{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":50160,"databundleVersionId":7602123,"sourceType":"competition"}],"dockerImageVersionId":30646,"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 polars as pl\nimport numpy as np\nimport pandas as pd\nimport lightgbm as lgb\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score \nimport warnings\n\nwarnings.filterwarnings('ignore')\n\ndataPath = \"/kaggle/input/home-credit-credit-risk-model-stability/\"\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","_kg_hide-output":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2024-02-10T17:08:39.980958Z","iopub.execute_input":"2024-02-10T17:08:39.981387Z","iopub.status.idle":"2024-02-10T17:08:40.004389Z","shell.execute_reply.started":"2024-02-10T17:08:39.981360Z","shell.execute_reply":"2024-02-10T17:08:40.003469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Goal of the Competition**\n\nThe goal of this competition is to predict which clients are more likely to default on their loans. The evaluation will favor solutions that are stable over time.\n\nYour participation may offer consumer finance providers a more reliable and longer-lasting way to assess a potential client’s default risk.\n\n### I will continue to work and update this notebook. Please upvote it if you find it useful in this interesting challenge!\n\nWe are improving on the [Home Credit 2024 Starter Notebook](https://www.kaggle.com/code/jetakow/home-credit-2024-starter-notebook/notebook) \n\n## **Loading the Data**\n\nWe will now load the data and get it in the right format. We will conduct exploratory data analysis and create a baseline submission that we can iterate and improve on.","metadata":{}},{"cell_type":"code","source":"def set_table_dtypes(df: pl.DataFrame) -> pl.DataFrame:\n    # implement here all desired dtypes for tables\n    # the following is just an example\n    for col in df.columns:\n        # last letter of column name will help you determine the type\n        if col[-1] in (\"P\", \"A\"):\n            df = df.with_columns(pl.col(col).cast(pl.Float64).alias(col))\n\n    return df\n\ndef convert_strings(df: pd.DataFrame) -> pd.DataFrame:\n    for col in df.columns:  \n        if df[col].dtype.name in ['object', 'string']:\n            df[col] = df[col].astype(\"string\").astype('category')\n            current_categories = df[col].cat.categories\n            new_categories = current_categories.to_list() + [\"Unknown\"]\n            new_dtype = pd.CategoricalDtype(categories=new_categories, ordered=True)\n            df[col] = df[col].astype(new_dtype)\n    return df","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:08:40.031002Z","iopub.execute_input":"2024-02-10T17:08:40.031960Z","iopub.status.idle":"2024-02-10T17:08:40.040300Z","shell.execute_reply.started":"2024-02-10T17:08:40.031889Z","shell.execute_reply":"2024-02-10T17:08:40.039381Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_basetable = pl.read_csv(dataPath + \"csv_files/train/train_base.csv\")\ntrain_static = pl.concat(\n    [\n        pl.read_csv(dataPath + \"csv_files/train/train_static_0_0.csv\").pipe(set_table_dtypes),\n        pl.read_csv(dataPath + \"csv_files/train/train_static_0_1.csv\").pipe(set_table_dtypes),\n    ],\n    how=\"vertical_relaxed\",\n)\ntrain_static_cb = pl.read_csv(dataPath + \"csv_files/train/train_static_cb_0.csv\").pipe(set_table_dtypes)\ntrain_person_1 = pl.read_csv(dataPath + \"csv_files/train/train_person_1.csv\").pipe(set_table_dtypes) \ntrain_credit_bureau_b_2 = pl.read_csv(dataPath + \"csv_files/train/train_credit_bureau_b_2.csv\").pipe(set_table_dtypes) ","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:08:40.085170Z","iopub.execute_input":"2024-02-10T17:08:40.085820Z","iopub.status.idle":"2024-02-10T17:08:52.364782Z","shell.execute_reply.started":"2024-02-10T17:08:40.085786Z","shell.execute_reply":"2024-02-10T17:08:52.363750Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_static","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:08:52.368561Z","iopub.execute_input":"2024-02-10T17:08:52.370523Z","iopub.status.idle":"2024-02-10T17:08:52.420907Z","shell.execute_reply.started":"2024-02-10T17:08:52.370492Z","shell.execute_reply":"2024-02-10T17:08:52.419962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_basetable = pl.read_csv(dataPath + \"csv_files/test/test_base.csv\")\ntest_static = pl.concat(\n    [\n        pl.read_csv(dataPath + \"csv_files/test/test_static_0_0.csv\").pipe(set_table_dtypes),\n        pl.read_csv(dataPath + \"csv_files/test/test_static_0_1.csv\").pipe(set_table_dtypes),\n        pl.read_csv(dataPath + \"csv_files/test/test_static_0_2.csv\").pipe(set_table_dtypes),\n    ],\n    how=\"vertical_relaxed\",\n)\ntest_static_cb = pl.read_csv(dataPath + \"csv_files/test/test_static_cb_0.csv\").pipe(set_table_dtypes)\ntest_person_1 = pl.read_csv(dataPath + \"csv_files/test/test_person_1.csv\").pipe(set_table_dtypes) \ntest_credit_bureau_b_2 = pl.read_csv(dataPath + \"csv_files/test/test_credit_bureau_b_2.csv\").pipe(set_table_dtypes) ","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:08:52.424697Z","iopub.execute_input":"2024-02-10T17:08:52.426023Z","iopub.status.idle":"2024-02-10T17:08:52.461087Z","shell.execute_reply.started":"2024-02-10T17:08:52.425981Z","shell.execute_reply":"2024-02-10T17:08:52.460231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Feature Engineering**\n\nWe will join the tables on case_id. There are additional ways we can work with the data, but we will leave this as is for now.","metadata":{}},{"cell_type":"code","source":"# We need to use aggregation functions in tables with depth > 1, so tables that contain num_group1 column or \n# also num_group2 column.\ntrain_person_1_feats_1 = train_person_1.group_by(\"case_id\").agg(\n    pl.col(\"mainoccupationinc_384A\").max().alias(\"mainoccupationinc_384A_max\"),\n    (pl.col(\"incometype_1044T\") == \"SELFEMPLOYED\").max().alias(\"mainoccupationinc_384A_any_selfemployed\")\n)\n\n# Here num_group1=0 has special meaning, it is the person who applied for the loan.\ntrain_person_1_feats_2 = train_person_1.select([\"case_id\", \"num_group1\", \"housetype_905L\"]).filter(\n    pl.col(\"num_group1\") == 0\n).drop(\"num_group1\").rename({\"housetype_905L\": \"person_housetype\"})\n\n# Here we have num_goup1 and num_group2, so we need to aggregate again.\ntrain_credit_bureau_b_2_feats = train_credit_bureau_b_2.group_by(\"case_id\").agg(\n    pl.col(\"pmts_pmtsoverdue_635A\").max().alias(\"pmts_pmtsoverdue_635A_max\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).max().alias(\"pmts_dpdvalue_108P_over31\")\n)\n\n# We will process in this examples only A-type and M-type columns, so we need to select them.\nselected_static_cols = []\nfor col in train_static.columns:\n    if col[-1] in (\"A\", \"M\"):\n        selected_static_cols.append(col)\nprint(selected_static_cols)\n\nselected_static_cb_cols = []\nfor col in train_static_cb.columns:\n    if col[-1] in (\"A\", \"M\"):\n        selected_static_cb_cols.append(col)\nprint(selected_static_cb_cols)\n\n# Join all tables together.\ndata = train_basetable.join(\n    train_static.select([\"case_id\"]+selected_static_cols), how=\"left\", on=\"case_id\"\n).join(\n    train_static_cb.select([\"case_id\"]+selected_static_cb_cols), how=\"left\", on=\"case_id\"\n).join(\n    train_person_1_feats_1, how=\"left\", on=\"case_id\"\n).join(\n    train_person_1_feats_2, how=\"left\", on=\"case_id\"\n).join(\n    train_credit_bureau_b_2_feats, how=\"left\", on=\"case_id\"\n)","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:08:52.464037Z","iopub.execute_input":"2024-02-10T17:08:52.464823Z","iopub.status.idle":"2024-02-10T17:08:54.794264Z","shell.execute_reply.started":"2024-02-10T17:08:52.464783Z","shell.execute_reply":"2024-02-10T17:08:54.793298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_person_1_feats_1 = test_person_1.group_by(\"case_id\").agg(\n    pl.col(\"mainoccupationinc_384A\").max().alias(\"mainoccupationinc_384A_max\"),\n    (pl.col(\"incometype_1044T\") == \"SELFEMPLOYED\").max().alias(\"mainoccupationinc_384A_any_selfemployed\")\n)\n\ntest_person_1_feats_2 = test_person_1.select([\"case_id\", \"num_group1\", \"housetype_905L\"]).filter(\n    pl.col(\"num_group1\") == 0\n).drop(\"num_group1\").rename({\"housetype_905L\": \"person_housetype\"})\n\ntest_credit_bureau_b_2_feats = test_credit_bureau_b_2.group_by(\"case_id\").agg(\n    pl.col(\"pmts_pmtsoverdue_635A\").max().alias(\"pmts_pmtsoverdue_635A_max\"),\n    (pl.col(\"pmts_dpdvalue_108P\") > 31).max().alias(\"pmts_dpdvalue_108P_over31\")\n)\n\ndata_submission = test_basetable.join(\n    test_static.select([\"case_id\"]+selected_static_cols), how=\"left\", on=\"case_id\"\n).join(\n    test_static_cb.select([\"case_id\"]+selected_static_cb_cols), how=\"left\", on=\"case_id\"\n).join(\n    test_person_1_feats_1, how=\"left\", on=\"case_id\"\n).join(\n    test_person_1_feats_2, how=\"left\", on=\"case_id\"\n).join(\n    test_credit_bureau_b_2_feats, how=\"left\", on=\"case_id\"\n)","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:08:54.795959Z","iopub.execute_input":"2024-02-10T17:08:54.796761Z","iopub.status.idle":"2024-02-10T17:08:54.809304Z","shell.execute_reply.started":"2024-02-10T17:08:54.796719Z","shell.execute_reply":"2024-02-10T17:08:54.808129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"case_ids = data[\"case_id\"].unique().shuffle(seed=1)\ncase_ids_train, case_ids_test = train_test_split(case_ids, train_size=0.6, random_state=1)\ncase_ids_valid, case_ids_test = train_test_split(case_ids_test, train_size=0.5, random_state=1)\n\ncols_pred = []\nfor col in data.columns:\n    if col[-1].isupper() and col[:-1].islower():\n        cols_pred.append(col)\n\nprint(cols_pred)\n\ndef from_polars_to_pandas(case_ids: pl.DataFrame) -> pl.DataFrame:\n    return (\n        data.filter(pl.col(\"case_id\").is_in(case_ids))[[\"case_id\", \"WEEK_NUM\", \"target\"]].to_pandas(),\n        data.filter(pl.col(\"case_id\").is_in(case_ids))[cols_pred].to_pandas(),\n        data.filter(pl.col(\"case_id\").is_in(case_ids))[\"target\"].to_pandas()\n    )\n\nbase_train, X_train, y_train = from_polars_to_pandas(case_ids_train)\nbase_valid, X_valid, y_valid = from_polars_to_pandas(case_ids_valid)\nbase_test, X_test, y_test = from_polars_to_pandas(case_ids_test)\n\nfor df in [X_train, X_valid, X_test]:\n    df = convert_strings(df)","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:08:54.810936Z","iopub.execute_input":"2024-02-10T17:08:54.811341Z","iopub.status.idle":"2024-02-10T17:09:01.200588Z","shell.execute_reply.started":"2024-02-10T17:08:54.811303Z","shell.execute_reply":"2024-02-10T17:09:01.199386Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(f\"Train: {X_train.shape}\")\nprint(f\"Valid: {X_valid.shape}\")\nprint(f\"Test: {X_test.shape}\")","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:09:01.202363Z","iopub.execute_input":"2024-02-10T17:09:01.202801Z","iopub.status.idle":"2024-02-10T17:09:01.209031Z","shell.execute_reply.started":"2024-02-10T17:09:01.202760Z","shell.execute_reply":"2024-02-10T17:09:01.207751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Machine Learning**\n\nWe will train a LightGBM model and submit predictions. \n\n## **Optuna Hyperparameter Optimization**\n[Optuna](https://optuna.org) is an open source hyperparameter optimization framework to automate hyperparameter search. It offers an efficient way to find ideal hyperparameters and many visualizations of the results and search space. ","metadata":{}},{"cell_type":"code","source":"import optuna\n\n# Prepare LightGBM datasets\nlgb_train = lgb.Dataset(X_train, label=y_train)\nlgb_valid = lgb.Dataset(X_valid, label=y_valid)\n\n# Define the gini_stability function\ndef gini_stability(base, w_fallingrate=88.0, w_resstd=-0.5):\n    gini_in_time = base.loc[:, [\"WEEK_NUM\", \"target\", \"score\"]]\\\n        .sort_values(\"WEEK_NUM\")\\\n        .groupby(\"WEEK_NUM\")[[\"target\", \"score\"]]\\\n        .apply(lambda x: 2*roc_auc_score(x[\"target\"], x[\"score\"])-1).tolist()\n    \n    x = np.arange(len(gini_in_time))\n    y = gini_in_time\n    a, b = np.polyfit(x, y, 1)\n    y_hat = a*x + b\n    residuals = y - y_hat\n    res_std = np.std(residuals)\n    avg_gini = np.mean(gini_in_time)\n    return avg_gini + w_fallingrate * min(0, a) + w_resstd * res_std\n\n# Define the Optuna objective function\ndef objective(trial):\n    # Hyperparameters to tune\n    param = {\n        \"boosting_type\": \"gbdt\",\n        \"objective\": \"binary\",\n        \"metric\": \"auc\",\n        \"max_depth\": trial.suggest_int(\"max_depth\", 1, 10),\n        \"num_leaves\": trial.suggest_int(\"num_leaves\", 20, 60),\n        \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.01, 0.2),\n        \"feature_fraction\": trial.suggest_float(\"feature_fraction\", 0.6, 1.0),\n        \"bagging_fraction\": trial.suggest_float(\"bagging_fraction\", 0.6, 1.0),\n        \"bagging_freq\": trial.suggest_int(\"bagging_freq\", 1, 10),\n        \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 5, 100),\n        \"lambda_l1\": trial.suggest_loguniform(\"lambda_l1\", 1e-8, 10.0),\n        \"lambda_l2\": trial.suggest_loguniform(\"lambda_l2\", 1e-8, 10.0),\n        \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 10000),\n        \"feature_pre_filter\": False,\n        \"verbose\": -1,\n    }\n    \n    # Train the model\n    gbm = lgb.train(\n        param,\n        lgb_train,\n        valid_sets=lgb_valid,\n        callbacks=[lgb.log_evaluation(50), lgb.early_stopping(10)]\n    )\n    # Make predictions on the validation set\n    base_valid['score'] = gbm.predict(X_valid)\n    \n    # Calculate the Gini stability score\n    stability_score_valid = gini_stability(base_valid)\n    \n    return stability_score_valid\n\n# Create and run the Optuna study\nstudy = optuna.create_study(direction=\"maximize\")\nstudy.optimize(objective, n_trials=100)\n\n# Output the results\nprint(\"Best trial:\")\ntrial = study.best_trial\nprint(f\"Stability Score: {trial.value}\")\nprint(\"Best hyperparameters:\")\nfor key, value in trial.params.items():\n    print(f\"{key}: {value}\")","metadata":{"execution":{"iopub.status.busy":"2024-02-10T17:09:01.210823Z","iopub.execute_input":"2024-02-10T17:09:01.211339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Plot Visuals From Optuna Hyperparameter Optimization\n\n**plot_optimization_history** plots optimization history of all trials in a study. The blue dots show the gini stability on each trial, and the red line - the best value attained.","metadata":{}},{"cell_type":"code","source":"from optuna.visualization import plot_optimization_history\n\nplot_optimization_history(study)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We will now use the best hyperparameters found by Optuna and train the model and make predictions.","metadata":{}},{"cell_type":"code","source":"# Use the best parameters to train the final model\nbest_params = trial.params\nbest_params","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# You must manually add the parameters that were not tuned by Optuna back into the best_params dictionary\nbest_params.update({\n    \"boosting_type\": \"gbdt\",\n    \"objective\": \"binary\",\n    \"metric\": \"auc\",\n    \"verbose\": -1,\n    \"feature_pre_filter\": False,  \n})\n\n# Train the final model with the best parameters found\ngbm_final = lgb.train(\n    best_params,\n    lgb_train,\n    valid_sets=lgb_valid,\n    callbacks=[lgb.log_evaluation(50), lgb.early_stopping(10)]\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for base, X in [(base_train, X_train), (base_valid, X_valid), (base_test, X_test)]:\n    y_pred = gbm_final.predict(X, num_iteration=gbm_final.best_iteration)\n    base[\"score\"] = y_pred\n\nprint(f'The AUC score on the train set is: {roc_auc_score(base_train[\"target\"], base_train[\"score\"])}') \nprint(f'The AUC score on the valid set is: {roc_auc_score(base_valid[\"target\"], base_valid[\"score\"])}') \nprint(f'The AUC score on the test set is: {roc_auc_score(base_test[\"target\"], base_test[\"score\"])}')  ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"stability_score_train = gini_stability(base_train)\nstability_score_valid = gini_stability(base_valid)\nstability_score_test = gini_stability(base_test)\n\nprint(f'The stability score on the train set is: {stability_score_train}') \nprint(f'The stability score on the valid set is: {stability_score_valid}') \nprint(f'The stability score on the test set is: {stability_score_test}')  ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Feature Importance**\n\nModel features are evaluated using SHAP (SHapley Additive exPlanations). SHAP values are