kidney disease with chatgpt 3.5 dataset.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":2005,"sourceType":"datasetVersion","datasetId":1111},{"sourceId":960570,"sourceType":"datasetVersion","datasetId":515409},{"sourceId":8413788,"sourceType":"datasetVersion","datasetId":5007956},{"sourceId":8414434,"sourceType":"datasetVersion","datasetId":5008428},{"sourceId":8414488,"sourceType":"datasetVersion","datasetId":5008469},{"sourceId":8438461,"sourceType":"datasetVersion","datasetId":5026511}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/youssef22ashraf/kidney-disease-with-chatgpt3-5-dataset?scriptVersionId=178502350\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"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":"2024-05-19T08:28:34.647725Z","iopub.execute_input":"2024-05-19T08:28:34.64874Z","iopub.status.idle":"2024-05-19T08:28:35.720061Z","shell.execute_reply.started":"2024-05-19T08:28:34.648697Z","shell.execute_reply":"2024-05-19T08:28:35.718927Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/dataaa/augmented_dataset.csv\n/kaggle/input/finaaaal/Untitled spreadsheet (2).xlsx\n/kaggle/input/jddbnd/real and ChatGPT 3.5.csv\n/kaggle/input/jddbnd/ChatGPT 3.5.csv\n/kaggle/input/kidney/kidney_disease.csv\n/kaggle/input/ckdisease/kidney_disease.csv\n/kaggle/input/chronic-kidney-disease/new_model.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\n# Load the original dataset\noriginal_data = pd.read_csv(\"/kaggle/input/kidney/kidney_disease.csv\")\n\n# Number of new instances to generate\nnum_new_instances = 5000\n\n# Generate new data instances by sampling from the original dataset\nnew_instances = original_data.sample(n=num_new_instances, replace=True).reset_index(drop=True)\n\n# Define features with null values and their corresponding new data generation strategies\nfeatures_to_fill_manually = {\n    'age': lambda: np.random.randint(1, 100),\n    'bp': lambda: np.random.randint(50, 180),\n    'sg': lambda: round(np.random.uniform(1.005, 1.030), 3),\n    'al': lambda: np.random.randint(0, 5),\n    'su': lambda: np.random.randint(0, 5),\n    'rbc': lambda: np.random.choice(['normal', 'abnormal']),\n    'pc': lambda: np.random.choice(['normal', 'abnormal']),\n    'pcc': lambda: np.random.choice(['present', 'notpresent']),\n    'ba': lambda: np.random.choice(['present', 'notpresent']),\n    'bgr': lambda: round(np.random.uniform(70, 490), 1),\n    'bu': lambda: round(np.random.uniform(10, 300), 1),\n    'sc': lambda: round(np.random.uniform(0.3, 10), 2),\n    'sod': lambda: round(np.random.uniform(100, 200), 1),\n    'pot': lambda: round(np.random.uniform(2.5, 7), 1),\n    'hemo': lambda: round(np.random.uniform(3, 17), 1),\n    'pcv': lambda: np.random.randint(15, 55),\n    'wc': lambda: np.random.randint(4000, 18000),\n    'rc': lambda: round(np.random.uniform(2.5, 7.5), 1),\n    'htn': lambda: np.random.choice(['yes', 'no']),\n    'dm': lambda: np.random.choice(['yes', 'no']),\n    'cad': lambda: np.random.choice(['yes', 'no']),\n    'appet': lambda: np.random.choice(['good', 'poor']),\n    'pe': lambda: np.random.choice(['yes', 'no']),\n    'ane': lambda: np.random.choice(['yes', 'no'])\n}\n\n# Fill null values manually for features requiring manual filling\nfor feature, fill_func in features_to_fill_manually.items():\n    if new_instances[feature].isnull().any():\n        new_instances[feature] = new_instances[feature].fillna(new_instances.apply(lambda row: fill_func(), axis=1))\n\n# Save the augmented dataset\naugmented_data = pd.concat([original_data, new_instances], ignore_index=True)\naugmented_data.to_csv(\"augmented_dataset.csv\", index=False)\n\naugmented_data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-14T19:06:44.735607Z","iopub.execute_input":"2024-05-14T19:06:44.736031Z","iopub.status.idle":"2024-05-14T19:06:45.349885Z","shell.execute_reply.started":"2024-05-14T19:06:44.736002Z","shell.execute_reply":"2024-05-14T19:06:45.348716Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"id                  0\nage                 9\nbp                 12\nsg                 47\nal                 46\nsu                 49\nrbc               152\npc                 65\npcc                 4\nba                  4\nbgr                44\nbu                 19\nsc                 17\nsod                87\npot                88\nhemo               52\npcv                70\nwc                105\nrc                130\nhtn                 2\ndm                  2\ncad                 2\nappet               1\npe                  1\nane                 1\nclassification      0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"categorical_features = new_instances.select_dtypes(include=['object']).columns\nnumerical_features = new_instances.select_dtypes(include=[np.number]).columns\n\nfor feature in categorical_features:\n    if new_instances[feature].isnull().any():\n        mode_value = new_instances[feature].mode().iloc[0]\n        new_instances[feature].fillna(mode_value, inplace=True)\n\nfor feature in numerical_features:\n    if new_instances[feature].isnull().any():\n        median_value = new_instances[feature].median()\n        new_instances[feature].fillna(median_value, inplace=True)\n        \naugmented_data = pd.concat([original_data, new_instances], ignore_index=True)\naugmented_data.to_csv(\"augmented_dataset.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T19:09:48.816971Z","iopub.execute_input":"2024-05-14T19:09:48.817696Z","iopub.status.idle":"2024-05-14T19:09:48.866383Z","shell.execute_reply.started":"2024-05-14T19:09:48.817658Z","shell.execute_reply":"2024-05-14T19:09:48.865376Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"augmented_data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-14T19:09:50.6622Z","iopub.execute_input":"2024-05-14T19:09:50.662575Z","iopub.status.idle":"2024-05-14T19:09:50.679054Z","shell.execute_reply.started":"2024-05-14T19:09:50.662546Z","shell.execute_reply":"2024-05-14T19:09:50.677866Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"id                  0\nage                 9\nbp                 12\nsg                 47\nal                 46\nsu                 49\nrbc               152\npc                 65\npcc                 4\nba                  4\nbgr                44\nbu                 19\nsc                 17\nsod                87\npot                88\nhemo               52\npcv                70\nwc                105\nrc                130\nhtn                 2\ndm                  2\ncad                 2\nappet               1\npe                  1\nane                 1\nclassification      0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\n\n\noriginal_data = pd.read_csv(\"/kaggle/input/kidney/kidney_disease.csv\")\n\n# Fill categorical null values with mode\n\ncategorical_columns = original_data.select_dtypes(include=['object']).columns\noriginal_data[categorical_columns] = original_data[categorical_columns].fillna(original_data[categorical_columns].mode().iloc[0])\n\n# Fill numerical null values with median\nnumerical_columns = original_data.select_dtypes(include=['number']).columns\noriginal_data[numerical_columns] = original_data[numerical_columns].fillna(original_data[numerical_columns].median())\n\noriginal_data\n","metadata":{"execution":{"iopub.status.busy":"2024-05-17T13:09:25.518521Z","iopub.execute_input":"2024-05-17T13:09:25.518931Z","iopub.status.idle":"2024-05-17T13:09:25.61763Z","shell.execute_reply.started":"2024-05-17T13:09:25.5189Z","shell.execute_reply":"2024-05-17T13:09:25.616211Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"      id   age    bp     sg   al   su     rbc        pc         pcc  \\\n0      0  48.0  80.0  1.020  1.0  0.0  normal    normal  notpresent   \n1      1   7.0  50.0  1.020  4.0  0.0  normal    normal  notpresent   \n2      2  62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent   \n3      3  48.0  70.0  1.005  4.0  0.0  normal  abnormal     present   \n4      4  51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent   \n..   ...   ...   ...    ...  ...  ...     ...       ...         ...   \n395  395  55.0  80.0  1.020  0.0  0.0  normal    normal  notpresent   \n396  396  42.0  70.0  1.025  0.0  0.0  normal    normal  notpresent   \n397  397  12.0  80.0  1.020  0.0  0.0  normal    normal  notpresent   \n398  398  17.0  60.0  1.025  0.0  0.0  normal    normal  notpresent   \n399  399  58.0  80.0  1.025  0.0  0.0  normal    normal  notpresent   \n\n             ba  ...  pcv    wc   rc  htn   dm  cad appet   pe  ane  \\\n0    notpresent  ...   44  7800  5.2  yes  yes   no  good   no   no   \n1    notpresent  ...   38  6000  5.2   no   no   no  good   no   no   \n2    notpresent  ...   31  7500  5.2   no  yes   no  poor   no  yes   \n3    notpresent  ...   32  6700  3.9  yes   no   no  poor  yes  yes   \n4    notpresent  ...   35  7300  4.6   no   no   no  good   no   no   \n..          ...  ...  ...   ...  ...  ...  ...  ...   ...  ...  ...   \n395  notpresent  ...   47  6700  4.9   no   no   no  good   no   no   \n396  notpresent  ...   54  7800  6.2   no   no   no  good   no   no   \n397  notpresent  ...   49  6600  5.4   no   no   no  good   no   no   \n398  notpresent  ...   51  7200  5.9   no   no   no  good   no   no   \n399  notpresent  ...   53  6800  6.1   no   no   no  good   no   no   \n\n    classification  \n0              ckd  \n1              ckd  \n2              ckd  \n3              ckd  \n4              ckd  \n..             ...  \n395         notckd  \n396         notckd  \n397         notckd  \n398         notckd  \n399         notckd  \n\n[400 rows x 26 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>id</th>\n      <th>age</th>\n      <th>bp</th>\n      <th>sg</th>\n      <th>al</th>\n      <th>su</th>\n      <th>rbc</th>\n      <th>pc</th>\n      <th>pcc</th>\n      <th>ba</th>\n      <th>...</th>\n      <th>pcv</th>\n      <th>wc</th>\n      <th>rc</th>\n      <th>htn</th>\n      <th>dm</th>\n      <th>cad</th>\n      <th>appet</th>\n      <th>pe</th>\n      <th>ane</th>\n      <th>classification</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>48.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>44</td>\n      <td>7800</td>\n      <td>5.2</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>7.0</td>\n      <td>50.0</td>\n      <td>1.020</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>38</td>\n      <td>6000</td>\n      <td>5.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>62.0</td>\n      <td>80.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>3.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>31</td>\n      <td>7500</td>\n      <td>5.2</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>48.0</td>\n      <td>70.0</td>\n      <td>1.005</td>\n      <td>4.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>abnormal</td>\n      <td>present</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>32</td>\n      <td>6700</td>\n      <td>3.9</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>51.0</td>\n      <td>80.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>35</td>\n      <td>7300</td>\n      <td>4.6</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>395</td>\n      <td>55.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>47</td>\n      <td>6700</td>\n      <td>4.9</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>396</td>\n      <td>42.0</td>\n      <td>70.0</td>\n      <td>1.025</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>54</td>\n      <td>7800</td>\n      <td>6.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>397</td>\n      <td>12.