diabeties-diagnosis-randomforestclassifier.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":7737512,"sourceType":"datasetVersion","datasetId":4522151}],"dockerImageVersionId":30664,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-03-21T06:13:39.515201Z","iopub.execute_input":"2024-03-21T06:13:39.515583Z","iopub.status.idle":"2024-03-21T06:13:39.527233Z","shell.execute_reply.started":"2024-03-21T06:13:39.515537Z","shell.execute_reply":"2024-03-21T06:13:39.526142Z"},"trusted":true},"execution_count":187,"outputs":[{"name":"stdout","text":"/kaggle/input/diabetes-classification-dataset/Diabetes Classification.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Import Libraries**","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.529500Z","iopub.execute_input":"2024-03-21T06:13:39.530583Z","iopub.status.idle":"2024-03-21T06:13:39.537779Z","shell.execute_reply.started":"2024-03-21T06:13:39.530523Z","shell.execute_reply":"2024-03-21T06:13:39.536804Z"},"trusted":true},"execution_count":188,"outputs":[]},{"cell_type":"markdown","source":"**Load Data**","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/diabetes-classification-dataset/Diabetes Classification.csv')\n\ndf.columns","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.539004Z","iopub.execute_input":"2024-03-21T06:13:39.539531Z","iopub.status.idle":"2024-03-21T06:13:39.562983Z","shell.execute_reply.started":"2024-03-21T06:13:39.539502Z","shell.execute_reply":"2024-03-21T06:13:39.561849Z"},"trusted":true},"execution_count":189,"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":"Index(['Unnamed: 0', 'Age', 'Gender', 'BMI', 'Chol', 'TG', 'HDL', 'LDL', 'Cr',\n       'BUN', 'Diagnosis'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.564412Z","iopub.execute_input":"2024-03-21T06:13:39.564767Z","iopub.status.idle":"2024-03-21T06:13:39.581618Z","shell.execute_reply.started":"2024-03-21T06:13:39.564737Z","shell.execute_reply":"2024-03-21T06:13:39.580660Z"},"trusted":true},"execution_count":190,"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":"   Unnamed: 0  Age Gender  BMI  Chol   TG  HDL  LDL    Cr  BUN  Diagnosis\n0           0   50      F   24   4.2  0.9  2.4  1.4  46.0  4.7          0\n1           1   26      M   23   3.7  1.4  1.1  2.1  62.0  4.5          0\n2           2   33      M   21   4.9  1.0  0.8  2.0  46.0  7.1          0\n3           3   45      F   21   2.9  1.0  1.0  1.5  24.0  2.3          0\n4           4   50      F   24   3.6  1.3  0.9  2.1  50.0  2.0          0","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>Unnamed: 0</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>BMI</th>\n      <th>Chol</th>\n      <th>TG</th>\n      <th>HDL</th>\n      <th>LDL</th>\n      <th>Cr</th>\n      <th>BUN</th>\n      <th>Diagnosis</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>50</td>\n      <td>F</td>\n      <td>24</td>\n      <td>4.2</td>\n      <td>0.9</td>\n      <td>2.4</td>\n      <td>1.4</td>\n      <td>46.0</td>\n      <td>4.7</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>26</td>\n      <td>M</td>\n      <td>23</td>\n      <td>3.7</td>\n      <td>1.4</td>\n      <td>1.1</td>\n      <td>2.1</td>\n      <td>62.0</td>\n      <td>4.5</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>33</td>\n      <td>M</td>\n      <td>21</td>\n      <td>4.9</td>\n      <td>1.0</td>\n      <td>0.8</td>\n      <td>2.0</td>\n      <td>46.0</td>\n      <td>7.1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>45</td>\n      <td>F</td>\n      <td>21</td>\n      <td>2.9</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.5</td>\n      <td>24.0</td>\n      <td>2.3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>50</td>\n      <td>F</td>\n      <td>24</td>\n      <td>3.6</td>\n      <td>1.3</td>\n      <td>0.9</td>\n      <td>2.1</td>\n      <td>50.0</td>\n      <td>2.0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"unq_num_gender = set(df['Gender'])\n\nprint(unq_num_gender)","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.584139Z","iopub.execute_input":"2024-03-21T06:13:39.584831Z","iopub.status.idle":"2024-03-21T06:13:39.593939Z","shell.execute_reply.started":"2024-03-21T06:13:39.584798Z","shell.execute_reply":"2024-03-21T06:13:39.592513Z"},"trusted":true},"execution_count":191,"outputs":[{"name":"stdout","text":"{'F', 'M', 'f'}\n","output_type":"stream"}]},{"cell_type":"code","source":"num_gender = []\n\nfor i in df['Gender']:\n    if(i == 'F'):\n        num_gender.append(1)\n    elif(i == 'M'):\n        num_gender.append(2)\n    elif(i == 'f'):\n        num_gender.append(1)\n    \ndf['num_gender'] = num_gender\n\ndf.drop(columns = 'Gender', inplace = True)\n\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.595593Z","iopub.execute_input":"2024-03-21T06:13:39.596048Z","iopub.status.idle":"2024-03-21T06:13:39.624255Z","shell.execute_reply.started":"2024-03-21T06:13:39.596005Z","shell.execute_reply":"2024-03-21T06:13:39.623045Z"},"trusted":true},"execution_count":192,"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":"   Unnamed: 0  Age  BMI  Chol   TG  HDL  LDL    Cr  BUN  Diagnosis  num_gender\n0           0   50   24   4.2  0.9  2.4  1.4  46.0  4.7          0           1\n1           1   26   23   3.7  1.4  1.1  2.1  62.0  4.5          0           2\n2           2   33   21   4.9  1.0  0.8  2.0  46.0  7.1          0           2\n3           3   45   21   2.9  1.0  1.0  1.5  24.0  2.3          0           1\n4           4   50   24   3.6  1.3  0.9  2.1  50.0  2.0          0           1","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>Unnamed: 0</th>\n      <th>Age</th>\n      <th>BMI</th>\n      <th>Chol</th>\n      <th>TG</th>\n      <th>HDL</th>\n      <th>LDL</th>\n      <th>Cr</th>\n      <th>BUN</th>\n      <th>Diagnosis</th>\n      <th>num_gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>50</td>\n      <td>24</td>\n      <td>4.2</td>\n      <td>0.9</td>\n      <td>2.4</td>\n      <td>1.4</td>\n      <td>46.0</td>\n      <td>4.7</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>26</td>\n      <td>23</td>\n      <td>3.7</td>\n      <td>1.4</td>\n      <td>1.1</td>\n      <td>2.1</td>\n      <td>62.0</td>\n      <td>4.5</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>33</td>\n      <td>21</td>\n      <td>4.9</td>\n      <td>1.0</td>\n      <td>0.8</td>\n      <td>2.0</td>\n      <td>46.0</td>\n      <td>7.1</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>45</td>\n      <td>21</td>\n      <td>2.9</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.5</td>\n      <td>24.0</td>\n      <td>2.3</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>50</td>\n      <td>24</td>\n      <td>3.6</td>\n      <td>1.3</td>\n      <td>0.9</td>\n      <td>2.1</td>\n      <td>50.0</td>\n      <td>2.0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"X = df.drop('Diagnosis', axis = 1)\n\ny = df['Diagnosis']","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.625755Z","iopub.execute_input":"2024-03-21T06:13:39.626232Z","iopub.status.idle":"2024-03-21T06:13:39.631566Z","shell.execute_reply.started":"2024-03-21T06:13:39.626204Z","shell.execute_reply":"2024-03-21T06:13:39.630637Z"},"trusted":true},"execution_count":193,"outputs":[]},{"cell_type":"code","source":"X","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.632868Z","iopub.execute_input":"2024-03-21T06:13:39.633183Z","iopub.status.idle":"2024-03-21T06:13:39.660936Z","shell.execute_reply.started":"2024-03-21T06:13:39.633155Z","shell.execute_reply":"2024-03-21T06:13:39.659345Z"},"trusted":true},"execution_count":194,"outputs":[{"execution_count":194,"output_type":"execute_result","data":{"text/plain":"      