uploading Files

.gitattributes

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+*.joblib filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/EDA-NOTEBOOK-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6219ac96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn import svm\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "16859c5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('diabetes.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "69c54515",
+   "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>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",
+       "  </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": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4dd13ad6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data.drop(['Outcome'],axis=1)\n",
+    "y = data['Outcome']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f5945059",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
+    "scale_data = scaler.fit_transform(x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "583174fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a93dffad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train,y_test = train_test_split(x,y ,test_size=0.2, stratify=y, random_state=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "bc4473ce",
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>0.141</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>6</td>\n",
+       "      <td>105</td>\n",
+       "      <td>70</td>\n",
+       "      <td>32</td>\n",
+       "      <td>68</td>\n",
+       "      <td>30.8</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>189</td>\n",
+       "      <td>60</td>\n",
+       "      <td>23</td>\n",
+       "      <td>846</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>0.398</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>2</td>\n",
+       "      <td>105</td>\n",
+       "      <td>80</td>\n",
+       "      <td>45</td>\n",
+       "      <td>191</td>\n",
+       "      <td>33.7</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0</td>\n",
+       "      <td>180</td>\n",
+       "      <td>66</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>1.893</td>\n",
+       "      <td>25</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>303</th>\n",
+       "      <td>5</td>\n",
+       "      <td>115</td>\n",
+       "      <td>98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.9</td>\n",
+       "      <td>0.209</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>3</td>\n",
+       "      <td>132</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.4</td>\n",
+       "      <td>0.402</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11</td>\n",
+       "      <td>85</td>\n",
+       "      <td>74</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>4</td>\n",
+       "      <td>112</td>\n",
+       "      <td>78</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39.4</td>\n",
+       "      <td>0.236</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>68</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.8</td>\n",
+       "      <td>0.238</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>614 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "619            0      119              0              0        0  32.4   \n",
+       "329            6      105             70             32       68  30.8   \n",
+       "13             1      189             60             23      846  30.1   \n",
+       "476            2      105             80             45      191  33.7   \n",
+       "45             0      180             66             39        0  42.0   \n",
+       "..           ...      ...            ...            ...      ...   ...   \n",
+       "303            5      115             98              0        0  52.9   \n",
+       "592            3      132             80              0        0  34.4   \n",
+       "559           11       85             74              0        0  30.1   \n",
+       "725            4      112             78             40        0  39.4   \n",
+       "253            0       86             68             32        0  35.8   \n",
+       "\n",
+       "     DiabetesPedigreeFunction  Age  \n",
+       "619                     0.141   24  \n",
+       "329                     0.122   37  \n",
+       "13                      0.398   59  \n",
+       "476                     0.711   29  \n",
+       "45                      1.893   25  \n",
+       "..                        ...  ...  \n",
+       "303                     0.209   28  \n",
+       "592                     0.402   44  \n",
+       "559                     0.300   35  \n",
+       "725                     0.236   38  \n",
+       "253                     0.238   25  \n",
+       "\n",
+       "[614 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "abafb2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7833876221498371"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier = svm.SVC(kernel='linear')\n",
+    "classifier.fit(x_train,y_train)\n",
+    "y_predict = classifier.predict(x_train)\n",
+    "accuracy_score(y_predict, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "9b013f42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7727272727272727"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test_pred = classifier.predict(x_test)\n",
+    "accuracy_score(y_test_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "55e83896",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\n",
+      "0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\danielle.DESKTOP-EM4NRIV\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_data  = np.asarray((7,107,74,0,0,29.6,0.254,31)).reshape(1,-1)\n",
+    "\n",
+    "predict = classifier.predict(input_data)\n",
+    "print(predict)\n",
+    "print(accuracy_score(predict,[1]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dab6b96",
+   "metadata": {},
+   "source": [
+    "# using knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "4438351c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "277f89e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = KNeighborsClassifier(n_neighbors=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "b9916c2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=2)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "18a66aa2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8306188925081434"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "2c3a9475",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(n_neighbors=2),\n",
+       "             param_grid={'n_neighbors': [3, 5, 7, 9, 11], 'p': [1, 2],\n",
+       "                         'weights': ['uniform', 'distance']},\n",
+       "             scoring='accuracy')"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid_k = {\n",
+    "    'n_neighbors': [3, 5, 7, 9, 11],   # Number of neighbors\n",
+    "    'weights': ['uniform', 'distance'],# Weight function\n",
+    "    'p': [1, 2]                         # Power parameter for Minkowski distance\n",
+    "}\n",
+    "grid_k = GridSearchCV(model, param_grid_k, cv=5)\n",
+    "grid = GridSearchCV(model, param_grid= param_grid_k,cv=5 ,scoring = 'accuracy' )\n",
+    "grid.fit(x_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "204e8231",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7361322137811541"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3e4634f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt_model_k = KNeighborsClassifier(**grid.best_params_)\n",
+    "opt_model_k.fit(x_train,y_train)\n",
+    "opt_model_k.score(x_test,y_test)\n",
+    "y_pred = opt_model_k.predict(x_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "103db5f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7467532467532467\n",
+      "0.7931596091205212\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(  accuracy_score(y_pred, y_test))\n",
+    "print(opt_model_k.score(x_train,y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f2f62f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Untitled.ipynb

