Automated Web Scraper and Data Pipeline
Objective: Created an automated Python-based web scraping tool to extract raw HTML data from online job boards and structure it into a clean database.
Data Engineering: Used BeautifulSoup and Requests libraries to target dynamic web elements, extracting key information like Job Titles, Company Names, and Locations.
Data Pipeline: Standardized unstructured web data directly into clean Pandas DataFrames for quick analytical processing.
Tech Stack: Python, BeautifulSoup, Requests, Pandas.
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Customer Churn Prediction (Classification)
Objective: Developed a predictive machine learning model to analyze telecom customer behavior and identify users at high risk of canceling services (churning).
Modeling & Evaluation: Implemented a Random Forest Classifier to evaluate metrics like customer tenure and monthly charges, evaluating results through a structured Confusion Matrix.
Impact: Provides actionable insights that help businesses proactively implement retention strategies for at-risk accounts.
Tech Stack: Python, Pandas, Scikit-Learn (Random Forest), Matplotlib.
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E-Commerce Sales Data Analysis & Dashboard
Objective: Built a data visualization dashboard to analyze e-commerce sales performance and customer purchasing trends across various product categories.
Data Processing: Simulated and aggregated a retail dataset using Pandas, tracking monthly sales volumes, order frequencies, and total revenue.
Insights & Visualization: Designed clean data visualizations using Matplotlib and Seaborn to highlight seasonal sales trends and top-performing categories.
Tech Stack: Python, Pandas, NumPy, Matplotlib, Seaborn.
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