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>49</td>\n      <td>6600</td>\n      <td>5.4</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>398</th>\n      <td>398</td>\n      <td>17.0</td>\n      <td>60.0</td>\n      <td>1.025</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>51</td>\n      <td>7200</td>\n      <td>5.9</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>399</th>\n      <td>399</td>\n      <td>58.0</td>\n      <td>80.0</td>\n      <td>1.025</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>53</td>\n      <td>6800</td>\n      <td>6.1</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n  </tbody>\n</table>\n<p>400 rows × 26 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"print(original_data.isna().sum())\nprint(original_data.duplicated().sum())","metadata":{"execution":{"iopub.status.busy":"2024-05-17T13:09:26.980008Z","iopub.execute_input":"2024-05-17T13:09:26.980871Z","iopub.status.idle":"2024-05-17T13:09:26.997251Z","shell.execute_reply.started":"2024-05-17T13:09:26.980838Z","shell.execute_reply":"2024-05-17T13:09:26.995896Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"id                0\nage               0\nbp                0\nsg                0\nal                0\nsu                0\nrbc               0\npc                0\npcc               0\nba                0\nbgr               0\nbu                0\nsc                0\nsod               0\npot               0\nhemo              0\npcv               0\nwc                0\nrc                0\nhtn               0\ndm                0\ncad               0\nappet             0\npe                0\nane               0\nclassification    0\ndtype: int64\n0\n","output_type":"stream"}]},{"cell_type":"code","source":"# Remove '\\t' and '?' characters and convert to numeric\noriginal_data['pcv'] = original_data['pcv'].str.replace('\\t', '').str.replace('?', '')\noriginal_data['wc'] = original_data['wc'].str.replace('\\t', '').str.replace('?', '')\noriginal_data['rc'] = original_data['rc'].str.replace('\\t', '').str.replace('?', '')\n\noriginal_data['pcv'] = pd.to_numeric(original_data['pcv'], errors='coerce')\noriginal_data['wc'] = pd.to_numeric(original_data['wc'], errors='coerce')\noriginal_data['rc'] = pd.to_numeric(original_data['rc'], errors='coerce')\n\n# Print data types of modified columns\nprint(original_data[['pcv', 'wc', 'rc']].dtypes)\n\n# Print values of 'rc', 'wc', 'pcv' columns\nprint(original_data['rc'].values)\nprint(original_data['wc'].values)\nprint(original_data['pcv'].values)","metadata":{"execution":{"iopub.status.busy":"2024-05-17T13:09:28.119246Z","iopub.execute_input":"2024-05-17T13:09:28.119687Z","iopub.status.idle":"2024-05-17T13:09:28.145743Z","shell.execute_reply.started":"2024-05-17T13:09:28.119656Z","shell.execute_reply":"2024-05-17T13:09:28.144349Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"pcv    float64\nwc     float64\nrc     float64\ndtype: object\n[5.2 5.2 5.2 3.9 4.6 4.4 5.2 5.  4.  3.7 5.2 3.8 3.4 5.2 2.6 2.8 5.2 5.2\n 4.3 3.7 3.2 3.6 3.4 5.2 4.6 3.7 4.  4.1 5.2 5.2 5.2 4.  4.  5.2 5.2 4.9\n 5.2 2.5 5.2 4.2 4.1 5.2 4.5 3.1 5.2 5.2 4.7 5.2 3.5 3.5 3.8 5.2 3.7 4.7\n 4.2 5.2 3.4 5.2 4.3 5.2 5.2 5.2 3.8 5.2 5.2 5.2 5.2 5.2 5.2 6.  5.2 3.2\n 5.2 5.2 3.4 5.2 4.  3.7 5.2 5.  3.8 3.7 5.2 5.2 2.1 5.2 5.2 5.  4.7 5.2\n 4.2 5.6 3.6 3.2 3.9 5.2 5.2 4.  2.3 4.2 5.2 3.9 5.2 4.2 5.2 5.2 5.2 4.4\n 4.2 5.2 4.7 3.9 5.2 5.2 5.2 4.3 5.2 4.4 5.2 5.2 5.2 5.2 5.2 5.2 3.6 5.2\n 4.5 4.3 2.9 5.2 2.7 5.2 2.7 8.  3.8 5.2 5.2 5.2 5.2 5.2 5.2 5.2 5.2 5.2\n 4.1 3.3 5.2 3.  5.2 5.2 5.2 5.2 5.2 2.9 3.  5.2 5.2 3.9 5.2 4.3 2.4 4.8\n nan 3.2 5.4 5.2 5.2 5.2 5.2 5.2 4.4 3.1 4.9 5.2 4.6 3.4 3.9 5.2 5.2 5.2\n 5.2 3.7 5.2 5.2 3.6 5.2 5.2 5.2 5.2 3.4 4.8 3.4 5.2 2.8 5.2 5.2 3.5 3.\n 3.9 3.2 3.9 5.2 5.2 5.2 5.2 5.2 5.2 4.6 4.9 5.2 3.9 5.2 3.4 3.7 5.2 5.2\n 5.2 4.3 5.2 3.3 5.2 5.2 5.2 4.6 5.2 4.5 3.4 3.8 5.2 3.8 5.2 5.2 5.2 5.2\n 5.2 5.2 5.2 5.2 5.2 5.2 4.1 3.9 3.3 6.1 4.6 2.6 2.5 5.2 4.1 2.1 4.5 5.\n 4.7 6.2 5.2 6.3 5.1 5.8 5.5 5.2 5.3 4.9 5.4 5.2 4.5 5.  5.3 4.8 4.9 5.3\n 5.  4.5 5.5 5.2 5.2 4.6 5.5 4.8 6.4 5.6 5.2 6.  4.8 5.7 6.  5.9 6.  5.2\n 5.1 5.3 5.9 5.7 5.  5.4 5.8 6.5 5.9 5.2 4.9 4.7 5.8 5.  5.2 6.1 4.5 5.2\n 5.7 4.5 4.9 5.9 5.4 5.6 6.1 4.8 4.7 4.4 5.2 4.9 5.3 5.2 6.2 4.8 4.9 4.5\n 5.2 6.5 5.2 5.8 6.5 5.1 5.2 4.5 6.1 5.5 4.5 5.6 5.2 6.2 4.5 4.9 5.9 4.7\n 6.3 5.7 4.7 6.4 5.8 5.5 6.4 6.1 4.5 4.7 5.2 4.5 5.1 4.6 6.1 4.9 5.6 4.5\n 6.2 5.8 4.8 5.2 4.7 6.3 5.3 6.1 5.9 4.8 5.4 5.  5.5 4.9 6.4 5.6 5.2 4.8\n 5.5 5.7 4.9 5.9 6.5 5.  4.5 5.1 6.5 5.2 6.4 5.8 5.3 6.3 5.5 5.4 4.6 4.9\n 6.2 5.4 5.9 6.1]\n[ 7800.  6000.  7500.  6700.  7300.  7800.  9800.  6900.  9600. 12100.\n  9800.  4500. 12200.  9800. 11000.  3800.  9800.  9800. 11400.  5300.\n  9200.  6200.  6900.  9800.  8300.  8400. 10300.  9600.  9800.  9800.\n  9800.  7800.  9600.  9800.  9800.  9800.  9800.  6900.  9800.  9800.\n  9100.  9800.  9200.  7900.  9800.  9800.  6400.  8600. 18900. 21600.\n 11000.  9800.  4300.  8500.  9800.  9800. 11300.  9800.  7200.  9800.\n  9800.  9800.  7700.  9800.  9800.  9800.  9800.  9800.  9800.  6900.\n  8300. 14600.  9800.  6300.  6400.  9800.  6200.  7100.  9800. 11800.\n  9400.  5500.  9800.  9800.  3800.  9800.  9800.  5800. 13200.  9800.\n  9800. 12500.  5600.  7000. 11900.  9800.  9800.  9100.  5800. 10400.\n  9800. 10700.  7000. 12700.  9800.  6800.  6500. 13600. 10200.  9800.\n 11400.  9000.  9800.  9800. 10300.  5600.  9800.  9800.  9800.  9800.\n  9800.  9800.  9800. 14900.  5500.  9800.  8200. 15200.  5000.  9800.\n 16300. 12400. 13200.  8400. 10200.  9800.  9800.  9200.  9800.  9800.\n  9800.  6500.  9800.  9800. 10500.  4200.  9800. 15200.  9800.  9800.\n  6700.  9800.  9800. 14600.  9800.  4700.  9800.  7900.  9800. 10900.\n  9400. 10200.  6000.  8100.  9500.  9800.  9800.  2200.  6700.  9800.\n  9600. 12800. 12800. 11200.  8600.  4200. 12400.  9800.  9800.  9800.\n  9800. 19100.  9800.  9800.  5800.    nan  9800. 12300.  9800.  7500.\n 16700.  9200.  9800.  2600.  9800.  9800.  9600.  4300. 26400. 10700.\n  7900.  9800.  9800.  9800.  9800.  9800.  9800. 10400.  6900.  6900.\n  9800.  9800.  8800.  7400.  4900.  9800.  9800. 10500.  7000.  8000.\n  8800.  9800.  9800.  9200.  9800. 12000.  7500.  7200.  9800. 15700.\n  9500.  9800.  9800.  9800.  4100.  5700.  9800.  9600.  9800.  9800.\n  5000.  4200. 11500. 11000.  9800.  7200.  5000.  9800. 10200.  5400.\n 10400.  9800.  9100.  6200.  8300.  9200. 10700.  9100.  8400.  4300.\n  9400.  5600.  7200.  8600.  5000. 10500.  6800.  9500.  8500.  6900.\n  7200.  9000.  5600.  9800.  9800.  4700.  4500.  6300.  9400.  5800.\n  8100.  9100.  5500. 10800.  6700.  5300.  9800.  9800. 11000.  7100.\n  8400.  7000. 10200.  4700.  8300.  9600.  6300.  7700.  8600. 11000.\n  9200.  7200.  9800.  6700.  6200.  9100.  4300.  7900.  6900.  8100.\n 11000.  7500.  6300.  8200.  9400.  7800.  5000.  8100.  7000.  9800.\n 10400.  9600.  4500.  7800.  9800.  6700.  8300. 10700.  8600.  7800.\n  9800.  6700. 10500.  4300.  5600. 10700.  9200.  7500.  4700.  7000.\n  6700.  7300.  7700.  5500.  8100.  7900.  4300.  7200.  7300.  9400.\n  9600.  9900.  7000.  5800.  6800.  6300.  7400. 11000.  5200.  6000.\n  5800.  5400. 10300.  5900.  9300. 10700. 10500.  6700.  9400. 10300.\n  9300.  6500. 10500.  9200.  8000.  9700.  9100.  6400.  7700.  5400.\n  6500.  5800.  6000.  5100. 11000.  8000.  5700.  6200.  9500.  7200.\n  6300.  5800.  6600.  7400.  9500.  6700.  7800.  6600.  7200.  6800.]\n[44. 38. 31. 32. 35. 39. 36. 44. 33. 29. 28. 32. 28. 41. 16. 24. 41. 41.\n 37. 30. 24. 32. 32. 41. 39. 29. 35. 37. 41. 38. 41. 30. 34. 29. 41. 36.\n 32. 28. 41. 40. 32. 41. 33. 24. 28. 41. 37. 45. 29. 29. 28. 33. 34. 40.\n 40. 28. 30. 41. 33. 41. 41. 41. 33. 27. 41. 48. nan 41. 37. 52. 44. 28.\n 41. 14. 29. 41. 36. 34. 30. 40. 31. 29. 41. 41. 22. 41. 41. 32. 41. 41.\n 40. 52. 33. 30. 36. 41. 36. 32. 18. 32. 41. 33. 52. 36. 41. 42. 17. 34.\n 37. 41. 34. 32. 33. 41. 41. 44. 41. 37. 41. 41. 41. 46. 41. 42. 28. 41.\n 37. 35. 23. 41. 22. 36. 24. 37. 33. 39. 41. 29. 41. 33. 41. 19. 41. 41.\n 33. 24. 41. 25. 41. 32. 41. 30. 32. 22. 26. 36. 41. 39. 31. 35. 35. 42.\n 37. 27. 40. 41. 41. 42. 39. 27. 33. 26. 34. 41. 35. 41. 23. 41. 34. 39.\n 28. 30. 35. 41. 31. 34. 41. 34. 38. 29. 30. 26. 41. 15. 41. 41. 24. 41.\n 30. 25. 37. 21. 24. 41. 31. 43. 41. 41. 41. 41. 20. 41. 31. 34. 43. 41.\n 38. 36. 34. 28. 36. 41. 41. 38. 41. 35. 26. 36. 41. 31. 41. 35. 41. 41.\n 44. 26. 25. 40. 28. 39. 36. 31. 28. 47. 40. 19. 26. 41. 37.  9. 48. 52.\n 46. 42. 49. 52. 41. 46. 44. 45. 45. 41. 43. 45. 50. 48. 41. 48. 53. 48.\n 41. 42. 42. 41. 44. 43. 41. 50. 44. 48. 52. 41. 44. 43. 52. 41. 44. 43.\n 50. 43. 52. 41. 52. 48. 40. 50. 41. 45. 48. 52. 43. 41. 48. 50. 45. 41.\n 52. 44. 48. 40. 44. 45. 48. 52. 41. 48. 48. 40. 52. 41. 44. 48. 43. 45.\n 41. 50. 41. 44. 45. 48. 42. 46. 44. 46. 43. 48. 40. 52. 44. 48. 43. 41.\n 50. 50. 42. 42. 52. 43. 44. 52. 43. 40. 48. 51. 41. 52. 47. 42. 45. 53.\n 43. 54. 40. 44. 45. 41. 46. 50. 45. 46. 50. 51. 52. 47. 40. 48. 53. 49.\n 42. 50. 54. 40. 51. 49. 42. 52. 43. 50. 46. 52. 52. 44. 46. 54. 45. 47.\n 54. 49. 51. 53.]\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nfrom scipy.stats import mode\n\n# Function to replace NaN with mean in numerical features and mode in categorical features\ndef replace_nan_with_mean_mode(df):\n    for column in df.columns:\n        if df[column].dtype in ['int64', 'float64']:\n            mean_value = round(df[column].mean(),2)\n            df[column] = df[column].fillna(mean_value)\n        else:\n            mode_value = df[column].mode()[0]\n            df[column] = df[column].fillna(mode_value)\n    return df\n\n# Example usage with a sample DataFrame\n# Assuming chatgpt3_5 is already defined\ncleaned_real = replace_nan_with_mean_mode(original_data)\n\n\n\n# Check the DataFrame\ncleaned_real.rc.unique()\n\ncleaned_real.duplicated().sum()\n","metadata":{"execution":{"iopub.status.busy":"2024-05-17T13:09:29.282323Z","iopub.execute_input":"2024-05-17T13:09:29.282736Z","iopub.status.idle":"2024-05-17T13:09:29.730832Z","shell.execute_reply.started":"2024-05-17T13:09:29.282706Z","shell.execute_reply":"2024-05-17T13:09:29.729602Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"cleaned_real['classification'] = cleaned_real['classification'].replace({'ckd\\t': 'ckd'})\n\ncleaned_real.classification.nunique\n","metadata":{"execution":{"iopub.status.busy":"2024-05-17T13:09:31.14914Z","iopub.execute_input":"2024-05-17T13:09:31.14954Z","iopub.status.idle":"2024-05-17T13:09:31.160366Z","shell.execute_reply.started":"2024-05-17T13:09:31.149512Z","shell.execute_reply":"2024-05-17T13:09:31.15887Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"<bound method IndexOpsMixin.nunique of 0         ckd\n1         ckd\n2         ckd\n3         ckd\n4         ckd\n        ...  \n395    notckd\n396    notckd\n397    notckd\n398    notckd\n399    notckd\nName: classification, Length: 400, dtype: object>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\n\nordinal_encoded_real = cleaned_real.copy()\n\nlabel_encoders = {}\nfor column in ordinal_encoded_real.select_dtypes(include=['object']).columns:\n    label_encoders[column] = LabelEncoder()\n    ordinal_encoded_real[column] = label_encoders[column].fit_transform(ordinal_encoded_real[column])\nordinal_encoded_real\nordinal_encoded_real.to_csv('cleaned_real.csv')","metadata":{"execution":{"iopub.status.busy":"2024-05-17T13:09:35.809684Z","iopub.execute_input":"2024-05-17T13:09:35.810175Z","iopub.status.idle":"2024-05-17T13:09:35.840161Z","shell.execute_reply.started":"2024-05-17T13:09:35.810141Z","shell.execute_reply":"2024-05-17T13:09:35.838946Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# Fill categorical null values with mode\ncategorical_columns = augmented_data.select_dtypes(include=['object']).columns\naugmented_data[categorical_columns] = augmented_data[categorical_columns].fillna(augmented_data[categorical_columns].mode().iloc[0])\n\n# Fill numerical null values with median\nnumerical_columns = augmented_data.select_dtypes(include=['number']).columns\naugmented_data[numerical_columns] = augmented_data[numerical_columns].fillna(augmented_data[numerical_columns].median())\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"augmented_data.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-14T19:15:05.384768Z","iopub.execute_input":"2024-05-14T19:15:05.385472Z","iopub.status.idle":"2024-05-14T19:15:05.400308Z","shell.execute_reply.started":"2024-05-14T19:15:05.385433Z","shell.execute_reply":"2024-05-14T19:15:05.398843Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"id                0\nage               0\nbp                0\nsg                0\nal                0\nsu                0\nrbc               0\npc                0\npcc               0\nba                0\nbgr               0\nbu                0\nsc                0\nsod               0\npot               0\nhemo              0\npcv               0\nwc                0\nrc                0\nhtn               0\ndm                0\ncad               0\nappet             0\npe                0\nane               0\nclassification    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"\n# Separate the new data from the filled data\nnew_data = augmented_data.iloc[402:]\n\n# Save the new data to a separate CSV file\nnew_data.to_csv(\"new_data.