{"cells":[{"cell_type":"code","source":[],"metadata":{"id":"_HeKT-ajBWXh","executionInfo":{"status":"ok","timestamp":1747299154667,"user_tz":-330,"elapsed":2,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"_HeKT-ajBWXh","execution_count":82,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4i_t5qxQBYMv","executionInfo":{"status":"ok","timestamp":1747299156431,"user_tz":-330,"elapsed":1762,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"a38f6c33-89f2-4b52-c22a-0dcc797f849d"},"id":"4i_t5qxQBYMv","execution_count":83,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["import os\n","# os.get_dir('/content/drive')\n","path='/content/drive/MyDrive/Deep Learning/mini_sample_project/'\n","os.listdir('/content/drive/MyDrive/Deep Learning/mini_sample_project')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u8nAtqyhBWUx","executionInfo":{"status":"ok","timestamp":1747299156432,"user_tz":-330,"elapsed":15,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"6e6db817-9dc0-4578-f38c-ccd96101de8f"},"id":"u8nAtqyhBWUx","execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['neuron.ipynb', 'MentalHealth_risk_identification.csv']"]},"metadata":{},"execution_count":84}]},{"cell_type":"code","execution_count":85,"id":"c6de1db9","metadata":{"id":"c6de1db9","executionInfo":{"status":"ok","timestamp":1747299156432,"user_tz":-330,"elapsed":5,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":86,"id":"f6557186","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"f6557186","executionInfo":{"status":"ok","timestamp":1747299156480,"user_tz":-330,"elapsed":51,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"570a5141-d1aa-4888-d25a-0234ef17944b"},"outputs":[{"output_type":"stream","name":"stdout","text":["2087\n"]},{"output_type":"execute_result","data":{"text/plain":["      Age  Gender  Work Hours Family History  Sleep Hours  Stress Level  \\\n","0      79    Male          20            Yes            7             7   \n","1      20  Others          31             No            8             7   \n","2      40    Male          39             No            8             4   \n","3      35  Female          66            Yes            7            10   \n","4      81  Female          42            Yes            6             2   \n","...   ...     ...         ...            ...          ...           ...   \n","2082   63    Male          38             No            7             0   \n","2083   96  Female          34             No            6             9   \n","2084   25    Male          62            Yes            7             7   \n","2085   96  Female          65            Yes            4             9   \n","2086   50    Male          46            Yes            7             6   \n","\n","      Physical Activity  Social Interaction Diet Quality Treatment  \n","0                    24                   2      Average       Yes  \n","1                     2                   2      Average        No  \n","2                     7                   8         Good       Yes  \n","3                    40                   2      Average       Yes  \n","4                    78                   2         Good       Yes  \n","...                 ...                 ...          ...       ...  \n","2082                 61                   5         Good        No  \n","2083                 97                   1      Average       Yes  \n","2084                138                   2         Poor       Yes  \n","2085                 76                   7         Poor       Yes  \n","2086                 51                   3      Average        No  \n","\n","[2087 rows x 10 columns]"],"text/html":["\n","  <div id=\"df-9b3bb592-0efd-431e-8268-0f52ba52c002\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Age</th>\n","      <th>Gender</th>\n","      <th>Work Hours</th>\n","      <th>Family History</th>\n","      <th>Sleep Hours</th>\n","      <th>Stress Level</th>\n","      <th>Physical Activity</th>\n","      <th>Social Interaction</th>\n","      <th>Diet Quality</th>\n","      <th>Treatment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>79</td>\n","      <td>Male</td>\n","      <td>20</td>\n","      <td>Yes</td>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>24</td>\n","      <td>2</td>\n","      <td>Average</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>20</td>\n","      <td>Others</td>\n","      <td>31</td>\n","      <td>No</td>\n","      <td>8</td>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>Average</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>40</td>\n","      <td>Male</td>\n","      <td>39</td>\n","      <td>No</td>\n","      <td>8</td>\n","      <td>4</td>\n","      <td>7</td>\n","      <td>8</td>\n","      <td>Good</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>35</td>\n","      <td>Female</td>\n","      <td>66</td>\n","      <td>Yes</td>\n","      <td>7</td>\n","      <td>10</td>\n","      <td>40</td>\n","      <td>2</td>\n","      <td>Average</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>81</td>\n","      <td>Female</td>\n","      <td>42</td>\n","      <td>Yes</td>\n","      <td>6</td>\n","      <td>2</td>\n","      <td>78</td>\n","      <td>2</td>\n","      <td>Good</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2082</th>\n","      <td>63</td>\n","      <td>Male</td>\n","      <td>38</td>\n","      <td>No</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>61</td>\n","      <td>5</td>\n","      <td>Good</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>2083</th>\n","      <td>96</td>\n","      <td>Female</td>\n","      <td>34</td>\n","      <td>No</td>\n","      <td>6</td>\n","      <td>9</td>\n","      <td>97</td>\n","      <td>1</td>\n","      <td>Average</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2084</th>\n","      <td>25</td>\n","      <td>Male</td>\n","      <td>62</td>\n","      <td>Yes</td>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>138</td>\n","      <td>2</td>\n","      <td>Poor</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2085</th>\n","      <td>96</td>\n","      <td>Female</td>\n","      <td>65</td>\n","      <td>Yes</td>\n","      <td>4</td>\n","      <td>9</td>\n","      <td>76</td>\n","      <td>7</td>\n","      <td>Poor</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2086</th>\n","      <td>50</td>\n","      <td>Male</td>\n","      <td>46</td>\n","      <td>Yes</td>\n","      <td>7</td>\n","      <td>6</td>\n","      <td>51</td>\n","      <td>3</td>\n","      <td>Average</td>\n","      <td>No</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2087 rows × 10 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9b3bb592-0efd-431e-8268-0f52ba52c002')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n"," 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\"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 20,\n        \"max\": 69,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          37,\n          25,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family History\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 10,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          7,\n          8\n        ],\n        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\"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diet Quality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Average\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treatment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":86}],"source":["df=pd.read_csv(path+\"MentalHealth_risk_identification.csv\")\n","df.drop(\"Unnamed: 0\",axis=1,inplace=True)\n","print(len(df))\n","# df[\"Treatment\"]=df[\"Treatment\"].map({\"Yes\": 1, \"No\": 0})\n","\n","df.head()\n","df"]},{"cell_type":"code","execution_count":87,"id":"531f8999","metadata":{"id":"531f8999","executionInfo":{"status":"ok","timestamp":1747299156483,"user_tz":-330,"elapsed":2,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler,OrdinalEncoder,OneHotEncoder\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer"]},{"cell_type":"code","source":["en=OneHotEncoder(sparse_output=False, drop='first')\n","df['Treatment']=en.fit_transform(df[['Treatment']])#.toarray()"],"metadata":{"id":"i7qmLmP-Hqm8","executionInfo":{"status":"ok","timestamp":1747299156505,"user_tz":-330,"elapsed":3,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"i7qmLmP-Hqm8","execution_count":88,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"RejiKiE6H0pd","executionInfo":{"status":"ok","timestamp":1747299156547,"user_tz":-330,"elapsed":37,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"8dec80a7-1598-4b57-c064-518c49960a34"},"id":"RejiKiE6H0pd","execution_count":89,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Age  Gender  Work Hours Family History  Sleep Hours  Stress Level  \\\n","0      79    Male          20            Yes            7             7   \n","1      20  Others          31             No            8             7   \n","2      40    Male          39             No            8             4   \n","3      35  Female          66            Yes            7            10   \n","4      81  Female          42            Yes            6             2   \n","...   ...     ...         ...            ...          ...           ...   \n","2082   63    Male          38             No            7             0   \n","2083   96  Female          34             No            6             9   \n","2084   25    Male          62            Yes            7             7   \n","2085   96  Female          65            Yes            4             9   \n","2086   50    Male          46            Yes            7             6   \n","\n","      Physical Activity  Social Interaction Diet Quality  Treatment  \n","0                    24                   2      Average        1.0  \n","1                     2                   2      Average        0.0  \n","2                     7                   8         Good        1.0  \n","3                    40                   2      Average        1.0  \n","4                    78                   2         Good        1.0  \n","...                 ...                 ...          ...        ...  \n","2082                 61                   5         Good        0.0  \n","2083                 97                   1      Average        1.0  \n","2084                138                   2         Poor        1.0  \n","2085                 76                   7         Poor        1.0  \n","2086                 51                   3      Average        0.0  \n","\n","[2087 rows x 10 columns]"],"text/html":["\n","  <div id=\"df-8c6f2ab2-aa98-4e3a-bb52-58a529044a53\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        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<td>2</td>\n","      <td>Average</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>40</td>\n","      <td>Male</td>\n","      <td>39</td>\n","      <td>No</td>\n","      <td>8</td>\n","      <td>4</td>\n","      <td>7</td>\n","      <td>8</td>\n","      <td>Good</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>35</td>\n","      <td>Female</td>\n","      <td>66</td>\n","      <td>Yes</td>\n","      <td>7</td>\n","      <td>10</td>\n","      <td>40</td>\n","      <td>2</td>\n","      <td>Average</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>81</td>\n","      <td>Female</td>\n","      <td>42</td>\n","      <td>Yes</td>\n","      <td>6</td>\n","      <td>2</td>\n","      <td>78</td>\n","      <td>2</td>\n","      <td>Good</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      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<td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2085</th>\n","      <td>96</td>\n","      <td>Female</td>\n","      <td>65</td>\n","      <td>Yes</td>\n","      <td>4</td>\n","      <td>9</td>\n","      <td>76</td>\n","      <td>7</td>\n","      <td>Poor</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2086</th>\n","      <td>50</td>\n","      <td>Male</td>\n","      <td>46</td>\n","      <td>Yes</td>\n","      <td>7</td>\n","      <td>6</td>\n","      <td>51</td>\n","      <td>3</td>\n","      <td>Average</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2087 rows × 10 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c6f2ab2-aa98-4e3a-bb52-58a529044a53')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg 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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stress Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Physical Activity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 0,\n        \"max\": 180,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          16,\n          151\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Social Interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diet Quality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Average\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treatment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43318563444454833,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":89}]},{"cell_type":"code","execution_count":90,"id":"c5ba1a82","metadata":{"id":"c5ba1a82","executionInfo":{"status":"ok","timestamp":1747299156567,"user_tz":-330,"elapsed":17,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["std=StandardScaler()"]},{"cell_type":"code","execution_count":91,"id":"7fa99037","metadata":{"id":"7fa99037","executionInfo":{"status":"ok","timestamp":1747299156576,"user_tz":-330,"elapsed":4,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["nominal=['Gender','Family History']\n","ordinal=['Diet Quality']\n","s=['Age','Work Hours','Sleep Hours','Stress Level','Physical Activity','Social Interaction']\n",""]},{"cell_type":"code","source":["from sklearn.preprocessing import