Cluj-Napoca Real Estate Data Analysis Project Overview
This project involves the analysis of real estate data in Cluj-Napoca. I utilized web scraping techniques to collect the data, performed data cleaning and manipulation using Pandas, and conducted statistical analysis using NumPy. The insights gained from this analysis are visualized using Matplotlib to create informative charts and graphs.
Key Points:
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Data Collection and Cleaning:
- Web scraped real estate data.
- Used Pandas for data manipulation and cleaning.
- Ensured the dataset is robust and ready for analysis.
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Statistical Analysis with NumPy:
- Applied NumPy for statistical analysis.
- Calculated averages and derived key metrics.
- Uncovered meaningful insights from the dataset.
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Visualization with Matplotlib:
- Created visually compelling charts and graphs.
- Transformed complex numerical data into easily understandable visual representations.
- Provided clear and concise visual insights into the real estate data.
Key Insights
- RealEstateAnalysis.ipynb: Jupyter Notebook containing the entire analysis.
- uncleaned_data.txt: The initial uncleaned dataset.
- clean_data.txt: The final cleaned and prepared dataset.
- listings_urls.txt: Text file containing every listing url.
Usage
Clone the repository:
bash
git clone https://github.com/PrialaRadu/-Cluj-Napoca-Real-Estate-Data-Analysis.git
Open the Jupyter Notebook: RealEstateAnalysis.ipynb.
Run the notebook cell by cell to reproduce the analysis.