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:32:10.355485Z","iopub.execute_input":"2024-05-14T21:32:10.355943Z","iopub.status.idle":"2024-05-14T21:32:10.377411Z","shell.execute_reply.started":"2024-05-14T21:32:10.355909Z","shell.execute_reply":"2024-05-14T21:32:10.376269Z"},"trusted":true},"execution_count":81,"outputs":[]},{"cell_type":"code","source":"new_data.shape","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:32:11.635026Z","iopub.execute_input":"2024-05-14T21:32:11.635435Z","iopub.status.idle":"2024-05-14T21:32:11.642208Z","shell.execute_reply.started":"2024-05-14T21:32:11.635403Z","shell.execute_reply":"2024-05-14T21:32:11.641178Z"},"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":"(541, 26)"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\n\nnew_data = pd.read_csv(\"/kaggle/working/new_data.csv\")\n\n# Generate two random rows\n# Define the new rows\nnew_rows = [\n    [252, 45, 70, 1.02, 2, 1, 'abnormal', 'abnormal', 'present', 'notpresent', 300, 50, 1.5, 140, 4.0, 12.0, 35, 11000, 3.9, 'yes', 'no', 'no', 'good', 'yes', 'no', 'ckd'],\n    [253, 32, 75, 1.025, 0, 0, 'normal', 'normal', 'notpresent', 'notpresent', 120, 30, 0.9, 137, 4.4, 14.5, 42, 9800, 4.2, 'no', 'no', 'no', 'good', 'no', 'no', 'notckd']\n]\n\n# Define column names\ncolumns = ['id', 'age', 'bp', 'sg', 'al', 'su', 'rbc', 'pc', 'pcc', 'ba', 'bgr', 'bu', 'sc', 'sod', 'pot', 'hemo', 'pcv', 'wc', 'rc', 'htn', 'dm', 'cad', 'appet', 'pe', 'ane', 'classification']\n\n# Convert new rows to DataFrame\nnew_rows_df = pd.DataFrame(new_rows, columns=columns)\n\n# Concatenate existing DataFrame with new rows DataFrame\nnew_data = pd.concat([new_data, new_rows_df], ignore_index=True)\n\n","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:35:18.108955Z","iopub.execute_input":"2024-05-14T21:35:18.109359Z","iopub.status.idle":"2024-05-14T21:35:18.131978Z","shell.execute_reply.started":"2024-05-14T21:35:18.109329Z","shell.execute_reply":"2024-05-14T21:35:18.130811Z"},"trusted":true},"execution_count":84,"outputs":[]},{"cell_type":"code","source":"new_data.shape","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:35:20.017184Z","iopub.execute_input":"2024-05-14T21:35:20.017558Z","iopub.status.idle":"2024-05-14T21:35:20.024275Z","shell.execute_reply.started":"2024-05-14T21:35:20.017529Z","shell.execute_reply":"2024-05-14T21:35:20.023166Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"(543, 26)"},"metadata":{}}]},{"cell_type":"code","source":"new_data.to_csv(\"new_data.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:35:24.285846Z","iopub.execute_input":"2024-05-14T21:35:24.286254Z","iopub.status.idle":"2024-05-14T21:35:24.307199Z","shell.execute_reply.started":"2024-05-14T21:35:24.286222Z","shell.execute_reply":"2024-05-14T21:35:24.306188Z"},"trusted":true},"execution_count":86,"outputs":[]},{"cell_type":"code","source":"# Count duplicate rows\nnum_duplicate_rows = augmented_data.duplicated().sum()\n\nprint(\"Number of duplicate rows:\", num_duplicate_rows)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:35:27.395287Z","iopub.execute_input":"2024-05-14T21:35:27.395693Z","iopub.status.idle":"2024-05-14T21:35:27.410006Z","shell.execute_reply.started":"2024-05-14T21:35:27.39566Z","shell.execute_reply":"2024-05-14T21:35:27.408754Z"},"trusted":true},"execution_count":87,"outputs":[{"name":"stdout","text":"Number of duplicate rows: 0\n","output_type":"stream"}]},{"cell_type":"code","source":"# Drop duplicate rows\naugmented_data = augmented_data.drop_duplicates()\n\n# Confirm the removal of duplicates\nprint(\"Number of rows after dropping duplicates:\", len(augmented_data))\n\n\n# Drop duplicate rows\nnew_data = new_data.drop_duplicates()\n\n# Confirm the removal of duplicates\nprint(\"Number of rows after dropping duplicates:\", len(new_data))","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:35:31.84275Z","iopub.execute_input":"2024-05-14T21:35:31.84315Z","iopub.status.idle":"2024-05-14T21:35:31.861895Z","shell.execute_reply.started":"2024-05-14T21:35:31.843122Z","shell.execute_reply":"2024-05-14T21:35:31.860792Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"Number of rows after dropping duplicates: 943\nNumber of rows after dropping duplicates: 543\n","output_type":"stream"}]},{"cell_type":"code","source":"# Save to CSV\naugmented_data.to_csv('filled_data.csv', index=False)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:36:09.095956Z","iopub.execute_input":"2024-05-14T21:36:09.096375Z","iopub.status.idle":"2024-05-14T21:36:09.124341Z","shell.execute_reply.started":"2024-05-14T21:36:09.096343Z","shell.execute_reply":"2024-05-14T21:36:09.123177Z"},"trusted":true},"execution_count":89,"outputs":[]},{"cell_type":"code","source":"data = pd.read_excel(\"/kaggle/input/finaaaal/Untitled spreadsheet (2).xlsx\")\ndata","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:36:12.587181Z","iopub.execute_input":"2024-05-14T21:36:12.588372Z","iopub.status.idle":"2024-05-14T21:36:12.999603Z","shell.execute_reply.started":"2024-05-14T21:36:12.588322Z","shell.execute_reply":"2024-05-14T21:36:12.998447Z"},"trusted":true},"execution_count":90,"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"      id  age   bp     sg  al  su       rbc        pc         pcc          ba  \\\n0     52   45   80  1.020   0   0    normal    normal  notpresent  notpresent   \n1     53   55   90  1.025   2   1  abnormal  abnormal     present  notpresent   \n2     54   70  100  1.010   3   2    normal    normal  notpresent  notpresent   \n3     55   60   70  1.015   1   0    normal    normal  notpresent  notpresent   \n4     56   35   85  1.020   1   1  abnormal  abnormal  notpresent     present   \n..   ...  ...  ...    ...  ..  ..       ...       ...         ...         ...   \n393  496   50   70  1.020   1   0    normal    normal  notpresent  notpresent   \n394  497   35   85  1.025   2   0    normal    normal  notpresent  notpresent   \n395  498   63   90  1.010   0   0  abnormal    normal  notpresent  notpresent   \n396  499   42   80  1.020   1   1    normal  abnormal     present     present   \n397  500   52  100  1.025   3   2  abnormal  abnormal  notpresent  notpresent   \n\n     ...  pcv    wc   rc  htn   dm  cad  appet   pe  ane classification  \n0    ...   44  7200  4.2   no   no   no   good   no   no         notckd  \n1    ...   39  7700  4.5  yes  yes   no   poor   no   no            ckd  \n2    ...   41  7400  3.8  yes  yes   no   poor  yes  yes            ckd  \n3    ...   43  7100  4.0   no   no   no   good   no   no         notckd  \n4    ...   38  7800  4.3  yes  yes   no   poor  yes  yes            ckd  \n..   ...  ...   ...  ...  ...  ...  ...    ...  ...  ...            ...  \n393  ...   41  7100  4.0   no   no   no   good   no   no         notckd  \n394  ...   39  7200  4.2  yes   no   no   good   no   no            ckd  \n395  ...   46  7000  3.7   no   no   no   good   no   no         notckd  \n396  ...   34  7500  4.5  yes  yes  yes   poor  yes   no            ckd  \n397  ...   37  7600  4.8  yes  yes   no   poor   no  yes            ckd  \n\n[398 rows x 26 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>id</th>\n      <th>age</th>\n      <th>bp</th>\n      <th>sg</th>\n      <th>al</th>\n      <th>su</th>\n      <th>rbc</th>\n      <th>pc</th>\n      <th>pcc</th>\n      <th>ba</th>\n      <th>...</th>\n      <th>pcv</th>\n      <th>wc</th>\n      <th>rc</th>\n      <th>htn</th>\n      <th>dm</th>\n      <th>cad</th>\n      <th>appet</th>\n      <th>pe</th>\n      <th>ane</th>\n      <th>classification</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>52</td>\n      <td>45</td>\n      <td>80</td>\n      <td>1.020</td>\n      <td>0</td>\n      <td>0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>44</td>\n      <td>7200</td>\n      <td>4.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>53</td>\n      <td>55</td>\n      <td>90</td>\n      <td>1.025</td>\n      <td>2</td>\n      <td>1</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>present</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>39</td>\n      <td>7700</td>\n      <td>4.5</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>54</td>\n      <td>70</td>\n      <td>100</td>\n      <td>1.010</td>\n      <td>3</td>\n      <td>2</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>41</td>\n      <td>7400</td>\n      <td>3.8</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>55</td>\n      <td>60</td>\n      <td>70</td>\n      <td>1.015</td>\n      <td>1</td>\n      <td>0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>43</td>\n      <td>7100</td>\n      <td>4.0</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>56</td>\n      <td>35</td>\n      <td>85</td>\n      <td>1.020</td>\n      <td>1</td>\n      <td>1</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>notpresent</td>\n      <td>present</td>\n      <td>...</td>\n      <td>38</td>\n      <td>7800</td>\n      <td>4.3</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>ckd</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>393</th>\n      <td>496</td>\n      <td>50</td>\n      <td>70</td>\n      <td>1.020</td>\n      <td>1</td>\n      <td>0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>41</td>\n      <td>7100</td>\n      <td>4.0</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>394</th>\n      <td>497</td>\n      <td>35</td>\n      <td>85</td>\n      <td>1.025</td>\n      <td>2</td>\n      <td>0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>39</td>\n      <td>7200</td>\n      <td>4.2</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>395</th>\n      <td>498</td>\n      <td>63</td>\n      <td>90</td>\n      <td>1.010</td>\n      <td>0</td>\n      <td>0</td>\n      <td>abnormal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>46</td>\n      <td>7000</td>\n      <td>3.7</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>396</th>\n      <td>499</td>\n      <td>42</td>\n      <td>80</td>\n      <td>1.020</td>\n      <td>1</td>\n      <td>1</td>\n      <td>normal</td>\n      <td>abnormal</td>\n      <td>present</td>\n      <td>present</td>\n      <td>...</td>\n      <td>34</td>\n      <td>7500</td>\n      <td>4.5</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>397</th>\n      <td>500</td>\n      <td>52</td>\n      <td>100</td>\n      <td>1.025</td>\n      <td>3</td>\n      <td>2</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>37</td>\n      <td>7600</td>\n      <td>4.8</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n  </tbody>\n</table>\n<p>398 rows × 26 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.shape","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:06:59.166781Z","iopub.execute_input":"2024-05-14T21:06:59.16719Z","iopub.status.idle":"2024-05-14T21:06:59.175873Z","shell.execute_reply.started":"2024-05-14T21:06:59.167159Z","shell.execute_reply":"2024-05-14T21:06:59.174645Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"Number of rows after dropping rows after index 399: 400\n","output_type":"stream"},{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"(400, 30)"},"metadata":{}}]},{"cell_type":"code","source":"merged_df = pd.concat([data, augmented_data], ignore_index=True)\nmerged_df","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:27:59.60313Z","iopub.execute_input":"2024-05-14T21:27:59.603514Z","iopub.status.idle":"2024-05-14T21:27:59.645509Z","shell.execute_reply.started":"2024-05-14T21:27:59.603485Z","shell.execute_reply":"2024-05-14T21:27:59.644455Z"},"trusted":true},"execution_count":73,"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"       id   age     bp     sg   al   su       rbc        pc         pcc  \\\n0      52  45.0   80.0  1.020  0.0  0.0    normal    normal  notpresent   \n1      53  55.0   90.0  1.025  2.0  1.0  abnormal  abnormal     present   \n2      54  70.0  100.0  1.010  3.0  2.0    normal    normal  notpresent   \n3      55  60.0   70.0  1.015  1.0  0.0    normal    normal  notpresent   \n4      56  35.0   85.0  1.020  1.0  1.0  abnormal  abnormal  notpresent   \n...   ...   ...    ...    ...  ...  ...       ...       ...         ...   \n1336  110  63.0   90.0  1.015  0.0  0.0  abnormal    normal  notpresent   \n1337   82  38.0   70.0  1.011  0.0  1.0    normal    normal  notpresent   \n1338   67  45.0   80.0  1.020  3.0  0.0    normal  abnormal  notpresent   \n1339  187   3.0  115.0  1.010  2.0  0.0    normal    normal  notpresent   \n1340   85  70.0   70.0  1.015  2.0  4.0  abnormal  abnormal  notpresent   \n\n              ba  ...  pcv     wc   rc  htn   dm  cad appet   pe  ane  \\\n0     notpresent  ...   44   7200  4.2   no   no   no  good   no   no   \n1     notpresent  ...   39   7700  4.5  yes  yes   no  poor   no   no   \n2     notpresent  ...   41   7400  3.8  yes  yes   no  poor  yes  yes   \n3     notpresent  ...   43   7100  4.0   no   no   no  good   no   no   \n4        present  ...   38   7800  4.3  yes  yes   no  poor  yes  yes   \n...          ...  ...  ...    ...  ...  ...  ...  ...   ...  ...  ...   \n1336  notpresent  ...   34  11400  4.7   no   no   no  good   no   no   \n1337  notpresent  ...   51   7291  5.2  yes   no   no  poor  yes   no   \n1338  notpresent  ...   