notebook8ad570467f.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":1481789,"sourceType":"datasetVersion","datasetId":869651}],"dockerImageVersionId":30170,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-08-09T12:46:28.106325Z","iopub.execute_input":"2024-08-09T12:46:28.106654Z","iopub.status.idle":"2024-08-09T12:46:28.118908Z","shell.execute_reply.started":"2024-08-09T12:46:28.106620Z","shell.execute_reply":"2024-08-09T12:46:28.117750Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stdout","text":"/kaggle/input/credit-card-customer-churn-prediction/Churn_Modelling.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.read_csv('/kaggle/input/credit-card-customer-churn-prediction/Churn_Modelling.csv')","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.121110Z","iopub.execute_input":"2024-08-09T12:46:28.121492Z","iopub.status.idle":"2024-08-09T12:46:28.165352Z","shell.execute_reply.started":"2024-08-09T12:46:28.121439Z","shell.execute_reply":"2024-08-09T12:46:28.164302Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.167050Z","iopub.execute_input":"2024-08-09T12:46:28.167741Z","iopub.status.idle":"2024-08-09T12:46:28.187875Z","shell.execute_reply.started":"2024-08-09T12:46:28.167695Z","shell.execute_reply":"2024-08-09T12:46:28.186730Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"   RowNumber  CustomerId   Surname  CreditScore Geography  Gender  Age  \\\n0          1    15634602  Hargrave          619    France  Female   42   \n1          2    15647311      Hill          608     Spain  Female   41   \n2          3    15619304      Onio          502    France  Female   42   \n3          4    15701354      Boni          699    France  Female   39   \n4          5    15737888  Mitchell          850     Spain  Female   43   \n\n   Tenure    Balance  NumOfProducts  HasCrCard  IsActiveMember  \\\n0       2       0.00              1          1               1   \n1       1   83807.86              1          0               1   \n2       8  159660.80              3          1               0   \n3       1       0.00              2          0               0   \n4       2  125510.82              1          1               1   \n\n   EstimatedSalary  Exited  \n0        101348.88       1  \n1        112542.58       0  \n2        113931.57       1  \n3         93826.63       0  \n4         79084.10       0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>RowNumber</th>\n      <th>CustomerId</th>\n      <th>Surname</th>\n      <th>CreditScore</th>\n      <th>Geography</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>15634602</td>\n      <td>Hargrave</td>\n      <td>619</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>15647311</td>\n      <td>Hill</td>\n      <td>608</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>15619304</td>\n      <td>Onio</td>\n      <td>502</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>8</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>15701354</td>\n      <td>Boni</td>\n      <td>699</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>15737888</td>\n      <td>Mitchell</td>\n      <td>850</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>43</td>\n      <td>2</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.drop(columns = ['RowNumber','CustomerId','Surname'],inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.190930Z","iopub.execute_input":"2024-08-09T12:46:28.191349Z","iopub.status.idle":"2024-08-09T12:46:28.204373Z","shell.execute_reply.started":"2024-08-09T12:46:28.191298Z","shell.execute_reply":"2024-08-09T12:46:28.203291Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.206024Z","iopub.execute_input":"2024-08-09T12:46:28.206342Z","iopub.status.idle":"2024-08-09T12:46:28.232175Z","shell.execute_reply.started":"2024-08-09T12:46:28.206305Z","shell.execute_reply":"2024-08-09T12:46:28.231257Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n0          619    France  Female   42       2       0.00              1   \n1          608     Spain  Female   41       1   83807.86              1   \n2          502    France  Female   42       8  159660.80              3   \n3          699    France  Female   39       1       0.00              2   \n4          850     Spain  Female   43       2  125510.82              1   \n\n   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n0          1               1        101348.88       1  \n1          0               1        112542.58       0  \n2          1               0        113931.57       1  \n3          0               0         93826.63       0  \n4          1               1         79084.10       0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CreditScore</th>\n      <th>Geography</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>619</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>608</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>502</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>8</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>699</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>850</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>43</td>\n      <td>2</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df['Geography'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.233596Z","iopub.execute_input":"2024-08-09T12:46:28.233900Z","iopub.status.idle":"2024-08-09T12:46:28.252068Z","shell.execute_reply.started":"2024-08-09T12:46:28.233859Z","shell.execute_reply":"2024-08-09T12:46:28.250962Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"France     5014\nGermany    2509\nSpain      2477\nName: Geography, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df['Gender'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.253642Z","iopub.execute_input":"2024-08-09T12:46:28.254294Z","iopub.status.idle":"2024-08-09T12:46:28.269125Z","shell.execute_reply.started":"2024-08-09T12:46:28.254253Z","shell.execute_reply":"2024-08-09T12:46:28.268094Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"Male      5457\nFemale    4543\nName: Gender, dtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.270658Z","iopub.execute_input":"2024-08-09T12:46:28.271001Z","iopub.status.idle":"2024-08-09T12:46:28.294789Z","shell.execute_reply.started":"2024-08-09T12:46:28.270956Z","shell.execute_reply":"2024-08-09T12:46:28.293490Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"   