Multiple disease prediction system - heart.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Multiple disease prediction system - heart.ipynb","provenance":[],"authorship_tag":"ABX9TyNkEJ4hIKtyX260ZMlxNVm4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"aTb-9TFFqprC"},"source":["Importing the Dependencies"]},{"cell_type":"code","metadata":{"id":"3q9U3S_whh3-","executionInfo":{"status":"ok","timestamp":1653200233515,"user_tz":-330,"elapsed":1659,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import numpy as np\n","import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"egMd5zeurTMR"},"source":["Data Collection and Processing"]},{"cell_type":"code","metadata":{"id":"0q-3-LkQrREV","executionInfo":{"status":"ok","timestamp":1653200233516,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the csv data to a Pandas DataFrame\n","heart_data = pd.read_csv('/content/heart.csv')"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"M8dQxSTqriWD","executionInfo":{"status":"ok","timestamp":1653200233516,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"0ab48855-8403-4103-c7c8-e307c07da055"},"source":["# print first 5 rows of the dataset\n","heart_data.head()"],"execution_count":3,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n","0   63    1   3       145   233    1        0      150      0      2.3      0   \n","1   37    1   2       130   250    0        1      187      0      3.5      0   \n","2   41    0   1       130   204    0        0      172      0      1.4      2   \n","3   56    1   1       120   236    0        1      178      0      0.8      2   \n","4   57    0   0       120   354    0        1      163      1      0.6      2   \n","\n","   ca  thal  target  \n","0   0     1       1  \n","1   0     2       1  \n","2   0     2       1  \n","3   0     2       1  \n","4   0     2       1  "],"text/html":["\n","  <div id=\"df-c22c32f4-9008-4881-915a-af9a1cc44c73\">\n","    <div 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>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>145</td>\n","      <td>233</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>150</td>\n","      <td>0</td>\n","      <td>2.3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>37</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>130</td>\n","      <td>250</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>187</td>\n","      <td>0</td>\n","      <td>3.5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>204</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>172</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>56</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>120</td>\n","      <td>236</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>178</td>\n","      <td>0</td>\n","      <td>0.8</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>57</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>354</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>163</td>\n","      <td>1</td>\n","      <td>0.6</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c22c32f4-9008-4881-915a-af9a1cc44c73')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 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dataset\n","heart_data.tail()"],"execution_count":4,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","298   57    0   0       140   241    0        1      123      1      0.2   \n","299   45    1   3       110   264    0        1      132      0      1.2   \n","300   68    1   0       144   193    1        1      141      0      3.4   \n","301   57    1   0       130   131    0        1      115      1      1.2   \n","302   57    0   1       130   236    0        0      174      0      0.0   \n","\n","     slope  ca  thal  target  \n","298      1   0     3       0  \n","299      1   0     3       0  \n","300      1   2     3       0  \n","301      1   1     3       0  \n","302      1   1     2       0  "],"text/html":["\n","  <div id=\"df-8a23c18a-4205-4dce-9342-e086c7334c7b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr 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dataset\n","heart_data.shape"],"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(303, 14)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7_xTcw1Sr6aJ","executionInfo":{"status":"ok","timestamp":1653200234315,"user_tz":-330,"elapsed":26,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"3dfcf6cf-f814-4b3b-f8be-57e2ea6ea7b2"},"source":["# getting some info about the data\n","heart_data.info()"],"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 303 entries, 0 to 302\n","Data columns (total 14 columns):\n"," #   Column    Non-Null Count  Dtype  \n","---  ------    --------------  -----  \n"," 0   age       303 non-null    int64  \n"," 1   sex       303 non-null    int64  \n"," 2   cp        303 non-null    int64  \n"," 3   trestbps  303 