OneHotEncoder"],"metadata":{"id":"6wU03PY8CV-Q","executionInfo":{"status":"ok","timestamp":1747299156595,"user_tz":-330,"elapsed":17,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"6wU03PY8CV-Q","execution_count":92,"outputs":[]},{"cell_type":"code","execution_count":93,"id":"b0e87e39","metadata":{"id":"b0e87e39","executionInfo":{"status":"ok","timestamp":1747299156598,"user_tz":-330,"elapsed":1,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["ord_p=Pipeline([(\"ordnal\",OrdinalEncoder())])\n","nom_p=Pipeline([(\"nomianl\",OneHotEncoder(sparse_output=False, drop='first'))])\n","scale=Pipeline([(\"s\",StandardScaler())])"]},{"cell_type":"code","execution_count":94,"id":"586f583b","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":148},"id":"586f583b","executionInfo":{"status":"ok","timestamp":1747299156687,"user_tz":-330,"elapsed":88,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"dc1442cb-f99f-4c27-ed1e-4d9ff1dac379"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["ColumnTransformer(remainder='passthrough',\n","                  transformers=[('ord',\n","                                 Pipeline(steps=[('ordnal', OrdinalEncoder())]),\n","                                 ['Diet Quality']),\n","                                ('nominal',\n","                                 Pipeline(steps=[('nomianl',\n","                                                  OneHotEncoder(drop='first',\n","                                                                sparse_output=False))]),\n","                                 ['Gender', 'Family History']),\n","                                ('scaling',\n","                                 Pipeline(steps=[('s', StandardScaler())]),\n","                                 ['Age', 'Work Hours', 'Sleep Hours',\n","                                  'Stress Level', 'Physical Activity',\n","                                  'Social Interaction'])])"],"text/html":["<style>#sk-container-id-2 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: #000;\n","  --sklearn-color-text-muted: #666;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-2 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-2 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-2 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-2 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-2 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-2 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-2 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-2 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-2 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-2 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-2 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: flex;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","  align-items: start;\n","  justify-content: space-between;\n","  gap: 0.5em;\n","}\n","\n","#sk-container-id-2 label.sk-toggleable__label .caption {\n","  font-size: 0.6rem;\n","  font-weight: lighter;\n","  color: var(--sklearn-color-text-muted);\n","}\n","\n","#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-2 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-2 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-2 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-2 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-2 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-2 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 0.5em;\n","  text-align: center;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-2 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-2 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n","                  transformers=[(&#x27;ord&#x27;,\n","                                 Pipeline(steps=[(&#x27;ordnal&#x27;, OrdinalEncoder())]),\n","                                 [&#x27;Diet Quality&#x27;]),\n","                                (&#x27;nominal&#x27;,\n","                                 Pipeline(steps=[(&#x27;nomianl&#x27;,\n","                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n","                                                                sparse_output=False))]),\n","                                 [&#x27;Gender&#x27;, &#x27;Family History&#x27;]),\n","                                (&#x27;scaling&#x27;,\n","                                 Pipeline(steps=[(&#x27;s&#x27;, StandardScaler())]),\n","                                 [&#x27;Age&#x27;, &#x27;Work Hours&#x27;, &#x27;Sleep Hours&#x27;,\n","                                  &#x27;Stress Level&#x27;, &#x27;Physical Activity&#x27;,\n","                                  &#x27;Social Interaction&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n","                  transformers=[(&#x27;ord&#x27;,\n","                                 Pipeline(steps=[(&#x27;ordnal&#x27;, OrdinalEncoder())]),\n","                                 [&#x27;Diet Quality&#x27;]),\n","                                (&#x27;nominal&#x27;,\n","                                 