"execution_count": 84
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VCl5BgGAYevp"
},
"source": [
"pred= log.predict(x_test)"
],
"execution_count": 85,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9SoB54ZDc-o1",
"outputId": "11b04b09-39f3-48e9-cc57-770c8b059e64"
},
"source": [
"pred"
],
"execution_count": 86,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
" 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
" 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
" 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
" 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
" 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0])"
]
},
"metadata": {
"tags": []
},
"execution_count": 86
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rk5YpV2YYp2v"
},
"source": [
"#to check the accuracy of prediction\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WEVxF0xcYxH5",
"outputId": "c2076b23-14bc-4281-b0d2-9eb0efe78d7d"
},
"source": [
"from sklearn import metrics\n",
"metrics.accuracy_score(y_test,pred)"
],
"execution_count": 87,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.7987012987012987"
]
},
"metadata": {
"tags": []
},
"execution_count": 87
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "h0-giaZEax_K",
"outputId": "0737bb98-d85a-4f04-ba6f-fbe35a7b8712"
},
"source": [
"metrics.confusion_matrix(y_test, pred)"
],
"execution_count": 88,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[94, 13],\n",
" [18, 29]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 88
}
]
}
]
}
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