Statistical Data Analyzer

Overview

The Statistical Data Analyzer is a Python-based application designed for quick and efficient statistical analysis of datasets. It provides users with tools for descriptive statistics, data visualization, hypothesis testing, and correlation analysis. The tool is user-friendly and accessible through a command-line interface.

Features

1. Dataset Summary:

   • Provides an overview of the dataset structure, including column types and missing values.
   • Generates descriptive statistics (mean, median, standard deviation, etc.).

2. Data Visualization:

   • Visualize the distribution of data in individual columns using histograms and density plots.

3. Hypothesis Testing:

   • Perform one-sample t-tests to evaluate if the mean of a column differs from a specified value.

4. Correlation Analysis:

   • Generate a heatmap to visualize correlations between numerical variables.

How to Use

Prerequisites

• Python 3.6 or higher
• Required libraries: pandas, numpy, scipy, matplotlib, seaborn

Installation

1. Clone this repository:

   git clone https://github.com/your-username/StatisticalDataAnalyzer.git

2. Navigate to the project directory:

   cd StatisticalDataAnalyzer

3. Install the required dependencies:

   pip install -r requirements.txt

Running the Application

1. Run the script:

   python statistical_data_analyzer.py

2. Follow the prompts to:
   • Load your dataset (CSV format).
   • Perform desired analyses (e.g., summary, visualizations, tests).

Example Usage

Input

Enter the path to your CSV file: data.csv

Options:
1. Display dataset summary
2. Visualize data distribution
3. Perform hypothesis test
4. Visualize correlation matrix
5. Exit
Enter your choice: 1

Output

--- Dataset Summary ---
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100 entries, 0 to 99
Data columns (total 5 columns):
...

--- Descriptive Statistics ---
          Column1   Column2
mean       ...        ...
std        ...        ...
...

Contribution

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature-name

3. Commit your changes and push the branch:

   git push origin feature-name

4. Open a pull request.

License

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

Contact

• Author: [Your Name]
• Email: [Your Email]
• GitHub: https://github.com/your-username

Future Enhancements

• Add support for additional statistical tests (e.g., ANOVA, chi-square).
• Include time series analysis features.
• Integrate with Jupyter Notebook for enhanced interactivity.