House Price Predictions – Netherlands

A machine learning model to predict house prices in the Netherlands based on historical property and location data. This project demonstrates data cleaning, feature engineering, exploratory data analysis (EDA), and predictive modeling using Python.

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Project Overview

Objective:
Predict property prices accurately using machine learning techniques to support real estate decision-making and investment analysis.

Dataset:

• Historical property listings and transaction data in the Netherlands
• Features include: property type, size (sqm), location, number of rooms, age of property, and other relevant housing attributes

Tools & Libraries:

• Python: Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn,
• Machine Learning: Linear Regression, Random Forest, XGBoost, Ridge, Lasso, ElasticNet, SGDRegressor, BayesianRidge, RandomForestRegressor, XGBRegressor, SVR, SVC
• Visualization & Analysis: EDA, feature importance plots, correlation heatmaps

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Key Steps & Methodology

1. Data Collection & Cleaning
2. Exploratory Data Analysis (EDA)
3. Feature Engineering
4. Modeling & Evaluation
5. Results & Insights

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How to Run

# Clone the repo
# Navigate to the project folder
# Install dependencies
# Run