Pipeline(steps=[(&#x27;nomianl&#x27;,\n","                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n","                                                                sparse_output=False))]),\n","                                 [&#x27;Gender&#x27;, &#x27;Family History&#x27;]),\n","                                (&#x27;scaling&#x27;,\n","                                 Pipeline(steps=[(&#x27;s&#x27;, StandardScaler())]),\n","                                 [&#x27;Age&#x27;, &#x27;Work Hours&#x27;, &#x27;Sleep Hours&#x27;,\n","                                  &#x27;Stress Level&#x27;, &#x27;Physical Activity&#x27;,\n","                                  &#x27;Social Interaction&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ord</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Diet Quality&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OrdinalEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>nominal</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Gender&#x27;, &#x27;Family History&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>scaling</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Age&#x27;, &#x27;Work Hours&#x27;, &#x27;Sleep Hours&#x27;, &#x27;Stress Level&#x27;, &#x27;Physical Activity&#x27;, &#x27;Social Interaction&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content \"><pre></pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":94}],"source":["pip=ColumnTransformer([(\"ord\",ord_p,ordinal),('nominal',nom_p,nominal),('scaling',scale,s)],remainder=\"passthrough\")\n","pip"]},{"cell_type":"code","execution_count":95,"id":"8d49a873","metadata":{"id":"8d49a873","executionInfo":{"status":"ok","timestamp":1747299156698,"user_tz":-330,"elapsed":9,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["x=df.drop(\"Treatment\",axis=1)\n","y=df['Treatment']"]},{"cell_type":"code","execution_count":96,"id":"12ce1044","metadata":{"id":"12ce1044","executionInfo":{"status":"ok","timestamp":1747299156744,"user_tz":-330,"elapsed":42,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["x=pip.fit_transform(x)"]},{"cell_type":"code","execution_count":97,"id":"7f58f1d5","metadata":{"id":"7f58f1d5","executionInfo":{"status":"ok","timestamp":1747299156747,"user_tz":-330,"elapsed":29,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=27)"]},{"cell_type":"code","execution_count":98,"id":"a3d1ba4d","metadata":{"id":"a3d1ba4d","executionInfo":{"status":"ok","timestamp":1747299156748,"user_tz":-330,"elapsed":20,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["import keras"]},{"cell_type":"code","execution_count":99,"id":"012633cb","metadata":{"id":"012633cb","executionInfo":{"status":"ok","timestamp":1747299156750,"user_tz":-330,"elapsed":20,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["from keras.layers import Input, Dense, BatchNormalization"]},{"cell_type":"code","source":["from keras.models import Sequential"],"metadata":{"id":"UFPMNun_Cxp7","executionInfo":{"status":"ok","timestamp":1747299156753,"user_tz":-330,"elapsed":21,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"UFPMNun_Cxp7","execution_count":100,"outputs":[]},{"cell_type":"code","source":["model = Sequential()"],"metadata":{"id":"Z84dxzW_C4Zq","executionInfo":{"status":"ok","timestamp":1747299156765,"user_tz":-330,"elapsed":11,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"Z84dxzW_C4Zq","execution_count":101,"outputs":[]},{"cell_type":"code","source":["from keras.initializers import HeNormal, HeUniform, GlorotUniform, GlorotNormal"],"metadata":{"id":"tT4QtidkC7Ke","executionInfo":{"status":"ok","timestamp":1747299156770,"user_tz":-330,"elapsed":3,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"tT4QtidkC7Ke","execution_count":102,"outputs":[]},{"cell_type":"code","source":["h = HeNormal()"],"metadata":{"id":"nGqaZbC8C9Dv","executionInfo":{"status":"ok","timestamp":1747299156774,"user_tz":-330,"elapsed":2,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"nGqaZbC8C9Dv","execution_count":103,"outputs":[]},{"cell_type":"code","source":["model.add(Input(shape = (10,)))\n","model.add(BatchNormalization())\n","model.add(Dense(10, activation = 'relu', kernel_initializer = h))\n","model.add(BatchNormalization())\n","model.add(Dense(10, activation = 'relu', kernel_initializer = h))\n","# model\n","model.add(Dense(1,activation=\"sigmoid\",kernel_initializer=h))"],"metadata":{"id":"aDQLrl2jC--X","executionInfo":{"status":"ok","timestamp":1747299156776,"user_tz":-330,"elapsed":1,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"aDQLrl2jC--X","execution_count":104,"outputs":[]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":321},"id":"15I7WO7SD94d","executionInfo":{"status":"ok","timestamp":1747299156832,"user_tz":-330,"elapsed":55,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"f0c26576-f7f6-454d-f34a-8f9e14e9f635"},"id":"15I7WO7SD94d","execution_count":105,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_1\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │            \u001b[38;5;34m40\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m110\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │            \u001b[38;5;34m40\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m110\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m11\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m311\u001b[0m (1.21 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">311</span> (1.21 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m271\u001b[0m (1.06 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">271</span> (1.06 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m40\u001b[0m (160.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">40</span> (160.00 B)\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["model.compile(optimizer='adam',loss=\"binary_crossentropy\",metrics=['accuracy'])"],"metadata":{"id":"ZlGao5vnE7-D","executionInfo":{"status":"ok","timestamp":1747299156833,"user_tz":-330,"elapsed":11,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"ZlGao5vnE7-D","execution_count":106,"outputs":[]},{"cell_type":"code","source":["model.fit(x_train,y_train,epochs=30,validation_split=0.2,batch_size=30)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TEn1hVh4FGow","executionInfo":{"status":"ok","timestamp":1747299170222,"user_tz":-330,"elapsed":13396,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"2dcb523c-e15c-477f-bd98-844446cab1f7"},"id":"TEn1hVh4FGow","execution_count":107,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.6232 - loss: 0.6828 - val_accuracy: 0.7844 - val_loss: 0.4737\n","Epoch 2/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.6873 - loss: 0.5785 - val_accuracy: 0.7784 - val_loss: 0.4527\n","Epoch 3/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7258 - loss: 0.5342 - val_accuracy: 0.7754 - val_loss: 0.4334\n","Epoch 4/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7735 - loss: 0.5094 - val_accuracy: 0.7964 - val_loss: 0.4145\n","Epoch 5/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.7566 - loss: 0.5060 - val_accuracy: 0.7934 - val_loss: 0.4003\n","Epoch 6/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7718 - loss: 0.4710 - val_accuracy: 0.8054 - val_loss: 0.3871\n","Epoch 7/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7742 - loss: 0.4832 - val_accuracy: 0.8084 - val_loss: 0.3771\n","Epoch 8/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8003 - loss: 0.4416 - val_accuracy: 0.8204 - val_loss: 0.3655\n","Epoch 9/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7815 - loss: 0.4451 - val_accuracy: 0.8293 - val_loss: 0.3586\n","Epoch 10/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8084 - loss: 0.4219 - val_accuracy: 0.8323 - val_loss: 0.3506\n","Epoch 11/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7794 - loss: 0.4470 - val_accuracy: 0.8323 - val_loss: 0.3466\n","Epoch 12/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7999 - loss: 0.4202 - val_accuracy: 0.8323 - val_loss: 0.3424\n","Epoch 13/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8004 - loss: 0.4146 - val_accuracy: 0.8293 - val_loss: 0.3396\n","Epoch 14/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8137 - loss: 0.4199 - val_accuracy: 0.8323 - val_loss: 0.3353\n","Epoch 15/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8026 - loss: 0.4003 - val_accuracy: 0.8353 - val_loss: 0.3311\n","Epoch 16/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8048 - loss: 0.4034 - val_accuracy: 0.8383 - val_loss: 0.3284\n","Epoch 17/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8126 - loss: 0.3755 - val_accuracy: 0.8413 - val_loss: 0.3254\n","Epoch 18/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8069 - loss: 0.4014 - val_accuracy: 0.8443 - val_loss: 0.3248\n","Epoch 19/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8253 - loss: 0.3790 - val_accuracy: 0.8413 - val_loss: 0.3235\n","Epoch 20/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8247 - loss: 0.3805 - val_accuracy: 0.8383 - val_loss: 0.3230\n","Epoch 21/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8185 - loss: 0.3970 - val_accuracy: 0.8383 - val_loss: 0.3222\n","Epoch 22/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8063 - loss: 0.4218 - val_accuracy: 0.8413 - val_loss: 0.3190\n","Epoch 23/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8066 - loss: 0.3963 - val_accuracy: 0.8413 - val_loss: 0.3185\n","Epoch 24/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7945 - loss: 0.3924 - val_accuracy: 0.8383 - val_loss: 0.3161\n","Epoch 25/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8165 - loss: 0.3983 - val_accuracy: 0.8353 - val_loss: 0.3181\n","Epoch 26/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8058 - loss: 0.3890 - val_accuracy: 0.8323 - val_loss: 0.3155\n","Epoch 27/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7941 - loss: 0.4248 - val_accuracy: 0.8353 - val_loss: 0.3152\n","Epoch 28/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8023 - loss: 0.3983 - val_accuracy: 0.8383 - val_loss: 0.3151\n","Epoch 29/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8038 - loss: 0.4004 - val_accuracy: 0.8323 - val_loss: 0.3143\n","Epoch 30/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8205 - loss: 0.3855 - val_accuracy: 0.8383 - val_loss: 0.3142\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.src.callbacks.history.History at 0x7db25d815050>"]},"metadata":{},"execution_count":107}]},{"cell_type":"code","source":["model.evaluate(x_test,y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6gDAuGr6FfbG","executionInfo":{"status":"ok","timestamp":1747299170470,"user_tz":-330,"elapsed":245,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"0ae407a1-89f7-4423-f336-82eec67fbc32"},"id":"6gDAuGr6FfbG","execution_count":108,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8193 - loss: 0.3716 \n"]},{"output_type":"execute_result","data":{"text/plain":["[0.37067076563835144, 0.8253588676452637]"]},"metadata":{},"execution_count":108}]},{"cell_type":"code","source":["x_test[0].reshape(1,-1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IruIsuC9G2aG","executionInfo":{"status":"ok","timestamp":1747299170482,"user_tz":-330,"elapsed":13,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"9296c698-5e72-4a7f-f235-75a8b8758858"},"id":"IruIsuC9G2aG","execution_count":109,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0.        ,  1.        ,  0.        ,  0.        , -1.10039259,\n","        -0.61261705,  0.31432239,  0.64045774,  0.79635556,  0.64158901]])"]},"metadata":{},"execution_count":109}]},{"cell_type":"code","source":["model.predict(x_test[0].reshape(1,-1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h-VAIOg8Gs7L","executionInfo":{"status":"ok","timestamp":1747299170701,"user_tz":-330,"elapsed":206,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"44672033-de6f-4279-a10c-e1b405f7a4ff"},"id":"h-VAIOg8Gs7L","execution_count":110,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[0.1687277]], dtype=float32)"]},"metadata":{},"execution_count":110}]},{"cell_type":"code","source":["import numpy as np"],"metadata":{"id":"bFbHTw6cHUU0","executionInfo":{"status":"ok","timestamp":1747299170754,"user_tz":-330,"elapsed":52,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"bFbHTw6cHUU0","execution_count":111,"outputs":[]},{"cell_type":"code","source":["np.argmax(model.predict(x_test[0].reshape(1,-1)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mMerNJ1kHNkx","executionInfo":{"status":"ok","timestamp":1747299170875,"user_tz":-330,"elapsed":158,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"27c02805-9a6f-4cea-eb2b-9eac3df3a6e4"},"id":"mMerNJ1kHNkx","execution_count":112,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["np.int64(0)"]},"metadata":{},"execution_count":112}]},{"cell_type":"code","source":["en.inverse_transform(np.array(y_test)[1].reshape(1,-1))\n","# en.inverse_transform(np.array([[1]]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0XC0TjuHHakJ","executionInfo":{"status":"ok","timestamp":1747299795173,"user_tz":-330,"elapsed":54,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"d2fa44d2-f69c-4ca5-923b-e46b1636d5a2"},"id":"0XC0TjuHHakJ","execution_count":128,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([['No']], dtype=object)"]},"metadata":{},"execution_count":128}]},{"cell_type":"code","source":["import pickle as pkl"],"metadata":{"id":"9vP3zNFMJrwr","executionInfo":{"status":"ok","timestamp":1747299416783,"user_tz":-330,"elapsed":5,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"9vP3zNFMJrwr","execution_count":120,"outputs":[]},{"cell_type":"code","source":["with open(path+\"model.pkl\",\"wb\") as f:\n","  pkl.dump(model,f)\n","with open(path+\"pip.pkl\",\"wb\") as f:\n","  pkl.dump(pip,f)\n","with open(path+\"encoding.pkl\",\"wb\") as f:\n","  pkl.dump(en,f)"],"metadata":{"id":"bhBheETTKUG0","executionInfo":{"status":"ok","timestamp":1747299501836,"user_tz":-330,"elapsed":120,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"bhBheETTKUG0","execution_count":121,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.4"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}