a widely used approach to evaluate the output of a machine learning model. SHAP values identify the most important features in the prediction from a machine learning model. They also provide accurate local explanations.","metadata":{}},{"cell_type":"code","source":"import shap\n\n# Create the SHAP Explainer\nexplainer = shap.TreeExplainer(gbm_final)\n\n# Calculate SHAP values for the validation set\nshap_values = explainer.shap_values(X_valid)\n\n# Summarize the effects of all the features\nshap.summary_plot(shap_values, X_valid, plot_type=\"bar\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Submission**\n\nScoring the submission dataset is below, we need to take care of new categories. Then we save the score as a last step.","metadata":{}},{"cell_type":"code","source":"X_submission = data_submission[cols_pred].to_pandas()\nX_submission = convert_strings(X_submission)\ncategorical_cols = X_train.select_dtypes(include=['category']).columns\n\nfor col in categorical_cols:\n    train_categories = set(X_train[col].cat.categories)\n    submission_categories = set(X_submission[col].cat.categories)\n    new_categories = submission_categories - train_categories\n    X_submission.loc[X_submission[col].isin(new_categories), col] = \"Unknown\"\n    new_dtype = pd.CategoricalDtype(categories=train_categories, ordered=True)\n    X_train[col] = X_train[col].astype(new_dtype)\n    X_submission[col] = X_submission[col].astype(new_dtype)\n\ny_submission_pred = gbm_final.predict(X_submission, num_iteration=gbm_final.best_iteration)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.DataFrame({\n    \"case_id\": data_submission[\"case_id\"].to_numpy(),\n    \"score\": y_submission_pred\n}).set_index('case_id')\nsubmission.to_csv(\"./submission.csv\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## **Next Steps**\n\nThis section outlines the next steps to enhance the model's performance further. These steps include additional feature engineering, feature selection, feature importance analysis, and evaluating other models.\n\n### Additional Feature Engineering\n\n- **Create Interaction Features**: Investigate potential interactions between features that could be predictive of the outcome.\n- **Polynomial Features**: Generate polynomial features for the numeric variables to capture non-linear relationships.\n- **Encoding Categorical Variables**: Explore different encoding strategies (e.g., one-hot encoding, target encoding) for categorical variables.\n\n### Feature Selection\n\n- **Correlation Analysis**: Remove highly correlated features to reduce multicollinearity.\n- **Importance-Based Selection**: Utilize model-based feature importance to retain the most relevant features.\n- **Wrapper Methods**: Experiment with forward selection, backward elimination, or recursive feature elimination (RFE) techniques.\n\n### Feature Importance Analysis\n\n- **Model-Based Importance**: Use tree-based models (e.g., Random Forest, XGBoost) to evaluate feature importance.\n- **Permutation Importance**: Assess the impact of shuffling each feature on the model's performance to identify crucial features.\n- **SHAP Values**: Utilize SHAP (SHapley Additive exPlanations) to interpret the model's predictions and understand the impact of each feature.\n\n### Evaluating Other Models\n\n- **Compare Different Models**: Evaluate the performance of various machine learning models (e.g., SVM, Neural Networks, Ensemble Models).\n- **Hyperparameter Tuning**: Perform hyperparameter tuning on other models to find the optimal settings.\n- **Stacking/Ensembling**: Explore stacking or ensembling methods to combine predictions from multiple models for improved accuracy.\n\n### Model Evaluation and Validation\n\n- **Cross-Validation**: Use cross-validation techniques to assess the model's performance more reliably.\n- **Performance Metrics**: Evaluate the models using appropriate metrics (e.g., AUC, accuracy, F1 score) for your specific problem.\n- **Validation Curves**: Plot validation curves to identify overfitting or underfitting.\n- **Learning Curves**: Analyze learning curves to understand how well the model is learning from the training data.\n","metadata":{}}]}