https://github.com/mhmudfzli/exploring-mental-health-data

This project demonstrates a comprehensive approach to solving a regression problem using various machine learning models. The notebook includes: Data Preprocessing, Exploratory Data Analysis (EDA), Model Training, Hyperparameter Tuning, Model Evaluation, Feature Importance
https://github.com/mhmudfzli/exploring-mental-health-data

catboost lightgbm matplotlib numpy pandas scikit-learn seaborn xgboost

Last synced: 3 months ago
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This project demonstrates a comprehensive approach to solving a regression problem using various machine learning models. The notebook includes: Data Preprocessing, Exploratory Data Analysis (EDA), Model Training, Hyperparameter Tuning, Model Evaluation, Feature Importance

• Host: GitHub
• URL: https://github.com/mhmudfzli/exploring-mental-health-data
• Owner: MhmudFzli
• Created: 2025-01-18T10:18:16.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-18T15:15:41.000Z (over 1 year ago)
• Last Synced: 2025-03-20T07:47:17.850Z (over 1 year ago)
• Topics: catboost, lightgbm, matplotlib, numpy, pandas, scikit-learn, seaborn, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 115 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Exploring Mental Health Data

Goal: the goal is to use data from a mental health survey to explore factors that may cause individuals to experience depression.

#### Here are the Steps wee took for the project:

1. Load and Concat Original with Competition Dataset

2. EDA

3. Preprocess

4. Custom Feature Engineering

#### Results:

* Train Catboost Model with Optuna:

![image](https://github.com/user-attachments/assets/914456d2-3082-49d0-a93f-74503dd3e787)

* Train LGBM Model with Optuna:

![image](https://github.com/user-attachments/assets/c6123514-9864-40a6-b360-88cec1b12a43)

* AutoGloun:

![image](https://github.com/user-attachments/assets/c097d688-7f74-4f50-8672-5a266c09f676)

* Combine CatBoost+LGBM with Lasso:

![image](https://github.com/user-attachments/assets/852bd7e2-184c-4f90-bb0e-70d0082bc798)

### Leaderboard (top 6%!):

![image](https://github.com/user-attachments/assets/08d669d3-1acc-48e8-9bdb-2e4763f5fe2e)