Project - Ilham Ilyas R

Bali Lodging Price Estimation Create web app to predict the price of lodging in Bali and gather insight to better understand what features are important for pricing. Data for this project obtained from scrapping Traveloka website. Target Feature that is used in this model is pricing without tax and service fees. Machine learning algorithm used in this project is XGBoost.

Mobile Price Prediction This project used data from Kaggle for classification. In this experiment, the data will be split into two groups: 1. Data with all the features 2. data contains only 50% of features that have a high correlation with the target feature. Decission Tree, Random Forest, and SVM are the machine learning algorithms used.

Customer Segmentatin with RFM This project is geared towards helping businesses boost sales by analyzing customer RFM (Recency, Frequency, Monetary) data. Through this analysis, we aim to provide actionable conclusions and recommendations tailored to each customer segment. This project will guide businesses in crafting effective strategies to maximize sales and enhance customer engagement based on RFM insights.

Yogyakarta Hotel Price Regression This project is designed as an illustrative example for other students who are learning data science. The notebook used in this project is filled with instructions, notes, and explanations. The scope of this project includes data analysis, data validation, modeling, and deployment.