Predictive Modeling and Sentiment-Driven Recommendation System for Enhancing Customer Satisfaction in E-commerce
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Updated
Nov 1, 2024 - Jupyter Notebook
Predictive Modeling and Sentiment-Driven Recommendation System for Enhancing Customer Satisfaction in E-commerce
Hello there! This repo contains python project based on E-Commerce Customer Behavior analysis.
Machine learning system for predicting a customer's next product from historical purchase sequences.
Conducted business analysis using Python to support data-driven decisions based on customer behavior and regional perfomance.
Power BI dashboard analyzing customer drop-off, conversion behavior, revenue leakage, and high-risk segments in an e-commerce environment using 3,500+ simulated customer journey records.
Customer Shopping Behavior Analysis is an end-to-end data analytics project that analyzes customer purchasing patterns, spending behavior, purchase frequency, and satisfaction levels. The project uses Python for data cleaning and EDA, PostgreSQL for SQL-based analysis, and Power BI to create an interactive dashboard and generate actionable business
As a Data Scientist at Megaline, a leading mobile operator, I developed a model to analyze consumer behavior. I aimed to recommend either the Smart or Ultra package from Megaline's latest offerings, with a minimum accuracy of 0.75.
E-Commerce Data Analysis using Python
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