@@ -0,0 +1,704 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6219ac96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn import svm\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e7b1cf66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model Accuracy: 72.07792207792207%\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python train.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a6a89418",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " -V:3.13 *        Python 3.13 (Store)\n",
+      " -V:3.10          Python 3.10 (64-bit)\n",
+      " -V:ContinuumAnalytics/Anaconda39-64 Anaconda 2022.05\n"
+     ]
+    }
+   ],
+   "source": [
+    "!py --list\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "16859c5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('diabetes.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "69c54515",
+   "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>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",
+       "  </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": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4dd13ad6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = data.drop(['Outcome'],axis=1)\n",
+    "y = data['Outcome']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f5945059",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
+    "scale_data = scaler.fit_transform(x)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "583174fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a93dffad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train,y_test = train_test_split(x,y ,test_size=0.2, stratify=y, random_state=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "bc4473ce",
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>0</td>\n",
+       "      <td>119</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>0.141</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>6</td>\n",
+       "      <td>105</td>\n",
+       "      <td>70</td>\n",
+       "      <td>32</td>\n",
+       "      <td>68</td>\n",
+       "      <td>30.8</td>\n",
+       "      <td>0.122</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>189</td>\n",
+       "      <td>60</td>\n",
+       "      <td>23</td>\n",
+       "      <td>846</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>0.398</td>\n",
+       "      <td>59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>476</th>\n",
+       "      <td>2</td>\n",
+       "      <td>105</td>\n",
+       "      <td>80</td>\n",
+       "      <td>45</td>\n",
+       "      <td>191</td>\n",
+       "      <td>33.7</td>\n",
+       "      <td>0.711</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>0</td>\n",
+       "      <td>180</td>\n",
+       "      <td>66</td>\n",
+       "      <td>39</td>\n",
+       "      <td>0</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>1.893</td>\n",
+       "      <td>25</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>303</th>\n",
+       "      <td>5</td>\n",
+       "      <td>115</td>\n",
+       "      <td>98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52.9</td>\n",
+       "      <td>0.209</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>3</td>\n",
+       "      <td>132</td>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34.4</td>\n",
+       "      <td>0.402</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>559</th>\n",
+       "      <td>11</td>\n",
+       "      <td>85</td>\n",
+       "      <td>74</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30.1</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>725</th>\n",
+       "      <td>4</td>\n",
+       "      <td>112</td>\n",
+       "      <td>78</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0</td>\n",
+       "      <td>39.4</td>\n",
+       "      <td>0.236</td>\n",
+       "      <td>38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>0</td>\n",
+       "      <td>86</td>\n",
+       "      <td>68</td>\n",
+       "      <td>32</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35.8</td>\n",
+       "      <td>0.238</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>614 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
+       "619            0      119              0              0        0  32.4   \n",
+       "329            6      105             70             32       68  30.8   \n",
+       "13             1      189             60             23      846  30.1   \n",
+       "476            2      105             80             45      191  33.7   \n",
+       "45             0      180             66             39        0  42.0   \n",
+       "..           ...      ...            ...            ...      ...   ...   \n",
+       "303            5      115             98              0        0  52.9   \n",
+       "592            3      132             80              0        0  34.4   \n",
+       "559           11       85             74              0        0  30.1   \n",
+       "725            4      112             78             40        0  39.4   \n",
+       "253            0       86             68             32        0  35.8   \n",
+       "\n",
+       "     DiabetesPedigreeFunction  Age  \n",
+       "619                     0.141   24  \n",
+       "329                     0.122   37  \n",
+       "13                      0.398   59  \n",
+       "476                     0.711   29  \n",
+       "45                      1.893   25  \n",
+       "..                        ...  ...  \n",
+       "303                     0.209   28  \n",
+       "592                     0.402   44  \n",
+       "559                     0.300   35  \n",
+       "725                     0.236   38  \n",
+       "253                     0.238   25  \n",
+       "\n",
+       "[614 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "abafb2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7833876221498371"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier = svm.SVC(kernel='linear')\n",
+    "classifier.fit(x_train,y_train)\n",
+    "y_predict = classifier.predict(x_train)\n",
+    "accuracy_score(y_predict, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "9b013f42",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7727272727272727"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test_pred = classifier.predict(x_test)\n",
+    "accuracy_score(y_test_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "55e83896",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0]\n",
+      "0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\danielle.DESKTOP-EM4NRIV\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but SVC was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "input_data  = np.asarray((7,107,74,0,0,29.6,0.254,31)).reshape(1,-1)\n",
+    "\n",
+    "predict = classifier.predict(input_data)\n",
+    "print(predict)\n",
+    "print(accuracy_score(predict,[1]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9dab6b96",
+   "metadata": {},
+   "source": [
+    "# using knn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "4438351c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.model_selection import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "277f89e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = KNeighborsClassifier(n_neighbors=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "b9916c2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=2)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "18a66aa2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8306188925081434"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.score(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "2c3a9475",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(n_neighbors=2),\n",
+       "             param_grid={'n_neighbors': [3, 5, 7, 9, 11], 'p': [1, 2],\n",
+       "                         'weights': ['uniform', 'distance']},\n",
+       "             scoring='accuracy')"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid_k = {\n",
+    "    'n_neighbors': [3, 5, 7, 9, 11],   # Number of neighbors\n",
+    "    'weights': ['uniform', 'distance'],# Weight function\n",
+    "    'p': [1, 2]                         # Power parameter for Minkowski distance\n",
+    "}\n",
+    "grid_k = GridSearchCV(model, param_grid_k, cv=5)\n",
+    "grid = GridSearchCV(model, param_grid= param_grid_k,cv=5 ,scoring = 'accuracy' )\n",
+    "grid.fit(x_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "204e8231",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7361322137811541"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid.best_score_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "3e4634f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "opt_model_k = KNeighborsClassifier(**grid.best_params_)\n",
+    "opt_model_k.fit(x_train,y_train)\n",
+    "opt_model_k.score(x_test,y_test)\n",
+    "y_pred = opt_model_k.predict(x_test)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "103db5f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.7467532467532467\n",
+      "0.7931596091205212\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(  accuracy_score(y_pred, y_test))\n",
+    "print(opt_model_k.score(x_train,y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f2f62f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -0,0 +1,27 @@
+import streamlit as st
+import joblib
+import pandas as pd
+
+# Load trained model
+model = joblib.load("diabetes_model.joblib")
+
+# Streamlit UI
+st.title("Diabetes Prediction App")
+st.write("Enter patient data to predict diabetes")
+
+# Input fields
+pregnancies = st.number_input("Pregnancies", min_value=0, max_value=20)
+glucose = st.number_input("Glucose Level", min_value=0, max_value=200)
+blood_pressure = st.number_input("Blood Pressure", min_value=0, max_value=200)
+skin_thickness = st.number_input("Skin Thickness", min_value=0, max_value=100)
+insulin = st.number_input("Insulin Level", min_value=0, max_value=900)
+bmi = st.number_input("BMI", min_value=0.0, max_value=60.0)
+dpf = st.number_input("Diabetes Pedigree Function", min_value=0.0, max_value=3.0)
+age = st.number_input("Age", min_value=0, max_value=120)
+
+# Prediction
+if st.button("Predict"):
+    features = [[pregnancies, glucose, blood_pressure, skin_thickness, insulin, bmi, dpf, age]]
+    prediction = model.predict(features)[0]
+    result = "Diabetic" if prediction == 1 else "Not Diabetic"
+    st.write(f"Prediction: **{result}**")

