Titanic Survival Prediction - Machine Learning Model (76.54% Accuracy) -- 2
Budget: $10 – $30 USD
Successfully developed a Machine Learning model to predict passenger survival outcomes based on historical maritime data from the Titanic dataset.
Key Responsibilities & Tech Stack:
Data Preprocessing & Cleaning: Handled missing data thoroughly, including advanced imputation for missing ages using passenger titles.
Feature Engineering: Extracted meaningful features like passenger 'Titles' and engineered a 'Family_Size' metric to improve model accuracy.
Model Training & Evaluation: Implemented and compared classification algorithms (like Random Forest / XGBoost) to optimize performance, achieving a solid accuracy of 76.54%.
Tools used: Python, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn.
This project demonstrates strong capabilities in data analysis, feature engineering, and deploying predictive ML models."
Key Responsibilities & Tech Stack:
Data Preprocessing & Cleaning: Handled missing data thoroughly, including advanced imputation for missing ages using passenger titles.
Feature Engineering: Extracted meaningful features like passenger 'Titles' and engineered a 'Family_Size' metric to improve model accuracy.
Model Training & Evaluation: Implemented and compared classification algorithms (like Random Forest / XGBoost) to optimize performance, achieving a solid accuracy of 76.54%.
Tools used: Python, Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn.
This project demonstrates strong capabilities in data analysis, feature engineering, and deploying predictive ML models."
Related categories:
Python
Machine Learning (ML)
Data Mining
Statistical Analysis
Data Science
Data Visualization
Data Analysis
Pandas