CreditScore Geography  Gender  Age  Tenure    Balance  NumOfProducts  \\\n0          619    France  Female   42       2       0.00              1   \n1          608     Spain  Female   41       1   83807.86              1   \n2          502    France  Female   42       8  159660.80              3   \n3          699    France  Female   39       1       0.00              2   \n4          850     Spain  Female   43       2  125510.82              1   \n\n   HasCrCard  IsActiveMember  EstimatedSalary  Exited  \n0          1               1        101348.88       1  \n1          0               1        112542.58       0  \n2          1               0        113931.57       1  \n3          0               0         93826.63       0  \n4          1               1         79084.10       0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CreditScore</th>\n      <th>Geography</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>619</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>608</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>502</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>42</td>\n      <td>8</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>699</td>\n      <td>France</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>850</td>\n      <td>Spain</td>\n      <td>Female</td>\n      <td>43</td>\n      <td>2</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df = pd.get_dummies(df,columns=['Geography','Gender'],drop_first=True)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.296686Z","iopub.execute_input":"2024-08-09T12:46:28.297048Z","iopub.status.idle":"2024-08-09T12:46:28.319610Z","shell.execute_reply.started":"2024-08-09T12:46:28.297000Z","shell.execute_reply":"2024-08-09T12:46:28.318575Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.324232Z","iopub.execute_input":"2024-08-09T12:46:28.324604Z","iopub.status.idle":"2024-08-09T12:46:28.350762Z","shell.execute_reply.started":"2024-08-09T12:46:28.324557Z","shell.execute_reply":"2024-08-09T12:46:28.349805Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"      CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n0             619   42       2       0.00              1          1   \n1             608   41       1   83807.86              1          0   \n2             502   42       8  159660.80              3          1   \n3             699   39       1       0.00              2          0   \n4             850   43       2  125510.82              1          1   \n...           ...  ...     ...        ...            ...        ...   \n9995          771   39       5       0.00              2          1   \n9996          516   35      10   57369.61              1          1   \n9997          709   36       7       0.00              1          0   \n9998          772   42       3   75075.31              2          1   \n9999          792   28       4  130142.79              1          1   \n\n      IsActiveMember  EstimatedSalary  Exited  Geography_Germany  \\\n0                  1        101348.88       1                  0   \n1                  1        112542.58       0                  0   \n2                  0        113931.57       1                  0   \n3                  0         93826.63       0                  0   \n4                  1         79084.10       0                  0   \n...              ...              ...     ...                ...   \n9995               0         96270.64       0                  0   \n9996               1        101699.77       0                  0   \n9997               1         42085.58       1                  0   \n9998               0         92888.52       1                  1   \n9999               0         38190.78       0                  0   \n\n      Geography_Spain  Gender_Male  \n0                   0            0  \n1                   1            0  \n2                   0            0  \n3                   0            0  \n4                   1            0  \n...               ...          ...  \n9995                0            1  \n9996                0            1  \n9997                0            0  \n9998                0            1  \n9999                0            0  \n\n[10000 rows x 12 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>CreditScore</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Exited</th>\n      <th>Geography_Germany</th>\n      <th>Geography_Spain</th>\n      <th>Gender_Male</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>619</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>608</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>502</td>\n      <td>42</td>\n      <td>8</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>699</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>850</td>\n      <td>43</td>\n      <td>2</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>9995</th>\n      <td>771</td>\n      <td>39</td>\n      <td>5</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>96270.64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>516</td>\n      <td>35</td>\n      <td>10</td>\n      <td>57369.61</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101699.77</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>709</td>\n      <td>36</td>\n      <td>7</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>42085.58</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>772</td>\n      <td>42</td>\n      <td>3</td>\n      <td>75075.31</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>92888.52</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>792</td>\n      <td>28</td>\n      <td>4</td>\n      <td>130142.79</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>38190.78</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 12 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"X = df.drop(columns=['Exited'])\ny = df['Exited']\n\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.352475Z","iopub.execute_input":"2024-08-09T12:46:28.352817Z","iopub.status.idle":"2024-08-09T12:46:28.368053Z","shell.execute_reply.started":"2024-08-09T12:46:28.352762Z","shell.execute_reply":"2024-08-09T12:46:28.367135Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"X_train.shape","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.369515Z","iopub.execute_input":"2024-08-09T12:46:28.369789Z","iopub.status.idle":"2024-08-09T12:46:28.377579Z","shell.execute_reply.started":"2024-08-09T12:46:28.369756Z","shell.execute_reply":"2024-08-09T12:46:28.376454Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"(8000, 11)"},"metadata":{}}]},{"cell_type":"code","source":"X","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.378977Z","iopub.execute_input":"2024-08-09T12:46:28.379323Z","iopub.status.idle":"2024-08-09T12:46:28.408548Z","shell.execute_reply.started":"2024-08-09T12:46:28.379274Z","shell.execute_reply":"2024-08-09T12:46:28.407325Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"      CreditScore  Age  Tenure    Balance  NumOfProducts  HasCrCard  \\\n0             619   42       2       0.00              1          1   \n1             608   41       1   83807.86              1          0   \n2             502   42       8  159660.80              3          1   \n3             699   39       1       0.00              2          0   \n4             850   43       2  125510.82              1          1   \n...           ...  ...     ...        ...            ...        ...   \n9995          771   39       5       0.00              2          1   \n9996          516   35      10   57369.61              1          1   \n9997          709   36       7       0.00              1          0   \n9998          772   42       3   75075.31              2          1   \n9999          792   28       4  130142.79              1          1   \n\n      IsActiveMember  EstimatedSalary  Geography_Germany  Geography_Spain  \\\n0                  1        101348.88                  0                0   \n1                  1        112542.58                  0                1   \n2                  0        113931.57                  0                0   \n3                  0         93826.63                  0                0   \n4                  1         79084.10                  0                1   \n...              ...              ...                ...              ...   \n9995               0         96270.64                  0                0   \n9996               1        101699.77                  0                0   \n9997               1         42085.58                  0                0   \n9998               0         92888.52                  1                0   \n9999               0         38190.78                  0                0   \n\n      Gender_Male  \n0               0  \n1               0  \n2               0  \n3               0  \n4               0  \n...           ...  \n9995            1  \n9996            1  \n9997            0  \n9998            1  \n9999            0  \n\n[10000 rows x 11 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>CreditScore</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>Balance</th>\n      <th>NumOfProducts</th>\n      <th>HasCrCard</th>\n      <th>IsActiveMember</th>\n      <th>EstimatedSalary</th>\n      <th>Geography_Germany</th>\n      <th>Geography_Spain</th>\n      <th>Gender_Male</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>619</td>\n      <td>42</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101348.88</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>608</td>\n      <td>41</td>\n      <td>1</td>\n      <td>83807.86</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>112542.58</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>502</td>\n      <td>42</td>\n      <td>8</td>\n      <td>159660.80</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>113931.57</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>699</td>\n      <td>39</td>\n      <td>1</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>93826.63</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>850</td>\n      <td>43</td>\n      <td>2</td>\n      <td>125510.82</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>79084.10</td>\n      <td>0</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    </tr>\n    <tr>\n      <th>9995</th>\n      <td>771</td>\n      <td>39</td>\n      <td>5</td>\n      <td>0.00</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>96270.64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>516</td>\n      <td>35</td>\n      <td>10</td>\n      <td>57369.61</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>101699.77</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>709</td>\n      <td>36</td>\n      <td>7</td>\n      <td>0.00</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>42085.58</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>772</td>\n      <td>42</td>\n      <td>3</td>\n      <td>75075.31</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>92888.52</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>792</td>\n      <td>28</td>\n      <td>4</td>\n      <td>130142.79</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>38190.78</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 11 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\n\nX_train_trf = scaler.fit_transform(X_train)\nX_test_trf = scaler.