/Comprehensive-Analysis-of-Beijing-City-Houses-Dataset

This project delves into the Beijing City Houses dataset, offering insights through data preprocessing, feature engineering, and visualization. It helps stakeholders in housing and urban planning make informed decisions. Access the full project here:

MIT LicenseMIT

Project Title: Comprehensive Analysis of Beijing City Houses Dataset

Project Summary:

This project focuses on the comprehensive analysis and visualization of the Beijing City Houses dataset. The activities carried out in this project are divided into several key steps:

  1. Data Preparation and Cleaning:

    • Handling Unusual Characters: We began by reading files containing unusual characters and performed initial data exploration to understand the various features of each house. Unnecessary features were identified and removed.
    • Handling Missing Values: The dataset was examined for missing values. We identified the rows with missing data and implemented appropriate strategies to fill in these missing values.

  2. Data Formatting and Outlier Removal:

    • Formatting Data: Both numerical and non-numerical data were converted into appropriate formats to facilitate smooth analysis.
    • Removing Outliers: We identified and removed outlier data points, such as houses priced extremely high or low, to ensure a more accurate and meaningful analysis.

  3. Feature Engineering:

    • Creating New Features: Utilizing existing features, we generated new ones, such as calculating the price per square meter for each house. These new features provided additional insights into the housing market.
    • Visualization: We employed various visualization tools to explore relationships between different features, creating informative graphs and charts.

  4. Geospatial Analysis:

    • Mapping Houses: Leveraging the geographical coordinates available in the dataset, we used Matplotlib's scatter function to plot the houses on a map. This visual representation allowed us to analyze various spatial patterns.
    • Extracting Insights from Maps: By examining the density of different areas and the characteristics of houses in each region (e.g., presence of elevators, square footage, price), we derived valuable insights about the housing market in Beijing.

Through these steps, we transformed raw housing data into a structured, insightful, and visually appealing analysis, offering a deeper understanding of the Beijing housing market. This project demonstrates effective data cleaning, feature engineering, and the power of visualization in data analysis.

You can find the whole project at the below link: https://drive.google.com/drive/folders/1a3BNFICnXREB4tH5MJNw_wR6heF5t1XF?usp=drive_link

  • License

This project is licensed under the MIT License - see the LICENSE file for details.