36  12605  4.2   no   no   no  poor   no   no   \n1339  notpresent  ...   34  12300  4.4   no   no   no  good   no   no   \n1340  notpresent  ...   19   6802  3.8   no  yes   no  poor  yes   no   \n\n     classification  \n0            notckd  \n1               ckd  \n2               ckd  \n3            notckd  \n4               ckd  \n...             ...  \n1336            ckd  \n1337            ckd  \n1338            ckd  \n1339            ckd  \n1340            ckd  \n\n[1341 rows x 26 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>id</th>\n      <th>age</th>\n      <th>bp</th>\n      <th>sg</th>\n      <th>al</th>\n      <th>su</th>\n      <th>rbc</th>\n      <th>pc</th>\n      <th>pcc</th>\n      <th>ba</th>\n      <th>...</th>\n      <th>pcv</th>\n      <th>wc</th>\n      <th>rc</th>\n      <th>htn</th>\n      <th>dm</th>\n      <th>cad</th>\n      <th>appet</th>\n      <th>pe</th>\n      <th>ane</th>\n      <th>classification</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>52</td>\n      <td>45.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>44</td>\n      <td>7200</td>\n      <td>4.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>53</td>\n      <td>55.0</td>\n      <td>90.0</td>\n      <td>1.025</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>present</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>39</td>\n      <td>7700</td>\n      <td>4.5</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>54</td>\n      <td>70.0</td>\n      <td>100.0</td>\n      <td>1.010</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>41</td>\n      <td>7400</td>\n      <td>3.8</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>55</td>\n      <td>60.0</td>\n      <td>70.0</td>\n      <td>1.015</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>43</td>\n      <td>7100</td>\n      <td>4.0</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>56</td>\n      <td>35.0</td>\n      <td>85.0</td>\n      <td>1.020</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>notpresent</td>\n      <td>present</td>\n      <td>...</td>\n      <td>38</td>\n      <td>7800</td>\n      <td>4.3</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>ckd</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1336</th>\n      <td>110</td>\n      <td>63.0</td>\n      <td>90.0</td>\n      <td>1.015</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>abnormal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>34</td>\n      <td>11400</td>\n      <td>4.7</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1337</th>\n      <td>82</td>\n      <td>38.0</td>\n      <td>70.0</td>\n      <td>1.011</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>51</td>\n      <td>7291</td>\n      <td>5.2</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1338</th>\n      <td>67</td>\n      <td>45.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>3.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>abnormal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>36</td>\n      <td>12605</td>\n      <td>4.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1339</th>\n      <td>187</td>\n      <td>3.0</td>\n      <td>115.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>34</td>\n      <td>12300</td>\n      <td>4.4</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1340</th>\n      <td>85</td>\n      <td>70.0</td>\n      <td>70.0</td>\n      <td>1.015</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>19</td>\n      <td>6802</td>\n      <td>3.8</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n  </tbody>\n</table>\n<p>1341 rows × 26 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"merged_df.shape","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:36:17.132903Z","iopub.execute_input":"2024-05-14T21:36:17.133323Z","iopub.status.idle":"2024-05-14T21:36:17.141054Z","shell.execute_reply.started":"2024-05-14T21:36:17.133292Z","shell.execute_reply":"2024-05-14T21:36:17.139642Z"},"trusted":true},"execution_count":91,"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":"(1341, 26)"},"metadata":{}}]},{"cell_type":"code","source":"merged_df.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:28:14.825531Z","iopub.execute_input":"2024-05-14T21:28:14.826057Z","iopub.status.idle":"2024-05-14T21:28:14.842929Z","shell.execute_reply.started":"2024-05-14T21:28:14.826015Z","shell.execute_reply":"2024-05-14T21:28:14.841613Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"id                0\nage               0\nbp                0\nsg                0\nal                0\nsu                0\nrbc               0\npc                0\npcc               0\nba                0\nbgr               0\nbu                0\nsc                0\nsod               0\npot               0\nhemo              0\npcv               0\nwc                0\nrc                0\nhtn               0\ndm                0\ncad               0\nappet             0\npe                0\nane               0\nclassification    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"merged_df.duplicated().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:29:05.971351Z","iopub.execute_input":"2024-05-14T21:29:05.971813Z","iopub.status.idle":"2024-05-14T21:29:05.986179Z","shell.execute_reply.started":"2024-05-14T21:29:05.97178Z","shell.execute_reply":"2024-05-14T21:29:05.984908Z"},"trusted":true},"execution_count":80,"outputs":[{"execution_count":80,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}]},{"cell_type":"code","source":"# Save to CSV\nmerged_df.to_csv('real & ChatGpt 3.5.csv', index=False)\n","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:37:07.498596Z","iopub.execute_input":"2024-05-14T21:37:07.499024Z","iopub.status.idle":"2024-05-14T21:37:07.536183Z","shell.execute_reply.started":"2024-05-14T21:37:07.498992Z","shell.execute_reply":"2024-05-14T21:37:07.53505Z"},"trusted":true},"execution_count":92,"outputs":[]},{"cell_type":"code","source":"ChatGPT_df = pd.concat([data, new_data], ignore_index=True)\nprint(len(ChatGPT_df))\nprint(ChatGPT_df.isna().sum())\nprint(ChatGPT_df.duplicated().sum())\nChatGPT_df.to_csv('ChatGPT 3.5.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-14T21:41:45.419047Z","iopub.execute_input":"2024-05-14T21:41:45.419473Z","iopub.status.idle":"2024-05-14T21:41:45.462606Z","shell.execute_reply.started":"2024-05-14T21:41:45.419439Z","shell.execute_reply":"2024-05-14T21:41:45.461492Z"},"trusted":true},"execution_count":94,"outputs":[{"name":"stdout","text":"941\nid                0\nage               0\nbp                0\nsg                0\nal                0\nsu                0\nrbc               0\npc                0\npcc               0\nba                0\nbgr               0\nbu                0\nsc                0\nsod               0\npot               0\nhemo              0\npcv               0\nwc                0\nrc                0\nhtn               0\ndm                0\ncad               0\nappet             0\npe                0\nane               0\nclassification    0\ndtype: int64\n0\n","output_type":"stream"}]},{"cell_type":"code","source":"chatgpt3_5= pd.read_csv('/kaggle/input/jddbnd/ChatGPT 3.5.csv')\nreal_chatgpt3_5= pd.read_csv('/kaggle/input/jddbnd/real and ChatGPT 3.5.csv')\nchatgpt3_5","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:39.146788Z","iopub.execute_input":"2024-05-19T08:50:39.14726Z","iopub.status.idle":"2024-05-19T08:50:39.204075Z","shell.execute_reply.started":"2024-05-19T08:50:39.147227Z","shell.execute_reply":"2024-05-19T08:50:39.202902Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"       id   age     bp     sg   al   su       rbc        pc         pcc  \\\n0      52  45.0   80.0  1.020  0.0  0.0    normal    normal  notpresent   \n1      53  55.0   90.0  1.025  2.0  1.0  abnormal  abnormal     present   \n2      54  70.0  100.0  1.010  3.0  2.0    normal    normal  notpresent   \n3      55  60.0   70.0  1.015  1.0  0.0    normal    normal  notpresent   \n4      56  35.0   85.0  1.020  1.0  1.0  abnormal  abnormal  notpresent   \n...   ...   ...    ...    ...  ...  ...       ...       ...         ...   \n1645  235  45.0   70.0  1.010  2.0  0.0  abnormal    normal  notpresent   \n1646   35  65.0   90.0  1.020  2.0  1.0  abnormal    normal  notpresent   \n1647   49  60.0   70.0  1.010  2.0  0.0    normal  abnormal     present   \n1648  252  45.0   70.0  1.020  2.0  1.0  abnormal  abnormal     present   \n1649  253  32.0   75.0  1.025  0.0  0.0    normal    normal  notpresent   \n\n              ba  ...  pcv     wc   rc  htn   dm  cad appet   pe  ane  \\\n0     notpresent  ...   44   7200  4.2   no   no   no  good   no   no   \n1     notpresent  ...   39   7700  4.5  yes  yes   no  poor   no   no   \n2     notpresent  ...   41   7400  3.8  yes  yes   no  poor  yes  yes   \n3     notpresent  ...   43   7100  4.0   no   no   no  good   no   no   \n4        present  ...   38   7800  4.3  yes  yes   no  poor  yes  yes   \n...          ...  ...  ...    ...  ...  ...  ...  ...   ...  ...  ...   \n1645  notpresent  ...   26   5700  7.2   no   no  yes  good   no  yes   \n1646  notpresent  ...   36   9800  4.9  yes  yes   no  poor   no  yes   \n1647  notpresent  ...   29  21600  3.5  yes  yes   no  poor   no  yes   \n1648  notpresent  ...   35  11000  3.9  yes   no   no  good  yes   no   \n1649  notpresent  ...   42   9800  4.2   no   no   no  good   no   no   \n\n     classification  \n0            notckd  \n1               ckd  \n2               ckd  \n3            notckd  \n4               ckd  \n...             ...  \n1645            ckd  \n1646            ckd  \n1647            ckd  \n1648            ckd  \n1649         notckd  \n\n[1650 rows x 26 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>id</th>\n      <th>age</th>\n      <th>bp</th>\n      <th>sg</th>\n      <th>al</th>\n      <th>su</th>\n      <th>rbc</th>\n      <th>pc</th>\n      <th>pcc</th>\n      <th>ba</th>\n      <th>...</th>\n      <th>pcv</th>\n      <th>wc</th>\n      <th>rc</th>\n      <th>htn</th>\n      <th>dm</th>\n      <th>cad</th>\n      <th>appet</th>\n      <th>pe</th>\n      <th>ane</th>\n      <th>classification</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>52</td>\n      <td>45.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>44</td>\n      <td>7200</td>\n      <td>4.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>53</td>\n      <td>55.0</td>\n      <td>90.0</td>\n      <td>1.025</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>present</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>39</td>\n      <td>7700</td>\n      <td>4.5</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>54</td>\n      <td>70.0</td>\n      <td>100.0</td>\n      <td>1.010</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>41</td>\n      <td>7400</td>\n      <td>3.8</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>55</td>\n      <td>60.0</td>\n      <td>70.0</td>\n      <td>1.015</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>43</td>\n      <td>7100</td>\n      <td>4.0</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>56</td>\n      <td>35.0</td>\n      <td>85.0</td>\n      <td>1.020</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>notpresent</td>\n      <td>present</td>\n      <td>...</td>\n      <td>38</td>\n      <td>7800</td>\n      <td>4.3</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>ckd</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1645</th>\n      <td>235</td>\n      <td>45.0</td>\n      <td>70.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>abnormal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>26</td>\n      <td>5700</td>\n      <td>7.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>good</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1646</th>\n      <td>35</td>\n      <td>65.0</td>\n      <td>90.0</td>\n      <td>1.020</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>abnormal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>36</td>\n      <td>9800</td>\n      <td>4.9</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1647</th>\n      <td>49</td>\n      <td>60.0</td>\n      <td>70.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>abnormal</td>\n      <td>present</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>29</td>\n      <td>21600</td>\n      <td>3.5</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>poor</td>\n      <td>no</td>\n      <td>yes</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1648</th>\n      <td>252</td>\n      <td>45.0</td>\n      <td>70.0</td>\n      <td>1.020</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>abnormal</td>\n      <td>abnormal</td>\n      <td>present</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>35</td>\n      <td>11000</td>\n      <td>3.9</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>ckd</td>\n    </tr>\n    <tr>\n      <th>1649</th>\n      <td>253</td>\n      <td>32.0</td>\n      <td>75.0</td>\n      <td>1.025</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>normal</td>\n      <td>normal</td>\n      <td>notpresent</td>\n      <td>notpresent</td>\n      <td>...