Amazon Sales Prediction Challenge

Закрито

Приз:

$15 USD

Отримані роботи:

? HAGO Contest #2 – April ? For data scientists and analytics enthusiasts, we present a new challenge with Amazon sales dataset. ? Tasks: Explore the dataset in depth Analyze patterns and trends Build a predictive model using Python Submit results in a PDF file within our private community ? Prize: The winner receives $15 USD for the best analysis and prediction ? Don’t forget to join the IFAI Contest for future forecasting, where the winner earns $10 USD. ✨ Tips to increase your chances of winning: Make your analysis comprehensive yet easy to understand Add clear and insightful visualizations Focus on prediction accuracy and creative ideas https://www.skool.com/hago-8156

Необхідні навички

Data Analysis

Data Science

Data Visualization

Hadoop

Machine Learning (ML)

Predictive Analytics

Python

R Programming Language

Regression Analysis

Statistical Analysis

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Marwan M.

@Marwan328

3 months ago

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.

Amir S.

@amirm121

3 months ago

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

Rayane S.

@rayanes48

3 months ago

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