Medinsurance BI Case Analysis
Medinsurance BI Case Analysis Read More »
Business Intelligence
Leveraging RFM & CLV for a Data-Driven Marketing Strategy at All-U-Need Mart
Optimizing All-U-Need Mart’s marketing strategy with RFM & CLV analysis using Python, SQL, and Tableau. This project enhances customer retention, reduces churn, and refines discount strategies through data-driven segmentation and predictive analytics. Explore insights via an interactive dashboard!
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Olist E-Commerce Dashboard
An interactive Tableau dashboard providing both executive and operational insights into Olist’s sales trends, customer satisfaction, order fulfillment, and strategic performance for data-driven decision-making.
Olist E-Commerce Dashboard Read More »
Bank Customer Churn Prediction and Customer Lifetime Value (CLV) Optimization
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.
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Funnel & Cohort Analysis for Business Growth
Funnel and Cohort Analysis for Business Growth: An end-to-end project using Python, SQL, and Tableau to uncover customer behavior, retention trends, and sales insights. Includes interactive dashboards and actionable recommendations for data-driven decision-making
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Superstore RFM Analysis: Unlocking Customer Insights for Business Growth
Analyze customer data from the Superstore dataset through RFM segmentation and visualization. Perform EDA to identify key problems, calculate RFM scores, and derive actionable insights. Present findings in a clear, professional Google Slides report, including a Tableau dashboard for added value.
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Driving Growth with Uplift Modeling:Analyzing Marketing Promotions for Optimized Conversions
Uplift Modeling: Discount vs. BOGO (Control: No Offer) | This project analyzes the impact of Discount and BOGO offers compared to No Offer (Control) using S-Learner & Uplift Random Forest. It includes EDA, AUUC, Gain Chart, and model evaluation to optimize marketing conversions. #MarketingAnalytics #MachineLearning
Netflix Recommendation System: Analysis and Machine Learning Implementation
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.
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A/B Testing of Spotify’s Playlist Recommendation Algorithm
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.
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Optimization Strategy for Sales and Customer Loyalty at Kalbe Nutritionals
This project analyzes Sales, RFM, Cohort, Churn, and CLV using SQL, Python, and Tableau to help Kalbe Nutritionals understand sales trends, customer segmentation, and retention strategies.
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