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.410259Z","iopub.execute_input":"2024-08-09T12:46:28.410552Z","iopub.status.idle":"2024-08-09T12:46:28.431252Z","shell.execute_reply.started":"2024-08-09T12:46:28.410517Z","shell.execute_reply":"2024-08-09T12:46:28.430253Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"import tensorflow\nfrom tensorflow import keras\nfrom tensorflow.keras import Sequential \nfrom tensorflow.keras.layers import Dense","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.432607Z","iopub.execute_input":"2024-08-09T12:46:28.432966Z","iopub.status.idle":"2024-08-09T12:46:28.439895Z","shell.execute_reply.started":"2024-08-09T12:46:28.432921Z","shell.execute_reply":"2024-08-09T12:46:28.438734Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Dense(11,activation='sigmoid',input_dim=11))\nmodel.add(Dense(11,activation='sigmoid'))\nmodel.add(Dense(1,activation='sigmoid'))","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.441419Z","iopub.execute_input":"2024-08-09T12:46:28.441833Z","iopub.status.idle":"2024-08-09T12:46:28.486955Z","shell.execute_reply.started":"2024-08-09T12:46:28.441775Z","shell.execute_reply":"2024-08-09T12:46:28.486014Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.488167Z","iopub.execute_input":"2024-08-09T12:46:28.488424Z","iopub.status.idle":"2024-08-09T12:46:28.497166Z","shell.execute_reply.started":"2024-08-09T12:46:28.488393Z","shell.execute_reply":"2024-08-09T12:46:28.496017Z"},"trusted":true},"execution_count":51,"outputs":[{"name":"stdout","text":"Model: \"sequential_1\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_3 (Dense)              (None, 11)                132       \n_________________________________________________________________\ndense_4 (Dense)              (None, 11)                132       \n_________________________________________________________________\ndense_5 (Dense)              (None, 1)                 12        \n=================================================================\nTotal params: 276\nTrainable params: 276\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model.compile(optimizer='Adam',loss='binary_crossentropy',metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.498895Z","iopub.execute_input":"2024-08-09T12:46:28.499148Z","iopub.status.idle":"2024-08-09T12:46:28.514656Z","shell.execute_reply.started":"2024-08-09T12:46:28.499117Z","shell.execute_reply":"2024-08-09T12:46:28.513492Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"history = model.fit(X_train,y_train,batch_size=50,epochs=100,verbose=1,validation_split=0.2)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:46:28.516136Z","iopub.execute_input":"2024-08-09T12:46:28.516621Z","iopub.status.idle":"2024-08-09T12:47:02.399764Z","shell.execute_reply.started":"2024-08-09T12:46:28.516538Z","shell.execute_reply":"2024-08-09T12:47:02.398783Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"Epoch 1/100\n128/128 [==============================] - 1s 4ms/step - loss: 0.5056 - accuracy: 0.7958 - val_loss: 0.4992 - val_accuracy: 0.7969\nEpoch 2/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.5014 - accuracy: 0.7958 - val_loss: 0.4978 - val_accuracy: 0.7969\nEpoch 3/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.5007 - accuracy: 0.7958 - val_loss: 0.4968 - val_accuracy: 0.7969\nEpoch 4/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.5004 - accuracy: 0.7958 - val_loss: 0.4967 - val_accuracy: 0.7969\nEpoch 5/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.5002 - accuracy: 0.7958 - val_loss: 0.4965 - val_accuracy: 0.7969\nEpoch 6/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.5000 - accuracy: 0.7958 - val_loss: 0.4963 - val_accuracy: 0.7969\nEpoch 7/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.5000 - accuracy: 0.7958 - val_loss: 0.4964 - val_accuracy: 0.7969\nEpoch 8/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.5004 - accuracy: 0.7958 - val_loss: 0.4960 - val_accuracy: 0.7969\nEpoch 9/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4998 - accuracy: 0.7958 - val_loss: 0.4961 - val_accuracy: 0.7969\nEpoch 10/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4997 - accuracy: 0.7958 - val_loss: 0.4961 - val_accuracy: 0.7969\nEpoch 11/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4962 - val_accuracy: 0.7969\nEpoch 12/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\nEpoch 13/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 14/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 15/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.5008 - accuracy: 0.7958 - val_loss: 0.4959 - val_accuracy: 0.7969\nEpoch 16/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4998 - accuracy: 0.7958 - val_loss: 0.4958 - val_accuracy: 0.7969\nEpoch 17/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4997 - accuracy: 0.7958 - val_loss: 0.4956 - val_accuracy: 0.7969\nEpoch 18/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4962 - val_accuracy: 0.7969\nEpoch 19/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4999 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\nEpoch 20/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4957 - val_accuracy: 0.7969\nEpoch 21/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4959 - val_accuracy: 0.7969\nEpoch 22/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 23/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 24/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\nEpoch 25/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 26/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 27/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\nEpoch 28/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4995 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\nEpoch 29/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4997 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\nEpoch 30/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 31/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 32/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 33/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\nEpoch 34/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 35/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\nEpoch 36/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 37/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 38/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\nEpoch 39/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 40/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\nEpoch 41/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 42/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 43/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 44/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4959 - val_accuracy: 0.7969\nEpoch 45/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4995 - 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2ms/step - loss: 0.4996 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 53/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\nEpoch 54/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 55/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 