Unnamed: 0  Age  BMI  Chol    TG   HDL   LDL    Cr   BUN  num_gender\n0              0   50   24  4.20  0.90  2.40  1.40  46.0  4.70           1\n1              1   26   23  3.70  1.40  1.10  2.10  62.0  4.50           2\n2              2   33   21  4.90  1.00  0.80  2.00  46.0  7.10           2\n3              3   45   21  2.90  1.00  1.00  1.50  24.0  2.30           1\n4              4   50   24  3.60  1.30  0.90  2.10  50.0  2.00           1\n...          ...  ...  ...   ...   ...   ...   ...   ...   ...         ...\n5127        5127   54   23  5.00  1.50  1.24  2.98  77.0  3.50           2\n5128        5128   50   22  4.37  2.09  1.37  2.29  47.3  4.40           1\n5129        5129   67   24  3.89  1.38  1.14  2.17  70.6  4.73           2\n5130        5130   60   29  5.91  1.29  1.73  2.85  50.2  7.33           1\n5131        5131   37   34  5.42  2.66  1.08  2.87  75.5  4.61           2\n\n[5132 rows x 10 columns]","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>Unnamed: 0</th>\n      <th>Age</th>\n      <th>BMI</th>\n      <th>Chol</th>\n      <th>TG</th>\n      <th>HDL</th>\n      <th>LDL</th>\n      <th>Cr</th>\n      <th>BUN</th>\n      <th>num_gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>50</td>\n      <td>24</td>\n      <td>4.20</td>\n      <td>0.90</td>\n      <td>2.40</td>\n      <td>1.40</td>\n      <td>46.0</td>\n      <td>4.70</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>26</td>\n      <td>23</td>\n      <td>3.70</td>\n      <td>1.40</td>\n      <td>1.10</td>\n      <td>2.10</td>\n      <td>62.0</td>\n      <td>4.50</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>33</td>\n      <td>21</td>\n      <td>4.90</td>\n      <td>1.00</td>\n      <td>0.80</td>\n      <td>2.00</td>\n      <td>46.0</td>\n      <td>7.10</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>45</td>\n      <td>21</td>\n      <td>2.90</td>\n      <td>1.00</td>\n      <td>1.00</td>\n      <td>1.50</td>\n      <td>24.0</td>\n      <td>2.30</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>50</td>\n      <td>24</td>\n      <td>3.60</td>\n      <td>1.30</td>\n      <td>0.90</td>\n      <td>2.10</td>\n      <td>50.0</td>\n      <td>2.00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5127</th>\n      <td>5127</td>\n      <td>54</td>\n      <td>23</td>\n      <td>5.00</td>\n      <td>1.50</td>\n      <td>1.24</td>\n      <td>2.98</td>\n      <td>77.0</td>\n      <td>3.50</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5128</th>\n      <td>5128</td>\n      <td>50</td>\n      <td>22</td>\n      <td>4.37</td>\n      <td>2.09</td>\n      <td>1.37</td>\n      <td>2.29</td>\n      <td>47.3</td>\n      <td>4.40</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5129</th>\n      <td>5129</td>\n      <td>67</td>\n      <td>24</td>\n      <td>3.89</td>\n      <td>1.38</td>\n      <td>1.14</td>\n      <td>2.17</td>\n      <td>70.6</td>\n      <td>4.73</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>5130</th>\n      <td>5130</td>\n      <td>60</td>\n      <td>29</td>\n      <td>5.91</td>\n      <td>1.29</td>\n      <td>1.73</td>\n      <td>2.85</td>\n      <td>50.2</td>\n      <td>7.33</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5131</th>\n      <td>5131</td>\n      <td>37</td>\n      <td>34</td>\n      <td>5.42</td>\n      <td>2.66</td>\n      <td>1.08</td>\n      <td>2.87</td>\n      <td>75.5</td>\n      <td>4.61</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>5132 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"**Split data into train and test**","metadata":{}},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.662413Z","iopub.execute_input":"2024-03-21T06:13:39.662860Z","iopub.status.idle":"2024-03-21T06:13:39.672752Z","shell.execute_reply.started":"2024-03-21T06:13:39.662818Z","shell.execute_reply":"2024-03-21T06:13:39.671536Z"},"trusted":true},"execution_count":195,"outputs":[]},{"cell_type":"code","source":"X_train","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.674152Z","iopub.execute_input":"2024-03-21T06:13:39.675228Z","iopub.status.idle":"2024-03-21T06:13:39.695660Z","shell.execute_reply.started":"2024-03-21T06:13:39.675192Z","shell.execute_reply":"2024-03-21T06:13:39.694804Z"},"trusted":true},"execution_count":196,"outputs":[{"execution_count":196,"output_type":"execute_result","data":{"text/plain":"      Unnamed: 0  Age  BMI  Chol    TG       HDL       LDL    Cr   BUN  \\\n3740        3740   36   20  4.87  0.79  1.580000  2.980000  54.1  3.68   \n949          949   43   22  4.08  0.61  1.740000  2.030000  49.3  4.59   \n3826        3826   25   20  4.60  1.00  1.330000  2.630000  80.0  4.25   \n19            19   33   24  4.20  1.50  1.200000  2.300000  62.0  5.30   \n3049        3049   72   21  5.33  1.55  1.200000  2.940000  65.0  3.56   \n...          ...  ...  ...   ...   ...       ...       ...   ...   ...   \n4426        4426   58   27  4.98  2.48  4.860753  4.860753  68.3  6.24   \n466          466   63   30  3.60  5.10  0.900000  2.500000  63.0  5.90   \n3092        3092   40   16  4.00  0.00  1.000000  2.000000  67.0  4.00   \n3772        3772   42   23  4.00  0.80  1.120000  2.460000  96.0  5.49   \n860          860   78   25  5.83  2.10  1.080000  3.500000  81.4  4.30   \n\n      num_gender  \n3740           1  \n949            1  \n3826           2  \n19             1  \n3049           1  \n...          ...  \n4426           2  \n466            2  \n3092           2  \n3772           2  \n860            2  \n\n[4105 rows x 10 columns]","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>Unnamed: 0</th>\n      <th>Age</th>\n      <th>BMI</th>\n      <th>Chol</th>\n      <th>TG</th>\n      <th>HDL</th>\n      <th>LDL</th>\n      <th>Cr</th>\n      <th>BUN</th>\n      <th>num_gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3740</th>\n      <td>3740</td>\n      <td>36</td>\n      <td>20</td>\n      <td>4.87</td>\n      <td>0.79</td>\n      <td>1.580000</td>\n      <td>2.980000</td>\n      <td>54.1</td>\n      <td>3.68</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>949</th>\n      <td>949</td>\n      <td>43</td>\n      <td>22</td>\n      <td>4.08</td>\n      <td>0.61</td>\n      <td>1.740000</td>\n      <td>2.030000</td>\n      <td>49.3</td>\n      <td>4.59</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3826</th>\n      <td>3826</td>\n      <td>25</td>\n      <td>20</td>\n      <td>4.60</td>\n      <td>1.00</td>\n      <td>1.330000</td>\n      <td>2.630000</td>\n      <td>80.0</td>\n      <td>4.25</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>19</td>\n      <td>33</td>\n      <td>24</td>\n      <td>4.20</td>\n      <td>1.50</td>\n      <td>1.200000</td>\n      <td>2.300000</td>\n      <td>62.0</td>\n      <td>5.30</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3049</th>\n      <td>3049</td>\n      <td>72</td>\n      <td>21</td>\n      <td>5.33</td>\n      <td>1.55</td>\n      <td>1.200000</td>\n      <td>2.940000</td>\n      <td>65.0</td>\n      <td>3.56</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4426</th>\n      <td>4426</td>\n      <td>58</td>\n      <td>27</td>\n      <td>4.98</td>\n      <td>2.48</td>\n      <td>4.860753</td>\n      <td>4.860753</td>\n      <td>68.3</td>\n      <td>6.24</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>466</th>\n      <td>466</td>\n      <td>63</td>\n      <td>30</td>\n      <td>3.60</td>\n      <td>5.10</td>\n      <td>0.900000</td>\n      <td>2.500000</td>\n      <td>63.0</td>\n      <td>5.90</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3092</th>\n      <td>3092</td>\n      <td>40</td>\n      <td>16</td>\n      <td>4.00</td>\n      <td>0.00</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>67.0</td>\n      <td>4.00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3772</th>\n      <td>3772</td>\n      <td>42</td>\n      <td>23</td>\n      <td>4.00</td>\n      <td>0.80</td>\n      <td>1.120000</td>\n      <td>2.460000</td>\n      <td>96.0</td>\n      <td>5.49</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>860</th>\n      <td>860</td>\n      <td>78</td>\n      <td>25</td>\n      <td>5.83</td>\n      <td>2.10</td>\n      <td>1.080000</td>\n      <td>3.500000</td>\n      <td>81.4</td>\n      <td>4.30</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>4105 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"X_test","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.696621Z","iopub.execute_input":"2024-03-21T06:13:39.696923Z","iopub.status.idle":"2024-03-21T06:13:39.723626Z","shell.execute_reply.started":"2024-03-21T06:13:39.696898Z","shell.execute_reply":"2024-03-21T06:13:39.722509Z"},"trusted":true},"execution_count":197,"outputs":[{"execution_count":197,"output_type":"execute_result","data":{"text/plain":"      Unnamed: 0  Age  BMI  Chol    TG   HDL   LDL     Cr    BUN  num_gender\n5106        5106   68   29  5.22  3.55  0.87  2.46   93.8   4.89           2\n2186        2186   28   22  4.38  1.17  1.39  1.87   46.0   4.00           1\n2589        2589   54   18  4.09  0.96  1.43  2.50   81.7   7.68           2\n831          831   40   19  5.87  1.29  1.75  3.37   61.1   4.10           1\n1421        1421   41   22  4.50  0.50  1.75  1.94   52.0   3.12           1\n...          ...  ...  ...   ...   ...   ...   ...    ...    ...         ...\n1662        1662   53   23  4.03  1.57  1.03  2.56   72.4   6.00           2\n833          833   36   26  6.69  3.49  0.91  3.64   67.5   3.86           2\n366          366   69   32  5.30  3.80  1.40  2.30  243.0  14.50           1\n3778        3778   30   19  4.11  1.27  1.27  2.40   88.8   6.11           2\n1235        1235   78   27  4.87  1.40  1.05  3.03   47.4   5.60           1\n\n[1027 rows x 10 columns]","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>Unnamed: 0</th>\n      <th>Age</th>\n      <th>BMI</th>\n      <th>Chol</th>\n      <th>TG</th>\n      <th>HDL</th>\n      <th>LDL</th>\n      <th>Cr</th>\n      <th>BUN</th>\n      <th>num_gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5106</th>\n      <td>5106</td>\n      <td>68</td>\n      <td>29</td>\n      <td>5.22</td>\n      <td>3.55</td>\n      <td>0.87</td>\n      <td>2.46</td>\n      <td>93.8</td>\n      <td>4.89</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2186</th>\n      <td>2186</td>\n      <td>28</td>\n      <td>22</td>\n      <td>4.38</td>\n      <td>1.17</td>\n      <td>1.39</td>\n      <td>1.87</td>\n      <td>46.0</td>\n      <td>4.00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2589</th>\n      <td>2589</td>\n      <td>54</td>\n      <td>18</td>\n      <td>4.09</td>\n      <td>0.96</td>\n      <td>1.43</td>\n      <td>2.50</td>\n      <td>81.7</td>\n      <td>7.68</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>831</th>\n      <td>831</td>\n      <td>40</td>\n      <td>19</td>\n      <td>5.87</td>\n      <td>1.29</td>\n      <td>1.75</td>\n      <td>3.37</td>\n      <td>61.1</td>\n      <td>4.10</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1421</th>\n      <td>1421</td>\n      <td>41</td>\n      <td>22</td>\n      <td>4.50</td>\n      <td>0.50</td>\n      <td>1.75</td>\n      <td>1.94</td>\n      <td>52.0</td>\n      <td>3.12</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1662</th>\n      <td>1662</td>\n      <td>53</td>\n      <td>23</td>\n      <td>4.03</td>\n      <td>1.57</td>\n      <td>1.03</td>\n      <td>2.56</td>\n      <td>72.4</td>\n      <td>6.00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>833</th>\n      <td>833</td>\n      <td>36</td>\n      <td>26</td>\n      <td>6.69</td>\n      <td>3.49</td>\n      <td>0.91</td>\n      <td>3.64</td>\n      <td>67.5</td>\n      <td>3.86</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>366</th>\n      <td>366</td>\n      <td>69</td>\n      <td>32</td>\n      <td>5.30</td>\n      <td>3.80</td>\n      <td>1.40</td>\n      <td>2.30</td>\n      <td>243.0</td>\n      <td>14.50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3778</th>\n      <td>3778</td>\n      <td>30</td>\n      <td>19</td>\n      <td>4.11</td>\n      <td>1.27</td>\n      <td>1.27</td>\n      <td>2.40</td>\n      <td>88.8</td>\n      <td>6.11</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1235</th>\n      <td>1235</td>\n      <td>78</td>\n      <td>27</td>\n      <td>4.87</td>\n      <td>1.40</td>\n      <td>1.05</td>\n      <td>3.03</td>\n      <td>47.4</td>\n      <td>5.60</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>1027 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"y_train","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.725109Z","iopub.execute_input":"2024-03-21T06:13:39.725526Z","iopub.status.idle":"2024-03-21T06:13:39.734930Z","shell.execute_reply.started":"2024-03-21T06:13:39.725495Z","shell.execute_reply":"2024-03-21T06:13:39.733642Z"},"trusted":true},"execution_count":198,"outputs":[{"execution_count":198,"output_type":"execute_result","data":{"text/plain":"3740    0\n949     0\n3826    0\n19      0\n3049    0\n       ..\n4426    1\n466     1\n3092    0\n3772    0\n860     0\nName: Diagnosis, Length: 4105, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"y_test","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.736426Z","iopub.execute_input":"2024-03-21T06:13:39.736870Z","iopub.status.idle":"2024-03-21T06:13:39.749031Z","shell.execute_reply.started":"2024-03-21T06:13:39.736837Z","shell.execute_reply":"2024-03-21T06:13:39.747772Z"},"trusted":true},"execution_count":199,"outputs":[{"execution_count":199,"output_type":"execute_result","data":{"text/plain":"5106    1\n2186    0\n2589    0\n831     0\n1421    0\n       ..\n1662    0\n833     0\n366     1\n3778    0\n1235    0\nName: Diagnosis, Length: 1027, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"**Apply Model**","metadata":{}},{"cell_type":"code","source":"rf_classifier = RandomForestClassifier(random_state = 42)\nrf_classifier.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:39.753969Z","iopub.execute_input":"2024-03-21T06:13:39.754341Z","iopub.status.idle":"2024-03-21T06:13:40.353342Z","shell.execute_reply.started":"2024-03-21T06:13:39.754313Z","shell.execute_reply":"2024-03-21T06:13:40.352246Z"},"trusted":true},"execution_count":200,"outputs":[{"execution_count":200,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(random_state=42)","text/html":"<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = RandomForestClassifier.predict(rf_classifier, X_test)","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:40.354470Z","iopub.execute_input":"2024-03-21T06:13:40.355126Z","iopub.status.idle":"2024-03-21T06:13:40.377498Z","shell.execute_reply.started":"2024-03-21T06:13:40.355094Z","shell.execute_reply":"2024-03-21T06:13:40.376160Z"},"trusted":true},"execution_count":201,"outputs":[]},{"cell_type":"markdown","source":"**Check