LogisticRegression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "706db30a-23c8-42a2-a672-26cebf2d1a97",
   "metadata": {},
   "source": [
    "# Logistic Regression from Scratch on Diabetes Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "72c78807-4b1c-4985-95d4-999f4d725e9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64b7ba2-0ee5-4981-8fa9-727452709a2e",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "22bedc21-2022-4f11-acd5-d64387d187cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Logistic_Regression():\n",
    "    #declaring learning rate and no of iterations(hyperparameter)\n",
    "    def __init__(self,learning_rate,no_of_iterations):\n",
    "        self.learning_rate=learning_rate\n",
    "        self.no_of_iterations=no_of_iterations\n",
    "\n",
    "    #fit to train the dataset\n",
    "    def fit(self,X,Y):\n",
    "        #no of rows=m \n",
    "        #no of columns=n\n",
    "        self.m,self.n=X.shape\n",
    "        #initiating weight and bias\n",
    "        self.w=np.zeros(self.n)\n",
    "        self.b=0\n",
    "        self.X=X\n",
    "        self.Y=Y\n",
    "        #implementing Gradient Descent for Optimization\n",
    "        for i in range(self.no_of_iterations):\n",
    "            self.update_weights()\n",
    "\n",
    "    def update_weights(self):\n",
    "        #sigmoid function\n",
    "        z=self.X.dot(self.w)+self.b\n",
    "        Y_hat=1/(1+np.exp(-z))\n",
    "        #derivatives\n",
    "        dw=(1/self.m)*np.dot(self.X.T,(Y_hat -self.Y))\n",
    "        db=(1/self.m)*np.sum(Y_hat-self.Y)\n",
    "        #updating weight and bias using gradient descent\n",
    "        self.w=self.w-self.learning_rate*dw\n",
    "        self.b=self.b-self.learning_rate*db\n",
    "\n",
    "    #Sigmoid Equation and Decision Boundary\n",
    "    def predict(self,X):\n",
    "        z=X.dot(self.w)+self.b\n",
    "        Y_pred=1/(1+np.exp(-z))\n",
    "        Y_pred=np.where(Y_pred >0.5 ,1,0)\n",
    "        return Y_pred\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5887c17f-236c-4d43-8f90-54db921b9ce7",
   "metadata": {},
   "source": [
    "# Use on Diabetes Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b11fa9c3-e82f-4a03-98e2-d0831739ba89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ef68cd57-d04d-442f-adb7-3946ed96b322",
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_dataset=pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "182e2eb2-345a-44f4-aa23-8cce50d33cc1",
   "metadata": {},
   "outputs": [
    {
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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",
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       "    <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",
       "  </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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c4d173c6-101d-4f93-ad12-322732f84fc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7fd4150d-5ce7-466b-bc73-26491b07a324",
   "metadata": {},
   "outputs": [
    {
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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>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4138f744-a5b9-4c4a-95b1-9e5f5c9ccf59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset[\"Outcome\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "30422f63-193d-44ae-bf60-a32e434fe4ee",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Glucose</th>\n",
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       "      <th>SkinThickness</th>\n",
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       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <th>Outcome</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.298000</td>\n",
       "      <td>109.980000</td>\n",
       "      <td>68.184000</td>\n",
       "      <td>19.664000</td>\n",
       "      <td>68.792000</td>\n",
       "      <td>30.304200</td>\n",
       "      <td>0.429734</td>\n",
       "      <td>31.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.865672</td>\n",
       "      <td>141.257463</td>\n",
       "      <td>70.824627</td>\n",
       "      <td>22.164179</td>\n",
       "      <td>100.335821</td>\n",
       "      <td>35.142537</td>\n",
       "      <td>0.550500</td>\n",
       "      <td>37.067164</td>\n",
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      "text/plain": [
       "         Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "Outcome                                                                      \n",
       "0           3.298000  109.980000      68.184000      19.664000   68.792000   \n",
       "1           4.865672  141.257463      70.824627      22.164179  100.335821   \n",
       "\n",
       "               BMI  DiabetesPedigreeFunction        Age  \n",
       "Outcome                                                  \n",
       "0        30.304200                  0.429734  31.190000  \n",
       "1        35.142537                  0.550500  37.067164  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_dataset.groupby(\"Outcome\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9886a10-a9d5-4ff5-8ac3-93d44ad9eee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=diabetes_dataset.drop(columns='Outcome',axis=1)\n",
    "target=diabetes_dataset['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a8e6f8b1-d4a8-48c9-ba6e-9b906f43099e",
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       "      <th>Glucose</th>\n",
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       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
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       "      <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",
       "    </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",
