This project analyzes real estate transactions and property details using Python. The analysis includes exploring pricing trends, creating visualizations, and building a machine learning model to predict house prices.
This project aims to showcase Python capabilities in data analysis, visualization, and machine learning. It was utilizing a real estate dataset.
- Mix of categorical and numeric features
- Target variable: Price per square meter
- 400 transactions from 2012 to 2013
- Houses ranging from new construction to 40 years old
- Locations spread across coordinates in the Singapore area
RealEstateAlg.ipynb: Jupyter notebook containing the analysis and code.RealEstateAlg.py: Python script version of the analysis.requirements.txt: File listing dependencies for the project.Figure_1.png: Scatter plot visualizing house prices against the distance to the nearest MRT station.
- Install dependencies:
pip install -r requirements.txt
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MIT License
Copyright (c) 2023 Mahlodi Makobe
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