</td>\n      <td>42</td>\n      <td>9800</td>\n      <td>4.2</td>\n      <td>no</td>\n      <td>no</td>\n      <td>no</td>\n      <td>good</td>\n      <td>no</td>\n      <td>no</td>\n      <td>notckd</td>\n    </tr>\n  </tbody>\n</table>\n<p>1650 rows × 26 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"chatgpt3_5 = chatgpt3_5.drop(columns=['id'])\nreal_chatgpt3_5 = real_chatgpt3_5.drop(columns=['id'])\n\nprint(chatgpt3_5.shape)\nprint(real_chatgpt3_5.shape)","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:39.206087Z","iopub.execute_input":"2024-05-19T08:50:39.207274Z","iopub.status.idle":"2024-05-19T08:50:39.217698Z","shell.execute_reply.started":"2024-05-19T08:50:39.20723Z","shell.execute_reply":"2024-05-19T08:50:39.2163Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"(1650, 25)\n(1853, 25)\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# Assuming df is your DataFrame\n# Convert columns to numeric if they are not already\ndf=chatgpt3_5.copy()\ncolumns_to_plot = ['rbc', 'wc', 'rc', 'pcv']\nfor column in columns_to_plot:\n    df[column] = pd.to_numeric(df[column], errors='coerce')\n\n# Plot the boxplots\nplt.figure(figsize=(12, 6))\nfor i, column in enumerate(columns_to_plot, 1):\n    plt.subplot(1, len(columns_to_plot), i)\n    sns.boxplot(x=df[column])\n    plt.title(f'Boxplot of {column}')\n    plt.xlabel('Values')\n    plt.ylabel(column)\n\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:39.532292Z","iopub.execute_input":"2024-05-19T08:50:39.533247Z","iopub.status.idle":"2024-05-19T08:50:40.099725Z","shell.execute_reply.started":"2024-05-19T08:50:39.533206Z","shell.execute_reply":"2024-05-19T08:50:40.097264Z"},"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x600 with 4 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"print(chatgpt3_5.isna().sum())\nprint(\"---------------------------------\")\nprint(chatgpt3_5.duplicated().sum())\nprint(chatgpt3_5.shape)","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:40.247686Z","iopub.execute_input":"2024-05-19T08:50:40.248056Z","iopub.status.idle":"2024-05-19T08:50:40.263443Z","shell.execute_reply.started":"2024-05-19T08:50:40.248026Z","shell.execute_reply":"2024-05-19T08:50:40.262092Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"age               0\nbp                0\nsg                0\nal                0\nsu                0\nrbc               0\npc                0\npcc               0\nba                0\nbgr               0\nbu                0\nsc                0\nsod               0\npot               0\nhemo              0\npcv               0\nwc                0\nrc                0\nhtn               0\ndm                0\ncad               0\nappet             0\npe                0\nane               0\nclassification    0\ndtype: int64\n---------------------------------\n336\n(1650, 25)\n","output_type":"stream"}]},{"cell_type":"code","source":"chatgpt3_5=chatgpt3_5.drop_duplicates()\nchatgpt3_5.shape","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:41.139492Z","iopub.execute_input":"2024-05-19T08:50:41.139842Z","iopub.status.idle":"2024-05-19T08:50:41.153971Z","shell.execute_reply.started":"2024-05-19T08:50:41.139817Z","shell.execute_reply":"2024-05-19T08:50:41.15295Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"(1314, 25)"},"metadata":{}}]},{"cell_type":"code","source":"chatgpt3_5.info()","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:41.640713Z","iopub.execute_input":"2024-05-19T08:50:41.641918Z","iopub.status.idle":"2024-05-19T08:50:41.658954Z","shell.execute_reply.started":"2024-05-19T08:50:41.641878Z","shell.execute_reply":"2024-05-19T08:50:41.657757Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 1314 entries, 0 to 1649\nData columns (total 25 columns):\n #   Column          Non-Null Count  Dtype  \n---  ------          --------------  -----  \n 0   age             1314 non-null   float64\n 1   bp              1314 non-null   float64\n 2   sg              1314 non-null   float64\n 3   al              1314 non-null   float64\n 4   su              1314 non-null   float64\n 5   rbc             1314 non-null   object \n 6   pc              1314 non-null   object \n 7   pcc             1314 non-null   object \n 8   ba              1314 non-null   object \n 9   bgr             1314 non-null   float64\n 10  bu              1314 non-null   float64\n 11  sc              1314 non-null   float64\n 12  sod             1314 non-null   float64\n 13  pot             1314 non-null   float64\n 14  hemo            1314 non-null   float64\n 15  pcv             1314 non-null   object \n 16  wc              1314 non-null   object \n 17  rc              1314 non-null   object \n 18  htn             1314 non-null   object \n 19  dm              1314 non-null   object \n 20  cad             1314 non-null   object \n 21  appet           1314 non-null   object \n 22  pe              1314 non-null   object \n 23  ane             1314 non-null   object \n 24  classification  1314 non-null   object \ndtypes: float64(11), object(14)\nmemory usage: 266.9+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"chatgpt3_5.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:38:11.935408Z","iopub.execute_input":"2024-05-19T08:38:11.935749Z","iopub.status.idle":"2024-05-19T08:38:11.947976Z","shell.execute_reply.started":"2024-05-19T08:38:11.935718Z","shell.execute_reply":"2024-05-19T08:38:11.947216Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"age               0\nbp                0\nsg                0\nal                0\nsu                0\nrbc               0\npc                0\npcc               0\nba                0\nbgr               0\nbu                0\nsc                0\nsod               0\npot               0\nhemo              0\npcv               0\nwc                0\nrc                0\nhtn               0\ndm                0\ncad               0\nappet             0\npe                0\nane               0\nclassification    0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"chatgpt3_5['pcv'] = chatgpt3_5['pcv'].str.replace('\\t', '').str.replace('?', '')\nchatgpt3_5['wc'] = chatgpt3_5['wc'].str.replace('\\t', '').str.replace('?', '')\nchatgpt3_5['rc'] = chatgpt3_5['rc'].str.replace('\\t', '').str.replace('?', '')\n\nchatgpt3_5['pcv'] = pd.to_numeric(chatgpt3_5['pcv'], errors='coerce')\nchatgpt3_5['wc'] = pd.to_numeric(chatgpt3_5['wc'], errors='coerce')\nchatgpt3_5['rc'] = pd.to_numeric(chatgpt3_5['rc'], errors='coerce')\n\nprint(chatgpt3_5[['pcv', 'wc', 'rc']].dtypes)\n\n\nprint(chatgpt3_5['rc'].values)\nprint(chatgpt3_5['wc'].values)\nprint(chatgpt3_5['pcv'].values)","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:43.933886Z","iopub.execute_input":"2024-05-19T08:50:43.934271Z","iopub.status.idle":"2024-05-19T08:50:43.955993Z","shell.execute_reply.started":"2024-05-19T08:50:43.934242Z","shell.execute_reply":"2024-05-19T08:50:43.954946Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"pcv    float64\nwc     float64\nrc     float64\ndtype: object\n[4.2 4.5 3.8 ... 3.5 3.9 4.2]\n[ 7200.  7700.  7400. ... 21600. 11000.  9800.]\n[44. 39. 41. ... 29. 35. 42.]\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nfrom scipy.stats import mode\n\n# Function to replace NaN with mean in numerical features and mode in categorical features\ndef replace_nan_with_mean_mode(df):\n    for column in df.columns:\n        if df[column].dtype in ['int64', 'float64']:\n            mean_value = round(df[column].mean(),2)\n            df[column] = df[column].fillna(mean_value)\n        else:\n            mode_value = df[column].mode()[0]\n            df[column] = df[column].fillna(mode_value)\n    return df\n\n# Example usage with a sample DataFrame\n# Assuming chatgpt3_5 is already defined\nchatgpt3_5 = replace_nan_with_mean_mode(chatgpt3_5)\n\n\n\n# Check the DataFrame\nchatgpt3_5.rc.unique()","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:38:12.375212Z","iopub.execute_input":"2024-05-19T08:38:12.375565Z","iopub.status.idle":"2024-05-19T08:38:12.400573Z","shell.execute_reply.started":"2024-05-19T08:38:12.375538Z","shell.execute_reply":"2024-05-19T08:38:12.39963Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"array([4.2, 4.5, 3.8, 4. , 4.3, 3.6, 4.4, 4.1, 4.8, 3.9, 3.7, 3.5, 5.4,\n       6.3, 7.5, 6.2, 7.4, 5.3, 5.1, 4.7, 2.9, 4.9, 5.6, 7.2, 5.5, 5.2,\n       2.5, 6.1, 2.8, 7.3, 3. , 4.6, 5.8, 6.4, 2.6, 5. , 6. , 6.5, 7.1,\n       3.4, 3.2, 6.8, 5.9, 5.7, 3.1, 6.9, 7. , 3.3, 6.6, 2.3, 2.7, 2.1,\n       8. , 2.4, 6.7])"},"metadata":{}}]},{"cell_type":"code","source":"import seaborn as sns\nimport pandas as pd\n\n# Assuming chatgpt3_5 is your DataFrame\n# Replace 'classification' with the actual name of your classification column\nchatgpt3_5['classification'] = chatgpt3_5['classification'].replace({'ckd\\t': 'ckd'})\n\n\nsns.histplot(chatgpt3_5.classification)\nchatgpt3_5.classification.nunique","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:45.937793Z","iopub.execute_input":"2024-05-19T08:50:45.938254Z","iopub.status.idle":"2024-05-19T08:50:46.172507Z","shell.execute_reply.started":"2024-05-19T08:50:45.938221Z","shell.execute_reply":"2024-05-19T08:50:46.171731Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"<bound method IndexOpsMixin.nunique of 0       notckd\n1          ckd\n2          ckd\n3       notckd\n4          ckd\n         ...  \n1645       ckd\n1646       ckd\n1647       ckd\n1648       ckd\n1649    notckd\nName: classification, Length: 1314, dtype: object>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAkQAAAGwCAYAAABIC3rIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAArXElEQVR4nO3dfVRVdb7H8c/hQcAHQEwPMKFQPoH5UGqEOZrJSKldu7Ju2mXKyqIxsDGbNG6JDz1ozmSmQ3ltlVSj1bS6WuMqE/FqTSIoRimieUuDqwIZwVFTVM6+f7Tc6558SAk4R3/v11qs5dn7d87+bluM79lnc3BYlmUJAADAYH7eHgAAAMDbCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGC/A2wNcCtxutw4cOKB27drJ4XB4exwAAHABLMvS4cOHFR0dLT+/818DIoguwIEDBxQTE+PtMQAAQCNUVFToyiuvPO8agugCtGvXTtJPf6GhoaFengYAAFwIl8ulmJgY+9/x8yGILsDpt8lCQ0MJIgAALjEXcrsLN1UDAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeF4Nok8++US33XaboqOj5XA4tGrVKo/9lmUpOztbUVFRCgkJUXJysvbs2eOxpqamRmlpaQoNDVV4eLgmTpyoI0eOeKz58ssv9dvf/lbBwcGKiYnR/Pnzm/vUAADAJcSrQXT06FH17dtXOTk5Z90/f/58LVq0SEuWLFFhYaHatGmjlJQUHT9+3F6Tlpam0tJS5eXlafXq1frkk0+Unp5u73e5XBoxYoS6dOmi4uJi/fnPf9asWbO0dOnSZj8/AABwibB8hCRr5cqV9mO3221FRkZaf/7zn+1ttbW1VlBQkPXWW29ZlmVZO3futCRZW7Zssdd89NFHlsPhsPbv329ZlmW99NJLVvv27a36+np7zfTp060ePXpc8Gx1dXWWJKuurq6xpwcAAFrYxfz77bP3EO3du1eVlZVKTk62t4WFhSkxMVEFBQWSpIKCAoWHh2vAgAH2muTkZPn5+amwsNBeM2TIELVq1cpek5KSot27d+uHH34467Hr6+vlcrk8vgAAwOUrwNsDnEtlZaUkyel0emx3Op32vsrKSnXq1Mljf0BAgCIiIjzWxMXFnfEap/e1b9/+jGPPnTtXs2fPbpoTAeDzysvLdejQIW+PARjtiiuuUOfOnb12fJ8NIm/KysrS1KlT7ccul0sxMTFenAhAcykvL1fPnvE6duxHb48CGC0kpLV27SrzWhT5bBBFRkZKkqqqqhQVFWVvr6qqUr9+/ew11dXVHs87deqUampq7OdHRkaqqqrKY83px6fX/FxQUJCCgoKa5DwA+LZDhw7p2LEflXjfTIVGxXp7HMBIroP7VPjabB06dIgg+rm4uDhFRkYqPz/fDiCXy6XCwkJNmjRJkpSUlKTa2loVFxerf//+kqT169fL7XYrMTHRXvPEE0/o5MmTCgwMlCTl5eWpR48eZ327DICZQqNiFdG5h7fHAOAlXr2p+siRIyopKVFJSYmkn26kLikpUXl5uRwOh6ZMmaKnn35aH3zwgbZv3667775b0dHRuv322yVJ8fHxuuWWW/TAAw+oqKhIn332mTIzMzV+/HhFR0dLkv793/9drVq10sSJE1VaWqp33nlHL774osdbYgAAwGxevUK0detWDRs2zH58OlImTJig3NxcTZs2TUePHlV6erpqa2s1ePBgrVmzRsHBwfZzli9frszMTA0fPlx+fn5KTU3VokWL7P1hYWFau3atMjIy1L9/f11xxRXKzs72+KwiAABgNq8G0U033STLss653+FwaM6cOZozZ84510RERGjFihXnPU6fPn306aefNnpOAABwefPZzyECAABoKQQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4/l0EDU0NGjGjBmKi4tTSEiIrr76aj311FOyLMteY1mWsrOzFRUVpZCQECUnJ2vPnj0er1NTU6O0tDSFhoYqPDxcEydO1JEjR1r6dAAAgI/y6SB67rnn9PLLL+uvf/2rysrK9Nxzz2n+/PlavHixvWb+/PlatGiRlixZosLCQrVp00YpKSk6fvy4vSYtLU2lpaXKy8vT6tWr9cknnyg9Pd0bpwQAAHxQgLcHOJ9NmzZpzJgxGjVqlCQpNjZWb731loqKiiT9dHVo4cKFevLJJzVmzBhJ0htvvCGn06lVq1Zp/PjxKisr05o1a7RlyxYNGDBAkrR48WKNHDlSf/nLXxQdHX3Gcevr61VfX28/drlczX2qAADAi3z6CtGgQYOUn5+vr776SpL0xRdf6J///KduvfVWSdLevXtVWVmp5ORk+zlhYWFKTExUQUGBJKmgoEDh4eF2DElScnKy/Pz8VFhYeNbjzp07V2FhYfZXTExMc50iAADwAT59hejxxx+Xy+VSz5495e/vr4aGBj3zzDNKS0uTJFVWVkqSnE6nx/OcTqe9r7KyUp06dfLYHxAQoIiICHvNz2VlZWnq1Kn2Y5fLRRQBAHAZ8+kg+vvf/67ly5drxYoV6tWrl0pKSjRlyhRFR0drwoQJzXbcoKAgBQUFNdvrAwAA3+LTQfTYY4/p8ccf1/jx4yVJvXv31rfffqu5c+dqwoQJioyMlCRVVVUpKirKfl5VVZX69esnSYqMjFR1dbXH6546dUo1NTX28wEAgNl8+h6iH3/8UX5+niP6+/vL7XZLkuLi4hQZGan8/Hx7v8vlUmFhoZKSkiRJSUlJqq2tVXFxsb1m/fr1crvdSkxMbIGzAAAAvs6nrxDddttteuaZZ9S5c2f16tVLn3/+uRYsWKD77rtPkuRwODRlyhQ9/fTT6tatm+Li4jRjxgxFR0fr9ttvlyTFx8frlltu0QMPPKAlS5bo5MmTyszM1Pjx48/6E2YAAMA8Ph1Eixcv1owZM/TQQw+purpa0dHRevDBB5WdnW2vmTZtmo4ePar09HTV1tZq8ODBWrNmjYKDg+01y5cvV2ZmpoYPHy4/Pz+lpqZq0aJF3jglAADggxzW///YZ5yVy+VSWFiY6urqFBoa6u1xADShbdu2qX///vrdE8sU0bmHt8cBjFRTvlt5z9yr4uJiXXfddU32uhfz77dP30MEAADQEggiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGM/ng2j//v36/e9/rw4dOigkJES9e/fW1q1b7f2WZSk7O1tRUVEKCQlRcnKy9uzZ4/EaNTU1SktLU2hoqMLDwzVx4kQdOXKkpU8FAAD4KJ8Ooh9++EE33nijAgMD9dFHH2nnzp16/vnn1b59e3vN/PnztWjRIi1ZskSFhYVq06aNUlJSdPz4cXtNWlqaSktLlZeXp9WrV+uTTz5Renq6N04JAAD4oABvD3A+zz33nGJiYrRs2TJ7W1xcnP1ny7K0cOFCPfnkkxozZowk6Y033pDT6dSqVas0fvx4lZWVac2aNdqyZYsGDBggSVq8eLFGjhypv/zlL4qOjm7ZkwIAAD7Hp68QffDBBxowYID+7d/+TZ06ddK1116rV155xd6/d+9eVVZWKjk52d4WFhamxMREFRQUSJIKCgoUHh5ux5AkJScny8/PT4WFhWc9bn19vVwul8cXAAC4fPl0EH3zzTd6+eWX1a1bN3388ceaNGmSHn74Yb3++uuSpMrKSkmS0+n0eJ7T6bT3VVZWqlOnTh77AwICFBERYa/5ublz5yosLMz+iomJaepTAwAAPsSng8jtduu6667Ts88+q2uvvVbp6el64IEHtGTJkmY9blZWlurq6uyvioqKZj0eAADwLp8OoqioKCUkJHhsi4+PV3l5uSQpMjJSklRVVeWxpqqqyt4XGRmp6upqj/2nTp1STU2NvebngoKCFBoa6vEFAAAuXz4dRDfeeKN2797tse2rr75Sly5dJP10g3VkZKTy8/Pt/S6XS4WFhUpKSpIkJSUlqba2VsXFxfaa9evXy+12KzExsQXOAgAA+Dqf/imzRx55RIMGDdKzzz6rO+64Q0VFRVq6dKmWLl0qSXI4HJoyZYqefvppdevWTXFxcZoxY4aio6N1++23S/rpitItt9xiv9V28uRJZWZmavz48fyEGQAAkOTjQTRw4ECtXLlSWVlZmjNnjuLi4rRw4UKlpaXZa6ZNm6ajR48qPT1dtbW1Gjx4sNasWaPg4GB7zfLly5WZmanhw4fLz89PqampWrRokTdOCQAA+CCfDiJJGj16tEaPHn3O/Q6HQ3PmzNGcOXPOuSYiIkIrVqxojvEAAMBlwKfvIQIAAGgJBBEAADBeo4Loqquu0vfff3/G9traWl111VW/eigAAICW1Kgg2rdvnxoaGs7YXl9fr/379//qoQAAAFrSRd1U/cEHH9h//vjjjxUWFmY/bmhoUH5+vmJjY5tsOAAAgJZwUUF0+rN9HA6HJkyY4LEvMDBQsbGxev7555tsOAAAgJZwUUHkdrsl/fQJ0Vu2bNEVV1zRLEMBAAC0pEZ9DtHevXubeg4AAACvafQHM+bn5ys/P1/V1dX2laPTXnvttV89GAAAQEtpVBDNnj1bc+bM0YABAxQVFSWHw9HUcwEAALSYRgXRkiVLlJubq7vuuqup5wEAAGhxjfocohMnTmjQoEFNPQsAAIBXNCqI7r//fn5ZKgAAuGw06i2z48ePa+nSpVq3bp369OmjwMBAj/0LFixokuEAAABaQqOC6Msvv1S/fv0kSTt27PDYxw3WAADgUtOoIPrv//7vpp4DAADAaxp1DxEAAMDlpFFXiIYNG3bet8bWr1/f6IEAAABaWqOC6PT9Q6edPHlSJSUl2rFjxxm/9BUAAMDXNSqIXnjhhbNunzVrlo4cOfKrBgIAAGhpTXoP0e9//3t+jxkAALjkNGkQFRQUKDg4uClfEgAAoNk16i2zsWPHejy2LEsHDx7U1q1bNWPGjCYZDAAAoKU0KojCwsI8Hvv5+alHjx6aM2eORowY0SSDAQAAtJRGBdGyZcuaeg4AAACvaVQQnVZcXKyysjJJUq9evXTttdc2yVAAAAAtqVFBVF1drfHjx2vDhg0KDw+XJNXW1mrYsGF6++231bFjx6acEQAAoFk16qfMJk+erMOHD6u0tFQ1NTWqqanRjh075HK59PDDDzf1jAAAAM2qUVeI1qxZo3Xr1ik+Pt7elpCQoJycHG6qBgAAl5xGXSFyu90KDAw8Y3tgYKDcbvevHgoAAKAlNSqIbr75Zv3xj3/UgQMH7G379+/XI488ouHDhzfZcAAAAC2hUUH017/+VS6XS7Gxsbr66qt19dVXKy4uTi6XS4sXL27qGQEAAJpVo+4hiomJ0bZt27Ru3Trt2rVLkhQfH6/k5OQmHQ4AAKAlXNQVovXr1yshIUEul0sOh0O/+93vNHnyZE2ePFkDBw5Ur1699OmnnzbXrAAAAM3iooJo4cKFeuCBBxQaGnrGvrCwMD344INasGBBkw0HAADQEi4qiL744gvdcsst59w/YsQIFRcX/+qhAAAAWtJFBVFVVdVZf9z+tICAAH333Xe/eigAAICWdFFB9Jvf/EY7duw45/4vv/xSUVFRv3ooAACAlnRRQTRy5EjNmDFDx48fP2PfsWPHNHPmTI0ePbrJhgMAAGgJF/Vj908++aT+67/+S927d1dmZqZ69OghSdq1a5dycnLU0NCgJ554olkGBQAAaC4XFUROp1ObNm3SpEmTlJWVJcuyJEkOh0MpKSnKycmR0+lslkEBAACay0V/MGOXLl304Ycf6ocfftD//M//yLIsdevWTe3bt2+O+QAAAJpdoz6pWpLat2+vgQMHNuUsAAAAXtGo32UGAABwOSGIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGu6SCaN68eXI4HJoyZYq97fjx48rIyFCHDh3Utm1bpaamqqqqyuN55eXlGjVqlFq3bq1OnTrpscce06lTp1p4egAA4KsumSDasmWL/vM//1N9+vTx2P7II4/oH//4h959911t3LhRBw4c0NixY+39DQ0NGjVqlE6cOKFNmzbp9ddfV25urrKzs1v6FAAAgI+6JILoyJEjSktL0yuvvKL27dvb2+vq6vTqq69qwYIFuvnmm9W/f38tW7ZMmzZt0ubNmyVJa9eu1c6dO/W3v/1N/fr106233qqnnnpKOTk5OnHixFmPV19fL5fL5fEFAAAuX5dEEGVkZGjUqFFKTk722F5cXKyTJ096bO/Zs6c6d+6sgoICSVJBQYF69+4tp9Npr0lJSZHL5VJpaelZjzd37lyFhYXZXzExMc1wVgAAwFf4fBC9/fbb2rZtm+bOnXvGvsrKSrVq1Urh4eEe251OpyorK+01/z+GTu8/ve9ssrKyVFdXZ39VVFQ0wZkAAABfFeDtAc6noqJCf/zjH5WXl6fg4OAWO25QUJCCgoJa7HgAAMC7fPoKUXFxsaqrq3XdddcpICBAAQEB2rhxoxYtWqSAgAA5nU6dOHFCtbW1Hs+rqqpSZGSkJCkyMvKMnzo7/fj0GgAAYDafDqLhw4dr+/btKikpsb8GDBigtLQ0+8+BgYHKz8+3n7N7926Vl5crKSlJkpSUlKTt27erurraXpOXl6fQ0FAlJCS0+DkBAADf49NvmbVr107XXHONx7Y2bdqoQ4cO9vaJEydq6tSpioiIUGhoqCZPnqykpCTdcMMNkqQRI0YoISFBd911l+bPn6/Kyko9+eSTysjI4G0xAAAgyceD6EK88MIL8vPzU2pqqurr65WSkqKXXnrJ3u/v76/Vq1dr0qRJSkpKUps2bTRhwgTNmTPHi1MDAABfcskF0YYNGzweBwcHKycnRzk5Oed8TpcuXfThhx8282QAAOBS5dP3EAEAALQEgggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgvABvDwCpvLxchw4d8vYYgJHKysq8PQIAH0AQeVl5ebl69ozXsWM/ensUwGgn6094ewQAXkQQedmhQ4d07NiPSrxvpkKjYr09DmCcg9sLtOODpTp16pS3RwHgRQSRjwiNilVE5x7eHgMwjuvgPm+PAMAHcFM1AAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjOfTQTR37lwNHDhQ7dq1U6dOnXT77bdr9+7dHmuOHz+ujIwMdejQQW3btlVqaqqqqqo81pSXl2vUqFFq3bq1OnXqpMcee0ynTp1qyVMBAAA+zKeDaOPGjcrIyNDmzZuVl5enkydPasSIETp69Ki95pFHHtE//vEPvfvuu9q4caMOHDigsWPH2vsbGho0atQonThxQps2bdLrr7+u3NxcZWdne+OUAACADwrw9gDns2bNGo/Hubm56tSpk4qLizVkyBDV1dXp1Vdf1YoVK3TzzTdLkpYtW6b4+Hht3rxZN9xwg9auXaudO3dq3bp1cjqd6tevn5566ilNnz5ds2bNUqtWrbxxagAAwIf49BWin6urq5MkRURESJKKi4t18uRJJScn22t69uypzp07q6CgQJJUUFCg3r17y+l02mtSUlLkcrlUWlp61uPU19fL5XJ5fAEAgMvXJRNEbrdbU6ZM0Y033qhrrrlGklRZWalWrVopPDzcY63T6VRlZaW95v/H0On9p/edzdy5cxUWFmZ/xcTENPHZAAAAX3LJBFFGRoZ27Niht99+u9mPlZWVpbq6OvuroqKi2Y8JAAC8x6fvITotMzNTq1ev1ieffKIrr7zS3h4ZGakTJ06otrbW4ypRVVWVIiMj7TVFRUUer3f6p9BOr/m5oKAgBQUFNfFZAAAAX+XTV4gsy1JmZqZWrlyp9evXKy4uzmN///79FRgYqPz8fHvb7t27VV5erqSkJElSUlKStm/frurqantNXl6eQkNDlZCQ0DInAgAAfJpPXyHKyMjQihUr9P7776tdu3b2PT9hYWEKCQlRWFiYJk6cqKlTpyoiIkKhoaGaPHmykpKSdMMNN0iSRowYoYSEBN11112aP3++Kisr9eSTTyojI4OrQAAAQJKPB9HLL78sSbrppps8ti9btkz33HOPJOmFF16Qn5+fUlNTVV9fr5SUFL300kv2Wn9/f61evVqTJk1SUlKS2rRpowkTJmjOnDktdRoAAMDH+XQQWZb1i2uCg4OVk5OjnJycc67p0qWLPvzww6YcDQAAXEZ8+h4iAACAlkAQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMB5BBAAAjEcQAQAA4xFEAADAeAQRAAAwHkEEAACMRxABAADjEUQAAMB4BBEAADAeQQQAAIxHEAEAAOMRRAAAwHgEEQAAMJ5RQZSTk6PY2FgFBwcrMTFRRUVF3h4JAAD4AGOC6J133tHUqVM1c+ZMbdu2TX379lVKSoqqq6u9PRoAAPAyY4JowYIFeuCBB3TvvfcqISFBS5YsUevWrfXaa695ezQAAOBlAd4eoCWcOHFCxcXFysrKsrf5+fkpOTlZBQUFZ6yvr69XfX29/biurk6S5HK5mny2I0eOSJJqvt2tU/XHmvz1AZyf6+C3kqS6/XsUGODw8jSAmVyV5ZJ++jexKf+tPf1almX94lojgujQoUNqaGiQ0+n02O50OrVr164z1s+dO1ezZ88+Y3tMTEyzzVj8t3nN9toAftn2dxd6ewTAeEOHDm2W1z18+LDCwsLOu8aIILpYWVlZmjp1qv3Y7XarpqZGHTp0kMPB/4OEJ5fLpZiYGFVUVCg0NNTb4wDG4XsQ52JZlg4fPqzo6OhfXGtEEF1xxRXy9/dXVVWVx/aqqipFRkaesT4oKEhBQUEe28LDw5tzRFwGQkND+R9jwIv4HsTZ/NKVodOMuKm6VatW6t+/v/Lz8+1tbrdb+fn5SkpK8uJkAADAFxhxhUiSpk6dqgkTJmjAgAG6/vrrtXDhQh09elT33nuvt0cDAABeZkwQjRs3Tt99952ys7NVWVmpfv36ac2aNWfcaA1crKCgIM2cOfOMt1kBtAy+B9EUHNaF/CwaAADAZcyIe4gAAADOhyACAADGI4gAAIDxCCKgGeXm5l70Z1ht2LBBDodDtbW1zTITcDnYt2+fHA6HSkpKLup5DodDq1atapaZcGkjiIALNGvWLPXr18/bYwAAmgFBBAAAjEcQwRg33XSTHn74YU2bNk0RERGKjIzUrFmz7P3l5eUaM2aM2rZtq9DQUN1xxx32r3vJzc3V7Nmz9cUXX8jhcMjhcCg3N1eSVFtbqwcffFBOp1PBwcG65pprtHr16rPO8N1332nAgAH613/9V9XX10uSPvzwQ3Xv3l0hISEaNmyY9u3b15x/DcAlxe12a/78+eratauCgoLUuXNnPfPMM2esa2ho0H333aeePXuqvPyn35y+Z88eDRkyRMHBwUpISFBeXl5Lj49LiDEfzAhI0uuvv66pU6eqsLBQBQUFuueee3TjjTdq+PDhdgxt3LhRp06dUkZGhsaNG6cNGzZo3Lhx2rFjh9asWaN169ZJ+un347jdbt166606fPiw/va3v+nqq6/Wzp075e/vf8axKyoq9Lvf/U433HCDXn31Vfn7+6uiokJjx45VRkaG0tPTtXXrVj366KMt/dcC+KysrCy98soreuGFFzR48GAdPHhQu3bt8lhTX1+vO++8U/v27dOnn36qjh07yu12a+zYsXI6nSosLFRdXZ2mTJninZPApcECDDF06FBr8ODBHtsGDhxoTZ8+3Vq7dq3l7+9vlZeX2/tKS0stSVZRUZFlWZY1c+ZMq2/fvh7P//jjjy0/Pz9r9+7dZz3msmXLrLCwMGvXrl1WTEyM9fDDD1tut9ven5WVZSUkJHg8Z/r06ZYk64cffvgVZwtc+lwulxUUFGS98sorZ+zbu3evJcn69NNPreHDh1uDBw+2amtr7f0ff/yxFRAQYO3fv9/e9tFHH1mSrJUrV7bE+LjE8JYZjNKnTx+Px1FRUaqurlZZWZliYmIUExNj70tISFB4eLjKysrO+XolJSW68sor1b1793OuOXbsmH77299q7NixevHFF+VwOOx9ZWVlSkxM9FjPLxwGflJWVqb6+noNHz78nGvuvPNOHT16VGvXrvX4reanv6ejo6PtbXxv4XwIIhglMDDQ47HD4ZDb7W7064WEhPzimqCgICUnJ2v16tXav39/o48FmOZCvr9GjhypL7/8UgUFBS0wES5nBBEgKT4+XhUVFaqoqLC37dy5U7W1tUpISJAktWrVSg0NDR7P69Onj/73f/9XX3311Tlf28/PT2+++ab69++vYcOG6cCBAx7HLSoq8li/efPmpjgl4JLXrVs3hYSEKD8//5xrJk2apHnz5ulf/uVftHHjRnv76e/pgwcP2tv43sL5EESApOTkZPXu3VtpaWnatm2bioqKdPfdd2vo0KEaMGCAJCk2NlZ79+5VSUmJDh06pPr6eg0dOlRDhgxRamqq8vLytHfvXn300Udas2aNx+v7+/tr+fLl6tu3r26++WZVVlZKkv7whz9oz549euyxx7R7926tWLHC/uk1wHTBwcGaPn26pk2bpjfeeENff/21Nm/erFdffdVj3eTJk/X0009r9OjR+uc//ynpp+/p7t27a8KECfriiy/06aef6oknnvDGaeASQRAB+umts/fff1/t27fXkCFDlJycrKuuukrvvPOOvSY1NVW33HKLhg0bpo4dO+qtt96SJL333nsaOHCg7rzzTiUkJGjatGlnXEmSpICAAL311lvq1auXbr75ZlVXV6tz58567733tGrVKvXt21dLlizRs88+22LnDfi6GTNm6NFHH1V2drbi4+M1btw4VVdXn7FuypQpmj17tkaOHKlNmzbJz89PK1eu1LFjx3T99dfr/vvvP+uP6wOnOSzLsrw9BAAAgDdxhQgAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIQIvat2+fHA6HSkpKmv1Yubm5Cg8P99i2dOlSxcTEyM/PTwsXLtSsWbPUr1+/Zp8lNjZWCxcubPbjAGgcPqkaQIvat2+f4uLi9Pnnnzd7iBw7dkyHDx9Wp06dJEkul0tXXHGFFixYoNTUVIWFhcntdqu+vl4dOnRokmPm5uZqypQpqq2t9dj+3XffqU2bNmrdunWTHAdA0wrw9gAA0FxCQkIUEhJiPy4vL9fJkyc1atQoRUVF2dvbtm3b7LN07Nix2Y8BoPF4ywxAs3C73Zo/f766du2qoKAgde7c+ay/XLOhoUETJ05UXFycQkJC1KNHD7344oseazZs2KDrr79ebdq0UXh4uG688UZ9++23kqQvvvhCw4YNU7t27RQaGqr+/ftr69atkjzfMsvNzVXv3r0lSVdddZUcDof27dt31rfMXnvtNfXq1UtBQUGKiopSZmamvW/BggXq3bu32rRpo5iYGD300EM6cuSIPee9996ruro6ORwOORwOzZo1S9KZb5mVl5drzJgxatu2rUJDQ3XHHXeoqqrK3n96rjfffFOxsbEKCwvT+PHjdfjw4Yv/jwHgFxFEAJpFVlaW5s2bpxkzZmjnzp1asWKFnE7nGevcbreuvPJKvfvuu9q5c6eys7P1H//xH/r73/8uSTp16pRuv/12DR06VF9++aUKCgqUnp4uh8MhSUpLS9OVV16pLVu2qLi4WI8//rgCAwPPOM64ceO0bt06SVJRUZEOHjyomJiYM9a9/PLLysjIUHp6urZv364PPvhAXbt2tff7+flp0aJFKi0t1euvv67169dr2rRpkqRBgwZp4cKFCg0N1cGDB3Xw4EH96U9/Ous5jxkzRjU1Ndq4caPy8vL0zTffaNy4cR7rvv76a61atUqrV6/W6tWrtXHjRs2bN+9C/xMAuBgWADQxl8tlBQUFWa+88soZ+/bu3WtJsj7//PNzPj8jI8NKTU21LMuyvv/+e0uStWHDhrOubdeunZWbm3vWfcuWLbPCwsLsx59//rklydq7d6+9bebMmVbfvn3tx9HR0dYTTzxx7pP7mXfffdfq0KHDOY95WpcuXawXXnjBsizLWrt2reXv72+Vl5fb+0tLSy1JVlFRkT1X69atLZfLZa957LHHrMTExAueDcCF4woRgCZXVlam+vp6DR8+/ILW5+TkqH///urYsaPatm2rpUuXqry8XJIUERGhe+65RykpKbrtttv04osv6uDBg/Zzp06dqvvvv1/JycmaN2+evv7660bPXV1drQMHDpx37nXr1mn48OH6zW9+o3bt2umuu+7S999/rx9//PGCj1NWVqaYmBiPK1QJCQkKDw9XWVmZvS02Nlbt2rWzH0dFRam6uvoizwrAhSCIADS5/38j8y95++239ac//UkTJ07U2rVrVVJSonvvvVcnTpyw1yxbtkwFBQUaNGiQ3nnnHXXv3l2bN2+W9NO9NqWlpRo1apTWr1+vhIQErVy5slnm3rdvn0aPHq0+ffrovffeU3FxsXJyciTJY96m8vO3/hwOh9xud5MfBwBBBKAZdOvWTSEhIcrPz//FtZ999pkGDRqkhx56SNdee626du161qs81157rbKysrRp0yZdc801WrFihb2ve/fueuSRR7R27VqNHTtWy5Yta9Tc7dq1U2xs7DnnLi4ultvt1vPPP68bbrhB3bt314EDBzzWtGrVSg0NDec9Tnx8vCoqKlRRUWFv27lzp2pra5WQkNCo2QH8OgQRgCYXHBys6dOna9q0aXrjjTf09ddfa/PmzXr11VfPWNutWzdt3bpVH3/8sb766ivNmDFDW7Zssffv3btXWVlZKigo0Lfffqu1a9dqz549io+P17Fjx5SZmakNGzbo22+/1WeffaYtW7YoPj6+0bPPmjVLzz//vBYtWqQ9e/Zo27ZtWrx4sSSpa9euOnnypBYvXqxvvvlGb775ppYsWeLx/NjYWB05ckT5+fk6dOjQWd9KS05OVu/evZWWlqZt27apqKhId999t4YOHaoBAwY0enYAjUcQAWgWM2bM0KOPPqrs7GzFx8dr3LhxZ73/5cEHH9TYsWM1btw4JSYm6vvvv9dDDz1k72/durV27dql1NRUde/eXenp6crIyNCDDz4of39/ff/997r77rvVvXt33XHHHbr11ls1e/bsRs89YcIELVy4UC+99JJ69eql0aNHa8+ePZKkvn37asGCBXruued0zTXXaPny5Zo7d67H8wcNGqQ//OEPGjdunDp27Kj58+efcQyHw6H3339f7du315AhQ5ScnKyrrrpK77zzTqPnBvDr8EnVAADAeFwhAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYLz/A/CausCsX3pBAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.preprocessing import OneHotEncoder","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:48.862687Z","iopub.execute_input":"2024-05-19T08:50:48.864767Z","iopub.status.idle":"2024-05-19T08:50:48.86972Z","shell.execute_reply.started":"2024-05-19T08:50:48.864718Z","shell.execute_reply":"2024-05-19T08:50:48.868232Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"categorical_columns = chatgpt3_5.select_dtypes(include=['object']).columns\none_hot_encoded_chat3_5 = pd.get_dummies(chatgpt3_5, columns=categorical_columns)\n\none_hot_encoded_chat3_5\n","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:49.524877Z","iopub.execute_input":"2024-05-19T08:50:49.525263Z","iopub.status.idle":"2024-05-19T08:50:49.574017Z","shell.execute_reply.started":"2024-05-19T08:50:49.525237Z","shell.execute_reply":"2024-05-19T08:50:49.572895Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"       age     bp     sg   al   su    bgr    bu   sc    sod  pot  ...  cad_no  \\\n0     45.0   80.0  1.020  0.0  0.0  150.0  50.0  1.0  140.0  4.2  ...    True   \n1     55.0   90.0  1.025  2.0  1.0  200.0  70.0  2.0  138.0  4.5  ...    True   \n2     70.0  100.0  1.010  3.0  2.0  180.0  60.0  1.5  141.0  3.8  ...    True   \n3     60.0   70.0  1.015  1.0  0.0  135.0  45.0  1.2  139.0  4.0  ...    True   \n4     35.0   85.0  1.020  1.0  1.0  220.0  65.0  2.5  137.0  4.3  ...    True   \n...    ...    ...    ...  ...  ...    ...   ...  ...    ...  ...  ...     ...   \n1645  45.0   70.0  1.010  2.0  0.0  113.0  93.0  2.3  167.3  5.2  ...   False   \n1646  65.0   90.0  1.020  2.0  1.0  270.0  39.0  2.0  130.4  2.8  ...    True   \n1647  60.0   70.0  1.010  2.0  0.0  144.0  72.0  3.0  111.1  6.7  ...    True   \n1648  45.0   70.0  1.020  2.0  1.0  300.0  50.0  1.5  140.0  4.0  ...    True   \n1649  32.0   75.0  1.025  0.0  0.0  120.0  30.0  0.9  137.0  4.4  ...    