non-null    int64  \n"," 4   chol      303 non-null    int64  \n"," 5   fbs       303 non-null    int64  \n"," 6   restecg   303 non-null    int64  \n"," 7   thalach   303 non-null    int64  \n"," 8   exang     303 non-null    int64  \n"," 9   oldpeak   303 non-null    float64\n"," 10  slope     303 non-null    int64  \n"," 11  ca        303 non-null    int64  \n"," 12  thal      303 non-null    int64  \n"," 13  target    303 non-null    int64  \n","dtypes: float64(1), int64(13)\n","memory usage: 33.3 KB\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GjHtW31rsGlb","executionInfo":{"status":"ok","timestamp":1653200234316,"user_tz":-330,"elapsed":25,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"062af8fa-b836-480d-93dd-b84f335f0839"},"source":["# checking for missing values\n","heart_data.isnull().sum()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["age         0\n","sex         0\n","cp          0\n","trestbps    0\n","chol        0\n","fbs         0\n","restecg     0\n","thalach     0\n","exang       0\n","oldpeak     0\n","slope       0\n","ca          0\n","thal        0\n","target      0\n","dtype: int64"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":364},"id":"OHmcP7DJsSEP","executionInfo":{"status":"ok","timestamp":1653200234317,"user_tz":-330,"elapsed":24,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"b08b05fa-7f04-45bb-8f9b-c676a77834c3"},"source":["# statistical measures about the data\n","heart_data.describe()"],"execution_count":8,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              age         sex          cp    trestbps        chol         fbs  \\\n","count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n","mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n","std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n","min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n","25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n","50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n","75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n","max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n","\n","          restecg     thalach       exang     oldpeak       slope          ca  \\\n","count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n","mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n","std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n","min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n","25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n","50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n","75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n","max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n","\n","             thal      target  \n","count  303.000000  303.000000  \n","mean     2.313531    0.544554  \n","std      0.612277    0.498835  \n","min      0.000000    0.000000  \n","25%      2.000000    0.000000  \n","50%      2.000000    1.000000  \n","75%      3.000000    1.000000  \n","max      3.000000    1.000000  "],"text/html":["\n","  <div id=\"df-8077a748-f74f-4f21-94b6-9fc7e8a6e707\">\n","    <div 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>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>54.366337</td>\n","      <td>0.683168</td>\n","      <td>0.966997</td>\n","      <td>131.623762</td>\n","      <td>246.264026</td>\n","      <td>0.148515</td>\n","      <td>0.528053</td>\n","      <td>149.646865</td>\n","      <td>0.326733</td>\n","      <td>1.039604</td>\n","      <td>1.399340</td>\n","      <td>0.729373</td>\n","      <td>2.313531</td>\n","      <td>0.544554</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>9.082101</td>\n","      <td>0.466011</td>\n","      <td>1.032052</td>\n","      <td>17.538143</td>\n","      <td>51.830751</td>\n","      <td>0.356198</td>\n","      <td>0.525860</td>\n","      <td>22.905161</td>\n","      <td>0.469794</td>\n","      <td>1.161075</td>\n","      <td>0.616226</td>\n","      <td>1.022606</td>\n","      <td>0.612277</td>\n","      <td>0.498835</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>29.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>94.000000</td>\n","      <td>126.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>71.000000</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>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>47.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>120.000000</td>\n","      <td>211.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>133.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>55.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>130.000000</td>\n","      <td>240.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>153.000000</td>\n","      <td>0.000000</td>\n","      <td>0.800000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>61.