Analyze bank customer churn and optimize Customer Lifetime Value using 11 machine learning models (e.g., Logistic Regression, Random Forest, XGBoost). Includes EDA, churn prediction, CLV analysis, and model evaluation with metrics like Precision, Recall, F1-score, and Confusion Matrix.
Bank Customer Churn Prediction and Customer Lifetime Value (CLV) Optimization Read More »
A Netflix Recommendation System using machine learning models (KNN, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, K-Means) evaluated for accuracy and clustering, with insights into content trends and user preferences.
Netflix Recommendation System: Analysis and Machine Learning Implementation Read More »
This project analyzes the impact of a new playlist recommendation algorithm on engagement, retention, and conversion rates using t-tests and chi-square tests. Includes randomized test design, feature engineering, statistical analysis, and visualizations to guide optimization strategies.
A/B Testing of Spotify’s Playlist Recommendation Algorithm Read More »
This data science project predicts car selling prices using machine learning based on features like engine size, fuel type, and ownership history. It includes data preprocessing, EDA, feature engineering, model training, and evaluation.
Predicting Car Selling Prices with Machine Learning Read More »
In this case study, I explore how to predict Airbnb prices in Sydney using various features such as location, property attributes, and host characteristics. By building a machine learning model, I aim to uncover the key drivers behind listing prices and help hosts optimize their pricing strategy.
Predicting Airbnb Prices in Sydney Read More »
Predicting YouTube video views using regression models with data analysis and preprocessing on the YouTube Statistics dataset. Involves feature engineering, model selection (Linear Regression, Random Forest), and evaluation using RMSE and R2 metrics to ensure prediction accuracy. Code, Queries & Documentation Find the complete code, and documentation on my GitHub: https://github.com/hijirdella/YouTube-Supervised-Learning
YouTube Supervised Learning Read More »
Best Group and Best Student Awarded for Data Science Final Project predicting Home Credit customer default risk. Involves data preprocessing, EDA, feature engineering, and ML with XGBoost & Stacking. Provides actionable insights to enhance credit risk management and reduce default rates. Project Overview The Data Science Final Project focuses on predicting the risk of customer default using
Home Credit Default Risk Prediction: Data Science Final Project Read More »
