Marketing Campaign Effectiveness (MCE) Analysis

This project demonstrates how customer analytics can reveal actionable insights for targeted marketing. By segmenting customers based on demographics and purchase behavior, businesses can increase campaign effectiveness, improve ROI on marketing spend, enhance customer satisfaction through persona.

Credit Scoring with Business Metrics

This project builds a Credit Scoring Model using the German Credit Risk dataset. The goal is to help financial institutions decide whether to approve or reject loan applications by balancing: Business Metrics, ML Metrics, and Model Explainability.

Box Office Revenue Prediction

This project builds regression models to forecast movie revenue based on metadata and production details.

Walmart Sales Forecasting

This project analyzes and predicts weekly sales across Walmart stores using machine learning. By applying regression models and advanced boosting algorithms, the project provides actionable insights into sales drivers and helps optimize forecasting strategies.

Retail Sales Data

This project explores retail sales data (Superstore Dataset) to uncover business insights through data visualization. Using Python libraries like Matplotlib, Seaborn, and Plotly, I created static and interactive charts to analyze trends, customer behavior, product performance, and profitability.

Vehicle Coupon Recommendation

This project uses a vehicle coupon recommendation dataset to predict whether a driver will accept a coupon. By targeting the right customers with the right coupons, companies can increase redemption rates, reduce marketing costs, and improve customer satisfaction.

Flight Price Prediction

This project uses a flight booking dataset from the "Ease My Trip" website to conduct statistical hypothesis tests and regression analysis. The goal is to uncover insights that will be highly beneficial for passengers.

Bank Marketing Prediction

Using Python, I developed a classification model to predict whether a customer will deposit money. The model, which takes various factors into account, achieved an 84% accuracy rate in identifying these customers.

Smartwatch Data Analysis

Using Python, I analyzed how consumers use the FitBit app to uncover trends and insights for a new marketing strategy. The analysis revealed that most users (81.3%) primarily use the app to track sedentary activities, rather than for monitoring their health habits.