Project 3
Computer Price Prediction
Machine Learning[ Apr 2026 - May 2026 ]
Overview
Engineered a predictive model using a Random Forest Regressor to estimate custom computer prices based on complex hardware specifications, following the comprehensive CRISP-DM methodology across 100,000+ dataset rows with rigorous feature selection.
Key Features
- ◆End-to-end CRISP-DM pipeline structuring data understanding, preparation, modeling, and evaluation phases in Python
- ◆IQR Winsorization for outlier treatment preserving data stability and distribution integrity
- ◆Wrapper (RFE) feature selection method minimizing multicollinearity and optimizing dimensionality
- ◆87.85% R-squared test accuracy and $157.13 Mean Absolute Error, outperforming KNN and SVR baselines
- ◆20 high-quality statistical visualizations using Matplotlib and Seaborn for data variance and feature importance mapping
Skills
Machine LearningPythonData AnalyticsPredictive ModelingData Science
Gallery
Project Info
CategoryMachine Learning
PeriodApr 2026 - May 2026
TagPython / CRISP-DM
Tech Stack
Machine LearningPythonData AnalyticsPredictive ModelingData Science