Welcome to the Immoscout24.ch Exploratory Data Analysis (EDA) project! This project aims to analyze and present key insights from the data I scraped from Immoscout24.ch using the Immoscout24-scraper.
The goal of this analysis is to evaluate the information I collected and highlight key insights about the rental property market in Switzerland. Using various data visualization techniques and linear regression, we delve into the trends and characteristics of the properties listed on Immoscout24.ch.
The data for this project was scraped from Immoscout24.ch.
To run this project locally, you need to have Python installed. Then, install the required packages using pip:
pip install numpy pandas matplotlib seaborn scikit-learn- Clone this repository to your local machine.
- Open the Jupyter Notebook in your preferred environment (e.g., Jupyter Lab, Jupyter Notebook).
- Run the notebook cells to execute the analysis and generate visualizations.
- Data Visualization: I used Matplotlib and Seaborn to create insightful visualizations.
- Linear Regression: A linear regression model is applied to predict price trends.
- Customized Plots: The visualizations are customized with selected colors for better clarity and presentation.
- Matplotlib and Seaborn plots are extensively used to visualize the data.
- Trend analysis using linear regression is showcased.
- There is a focus on modern apartments with at least 2.5 rooms.
- These apartments typically have good access to highways or public transport and are close to schools and kindergartens.
- Older apartments are often renovated to modern standards.
- Significant price differences exist between various cantons.
- The price trend is generally increasing over time.
This project was created by me.
This project is licensed under the MIT License - feel free to use and modify it as you see fit.