bank-loan-prediction-model.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:10:44.018104Z","iopub.status.busy":"2023-11-30T08:10:44.017646Z","iopub.status.idle":"2023-11-30T08:10:44.028201Z","shell.execute_reply":"2023-11-30T08:10:44.026925Z","shell.execute_reply.started":"2023-11-30T08:10:44.018071Z"},"trusted":true},"outputs":[],"source":["import seaborn as sns\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","\n","from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.svm import SVC\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import accuracy_score, classification_report\n","from sklearn.model_selection import GridSearchCV\n","\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{},"source":["# Description of Dataset"]},{"cell_type":"markdown","metadata":{},"source":["File contains 14 columns and 5000 rows. Description of the columns are as follows:\n","\n","* ID: Customer ID\n","* Age : Customer Age\n","* Experience : Customer Experience\n","* Income : Income of the Customer\n","* ZipCode: Customer's residence zipcode\n","* Family : No of Family members of the customer\n","* CCAvg: Credit Card Average Score\n","* Education: Education of the customer\n","* Mortgage: Mortgage taken or not taken by the customer\n","* Personal Loan: 0 = No personal loan given , 1 = personal loan given\n","* Securities Account : Having or not having a Securities Account\n","* CD Account : Having or not having a CD Account\n","* Online : Having or not having online banking\n","* Credit Card : Having or not having a credit card"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:08:32.776848Z","iopub.status.busy":"2023-11-30T08:08:32.776221Z","iopub.status.idle":"2023-11-30T08:08:32.853779Z","shell.execute_reply":"2023-11-30T08:08:32.852584Z","shell.execute_reply.started":"2023-11-30T08:08:32.776808Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>ID</th>\n","      <th>Age</th>\n","      <th>Experience</th>\n","      <th>Income</th>\n","      <th>ZIP.Code</th>\n","      <th>Family</th>\n","      <th>CCAvg</th>\n","      <th>Education</th>\n","      <th>Mortgage</th>\n","      <th>Personal.Loan</th>\n","      <th>Securities.Account</th>\n","      <th>CD.Account</th>\n","      <th>Online</th>\n","      <th>CreditCard</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>25</td>\n","      <td>1</td>\n","      <td>49</td>\n","      <td>91107</td>\n","      <td>4</td>\n","      <td>1.6</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</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>2</td>\n","      <td>45</td>\n","      <td>19</td>\n","      <td>34</td>\n","      <td>90089</td>\n","      <td>3</td>\n","      <td>1.5</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>39</td>\n","      <td>15</td>\n","      <td>11</td>\n","      <td>94720</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>35</td>\n","      <td>9</td>\n","      <td>100</td>\n","      <td>94112</td>\n","      <td>1</td>\n","      <td>2.7</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</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>5</td>\n","      <td>35</td>\n","      <td>8</td>\n","      <td>45</td>\n","      <td>91330</td>\n","      <td>4</td>\n","      <td>1.0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>37</td>\n","      <td>13</td>\n","      <td>29</td>\n","      <td>92121</td>\n","      <td>4</td>\n","      <td>0.4</td>\n","      <td>2</td>\n","      <td>155</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>53</td>\n","      <td>27</td>\n","      <td>72</td>\n","      <td>91711</td>\n","      <td>2</td>\n","      <td>1.5</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>50</td>\n","      <td>24</td>\n","      <td>22</td>\n","      <td>93943</td>\n","      <td>1</td>\n","      <td>0.3</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>35</td>\n","      <td>10</td>\n","      <td>81</td>\n","      <td>90089</td>\n","      <td>3</td>\n","      <td>0.6</td>\n","      <td>2</td>\n","      <td>104</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>34</td>\n","      <td>9</td>\n","      <td>180</td>\n","      <td>93023</td>\n","      <td>1</td>\n","      <td>8.9</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   ID  