Kaggle Playground Prediction Competitions

This repository contains my solutions for various Kaggle Playground Series prediction competitions. Each competition is organized into its own directory, containing the notebooks, scripts, and data related to that specific challenge.

Competitions

Here's a breakdown of the competitions included in this repository:

Playground Series - Season 5, Episode 12: Diabetes Prediction Challenge

Goal: Predict the probability that a patient will be diagnosed with diabetes. Description: The goal of this competition is to predict the probability that a patient will be diagnosed with diabetes using a dataset generated from a deep learning model trained on the Diabetes Health Indicators Dataset.

Playground Series - Season 5, Episode 11: Loan Payback Prediction

Goal: Predict whether a loan will be paid back or not. Description: This project focuses on building a classification model to predict the probability of a borrower defaulting on a loan. The solution explores different models, including XGBoost and LightGBM, with hyperparameter tuning.

Playground Series - Season 5, Episode 10: Road Accidents

Goal: Predict the severity of road accidents. Description: This project involves analyzing a dataset of road accidents to build a model that can accurately predict the severity of an accident based on various factors like weather, road conditions, and time of day.

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General Approach

For each competition, I follow a general machine learning workflow:

1. Problem Definition: Understanding the competition's objective and evaluation metrics.
2. Data Loading and Exploration (EDA): Analyzing the dataset to understand its structure, identify patterns, and visualize relationships between features.
3. Data Preprocessing and Feature Engineering: Cleaning the data, handling missing values, and creating new features to improve model performance.
4. Model Selection and Training: Experimenting with different machine learning models (e.g., XGBoost, LightGBM, RandomForest) and training them on the prepared data.
5. Hyperparameter Tuning: Optimizing the models' hyperparameters to achieve the best possible performance.
6. Submission: Generating the submission file in the format required by the competition.

Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.