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Completedmachine-learning
House Price Prediction System
End-to-end ML pipeline to predict house prices using real-world datasets
January 2025 – March 2025Machine Learning Regression
Tech Stack
PythonPandasNumPyScikit-learnMatplotlib
What I Built
- 1Built end-to-end machine learning pipeline for house price prediction
- 2Performed data cleaning, feature engineering, and exploratory data analysis
- 3Implemented Linear Regression and Random Forest models with comparison
- 4Achieved improved prediction accuracy through hyperparameter tuning
- 5Evaluated models using RMSE and cross-validation metrics
Key Metrics
80+
features
2
models Compared
Cross-validation
evaluation Method
Problem Context
This project addressed machine learning regression challenges. The goal was to build a robust system that could handle real-world data and produce actionable insights for decision-making.
Architecture & Approach
┌─────────────────────────────────────────────────────────┐ │ Data Pipeline │ ├─────────────────────────────────────────────────────────┤ │ │ │ Raw Data ──▶ Preprocessing ──▶ Feature Engineering │ │ │ │ │ │ ▼ ▼ │ │ Data Cleaning Feature Selection │ │ │ │ │ │ └────────┬───────────┘ │ │ │ │ │ ▼ │ │ Model Training │ │ │ │ │ ▼ │ │ Evaluation & Tuning │ │ │ │ │ ▼ │ │ Final Predictions │ │ │ └─────────────────────────────────────────────────────────┘
Key Lessons
✓
Feature engineering has outsized impact on model performance. Domain knowledge matters more than model complexity.
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Cross-validation is essential for reliable evaluation. Single train-test splits can be misleading.
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Start simple (Linear/Logistic Regression) before complex models. Baselines provide crucial context.