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>140.000000</td>\n","      <td>274.500000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>166.000000</td>\n","      <td>1.000000</td>\n","      <td>1.600000</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>77.000000</td>\n","      <td>1.000000</td>\n","      <td>3.000000</td>\n","      <td>200.000000</td>\n","      <td>564.000000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","      <td>202.000000</td>\n","      <td>1.000000</td>\n","      <td>6.200000</td>\n","      <td>2.000000</td>\n","      <td>4.000000</td>\n","      <td>3.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8077a748-f74f-4f21-94b6-9fc7e8a6e707')\"\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 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 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Variable\n","heart_data['target'].value_counts()"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1    165\n","0    138\n","Name: target, dtype: int64"]},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","metadata":{"id":"aSOBu4qDtJy5"},"source":["1 --> Defective Heart\n","\n","0 --> Healthy Heart"]},{"cell_type":"markdown","metadata":{"id":"tW8i4igjtPRC"},"source":["Splitting the Features and Target"]},{"cell_type":"code","metadata":{"id":"Q6yfbswrs7m3","executionInfo":{"status":"ok","timestamp":1653200234318,"user_tz":-330,"elapsed":22,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X = heart_data.drop(columns='target', axis=1)\n","Y = heart_data['target']"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XJoCp4ZKtpZy","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":23,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"14301a08-f0ce-41ef-ea17-b6a315aebef5"},"source":["print(X)"],"execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","0     63    1   3       145   233    1        0      150      0      2.3   \n","1     37    1   2       130   250    0        1      187      0      3.5   \n","2     41    0   1       130   204    0        0      172      0      1.4   \n","3     56    1   1       120   236    0        1      178      0      0.8   \n","4     57    0   0       120   354    0        1      163      1      0.6   \n","..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n","298   57    0   0       140   241    0        1      123      1      0.2   \n","299   45    1   3       110   264    0        1      132      0      1.2   \n","300   68    1   0       144   193    1        1      141      0      3.4   \n","301   57    1   0       130   131    0        1      115      1      1.2   \n","302   57    0   1       130   236    0        0      174      0      0.0   \n","\n","     slope  ca  thal  \n","0        0   0     1  \n","1        0   0     2  \n","2        2   0     2  \n","3        2   0     2  \n","4        2   0     2  \n","..     ...  ..   ...  \n","298      1   0     3  \n","299      1   0     3  \n","300      1   2     3  \n","301      1   1     3  \n","302      1   1     2  \n","\n","[303 rows x 13 columns]\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nukuj-YItq1w","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"ee5e6451-0b29-4a02-aebf-24367d73874b"},"source":["print(Y)"],"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["0      1\n","1      1\n","2      1\n","3      1\n","4      1\n","      ..\n","298    0\n","299    0\n","300    0\n","301    0\n","302    0\n","Name: target, Length: 303, dtype: int64\n"]}]},{"cell_type":"markdown","metadata":{"id":"_EcjSE3Et18n"},"source":["Splitting the Data into Training data & Test Data"]},{"cell_type":"code","metadata":{"id":"a-UUfRUxtuga","executionInfo":{"status":"ok","timestamp":1653200234319,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x7PrjC6zuf6X","executionInfo":{"status":"ok","timestamp":1653200234320,"user_tz":-330,"elapsed":21,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"d78b7e79-4659-4101-c9d6-61df1d0d5981"},"source":["print(X.shape, X_train.shape, X_test.shape)"],"execution_count":14,"outputs":[{"output_type":"stream","name":"stdout","text":["(303, 13) (242, 13) (61, 13)\n"]}]},{"cell_type":"markdown","metadata":{"id":"beSkZmpVuvn9"},"source":["Model Training"]},{"cell_type":"markdown","metadata":{"id":"gi2NOWZjuxzw"},"source":["Logistic Regression"]},{"cell_type":"code","metadata":{"id":"4-Md74FYuqNL","executionInfo":{"status":"ok","timestamp":1653200234320,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["model = LogisticRegression()"],"execution_count":15,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kCdHYxGUu7XD","executionInfo":{"status":"ok","timestamp":1653200234321,"user_tz":-330,"elapsed":20,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"e24ea90f-2542-44ca-abbd-9d0c9cce231d"},"source":["# training the LogisticRegression model with Training