56/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4994 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 57/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 58/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 59/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 60/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 61/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\nEpoch 62/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\nEpoch 63/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 64/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 65/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 66/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\nEpoch 67/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 68/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\nEpoch 69/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\nEpoch 70/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\nEpoch 71/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4989 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\nEpoch 72/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4946 - val_accuracy: 0.7969\nEpoch 73/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\nEpoch 74/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\nEpoch 75/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4988 - accuracy: 0.7958 - val_loss: 0.4947 - val_accuracy: 0.7969\nEpoch 76/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\nEpoch 77/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\nEpoch 78/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4957 - val_accuracy: 0.7969\nEpoch 79/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\nEpoch 80/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4987 - accuracy: 0.7958 - val_loss: 0.4956 - val_accuracy: 0.7969\nEpoch 81/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4952 - val_accuracy: 0.7969\nEpoch 82/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 83/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4955 - val_accuracy: 0.7969\nEpoch 84/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 85/100\n128/128 [==============================] - 1s 5ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4953 - val_accuracy: 0.7969\nEpoch 86/100\n128/128 [==============================] - 0s 4ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 87/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 88/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 89/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4951 - val_accuracy: 0.7969\nEpoch 90/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 91/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4954 - val_accuracy: 0.7969\nEpoch 92/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 93/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4950 - val_accuracy: 0.7969\nEpoch 94/100\n128/128 [==============================] - 0s 3ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 95/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 96/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 97/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4991 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 98/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4993 - accuracy: 0.7958 - val_loss: 0.4948 - val_accuracy: 0.7969\nEpoch 99/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4990 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\nEpoch 100/100\n128/128 [==============================] - 0s 2ms/step - loss: 0.4992 - accuracy: 0.7958 - val_loss: 0.4949 - val_accuracy: 0.7969\n","output_type":"stream"}]},{"cell_type":"code","source":"y_pred = model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:47:02.404272Z","iopub.execute_input":"2024-08-09T12:47:02.404568Z","iopub.status.idle":"2024-08-09T12:47:02.579580Z","shell.execute_reply.started":"2024-08-09T12:47:02.404536Z","shell.execute_reply":"2024-08-09T12:47:02.578630Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"y_pred","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:47:02.580995Z","iopub.execute_input":"2024-08-09T12:47:02.581278Z","iopub.status.idle":"2024-08-09T12:47:02.588183Z","shell.execute_reply.started":"2024-08-09T12:47:02.581243Z","shell.execute_reply":"2024-08-09T12:47:02.587240Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"array([[0.24329603],\n       [0.24329603],\n       [0.2509605 ],\n       ...,\n       [0.2509605 ],\n       [0.256696  ],\n       [0.24329603]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"y_pred = y_pred.argmax(axis=-1)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:47:02.589466Z","iopub.execute_input":"2024-08-09T12:47:02.589710Z","iopub.status.idle":"2024-08-09T12:47:02.601588Z","shell.execute_reply.started":"2024-08-09T12:47:02.589679Z","shell.execute_reply":"2024-08-09T12:47:02.600640Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score\naccuracy_score(y_test,y_pred)","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:47:02.603133Z","iopub.execute_input":"2024-08-09T12:47:02.603503Z","iopub.status.idle":"2024-08-09T12:47:02.620381Z","shell.execute_reply.started":"2024-08-09T12:47:02.603466Z","shell.execute_reply":"2024-08-09T12:47:02.619215Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"0.7975"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:47:02.622158Z","iopub.execute_input":"2024-08-09T12:47:02.622589Z","iopub.status.idle":"2024-08-09T12:47:02.869900Z","shell.execute_reply.started":"2024-08-09T12:47:02.622507Z","shell.execute_reply":"2024-08-09T12:47:02.868813Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D at 0x7a3fdc5a1210>]"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.plot(history.history['accuracy'])\nplt.plot(history.history['val_accuracy'])","metadata":{"execution":{"iopub.status.busy":"2024-08-09T12:47:02.871446Z","iopub.execute_input":"2024-08-09T12:47:02.871820Z","iopub.status.idle":"2024-08-09T12:47:03.052735Z","shell.execute_reply.started":"2024-08-09T12:47:02.871773Z","shell.execute_reply":"2024-08-09T12:47:03.051749Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"[<matplotlib.lines.Line2D 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}