Predicting Depression - Kaggle Playground Series S4E11

This repository contains my solution for the Kaggle Playground Series - Season 4, Episode 11.

Summary of Results

In this competition, the goal was to predict depression using structured binary classification. The following models and results were achieved:

• CatBoostClassifier: 93.9%
• XGBoostClassifier: 93.8%
• Combined Model: 93.8%

The combination of models was implemented to maximize prediction accuracy. The provided notebook demonstrates the steps taken to preprocess the data, train the models, and evaluate their performance.

Repository Contents

• Predicting_Depression_Structured_Binary_Classification.ipynb: Main notebook containing the entire workflow, from data preprocessing to model evaluation.
• Supporting files and scripts for reproducing the results.

How to Use

1. Clone this repository:

   git clone https://github.com/yourusername/Depression-Prediction.git

2. Open the Jupyter Notebook:

   jupyter notebook Predicting_Depression_Structured_Binary_Classification.ipynb

3. Follow the steps in the notebook to preprocess data and train the models.

Results Visualization

Results were evaluated using a test dataset, which was uploaded into kaggle

Acknowledgments

Special thanks to Kaggle for organizing this competition and providing the dataset.