data\n","model.fit(X_train, Y_train)"],"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n","STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n","\n","Increase the number of iterations (max_iter) or scale the data as shown in:\n","    https://scikit-learn.org/stable/modules/preprocessing.html\n","Please also refer to the documentation for alternative solver options:\n","    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n","  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"]},{"output_type":"execute_result","data":{"text/plain":["LogisticRegression()"]},"metadata":{},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"ZYIw8Gi9vXfU"},"source":["Model Evaluation"]},{"cell_type":"markdown","metadata":{"id":"wmxAekfZvZa9"},"source":["Accuracy Score"]},{"cell_type":"code","metadata":{"id":"g19JaUTMvPKy","executionInfo":{"status":"ok","timestamp":1653200234321,"user_tz":-330,"elapsed":18,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy on training data\n","X_train_prediction = model.predict(X_train)\n","training_data_accuracy = accuracy_score(X_train_prediction, Y_train)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"uQBZvBh8v7R_","executionInfo":{"status":"ok","timestamp":1653200234908,"user_tz":-330,"elapsed":605,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"69f64655-7f79-4bdf-a0f1-6288620f032b"},"source":["print('Accuracy on Training data : ', training_data_accuracy)"],"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on Training data :  0.8512396694214877\n"]}]},{"cell_type":"code","metadata":{"id":"mDONDJdlwBIO","executionInfo":{"status":"ok","timestamp":1653200234908,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# accuracy on test data\n","X_test_prediction = model.predict(X_test)\n","test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"],"execution_count":19,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_MBS-OqdwYpf","executionInfo":{"status":"ok","timestamp":1653200234909,"user_tz":-330,"elapsed":13,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"bbe13e78-0dab-4925-e541-fb39be7d847f"},"source":["print('Accuracy on Test data : ', test_data_accuracy)"],"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy on Test data :  0.819672131147541\n"]}]},{"cell_type":"markdown","metadata":{"id":"jIruVh3Qwq0e"},"source":["Building a Predictive System"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9ercruC9wb4C","executionInfo":{"status":"ok","timestamp":1653200234909,"user_tz":-330,"elapsed":12,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"e4cddd3b-acdb-482c-a25f-b720f02111c9"},"source":["input_data = (62,0,0,140,268,0,0,160,0,3.6,0,2,2)\n","\n","# change the input data to a numpy array\n","input_data_as_numpy_array= np.asarray(input_data)\n","\n","# reshape the numpy array as we are predicting for only on instance\n","input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n","\n","prediction = model.predict(input_data_reshaped)\n","print(prediction)\n","\n","if (prediction[0]== 0):\n","  print('The Person does not have a Heart Disease')\n","else:\n","  print('The Person has Heart Disease')"],"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["[0]\n","The Person does not have a Heart Disease\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n","  \"X does not have valid feature names, but\"\n"]}]},{"cell_type":"markdown","metadata":{"id":"FCHCMHpshHU4"},"source":["Saving the trained model"]},{"cell_type":"code","metadata":{"id":"cdmTOR4MhHCB","executionInfo":{"status":"ok","timestamp":1653200234910,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["import pickle"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"4gN09lokhKuZ","executionInfo":{"status":"ok","timestamp":1653200234910,"user_tz":-330,"elapsed":10,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["filename = 'heart_disease_model.sav'\n","pickle.dump(model, open(filename, 'wb'))"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"IKW4D5CqhP5X","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"source":["# loading the saved model\n","loaded_model = pickle.load(open('heart_disease_model.sav', 'rb'))"],"execution_count":24,"outputs":[]},{"cell_type":"code","source":["for column in X.columns:\n","  print(column)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1SiD-oDpPSxY","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":11,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}},"outputId":"13c5618c-e337-4637-a35a-452e607e5ef4"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["age\n","sex\n","cp\n","trestbps\n","chol\n","fbs\n","restecg\n","thalach\n","exang\n","oldpeak\n","slope\n","ca\n","thal\n"]}]},{"cell_type":"code","source":[""],"metadata":{"id":"A23YZI2tPaFk","executionInfo":{"status":"ok","timestamp":1653200234911,"user_tz":-330,"elapsed":8,"user":{"displayName":"siddhardh selvam","userId":"13966379820454708749"}}},"execution_count":25,"outputs":[]}]}