Real Estate Price Estimator for Tokyo

This project develops a machine learning regression model to estimate Tokyo real estate prices based on historical housing data. It leverages data preprocessing, exploratory analysis, and predictive modeling to deliver accurate price estimations.

🚀 Technologies Used:

• Python.
• Pandas.
• NumPy.
• Machine Learning (Regression).
• Jupyter Notebook.

📊 Project Highlights:

• Exploratory data analysis on housing market trends.
• Development and tuning of regression models for price prediction.
• Evaluation of model performance and predictive accuracy.

📂 Files

• tokyo_real_estate_data.csv — raw data housing dataset.
• df_preprocessed.csv — pre-processed data housing dataset.
• final_dataset.csv — finalized data housing dataset.
• 01-Data Aggregation & Preprocessing.ipynb — Jupyter Notebook #1.
• 02-Exploratory Data Analysis.ipynb — Jupyter Notebook #2.
• 03-Correlation Analysis.ipynb — Jupyter Notebook #3.
• 04-Build Regression Model.ipynb — Jupyter Notebook #4.

▶️ How to View & Run:

Click in whaterver Jupyter Notebook in this repository that you want to see (recommended for non-technical people).

📄 License

MIT License

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