Telecom Customer Retention System
End-to-end ML pipeline predicting customer churn in telecom. Covers data normalization, feature engineering, model training, evaluation, and deployment-ready insight generation.
Python
Scikit-Learn
Pandas
NumPy
Movie Recommendation Engine
Content-based recommender using TF-IDF vectorization and cosine similarity to suggest films by analyzing genre patterns and user-generated metadata tags.
Python
NLP
Scikit-Learn
TF-IDF
Google Trends Analyzer
Market intelligence platform leveraging Google Trends API to map seasonal demand, identify high-value keywords, and uncover competitive patterns from live search data.
Python
Google API
Pandas
Plotly
JARVIS AI Assistant
Desktop AI assistant with two-tier NLP architecture. Processes natural voice commands to automate web browsing, play music, and retrieve live information hands-free.
Python
NLP
Speech Recognition
Automation
Facebook Auto Poster
Selenium-powered automation handling login, group navigation, media uploads, and anonymous post toggles with smart error recovery for reliable batch posting operations.
Python
Selenium
Web Scraping
FB Group Automator
Desktop GUI automation toolkit with modern dark interface, multi-profile management, and smart login handling for automating Facebook Group comment workflows at scale.
Python
Selenium
Tkinter
GUI
LMS-Result-Scraper-and-Analyzer
Python-based ETL pipeline that uses Selenium to scrape student results from the UAF LMS portal and Pandas to clean data, calculate GPAs, and track historical academic performance.
Python
Selenium
Pandas
Web Scraping
Ecommerce Data Analysis
The primary objective of this project is to explore retail sales data to identify trends, seasonal patterns, and profitability across different customer segments and geographic regions.
Python
Ploty
Pandas
Seaborn & Matplotlib
Website Performance Analysis
The goal of this analysis is to understand how users interact with the website over time. By correlating Sessions with Engagement Rates, the project seeks to identify high-traffic periods and determine the quality of user interaction during those times.
Python
Seaborn & Matplotlib
Pandas
