Data Analysis and Visualization Project

Welcome to the Data Analysis and Visualization Project! This repository contains code for analyzing a dataset, handling missing values, and visualizing correlations between different features. The project leverages Python libraries like pandas, seaborn, and matplotlib to perform data preprocessing and create insightful visualizations.

Project Structure

• analysis.py: Main Python script that loads the dataset, performs data preprocessing (including handling missing values and encoding categorical variables), calculates the correlation matrix, and visualizes the results as a heatmap.
• README.md: Documentation for the repository.
• requirements.txt: List of Python dependencies for easy installation.
• correlation_matrix_heatmap.png: Saved heatmap image file showing the correlation between various features in the dataset.

Features

1. Data Cleaning: Handles missing values by filling in median values for numerical columns and mode values for categorical columns.
2. Encoding: Converts categorical columns to numerical values for analysis.
3. Correlation Analysis: Calculates and visualizes the correlation matrix of the dataset.
4. Visualization Saving: Saves the generated heatmap as an image file in the repository.

Getting Started

Prerequisites

To run this project, you'll need the following Python libraries:

• pandas
• seaborn
• matplotlib

You can install the required libraries by running:

pip install -r requirements.txt

Running the Code

1. Clone the repository:

   git clone https://github.com/your-username/data-analysis-visualization.git
   cd data-analysis-visualization

2. Run the analysis script:

   python analysis.py

   This will execute the analysis, fill missing values, convert categorical columns, generate a correlation matrix, and save the heatmap as correlation_matrix_heatmap.png.

Example Usage

Here’s an example of how to run the analysis:

import pandas as pd
from analysis import run_analysis  # Assuming you've structured the script as a function

df = pd.read_csv('your_dataset.csv')
run_analysis(df)

Output

The output heatmap will be saved as correlation_matrix_heatmap.png in the current directory. This visualization provides insights into how features are correlated with each other, helping in understanding the relationships within the dataset.

License

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

Contributing

Contributions are welcome! Please feel free to submit a Pull Request if you have any enhancements or bug fixes.

Acknowledgements

• pandas for data manipulation
• seaborn and matplotlib for data visualization
• Python for being an awesome language for data analysis