Housing Data Analysis

This repository contains a Jupyter notebook for analyzing and transforming housing data. The analysis includes data cleaning, transformation, and visualization to derive insights from the housing dataset.

Table of Contents

• Introduction
• Installation
• Usage
• Notebook Overview
• Contributing

Introduction

The project aims to perform a comprehensive analysis of housing data, focusing on various attributes such as sale prices, square footage, and other features. The notebook includes steps for data cleaning, transformation, and visualization, providing insights into the housing market.

Installation

To run this project, you will need to have Python and Jupyter Notebook installed. Follow the instructions below to set up the environment.

1. Clone the repository:

   git clone https://github.com/yourusername/housing-data-analysis.git

2. Navigate to the project directory:

   cd housing-data-analysis

3. Create a virtual environment:

   python -m venv venv

4. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS and Linux:

     source venv/bin/activate

5. Install the required packages:

   pip install -r requirements.txt

Usage

1. Add the housing data CSV file to the repository. Name it housing_data.csv and place it in the root directory of the project.
2. Launch Jupyter Notebook:

   jupyter notebook

3. Open the Untitled.ipynb notebook and run the cells to perform the analysis.

Notebook Overview

The Jupyter notebook Untitled.ipynb includes the following steps and analyses:

1. Data Loading: The housing data is loaded from the CSV file.
2. Data Cleaning: The dataset is cleaned by handling missing values, removing duplicates, and correcting data types.
3. Data Transformation: Various transformations are applied to the data, such as binning of numerical values, encoding categorical variables, and creating new features.
4. Data Visualization: The transformed data is visualized using different plots to identify trends and patterns.
5. Statistical Analysis: Basic statistical analysis is performed to derive insights from the data.
6. Modeling (if applicable): Any machine learning models applied to the data are included here.

The notebook is well-documented with markdown cells explaining each step of the process, making it easy to follow and understand the analysis.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.