🔍 Passionate about transforming data into insightful stories
📊 Skilled in data analysis, cleaning, dashboarding, and storytelling using Python, SQL, Power BI, and Excel
🧹 Turning raw data into actionable business insights is what I love most

🕸️ Experienced in web scraping using BeautifulSoup and Selenium
🤖 Built several ML models for classification and regression problems

🎓 PG Program in Data Science – Data Trained Academy
💼 Internship at Flip Robo Technology (Python, SQL, ML)
🏋️‍♂️ Athlete mindset: always disciplined, focused & consistent — not just eat, code, sleep!

• 📊 Social Media Sentiment Analysis — 🔗 GitHub Link

• 🗂️ Customer Support Data Analysis — 🔗 GitHub Link

• 🚦 Road Accident Analysis — 🔗 GitHub Link

• 📌 Census Income Prediction – Classification with EDA & feature engineering
• 📌 Red Wine Quality Predictor – ML pipeline with evaluation metrics
• 📌 Medical Cost Estimator – Regression model using real insurance data
• 📌 Titanic Survival Prediction – Classification & feature handling
• 📌 World Happiness Report Analysis – Insights through data storytelling
• 📌 Glass Identification Project – Multiclass classification of glass types
• 📌 Baseball Case Study – EDA & statistical insights
• 📌 Avocado Price Prediction – Regression analysis & visualization
• 📌 IBM HR Analytics – Employee attrition and performance insights
• 📌 Zomato Data Analysis – Price prediction, cuisine trends, locality analysis
• 📌 Insurance Claim Fraud Detection – Binary classification & fraud insights
• 📌 Temperature Forecasting – Time series prediction using ML
• 📌 Loan Application Status Prediction – Classification using structured data
• 📌 Sentiment Analysis on Social Media – NLP with VADER

• 📌 YouTube & IMDb Scraper – Extracting video and series data
• 📌 Amazon Product Scraper – Scraped name, price, brand, delivery, return policy for 3 pages based on user search
• 📌 Flipkart Smartphone Scraper – RAM, storage, cameras, display, battery, price, and URL from page 1
• 📌 Google Maps Geolocation Scraper – Retrieved latitude & longitude of searched cities
• 📌 Flipkart Sneakers Scraper – Scraped first 100 sneaker listings with product details
• 📌 Flipkart iPhone Reviews Scraper – 100 reviews for iPhone 11 scraped using pagination
• 📌 Amazon Laptop (i7) Scraper – Searched & filtered Intel i7 laptops with details
• 📌 Top 1000 Quotes Scraper – Scraped quotes from a public quote website
• 📌 Most Expensive Cars Scraper – Scraped 50 most expensive cars with details
• 📌 Google Images Scraper – Scraped 10 images each for ‘fruits’, ‘cars’, ‘Machine Learning’, ‘Guitar’, and ‘Cakes’
• 📌 CNBC News Headlines Scraper – Extracted headline, date, and article link
• 📌 KeAi AI Journal Scraper – Scraped paper title, date, author from AI in Agriculture
• 📌 Bewakoof Product Scraper – Scraped top 10 popular products with name, price, image URL
• 📌 CoreyMS Patreon Scraper – Extracted video posts, content, likes, and YouTube links
• 📌 NoBroker Housing Scraper – Collected house title, location, EMI, area, and price for 3 Bangalore localities
• 📌 Digit Top Gaming Laptops Scraper – Extracted top laptop features from digit.in rankings

• 🔗 LinkedIn
• 📫 Email: abhianandakkap@gmail.com
• 💻 GitHub: github.com/Abhishek-K-Anand

"Data is everywhere, stories are rare — I focus on both."
Athlete at heart, analyst by brain, learner by soul.