In this project I used ML modeling and data analysis to predict ad clicks and significantly improve ad campaign performance, resulting in a 43.3% increase in profits. The selected model was Logistic Regression. The insights provided recommendations for personalized content, age-targeted ads, and income-level targeting, enhancing marketing strategy.

Our goal is to increase conversion rate up to 14%. Then we need to analyze factors that cause low conversion rate and also predict customer will convert or not.

The goals of this project is to predict whether the employee will be promoted or not. High promotion rate will give impact on decreasing hiring cost. We suggest our recommendation to be implemented and we provide information how much impact if we implement the recommendation

The objective of this project is to develop a model that predicts customer behavior whether it'll reponse/accept the marketing campaign or not and RFM Segmentation

Predict Clicked Ads Customer Classification by using Machine Learning

This project aim to reduce churn rate, with objective finding the importance factor affect churn customer.

Perform an exploratory data analysis and provide actionable insights for a food aggregator company to get a fair idea about the demand of different restaurants and cuisines, which will help them enhance their customer experience and improve the business

This project involves the implementation of RFM (Recency, Frequency, Monetary) analysis, a powerful technique in customer segmentation and targeting

Analyzing and Forecasting of two different Wines' Sales by using Time Series Forecasting modelling

This project involves the implementation of Market Basket Analysis, a powerful technique in retail analytics.

This project delves into Indonesia's dynamic hospitality industry, uncovering seasonal booking trends, the impact of stay duration on cancellations, and how lead time affects cancellation rates. Valuable insights guide hotels to optimize pricing, policies, and customer engagement, enhancing competitiveness in the Indonesian hospitality sector.

In today's competitive business landscape, understanding customer behavior is paramount for marketing success. By gaining insights into customers' unique personality traits and preferences, companies can tailor their strategies to boost engagement and conversion rates.

Analyzing e-Commerce company customer churn and providing business recommendations

In this project, we leverage time series forecasting techniques to make educated estimates of wine sales throughout the 20th century.