Diabetes Prediction.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"IatuJJAfNYKi"},"source":["Importing dependencies\n","\n","\n"]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":1078,"status":"ok","timestamp":1714147513658,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"16kmPoQOHRSB"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn import svm\n","from sklearn.metrics import accuracy_score\n","from matplotlib import pyplot as plt\n","import seaborn as sns"]},{"cell_type":"markdown","metadata":{"id":"4WhkxORWH6j-"},"source":["Data collection and analysis"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N1bckoaSIGc5"},"outputs":[],"source":["#loading diabetes dataset to pandas dataframe\n","diabetes_dataset = pd.read_csv('/diabetes.csv')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1713797287656,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"a6OpDpiKI6SS","outputId":"a9dd08b1-6d1b-40e8-c1ed-578d4af36575"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"diabetes_dataset\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"num_unique_values\": 186,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"num_unique_values\": 517,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe","variable_name":"diabetes_dataset"},"text/html":["\n","  <div id=\"df-ecbac9ab-d73c-4813-a98d-1d6ab1e1a66d\" 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</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","    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"]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["diabetes_dataset.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":861,"status":"ok","timestamp":1713797298969,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"Td1-D2kvJHY_","outputId":"9729df3e-f565-410b-9809-1e94e29a74d4"},"outputs":[{"data":{"text/plain":["(768, 9)"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["#number of rows and columns in this data set\n","diabetes_dataset.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"executionInfo":{"elapsed":672,"status":"ok","timestamp":1713797307859,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"2mfL48nuJTdS","outputId":"c9f4b4c5-4af5-492b-ee46-6005c291765e"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"diabetes_dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 269.85223453356366,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8450520833333335,\n          3.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 243.73802348295857,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.89453125,\n          117.0,\n          768.0\n        ],\n 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350.26059167945886,\n        \"min\": 0.0,\n        \"max\": 846.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          768.0,\n          79.79947916666667,\n          127.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 262.05117817552093,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          31.992578124999998,\n          32.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3005221658502,\n        \"min\": 0.078,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.47187630208333325,\n          0.3725,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.1941178528413,\n        \"min\": 11.760231540678685,\n        \"max\": 768.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33.240885416666664,\n          29.0,\n          768.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 271.3865920388932,\n        \"min\": 0.0,\n        \"max\": 768.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3489583333333333,\n          1.0,\n          0.47695137724279896\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe"},"text/html":["\n","  <div id=\"df-415afde7-7fbc-45b2-9de4-21cad57c8e3e\" class=\"colab-df-container\">\n","    <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>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>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-415afde7-7fbc-45b2-9de4-21cad57c8e3e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  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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":39,"metadata":{},"output_type":"execute_result"}],"source":["#statistical measures of the dataset\n","diabetes_dataset.describe()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P5jigw_4JvWg"},"outputs":[],"source":["diabetes_dataset['Outcome'].value_counts()"]},{"cell_type":"markdown","metadata":{"id":"RoTOlX5oJ9mG"},"source":["0 -> Non-diabetic\n","\n","1 -> diabetic"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":666,"status":"ok","timestamp":1713794364619,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"uMLFShV6d4xC","outputId":"a485216b-2c61-4b6e-ea05-bc85648b5cfa"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# @title Category\n","\n","diabetes_dataset.groupby('Outcome').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n","plt.gca().spines[['top', 'right',]].set_visible(False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"elapsed":550,"status":"ok","timestamp":1713794372528,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"CC6HpJWuKKgH","outputId":"33f9df94-3334-4445-8ec4-8deb126722a6"},"outputs":[{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"summary":"{\n  \"name\": \"diabetes_dataset\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.108511248584296,\n        \"min\": 3.298,\n        \"max\": 4.865671641791045,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4.865671641791045,\n          3.298\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.116505963980842,\n        \"min\": 109.98,\n        \"max\": 141.25746268656715,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          141.25746268656715,\n          109.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8672051632998017,\n        \"min\": 68.184,\n        \"max\": 70.82462686567165,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          70.82462686567165,\n          68.184\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7678935989570275,\n        \"min\": 19.664,\n        \"max\": 22.16417910447761,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          22.16417910447761,\n          19.664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.304849659757796,\n        \"min\": 68.792,\n        \"max\": 100.33582089552239,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100.33582089552239,\n          68.792\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.4212211239962618,\n        \"min\": 30.3042,\n        \"max\": 35.14253731343284,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          35.14253731343284,\n          30.3042\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08539445753677459,\n        \"min\": 0.429734,\n        \"max\": 0.5505,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.5505,\n          0.429734\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.155782645191446,\n        \"min\": 31.19,\n        \"max\": 37.06716417910448,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          37.06716417910448,\n          31.19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}","type":"dataframe"},"text/html":["\n","  <div id=\"df-2a975632-1c2b-445b-9842-93c417c5cc79\" class=\"colab-df-container\">\n","    <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","    <tr>\n","      <th>Outcome</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <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","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","  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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","\n","    </div>\n","  </div>\n"],"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":42,"metadata":{},"output_type":"execute_result"}],"source":["diabetes_dataset.groupby('Outcome').mean()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lgTlSH5DKgf1"},"outputs":[],"source":["#splitting the data nd label seperately\n","\n","X = diabetes_dataset.drop(columns = 'Outcome',axis=1 )\n","Y = diabetes_dataset['Outcome']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1713794443301,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"Rx25lqj0LHhG","outputId":"c5ea18bc-1a9a-46ff-a45d-74e84b5a9248"},"outputs":[{"name":"stdout","output_type":"stream","text":["     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]\n"]}],"source":["print(X)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1203,"status":"ok","timestamp":1713794458776,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"jTNAuP37LL92","outputId":"1b58491a-a8d9-4f9f-ebc8-82a2937f631f"},"outputs":[{"name":"stdout","output_type":"stream","text":["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\n"]}],"source":["print(Y)"]},{"cell_type":"markdown","metadata":{"id":"bHEqpPrCLYaY"},"source":["Data standarlization"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ILZ5IaalLbb6"},"outputs":[],"source":["scaler = StandardScaler()        #we standerdized the data to have common range bw them"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1713794465601,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"lcDWERN7Mex5","outputId":"7a678631-1bc2-4ba1-c826-32f924ae244b"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#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":19,"metadata":{},"output_type":"execute_result"}],"source":["scaler.fit(X)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HMQ-kpZLN0OG"},"outputs":[],"source":["standerdized_data = scaler.transform(X)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":543,"status":"ok","timestamp":1713794480913,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"WFMw51SYc92I","outputId":"1ad0078a-0b27-471e-c4ce-94136ce59ed8"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n","   1.4259954 ]\n"," [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n","  -0.19067191]\n"," [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n","  -0.10558415]\n"," ...\n"," [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n","  -0.27575966]\n"," [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n","   1.17073215]\n"," [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n","  -0.87137393]]\n"]}],"source":["print(standerdized_data)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_HJWypAIdqfj"},"outputs":[],"source":["X = standerdized_data\n","Y = diabetes_dataset['Outcome']"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":806,"status":"ok","timestamp":1713794489948,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"bWMe0PTud9uo","outputId":"ca3ff959-5e7f-4cde-d972-f441a54a05ec"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[ 0.63994726  0.84832379  0.14964075 ...  0.20401277  0.46849198\n","   1.4259954 ]\n"," [-0.84488505 -1.12339636 -0.16054575 ... -0.68442195 -0.36506078\n","  -0.19067191]\n"," [ 1.23388019  1.94372388 -0.26394125 ... -1.10325546  0.60439732\n","  -0.10558415]\n"," ...\n"," [ 0.3429808   0.00330087  0.14964075 ... -0.73518964 -0.68519336\n","  -0.27575966]\n"," [-0.84488505  0.1597866  -0.47073225 ... -0.24020459 -0.37110101\n","   1.17073215]\n"," [-0.84488505 -0.8730192   0.04624525 ... -0.20212881 -0.47378505\n","  -0.87137393]]\n","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\n"]}],"source":["print(X)\n","print(Y)"]},{"cell_type":"markdown","metadata":{"id":"fE28-4OpeEHx"},"source":["Train test split"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VCmBMmDAd5C5"},"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":647,"status":"ok","timestamp":1713794538222,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"IJq-mVdGevED","outputId":"0ae61666-dbc1-4b29-a16f-66c35bdeeb1a"},"outputs":[{"name":"stdout","output_type":"stream","text":["(768, 8) (614, 8) (154, 8)\n"]}],"source":["print(X.shape, X_train.shape, X_test.shape)"]},{"cell_type":"markdown","metadata":{"id":"ewaAeQ-9e-VI"},"source":["Training the model\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gYqZs2VrfA0n"},"outputs":[],"source":["classifier = svm.SVC(kernel='linear')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1713794581934,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"ErNeDJ3Ifgku","outputId":"499ff939-1270-45ae-b398-3c91a1689938"},"outputs":[{"data":{"text/html":["<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"],"text/plain":["SVC(kernel='linear')"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["#training the support vector machine classifier\n","classifier.fit(X_train , Y_train)"]},{"cell_type":"markdown","metadata":{"id":"bTHHCTJ7fxUl"},"source":["Model Evaluation and accuracy score"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"02FRc3q5f6hp"},"outputs":[],"source":["X_train_prediction = classifier.predict(X_train)\n","training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1713794626014,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"b8zufAl6gTZ1","outputId":"8826596b-d6f9-460e-e1ac-8a839e2a80cf"},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy score of training data:  0.7866449511400652\n"]}],"source":["print('Accuracy score of training data: ', training_data_accuracy)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JkGerHfKgnMq"},"outputs":[],"source":["X_test_prediction = classifier.predict(X_test)\n","test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1713794639458,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"xL8UPI7Ag1QM","outputId":"846a07a3-ad56-49ca-ca7e-c78c248bbeef"},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy score of test data:  0.7727272727272727\n"]}],"source":["print('Accuracy score of test data: ', test_data_accuracy)"]},{"cell_type":"markdown","metadata":{"id":"J0kHj-XehEKA"},"source":["Making a predictive system"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1713794672126,"user":{"displayName":"Ahamed Ayyash","userId":"17680496972396132693"},"user_tz":-300},"id":"Rj8cVSYchHIG","outputId":"4210e012-69e0-4e4a-bc82-5b11c31997fa"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[ 0.04601433 -0.34096773  1.18359575 -1.28821221 -0.69289057  0.71168975\n","  -0.84827977 -0.27575966]]\n","[0]\n","The person is not diabetic\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n","  warnings.warn(\n"]}],"source":["input_data = ( 4, 110, 92, 0 , 0 ,37.6, 0.191,30)\n","\n","input_data_as_numpy_array = np.asarray(input_data)         #changing ip data into numpy arrays\n","\n","input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)          #reshape array as we are predicting for one instance\n","\n","std_data = scaler.transform(input_data_reshaped)           #standerdized ip data\n","print(std_data)\n","\n","prediction = classifier.predict(std_data)\n","print(prediction)\n","\n","if(prediction[0]== 0):\n","  print('The person is not diabetic')\n","else:\n","  print('The person is diabetic')"]}],"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyP29AeMj4J1HkcOwFu+idEi"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}