House Price Prediction — Supervised Machine Learning Pipeline

End-to-End Data Science & Regression Workflow
Data Cleaning • Exploratory Data Analysis • Feature Normalization • Scikit-Learn Linear Regression

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📊 Overview

This project implements an end-to-end predictive machine learning pipeline for real estate valuation, developed for the ISTE Thapar Chapter Machine Learning Workshop.

It handles dirty, uncurated raw tabular data (house_price_dirty.csv) with missing attributes and outliers, cleans and standardizes numeric distributions, and trains a multivariate linear regression model to predict residential house valuations.

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🔬 Pipeline Architecture

Raw CSV Dataset (house_price_dirty.csv)
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     [ Stage 1: Data Cleansing ]
     - Missing value imputation
     - Duplicate elimination
     - Type coercion & outlier filtering
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     [ Stage 2: Feature Transformation ]
     - Numerical scaling via StandardScaler (Z-score normalization)
     - Correlation matrix evaluation
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     [ Stage 3: Train / Test Split ]
     - 80/20 train-test partitioning
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     [ Stage 4: Supervised Modeling ]
     - Multivariate Linear Regression (OLS)
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     [ Stage 5: Validation & Metrics ]
     - Mean Absolute Error (MAE)
     - Root Mean Squared Error (RMSE)
     - Coefficient of Determination (R² Score)

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🛠️ Tech Stack & Libraries

• Language: Python 3.9+
• Data Manipulation: pandas, numpy
• Visualization: matplotlib, seaborn
• Machine Learning: scikit-learn (LinearRegression, StandardScaler, train_test_split)

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🚀 How to Run

# Clone the repository
git clone https://github.com/LifeOfDevD/iste-wrokshop.git
cd iste-wrokshop

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install pandas numpy scikit-learn matplotlib seaborn

# Execute pipeline
python projectone.py