True   \n\n      cad_yes  appet_good  appet_poor  pe_no  pe_yes  ane_no  ane_yes  \\\n0       False        True       False   True   False    True    False   \n1       False       False        True   True   False    True    False   \n2       False       False        True  False    True   False     True   \n3       False        True       False   True   False    True    False   \n4       False       False        True  False    True   False     True   \n...       ...         ...         ...    ...     ...     ...      ...   \n1645     True        True       False   True   False   False     True   \n1646    False       False        True   True   False   False     True   \n1647    False       False        True   True   False   False     True   \n1648    False        True       False  False    True    True    False   \n1649    False        True       False   True   False    True    False   \n\n      classification_ckd  classification_notckd  \n0                  False                   True  \n1                   True                  False  \n2                   True                  False  \n3                  False                   True  \n4                   True                  False  \n...                  ...                    ...  \n1645                True                  False  \n1646                True                  False  \n1647                True                  False  \n1648                True                  False  \n1649               False                   True  \n\n[1314 rows x 40 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>age</th>\n      <th>bp</th>\n      <th>sg</th>\n      <th>al</th>\n      <th>su</th>\n      <th>bgr</th>\n      <th>bu</th>\n      <th>sc</th>\n      <th>sod</th>\n      <th>pot</th>\n      <th>...</th>\n      <th>cad_no</th>\n      <th>cad_yes</th>\n      <th>appet_good</th>\n      <th>appet_poor</th>\n      <th>pe_no</th>\n      <th>pe_yes</th>\n      <th>ane_no</th>\n      <th>ane_yes</th>\n      <th>classification_ckd</th>\n      <th>classification_notckd</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>45.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>150.0</td>\n      <td>50.0</td>\n      <td>1.0</td>\n      <td>140.0</td>\n      <td>4.2</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55.0</td>\n      <td>90.0</td>\n      <td>1.025</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>200.0</td>\n      <td>70.0</td>\n      <td>2.0</td>\n      <td>138.0</td>\n      <td>4.5</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>70.0</td>\n      <td>100.0</td>\n      <td>1.010</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>180.0</td>\n      <td>60.0</td>\n      <td>1.5</td>\n      <td>141.0</td>\n      <td>3.8</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>60.0</td>\n      <td>70.0</td>\n      <td>1.015</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>135.0</td>\n      <td>45.0</td>\n      <td>1.2</td>\n      <td>139.0</td>\n      <td>4.0</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>35.0</td>\n      <td>85.0</td>\n      <td>1.020</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>220.0</td>\n      <td>65.0</td>\n      <td>2.5</td>\n      <td>137.0</td>\n      <td>4.3</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1645</th>\n      <td>45.0</td>\n      <td>70.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>113.0</td>\n      <td>93.0</td>\n      <td>2.3</td>\n      <td>167.3</td>\n      <td>5.2</td>\n      <td>...</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1646</th>\n      <td>65.0</td>\n      <td>90.0</td>\n      <td>1.020</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>270.0</td>\n      <td>39.0</td>\n      <td>2.0</td>\n      <td>130.4</td>\n      <td>2.8</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1647</th>\n      <td>60.0</td>\n      <td>70.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>144.0</td>\n      <td>72.0</td>\n      <td>3.0</td>\n      <td>111.1</td>\n      <td>6.7</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1648</th>\n      <td>45.0</td>\n      <td>70.0</td>\n      <td>1.020</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>300.0</td>\n      <td>50.0</td>\n      <td>1.5</td>\n      <td>140.0</td>\n      <td>4.0</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>1649</th>\n      <td>32.0</td>\n      <td>75.0</td>\n      <td>1.025</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>120.0</td>\n      <td>30.0</td>\n      <td>0.9</td>\n      <td>137.0</td>\n      <td>4.4</td>\n      <td>...</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>False</td>\n      <td>False</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>1314 rows × 40 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\n\nordinal_encoded_chat3_5 = chatgpt3_5.copy()\n\nlabel_encoders = {}\nfor column in ordinal_encoded_chat3_5.select_dtypes(include=['object']).columns:\n    label_encoders[column] = LabelEncoder()\n    ordinal_encoded_chat3_5[column] = label_encoders[column].fit_transform(ordinal_encoded_chat3_5[column])\nordinal_encoded_chat3_5","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:38:13.501775Z","iopub.execute_input":"2024-05-19T08:38:13.50248Z","iopub.status.idle":"2024-05-19T08:38:13.541164Z","shell.execute_reply.started":"2024-05-19T08:38:13.502443Z","shell.execute_reply":"2024-05-19T08:38:13.54017Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"       age     bp     sg   al   su  rbc  pc  pcc  ba    bgr  ...   pcv  \\\n0     45.0   80.0  1.020  0.0  0.0    1   1    0   0  150.0  ...  44.0   \n1     55.0   90.0  1.025  2.0  1.0    0   0    1   0  200.0  ...  39.0   \n2     70.0  100.0  1.010  3.0  2.0    1   1    0   0  180.0  ...  41.0   \n3     60.0   70.0  1.015  1.0  0.0    1   1    0   0  135.0  ...  43.0   \n4     35.0   85.0  1.020  1.0  1.0    0   0    0   1  220.0  ...  38.0   \n...    ...    ...    ...  ...  ...  ...  ..  ...  ..    ...  ...   ...   \n1645  45.0   70.0  1.010  2.0  0.0    0   1    0   0  113.0  ...  26.0   \n1646  65.0   90.0  1.020  2.0  1.0    0   1    0   0  270.0  ...  36.0   \n1647  60.0   70.0  1.010  2.0  0.0    1   0    1   0  144.0  ...  29.0   \n1648  45.0   70.0  1.020  2.0  1.0    0   0    1   0  300.0  ...  35.0   \n1649  32.0   75.0  1.025  0.0  0.0    1   1    0   0  120.0  ...  42.0   \n\n           wc   rc  htn  dm  cad  appet  pe  ane  classification  \n0      7200.0  4.2    0   3    1      0   0    0               1  \n1      7700.0  4.5    1   4    1      1   0    0               0  \n2      7400.0  3.8    1   4    1      1   1    1               0  \n3      7100.0  4.0    0   3    1      0   0    0               1  \n4      7800.0  4.3    1   4    1      1   1    1               0  \n...       ...  ...  ...  ..  ...    ...  ..  ...             ...  \n1645   5700.0  7.2    0   3    2      0   0    1               0  \n1646   9800.0  4.9    1   4    1      1   0    1               0  \n1647  21600.0  3.5    1   4    1      1   0    1               0  \n1648  11000.0  3.9    1   3    1      0   1    0               0  \n1649   9800.0  4.2    0   3    1      0   0    0               1  \n\n[1314 rows x 25 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>age</th>\n      <th>bp</th>\n      <th>sg</th>\n      <th>al</th>\n      <th>su</th>\n      <th>rbc</th>\n      <th>pc</th>\n      <th>pcc</th>\n      <th>ba</th>\n      <th>bgr</th>\n      <th>...</th>\n      <th>pcv</th>\n      <th>wc</th>\n      <th>rc</th>\n      <th>htn</th>\n      <th>dm</th>\n      <th>cad</th>\n      <th>appet</th>\n      <th>pe</th>\n      <th>ane</th>\n      <th>classification</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>45.0</td>\n      <td>80.0</td>\n      <td>1.020</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>150.0</td>\n      <td>...</td>\n      <td>44.0</td>\n      <td>7200.0</td>\n      <td>4.2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>55.0</td>\n      <td>90.0</td>\n      <td>1.025</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>200.0</td>\n      <td>...</td>\n      <td>39.0</td>\n      <td>7700.0</td>\n      <td>4.5</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>70.0</td>\n      <td>100.0</td>\n      <td>1.010</td>\n      <td>3.0</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>180.0</td>\n      <td>...</td>\n      <td>41.0</td>\n      <td>7400.0</td>\n      <td>3.8</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>60.0</td>\n      <td>70.0</td>\n      <td>1.015</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>135.0</td>\n      <td>...</td>\n      <td>43.0</td>\n      <td>7100.0</td>\n      <td>4.0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>35.0</td>\n      <td>85.0</td>\n      <td>1.020</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>220.0</td>\n      <td>...</td>\n      <td>38.0</td>\n      <td>7800.0</td>\n      <td>4.3</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</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      <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      <td>...</td>\n    </tr>\n    <tr>\n      <th>1645</th>\n      <td>45.0</td>\n      <td>70.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>113.0</td>\n      <td>...</td>\n      <td>26.0</td>\n      <td>5700.0</td>\n      <td>7.2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1646</th>\n      <td>65.0</td>\n      <td>90.0</td>\n      <td>1.020</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>270.0</td>\n      <td>...</td>\n      <td>36.0</td>\n      <td>9800.0</td>\n      <td>4.9</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1647</th>\n      <td>60.0</td>\n      <td>70.0</td>\n      <td>1.010</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>144.0</td>\n      <td>...</td>\n      <td>29.0</td>\n      <td>21600.0</td>\n      <td>3.5</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1648</th>\n      <td>45.0</td>\n      <td>70.0</td>\n      <td>1.020</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>300.0</td>\n      <td>...</td>\n      <td>35.0</td>\n      <td>11000.0</td>\n      <td>3.9</td>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1649</th>\n      <td>32.0</td>\n      <td>75.0</td>\n      <td>1.025</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>120.0</td>\n      <td>...</td>\n      <td>42.0</td>\n      <td>9800.0</td>\n      <td>4.2</td>\n      <td>0</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>1314 rows × 25 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"ordinal_encoded_chat3_5.classification.unique()\nordinal_encoded_chat3_5.to_csv(\"cleaned_chatgpt3_5.csv\")\n","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:50:51.348695Z","iopub.execute_input":"2024-05-19T08:50:51.349187Z","iopub.status.idle":"2024-05-19T08:50:51.380239Z","shell.execute_reply.started":"2024-05-19T08:50:51.349154Z","shell.execute_reply":"2024-05-19T08:50:51.378954Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"x=ordinal_encoded_chat3_5.iloc[:,:-1].values\ny=ordinal_encoded_chat3_5.iloc[:,-1].values\nx","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:56:27.28153Z","iopub.execute_input":"2024-05-19T08:56:27.282005Z","iopub.status.idle":"2024-05-19T08:56:27.29476Z","shell.execute_reply.started":"2024-05-19T08:56:27.281972Z","shell.execute_reply":"2024-05-19T08:56:27.293694Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"array([[ 45.   ,  80.   ,   1.02 , ...,   0.   ,   0.   ,   0.   ],\n       [ 55.   ,  90.   ,   1.025, ...,   1.   ,   0.   ,   0.   ],\n       [ 70.   , 100.   ,   1.01 , ...,   1.   ,   1.   ,   1.   ],\n       ...,\n       [ 60.   ,  70.   ,   1.01 , ...,   1.   ,   0.   ,   1.   ],\n       [ 45.   ,  70.   ,   1.02 , ...,   0.   ,   1.   ,   0.   ],\n       [ 32.   ,  75.   ,   1.025, ...,   0.   ,   0.   ,   0.   ]])"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nimport xgboost as xgb\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import precision_score, recall_score,accuracy_score, f1_score ,confusion_matrix\nfrom sklearn.svm import SVC\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.tree import DecisionTreeClassifier\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nX_train, X_test, y_train, y_test = train_test_split(x, y, test_size = 1/3, random_state = 42)","metadata":{"execution":{"iopub.status.busy":"2024-05-19T09:02:23.464075Z","iopub.execute_input":"2024-05-19T09:02:23.465019Z","iopub.status.idle":"2024-05-19T09:02:23.473211Z","shell.execute_reply.started":"2024-05-19T09:02:23.464981Z","shell.execute_reply":"2024-05-19T09:02:23.472391Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"logreg_model = LogisticRegression(random_state=42)\nlogreg_model.fit(X_train, y_train)\ny_pred_logreg = logreg_model.predict(X_test)\naccuracy_logreg = accuracy_score(y_test, y_pred_logreg)\nprecision_logreg = precision_score(y_test, y_pred_logreg)\nrecall_logreg = recall_score(y_test, y_pred_logreg)\nf1_logreg = f1_score(y_test, y_pred_logreg)\nprint(\"Logistic Regression Model Accuracy:\", accuracy_logreg)\nprint(\"Logistic Regression Precision:\", precision_logreg)\nprint(\"Logistic Regression Recall:\", recall_logreg)\nprint(\"Logistic Regression F1 Score:\", f1_logreg)\n\nlogreg_cm = confusion_matrix(y_test, y_pred_logreg)\nplt.figure(figsize=(5, 4))\nsns.heatmap(logreg_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix - Logistic Regression')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:51:15.916057Z","iopub.execute_input":"2024-05-19T08:51:15.91648Z","iopub.status.idle":"2024-05-19T08:51:16.143901Z","shell.execute_reply.started":"2024-05-19T08:51:15.916449Z","shell.execute_reply":"2024-05-19T08:51:16.142892Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":"Logistic Regression Model Accuracy: 0.8995433789954338\nLogistic Regression Precision: 0.9\nLogistic Regression Recall: 0.6982758620689655\nLogistic Regression F1 Score: 0.7864077669902912\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndef knn_accuracy(range_start, range_end, X_train, X_test, y_train, y_test):\n    max_accuracy = 0\n    best_n_neighbors = 0\n    best_cm = None\n    best_precision = 0\n    best_recall = 0\n    best_f1 = 0\n\n    for i in range(range_start, range_end + 1):\n        if i % 2 != 0:\n            knn_model = KNeighborsClassifier(n_neighbors=i)\n            knn_model.fit(X_train, y_train)\n            y_pred_knn = knn_model.predict(X_test)\n            accuracy_knn = accuracy_score(y_test, y_pred_knn)\n            precision_knn = precision_score(y_test, y_pred_knn, average='macro', zero_division=0)\n            recall_knn = recall_score(y_test, y_pred_knn, average='macro', zero_division=0)\n            f1_knn = f1_score(y_test, y_pred_knn, average='macro', zero_division=0)\n            if accuracy_knn > max_accuracy:\n                max_accuracy = accuracy_knn\n                best_n_neighbors = i\n                best_cm = confusion_matrix(y_test, y_pred_knn)\n                best_precision = precision_knn\n                best_recall = recall_knn\n                best_f1 = f1_knn\n\n    print(f\"Maximum Accuracy: {max_accuracy}, Best Number of Neighbors: {best_n_neighbors}\")\n    print(f\"Precision: {best_precision} \\nRecall: {best_recall} \\nF1 Score: {best_f1}\")\n\n    plt.figure(figsize=(5, 4))\n    sns.heatmap(best_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\n    plt.title('Confusion Matrix - k-NN')\n    plt.xlabel('Predicted')\n    plt.ylabel('True')\n    plt.show()\n\n    return max_accuracy, best_n_neighbors\n","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:51:22.756006Z","iopub.execute_input":"2024-05-19T08:51:22.756495Z","iopub.status.idle":"2024-05-19T08:51:22.768074Z","shell.execute_reply.started":"2024-05-19T08:51:22.75646Z","shell.execute_reply":"2024-05-19T08:51:22.767041Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"max_accuracy, best_n_neighbors = knn_accuracy(3, 266, X_train, X_test, y_train, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-05-19T08:51:25.012964Z","iopub.execute_input":"2024-05-19T08:51:25.013421Z","iopub.status.idle":"2024-05-19T08:51:30.466078Z","shell.execute_reply.started":"2024-05-19T08:51:25.013388Z","shell.execute_reply":"2024-05-19T08:51:30.465302Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"Maximum Accuracy: 0.8721461187214612, Best Number of Neighbors: 3\nPrecision: 0.8486494597839136 \nRecall: 0.8110141357892482 \nF1 Score: 0.8268628059971201\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"dt_model = DecisionTreeClassifier(random_state=42)\ndt_model.fit(X_train, y_train)\ny_pred_dt = dt_model.predict(X_test)\naccuracy_dt = accuracy_score(y_test, y_pred_dt)\nprecision_dt = precision_score(y_test, y_pred_dt)\nrecall_dt = recall_score(y_test, y_pred_dt)\nf1_dt = f1_score(y_test, y_pred_dt)\nprint(\"Decision Tree Model Accuracy:\", accuracy_dt)\nprint(\"Decision Tree Precision:\", precision_dt)\nprint(\"Decision Tree Recall:\", recall_dt)\nprint(\"Decision Tree F1 Score:\", f1_dt)\ncm_dt = confusion_matrix(y_test, y_pred_dt)\nplt.figure(figsize=(5, 4))\nsns.heatmap(cm_dt, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix - Decision Tree')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-17T15:03:31.999248Z","iopub.execute_input":"2024-05-17T15:03:31.999647Z","iopub.status.idle":"2024-05-17T15:03:32.177522Z","shell.execute_reply.started":"2024-05-17T15:03:31.999614Z","shell.execute_reply":"2024-05-17T15:03:32.175247Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"Decision Tree Model Accuracy: 0.9771689497716894\nDecision Tree Precision: 0.9818181818181818\nDecision Tree Recall: 0.9310344827586207\nDecision Tree F1 Score: 0.9557522123893805\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score\n\n\n# Convert X_train to DataFrame\nX_train = pd.DataFrame(X_train)\n\n# Now you can use x_train_df as a DataFrame with all columns from X_train\n\n# Define the column transformer\n# Here we assume all features are numeric. Adjust if you have categorical features.\nnumeric_features = X_train.columns  # Assuming x_train is a DataFrame\nnumeric_transformer = Pipeline(steps=[\n    ('scaler', StandardScaler())\n])\n\ncolumn_transformer = ColumnTransformer(\n    transformers=[\n        ('num', numeric_transformer, numeric_features)\n    ]\n)\n\n# Fit and transform the training data\nX_train_transformed = column_transformer.fit_transform(X_train)\nX_test_transformed = column_transformer.transform(X_test)\n\n# Convert X_train_transformed back to DataFrame to access columns\nX_train_transformed_df = pd.DataFrame(X_train_transformed, columns=numeric_features)\n\n# Train the SVM classifier\nclf = SVC(kernel='rbf', gamma=0.01, C=1000, random_state=42)\nclf.fit(X_train_transformed, y_train)\n\n# Making predictions\ny_pred_svm = clf.predict(X_test_transformed)\n\n# Calculating accuracy, precision, recall, and F1 score\naccuracy_svm = accuracy_score(y_test, y_pred_svm)\nprecision_svm = precision_score(y_test, y_pred_svm, average='weighted', zero_division=0)\nrecall_svm = recall_score(y_test, y_pred_svm, average='weighted', zero_division=0)\nf1_svm = f1_score(y_test, y_pred_svm, average='weighted', zero_division=0)\n\n# Printing the results\nprint(\"SVM Model Accuracy:\", accuracy_svm)\nprint(\"SVM Precision:\", precision_svm)\nprint(\"SVM Recall:\", recall_svm)\nprint(\"SVM F1 Score:\", f1_svm)\n\n# Plotting the confusion matrix\nsvm_cm = confusion_matrix(y_test, y_pred_svm)\nplt.figure(figsize=(5, 4))\nsns.heatmap(svm_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix - SVM')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()\n\n","metadata":{"execution":{"iopub.status.busy":"2024-05-19T09:22:09.019469Z","iopub.execute_input":"2024-05-19T09:22:09.020337Z","iopub.status.idle":"2024-05-19T09:22:09.228557Z","shell.execute_reply.started":"2024-05-19T09:22:09.020295Z","shell.execute_reply":"2024-05-19T09:22:09.227425Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"SVM Model Accuracy: 0.9817351598173516\nSVM Precision: 0.9817401622385235\nSVM Recall: 0.9817351598173516\nSVM F1 Score: 0.9816313148845394\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"rf_model = RandomForestClassifier(random_state=42)\nrf_model.fit(X_train, y_train)\ny_pred_rf = rf_model.predict(X_test)\naccuracy_rf = accuracy_score(y_test, y_pred_rf)\nprecision_rf = precision_score(y_test, y_pred_rf)\nrecall_rf = recall_score(y_test, y_pred_rf)\nf1_rf = f1_score(y_test, y_pred_rf)\nprint(\"Random Forest Model Accuracy:\", accuracy_rf)\nprint(\"Random Forest Precision:\", precision_rf)\nprint(\"Random Forest Recall:\", recall_rf)\nprint(\"Random Forest F1 Score:\", f1_rf)\nrf_cm = confusion_matrix(y_test, y_pred_rf)\nplt.figure(figsize=(5, 4))\nsns.heatmap(rf_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix - Random Forest')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-17T15:21:46.897441Z","iopub.execute_input":"2024-05-17T15:21:46.897874Z","iopub.status.idle":"2024-05-17T15:21:47.293667Z","shell.execute_reply.started":"2024-05-17T15:21:46.897828Z","shell.execute_reply":"2024-05-17T15:21:47.29229Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"Random Forest Model Accuracy: 1.0\nRandom Forest Precision: 1.0\nRandom Forest Recall: 1.0\nRandom Forest F1 Score: 1.0\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"mlp_model = MLPClassifier(random_state=42)\nmlp_model.fit(X_train, y_train)\ny_pred_mlp = mlp_model.predict(X_test)\naccuracy_mlp = accuracy_score(y_test, y_pred_mlp)\nprecision_mlp = precision_score(y_test, y_pred_mlp)\nrecall_mlp = recall_score(y_test, y_pred_mlp)\nf1_mlp = f1_score(y_test, y_pred_mlp)\nprint(\"MLP Model Accuracy:\", accuracy_mlp)\nprint(\"MLP Precision:\", precision_mlp)\nprint(\"MLP Recall:\", recall_mlp)\nprint(\"MLP F1 Score:\", f1_mlp)\nmlp_cm = confusion_matrix(y_test, y_pred_mlp)\nplt.figure(figsize=(5, 4))\nsns.heatmap(mlp_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix - MLP')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-17T15:22:16.002773Z","iopub.execute_input":"2024-05-17T15:22:16.003199Z","iopub.status.idle":"2024-05-17T15:22:16.476028Z","shell.execute_reply.started":"2024-05-17T15:22:16.003169Z","shell.execute_reply":"2024-05-17T15:22:16.474933Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"MLP Model Accuracy: 0.8059360730593608\nMLP Precision: 0.8297872340425532\nMLP Recall: 0.33620689655172414\nMLP F1 Score: 0.47852760736196315\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom sklearn.metrics import accuracy_score\ndef create_mlp_model(input_dim):\n    model = Sequential([\n        Dense(64, activation='relu', input_dim=input_dim),\n        Dense(32, activation='relu'),\n        Dense(1, activation='sigmoid')\n    ])\n    return model\nmlp_model = create_mlp_model(input_dim=X_train.shape[1])\nmlp_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nmlp_model.fit(X_train, y_train, epochs=3, batch_size=32, validation_split=0.2)\nloss, accuracy = mlp_model.evaluate(X_test, y_test)\nprint(\"MLP Model Accuracy:\", accuracy)\ncm = confusion_matrix(y_test, y_pred_mlp)\nplt.figure(figsize=(5, 4))\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix - MLP')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-17T15:22:43.701254Z","iopub.execute_input":"2024-05-17T15:22:43.701656Z","iopub.status.idle":"2024-05-17T15:22:59.933042Z","shell.execute_reply.started":"2024-05-17T15:22:43.701625Z","shell.execute_reply":"2024-05-17T15:22:59.931745Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stderr","text":"2024-05-17 15:22:45.819755: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-05-17 15:22:45.819887: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-05-17 15:22:45.957582: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"},{"name":"stdout","text":"Epoch 1/3\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/keras/src/layers/core/dense.py:86: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - accuracy: 0.5633 - loss: 91.0894 - val_accuracy: 0.8580 - val_loss: 7.0953\nEpoch 2/3\n\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7078 - loss: 10.4465 - val_accuracy: 0.8011 - val_loss: 1.1745\nEpoch 3/3\n\u001b[1m22/22\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6068 - loss: 5.5059 - val_accuracy: 0.8580 - val_loss: 1.9304\n\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7202 - loss: 4.2980 \nMLP Model Accuracy: 0.7351598143577576\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"xgb_model = xgb.XGBClassifier(random_state=42)\nxgb_model.fit(X_train, y_train)\ny_pred_xgb = xgb_model.predict(X_test)\naccuracy_xgb = accuracy_score(y_test, y_pred_xgb)\nprecision_xgb = precision_score(y_test, y_pred_xgb)\nrecall_xgb = recall_score(y_test, y_pred_xgb)\nf1_xgb = f1_score(y_test, y_pred_xgb)\nprint(\"XGBoost Model Accuracy:\", accuracy_xgb)\nprint(\"XGBoost Precision:\", precision_xgb)\nprint(\"XGBoost Recall:\", recall_xgb)\nprint(\"XGBoost F1 Score:\", f1_xgb)\nxgb_cm = confusion_matrix(y_test, y_pred_xgb)\nplt.figure(figsize=(5, 4))\nsns.heatmap(xgb_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Confusion Matrix - XGBoost')\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-17T15:25:14.831005Z","iopub.execute_input":"2024-05-17T15:25:14.832038Z","iopub.status.idle":"2024-05-17T15:25:15.096496Z","shell.execute_reply.started":"2024-05-17T15:25:14.832002Z","shell.execute_reply":"2024-05-17T15:25:15.095295Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"XGBoost Model Accuracy: 1.0\nXGBoost Precision: 1.0\nXGBoost Recall: 1.0\nXGBoost F1 Score: 1.0\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 500x400 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}