- Author: Quan Hoang Ngoc
- Inspiration: Kaggle Competition after Completing Andrew Ng's ML Courses
- Date: Summer 2023
This repository hosts my inaugural self-practice project on Kaggle, inspired by the foundational teachings of Professor Andrew Ng. The aim of this project is to participate in the Spaceship Titanic competition and to apply the machine learning skills acquired through the course.
This project involves predicting which passengers survived the Spaceship Titanic disaster. Utilizing various machine learning techniques, I analyze data to develop and evaluate models, ultimately contributing to a competitive ranking on Kaggle.
Engaging in Kaggle competitions allows practitioners to:
- Apply theoretical knowledge in practical scenarios.
- Gain hands-on experience with real-world datasets and challenges.
- Enhance data analysis and model-building skills.
- Benchmark skills against a global community of data scientists.
This project is intended for:
- Aspiring Data Scientists: Individuals looking to practice and improve their ML skills.
- Machine Learning Enthusiasts: Anyone interested in understanding the application of ML in competitive scenarios.
- Students: Learners seeking practical experience post-course completion.
Check out my current Kaggle ranking and dive into the journey and results of the competition!
The project implementation involved several steps:
- Data Exploration: Analyzed the dataset to understand the variables and identify patterns.
- Data Cleaning: Handled missing values and transformed categorical variables for model training.
- Feature Engineering: Created additional features to enhance model performance.
- Model Selection: Experimented with various algorithms (e.g., Logistic Regression, Random Forest, XGBoost) to determine the best fit for the data.
- Model Evaluation: Used cross-validation and held-out validation sets to assess model performance.
- The importance of data preprocessing and feature engineering in improving model accuracy.
- How to effectively evaluate model performance using various metrics.
- Insights into the competitive data science landscape through participation in challenges.
- Developed a predictive model that ranks among the top competitors in the Kaggle Spaceship Titanic competition.
- Gained substantial experience in machine learning techniques, data handling, and effective competition strategies.
Feel free to explore the code, contribute, and join me on this exciting journey of machine learning and data science! 🌌 Happy coding!