Accuracy**","metadata":{}},{"cell_type":"code","source":"accuracy = accuracy_score(y_test, y_pred)\nprint(accuracy)","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:40.380235Z","iopub.execute_input":"2024-03-21T06:13:40.380627Z","iopub.status.idle":"2024-03-21T06:13:40.386419Z","shell.execute_reply.started":"2024-03-21T06:13:40.380592Z","shell.execute_reply":"2024-03-21T06:13:40.385407Z"},"trusted":true},"execution_count":202,"outputs":[{"name":"stdout","text":"0.9980525803310614\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Visualization**","metadata":{}},{"cell_type":"code","source":"test_zeros_sum = 0\ntest_ones_sum = 0\n\nfor i in y_test:\n    if(i == 0):\n        test_zeros_sum += 1\n    elif(i == 1):\n        test_ones_sum += 1","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:40.387539Z","iopub.execute_input":"2024-03-21T06:13:40.387861Z","iopub.status.idle":"2024-03-21T06:13:40.399716Z","shell.execute_reply.started":"2024-03-21T06:13:40.387835Z","shell.execute_reply":"2024-03-21T06:13:40.398720Z"},"trusted":true},"execution_count":203,"outputs":[]},{"cell_type":"code","source":"pred_zeros_sum = 0\npred_ones_sum = 0\n\nfor i in y_pred:\n    if(i == 0):\n        pred_zeros_sum += 1\n    elif(i == 1):\n        pred_ones_sum += 1","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:40.400918Z","iopub.execute_input":"2024-03-21T06:13:40.401227Z","iopub.status.idle":"2024-03-21T06:13:40.410882Z","shell.execute_reply.started":"2024-03-21T06:13:40.401201Z","shell.execute_reply":"2024-03-21T06:13:40.409922Z"},"trusted":true},"execution_count":204,"outputs":[]},{"cell_type":"code","source":"categories_test = ['0', '1']\nvalues = [test_zeros_sum, test_ones_sum]\n\nplt.bar(categories_test, values, linestyle='-')\nplt.title('Model Accuracy')\nplt.xlabel('')\nplt.ylabel('Sum')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:40.412125Z","iopub.execute_input":"2024-03-21T06:13:40.412419Z","iopub.status.idle":"2024-03-21T06:13:40.623893Z","shell.execute_reply.started":"2024-03-21T06:13:40.412394Z","shell.execute_reply":"2024-03-21T06:13:40.622639Z"},"trusted":true},"execution_count":205,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"categories_pred = ['0', '1']\nvalues = [pred_zeros_sum, pred_ones_sum]\n\nplt.bar(categories_test, values, linestyle='-')\nplt.title('Model Accuracy')\nplt.xlabel('')\nplt.ylabel('Sum')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:40.625409Z","iopub.execute_input":"2024-03-21T06:13:40.625837Z","iopub.status.idle":"2024-03-21T06:13:40.842830Z","shell.execute_reply.started":"2024-03-21T06:13:40.625795Z","shell.execute_reply":"2024-03-21T06:13:40.841680Z"},"trusted":true},"execution_count":206,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"# Create the scatter plot for test\nplt.plot(X_test['Unnamed: 0'], abs(y_test - y_pred), color='red')\n\n# Add labels and title\nplt.xlabel('Unnamed')\nplt.ylabel('Diagnosis')\nplt.title('Scatter Plot of Absolute of difference')\n\n# Show the plot\nplt.grid(True)  # Add gridlines\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-21T06:13:40.844120Z","iopub.execute_input":"2024-03-21T06:13:40.844477Z","iopub.status.idle":"2024-03-21T06:13:41.100727Z","shell.execute_reply.started":"2024-03-21T06:13:40.844441Z","shell.execute_reply":"2024-03-21T06:13:41.099303Z"},"trusted":true},"execution_count":207,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]}]}