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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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\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",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.627   50  \n",
       "1                       0.351   31  \n",
       "2                       0.672   32  \n",
       "3                       0.167   21  \n",
       "4                       2.288   33  \n",
       "..                        ...  ...  \n",
       "763                     0.171   63  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4a41af05-94d5-41bc-b225-9fc9b3997091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0bfee6f2-d4fb-4a60-8c21-41b0e00d89ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcfea1ca-09c4-4d52-a2f8-1380850a051c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "46bc8b7c-1a29-4c6c-a012-443d8f00034c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
       "         0.46849198,  1.4259954 ],\n",
       "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
       "        -0.36506078, -0.19067191],\n",
       "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
       "         0.60439732, -0.10558415],\n",
       "       ...,\n",
       "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
       "        -0.68519336, -0.27575966],\n",
       "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
       "        -0.37110101,  1.17073215],\n",
       "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
       "        -0.47378505, -0.87137393]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "standardized_data=scaler.transform(features)\n",
    "standardized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a272899-d56a-4bb4-a7de-bcb28bfbc276",
   "metadata": {},
   "outputs": [],
   "source": [
    "features=standardized_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cad27fdd-1840-4bff-b8f8-027091c2f603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n",
       "         0.46849198,  1.4259954 ],\n",
       "       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n",
       "        -0.36506078, -0.19067191],\n",
       "       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n",
       "         0.60439732, -0.10558415],\n",
       "       ...,\n",
       "       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n",
       "        -0.68519336, -0.27575966],\n",
       "       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n",
       "        -0.37110101,  1.17073215],\n",
       "       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n",
       "        -0.47378505, -0.87137393]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0057c3fe-17c8-47d7-bb13-cce22d37707c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b1336957-b7a9-41ed-bdc9-c55084eea29d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(features,target,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "463afc20-11fd-4f57-a315-be73962eda4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 8)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4fa5137d-ed9e-4ad7-bb5d-94eaffe66af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(154, 8)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "464664e5-0cbc-4a00-b5b5-501bccc10728",
   "metadata": {},
   "source": [
    "## Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "379cf19f-e251-4e00-9297-0d089594a4df",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier=Logistic_Regression(learning_rate=0.01,no_of_iterations=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72e2a89f-395e-4cba-902e-6912f1931d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e04abe-dd1d-4c2c-b069-3d2e13035988",
   "metadata": {},
   "source": [
    "### Evaluation / Accuracy Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649b4324-7eb3-4348-8e9f-a6bb980f264f",
   "metadata": {},
   "source": [
    "Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb9eb9da-96b8-446f-9da4-f0a487883ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_prediction=classifier.predict(x_train)\n",
    "training_data_accuracy=accuracy_score(y_train,x_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "823817db-0d60-4dd4-8976-04461aa16b57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7768729641693811"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa0f79d0-8f61-495d-94ff-5521401174bc",
   "metadata": {},
   "source": [
    "Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "610d6ee5-4a97-448f-8e17-96453cb9b4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_prediction=classifier.predict(x_test)\n",
    "test_data_accuracy=accuracy_score(y_test,x_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "32be7e40-6dfe-4286-814d-01b91a7278a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7662337662337663"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad18efca-2e6e-4d85-ac1e-7b068d72c923",
   "metadata": {},
   "source": [
    "# Prediction System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f7fb0d0c-0081-4b49-a5ee-e6f26f5c74f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diabetic\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gyanb\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\base.py:465: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "input_data=(0,180,72,19,175,25.8,0.587,51)\n",
    "#input to numpy array\n",
    "input_data_as_numpy_array=np.asarray(input_data)\n",
    "#reshape \n",
    "input=input_data_as_numpy_array.reshape(1,-1)\n",
    "#standerize data\n",
    "std_data=scaler.transform(input)\n",
    "#std_data\n",
    "prediction=classifier.predict(std_data)\n",
    "#prediction\n",
    "if(prediction[0]==0):\n",
    "    print(\"Not Diabetic\")\n",
    "else:\n",
    "    print(\"Diabetic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cf583a7-ba28-4ea9-9efb-1c502d487aa7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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