Age  Experience  Income  ZIP.Code  Family  CCAvg  Education  Mortgage  \\\n","0   1   25           1      49     91107       4    1.6          1         0   \n","1   2   45          19      34     90089       3    1.5          1         0   \n","2   3   39          15      11     94720       1    1.0          1         0   \n","3   4   35           9     100     94112       1    2.7          2         0   \n","4   5   35           8      45     91330       4    1.0          2         0   \n","5   6   37          13      29     92121       4    0.4          2       155   \n","6   7   53          27      72     91711       2    1.5          2         0   \n","7   8   50          24      22     93943       1    0.3          3         0   \n","8   9   35          10      81     90089       3    0.6          2       104   \n","9  10   34           9     180     93023       1    8.9          3         0   \n","\n","   Personal.Loan  Securities.Account  CD.Account  Online  CreditCard  \n","0              0                   1           0       0           0  \n","1              0                   1           0       0           0  \n","2              0                   0           0       0           0  \n","3              0                   0           0       0           0  \n","4              0                   0           0       0           1  \n","5              0                   0           0       1           0  \n","6              0                   0           0       1           0  \n","7              0                   0           0       0           1  \n","8              0                   0           0       1           0  \n","9              1                   0           0       0           0  "]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv('bankloan.csv')\n","\n","df.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["# Exploratory Data Analysis"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:08:32.855892Z","iopub.status.busy":"2023-11-30T08:08:32.855501Z","iopub.status.idle":"2023-11-30T08:08:32.864236Z","shell.execute_reply":"2023-11-30T08:08:32.862713Z","shell.execute_reply.started":"2023-11-30T08:08:32.85586Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(5000, 14)"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:08:32.868155Z","iopub.status.busy":"2023-11-30T08:08:32.867799Z","iopub.status.idle":"2023-11-30T08:08:32.896588Z","shell.execute_reply":"2023-11-30T08:08:32.895467Z","shell.execute_reply.started":"2023-11-30T08:08:32.868124Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 5000 entries, 0 to 4999\n","Data columns (total 14 columns):\n"," #   Column              Non-Null Count  Dtype  \n","---  ------              --------------  -----  \n"," 0   ID                  5000 non-null   int64  \n"," 1   Age                 5000 non-null   int64  \n"," 2   Experience          5000 non-null   int64  \n"," 3   Income              5000 non-null   int64  \n"," 4   ZIP.Code            5000 non-null   int64  \n"," 5   Family              5000 non-null   int64  \n"," 6   CCAvg               5000 non-null   float64\n"," 7   Education           5000 non-null   int64  \n"," 8   Mortgage            5000 non-null   int64  \n"," 9   Personal.Loan       5000 non-null   int64  \n"," 10  Securities.Account  5000 non-null   int64  \n"," 11  CD.Account          5000 non-null   int64  \n"," 12  Online              5000 non-null   int64  \n"," 13  CreditCard          5000 non-null   int64  \n","dtypes: float64(1), int64(13)\n","memory usage: 547.0 KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:08:32.898374Z","iopub.status.busy":"2023-11-30T08:08:32.898046Z","iopub.status.idle":"2023-11-30T08:08:32.912966Z","shell.execute_reply":"2023-11-30T08:08:32.91179Z","shell.execute_reply.started":"2023-11-30T08:08:32.898345Z"},"trusted":true},"outputs":[{"data":{"text/plain":["0"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.duplicated().sum()"]},{"cell_type":"markdown","metadata":{},"source":["Let's Check Outliers in our Columns"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:08:32.91455Z","iopub.status.busy":"2023-11-30T08:08:32.914166Z","iopub.status.idle":"2023-11-30T08:08:33.472027Z","shell.execute_reply":"2023-11-30T08:08:33.470915Z","shell.execute_reply.started":"2023-11-30T08:08:32.914519Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 