Hey there! I'm Haridev S, a passionate Data Science and Machine Learning enthusiast. I'm obsessed with the coolest AI technologies and love exploring new areas in the field. From mind-reading algorithms to self-driving cars, AI is transforming our world. I'm here to connect with fellow enthusiasts and dive deep into captivating AI projects. What are you working on? Any specific areas or technologies that excite you? Let's embark on this incredible journey together and make our mark in Data Science and Machine Learning! The possibilities are endless.

• 🌟 I'm currently diving deep into the mystical world of Machine Learning! 🌱
• 🚀 All my mind-blowing projects are waiting to be discovered on my Jovian Profile! 👨‍💻
• 📝 Brace yourself for some wild and mind-bending articles on Medium! 📚
• 💬 I'm a master of MySQL, Python, Statistics, Web scraping, and the art of Exploratory Data Analysis (EDA). Ask me anything! 💪
• 📧 Catch me on the intergalactic web at ! 📫
• 📄 Wanna see my supercharged resume? Here it is: Resume! 🚀

• Data Analysis, Machine Learning, Web Scraping, Data Visualization, SQL, Version Control, Problem Solving, Communication

• Machine Learning for Product Classification: The Otto Group Challenge(View)

  Cleaned & analyzed 93 feature product data of 70,000+ records for product classification using Python

  Trained logistic regression, tree based models, neural network model etc. with scikit-learn, LightBGM etc.

  Tuned LightGBM classifier to achieve a log_loss of 0.47, which is within the top 30% on Kaggle leaderboard

• A Data-Driven Look at the Luxury Fashion Market - Farfetch Dataset EDA (View)

  Cleaned and analyzed 360K luxury product records out of 5.8M containing 16 columns using Pandas

  Created visualizations (Histograms, pair plots, & word clouds etc.) using Matplotlib, and Plotly etc. libraries

  The luxury fashion market is dominated by a few popular brands, such as Gucci, Louis Vuitton, and Chanel

• Scraping Region-Specific Product Details from Amazon using Python(View)

  Python libraries like Requests & Beautiful Soup were used to extract various product detail from Amazon

  Built-in functions like page_extractor() & amazon_extractor() which makes project more flexible to parse data

  Created CSV files of 15K+ region-specific product data records using Pandas library

• Bookshop Revenue Analysis using Tableau(View)

  Cleaned and analyzed 5k+ rows of 10-column of Bookshop data using Excel

  Analyzed the bookshop's revenue by average monthly sales, genre, and book via visualization

  Created visualizations like bar chart, density chart, heatmap & published the dashboard to Tableau Public