diabetes.csv

@@ -0,0 +1,769 @@
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diabetes_model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1816ee4fd6de54406f17caf645cfb44a8c20621ca751513739ebfdb154a29530
+size 1709545

evaluation.py

@@ -0,0 +1,30 @@
+import joblib
+import json
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+# Load dataset again (same as in train.py)
+data = pd.read_csv("diabetes.csv")
+X = data.drop('Outcome', axis=1)
+y = data['Outcome']
+
+# Split data (same as in train.py)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Load trained model
+model = joblib.load('diabetes_model.joblib')
+
+# Make predictions
+y_pred = model.predict(X_test)
+
+# Calculate accuracy
+accuracy = accuracy_score(y_test, y_pred)
+
+# Save accuracy to a JSON file
+metrics = {"accuracy": accuracy * 100}
+with open("metrics.json", "w") as f:
+    json.dump(metrics, f)
+
+# Print accuracy (for debugging in GitHub Actions)
+print(f"Model Accuracy: {accuracy * 100}%")

metrics.json

@@ -0,0 +1 @@
+{"accuracy": 72.07792207792207}

requirements.txt

@@ -0,0 +1,4 @@
+streamlit
+scikit-learn
+pandas
+joblib

train.py

@@ -0,0 +1,30 @@
+import joblib
+import numpy as np
+import pandas as pd
+import json
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+# Load dataset
+data = pd.read_csv("diabetes.csv")
+X = data.drop('Outcome', axis=1)
+y = data['Outcome']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train model
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Evaluate
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+
+# Save model
+joblib.dump(model, 'diabetes_model.joblib')
+
+# Save accuracy
+with open("metrics.json", "w") as f:
+    json.dump({"accuracy": accuracy * 100}, f)
+
+print(f"Model Accuracy: {accuracy * 100}%")