864x576 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["\n","numerical_columns = df.select_dtypes(include=['number'])\n","\n","\n","plt.figure(figsize=(12, 8))\n","sns.boxplot(data=numerical_columns)\n","plt.title(\"Box Plot for Numerical Columns\")\n","\n","\n","plt.xticks(rotation=45, ha=\"right\")\n","\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["as we look at Box plots we have outliers in `Zip_code` column. Thus We Won't use it in Predictive Modeling then we will drop it"]},{"cell_type":"markdown","metadata":{},"source":["# Data Preprocessing"]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:08:33.474913Z","iopub.status.busy":"2023-11-30T08:08:33.474055Z","iopub.status.idle":"2023-11-30T08:08:33.481786Z","shell.execute_reply":"2023-11-30T08:08:33.480065Z","shell.execute_reply.started":"2023-11-30T08:08:33.474872Z"},"trusted":true},"outputs":[],"source":["\n","df.columns = [col.replace('.', '_') for col in df.columns]"]},{"cell_type":"markdown","metadata":{},"source":["# Baseline Model"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:15:16.065916Z","iopub.status.busy":"2023-11-30T08:15:16.065461Z","iopub.status.idle":"2023-11-30T08:15:16.073057Z","shell.execute_reply":"2023-11-30T08:15:16.071841Z","shell.execute_reply.started":"2023-11-30T08:15:16.065879Z"},"trusted":true},"outputs":[],"source":["# assuming 'ZIP_Code' and 'Personal_Loan' are columns in the dataFrame\n","X = df.drop(['ZIP_Code', 'Personal_Loan', 'ID'], axis=1)  \n","y = df['Personal_Loan']  # target variable"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:15:16.844148Z","iopub.status.busy":"2023-11-30T08:15:16.843426Z","iopub.status.idle":"2023-11-30T08:15:17.639904Z","shell.execute_reply":"2023-11-30T08:15:17.638279Z","shell.execute_reply.started":"2023-11-30T08:15:16.844108Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Random Forest Accuracy: 0.99\n","SVM Accuracy: 0.976\n","Logistic Regression Accuracy: 0.955\n"]}],"source":["\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n","\n","# pipeline with different classifiers\n","pipeline_rf = Pipeline([\n","    ('scaler', StandardScaler()),\n","    ('classifier', RandomForestClassifier())\n","])\n","\n","pipeline_svm = Pipeline([\n","    ('scaler', StandardScaler()),\n","    ('classifier', SVC())\n","])\n","\n","pipeline_lr = Pipeline([\n","    ('scaler', StandardScaler()),\n","    ('classifier', LogisticRegression())\n","])\n","\n","# fitting the pipelines\n","pipeline_rf.fit(X_train, y_train)\n","pipeline_svm.fit(X_train, y_train)\n","pipeline_lr.fit(X_train, y_train)\n","\n","# predictions\n","y_pred_rf = pipeline_rf.predict(X_test)\n","y_pred_svm = pipeline_svm.predict(X_test)\n","y_pred_lr = pipeline_lr.predict(X_test)\n","\n","# performance\n","accuracy_rf = accuracy_score(y_test, y_pred_rf)\n","accuracy_svm = accuracy_score(y_test, y_pred_svm)\n","accuracy_lr = accuracy_score(y_test, y_pred_lr)\n","\n","print(\"Random Forest Accuracy:\", accuracy_rf)\n","print(\"SVM Accuracy:\", accuracy_svm)\n","print(\"Logistic Regression Accuracy:\", accuracy_lr)"]},{"cell_type":"markdown","metadata":{},"source":["# Hyperparameter Tuning"]},{"cell_type":"markdown","metadata":{},"source":["Through this code we will use `GridSearchCV` and will print Best parameters can get Higher Performance"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:15:25.459663Z","iopub.status.busy":"2023-11-30T08:15:25.459248Z","iopub.status.idle":"2023-11-30T08:15:57.067828Z","shell.execute_reply":"2023-11-30T08:15:57.065976Z","shell.execute_reply.started":"2023-11-30T08:15:25.459632Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Random Forest Accuracy (with CV): 0.989\n","\n","Best Hyperparameters for RandomForestClassifier:\n","{'max_depth': None, 'min_samples_split': 2, 'n_estimators': 100}\n"]}],"source":["# hyperparameter grids for RandomForestClassifier\n","param_grid_rf = {\n","    'n_estimators': [10, 50, 100],\n","    'max_depth': [None, 10, 20],\n","    'min_samples_split': [2, 5, 10],\n","}\n","\n","\n","pipeline_rf_cv = Pipeline([\n","    ('scaler', StandardScaler()),\n","    ('classifier', GridSearchCV(RandomForestClassifier(), param_grid_rf, cv=5))\n","])\n","\n","# cross-validation and hyperparameter tuning\n","pipeline_rf_cv.fit(X_train, y_train)\n","\n","# best hyperparameters and predictions\n","y_pred_rf_cv = pipeline_rf_cv.predict(X_test)\n","\n","\n","accuracy_rf_cv = accuracy_score(y_test, y_pred_rf_cv)\n","print(\"Random Forest Accuracy (with CV):\", accuracy_rf_cv)\n","\n","\n","best_params_rf = pipeline_rf_cv.named_steps['classifier'].best_params_\n","print(\"\\nBest Hyperparameters for RandomForestClassifier:\")\n","print(best_params_rf)"]},{"cell_type":"markdown","metadata":{},"source":["# Model Evaluation"]},{"cell_type":"markdown","metadata":{},"source":["When using pipelines, model evaluation remains consistent with traditional workflows. The key advantage of pipelines is the seamless integration of preprocessing and modeling steps, making the overall process cleaner and more modular."]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:11:16.198314Z","iopub.status.busy":"2023-11-30T08:11:16.197615Z","iopub.status.idle":"2023-11-30T08:11:16.217107Z","shell.execute_reply":"2023-11-30T08:11:16.21596Z","shell.execute_reply.started":"2023-11-30T08:11:16.198276Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Classification Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.99      1.00      0.99       895\n","           1       0.98      0.91      0.95       105\n","\n","    accuracy                           0.99      1000\n","   macro avg       0.98      0.96      0.97      1000\n","weighted avg       0.99      0.99      0.99      1000\n","\n"]}],"source":["\n","classification_rep = classification_report(y_test, y_pred_rf_cv)\n","print(\"Classification Report:\\n\", classification_rep)"]},{"cell_type":"markdown","metadata":{},"source":["# Compare Scores and Values"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:14:02.036996Z","iopub.status.busy":"2023-11-30T08:14:02.036558Z","iopub.status.idle":"2023-11-30T08:14:34.761531Z","shell.execute_reply":"2023-11-30T08:14:34.760697Z","shell.execute_reply.started":"2023-11-30T08:14:02.036962Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Training Set Accuracy: 0.998\n","Test Set Accuracy: 0.991\n"]}],"source":["\n","pipeline_rf_cv.fit(X_train, y_train)\n","\n","\n","y_pred_train = pipeline_rf_cv.predict(X_train)\n","\n","\n","y_pred_test = pipeline_rf_cv.predict(X_test)\n","\n","\n","accuracy_train = accuracy_score(y_train, y_pred_train)\n","print(\"Training Set Accuracy:\", accuracy_train)\n","\n","\n","accuracy_test = accuracy_score(y_test, y_pred_test)\n","print(\"Test Set Accuracy:\", accuracy_test)"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2023-11-30T08:09:07.836548Z","iopub.status.busy":"2023-11-30T08:09:07.836198Z","iopub.status.idle":"2023-11-30T08:09:07.853563Z","shell.execute_reply":"2023-11-30T08:09:07.85236Z","shell.execute_reply.started":"2023-11-30T08:09:07.836519Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Actual: 0, Predicted: 0\n","Actual: 1, Predicted: 1\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 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1\n","Actual: 1, Predicted: 1\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 1, Predicted: 1\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 1, Predicted: 1\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 1, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 1, Predicted: 1\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 1, Predicted: 1\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Actual: 0, Predicted: 0\n","Random Forest Accuracy: 0.989\n"]}],"source":["\n","for actual, predicted in zip(y_test, y_pred_rf_cv):\n","    print(f\"Actual: {actual}, Predicted: {predicted}\")\n","\n","# performance eval\n","accuracy_rf = accuracy_score(y_test, y_pred_rf_cv)\n","print(\"Random Forest Accuracy:\", accuracy_rf)\n"]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":3967841,"sourceId":6908879,"sourceType":"datasetVersion"}],"dockerImageVersionId":30587,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"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":"3.10.11"}},"nbformat":4,"nbformat_minor":4}