I have completed a full end-to-end analysis and predictive modeling solution for this challenge. The project includes: - Data cleaning and preprocessing (handling missing values and duplicates) - Feature engineering (price-based features, review statistics, category encoding) - NLP processing using TF-IDF on combined review text, titles, and product descriptions - Model training and comparison (Linear Regression vs Random Forest) - Final model performance: RMSE ≈ 0.273 - Visualizations and feature importance analysis for deeper insights Due to submission limitations, only key results are attached here. The complete notebook, detailed analysis, and full report are available in my portfolio. I would be happy to provide full access or discuss the approach in more detail if needed.

Request contest holders to increase prize and upgrade contest by posting #extended, #guaranteed, #sealed, #increaseprize

Excited to participate in HAGO Contest #2! ? I explored the dataset, analyzed key patterns, and built a predictive model to identify high-rated products. Looking forward to feedback! number#19

Hi, Please check entry no #1

Hi, I’m unable to find the Amazon sales dataset for Contest #2. Could you please share the dataset link?

Can you join me in the inbox? I have some questions for you.

hi i am here and working on it