1st read research_guide.doc and then next_step.doc

Analysis of House Listing Dataset

This repository contains a Jupyter Notebook that performs statistical analysis on a House Listing Dataset. The analysis includes inference on features like property type, dimensions, age, locality, etc.

Dataset

The input CSV file named train.csv used for the analysis includes the columns as described in data_description.txt.

Contents

• main.ipynb: The Jupyter Notebook performing data preprocessing, analysis, and visualization.
• Input CSV Files i.e. train.csv: Dataset file containing the information used for analysis.

Features of the Notebook

1. Exploratory Data Analysis (EDA):
   • Descriptive statistics of the dataset.
   • Visualization of trends and correlations.
2. Statistical Inference:
   • Hypothesis testing.
   • Analysis of property-related features.
3. Recommendations:
   • Suggestions based on findings.

How to Run

1. Clone this repository:

   git clone https://github.com/samar-080301/house_price_EDA

2. Navigate to the repository folder:

   cd house_price_EDA

3. Install the required dependencies:

   pip install -r requirements.txt

4. Open the Jupyter Notebook:

   jupyter notebook main.ipynb

5. Run the notebook cells sequentially.

Dependencies

Ensure the following Python libraries are installed:

• pandas
• numpy
• matplotlib
• seaborn
• scipy

You can install them using:

pip install pandas numpy matplotlib seaborn scipy

Outputs

The notebook generates:

• Summary statistics of the dataset.
• Graphical visualizations (e.g., bar charts, scatter plots).
• Insights into property features and trends.

Author

Samar Pratap Singh
MBA Student | Data Analysis Enthusiast

License

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