Constituents of a Company: A Breakdown

This project provides a comprehensive Exploratory Data Analysis (EDA) on the various stakeholders within a company, both internal and external. Conducted in Python using Google Colab, this analysis examines the roles and impacts of management, employees, shareholders, customers, suppliers, creditors, government entities, and the local community in a corporate structure.

Dataset

The dataset used in this project is available at the following link:

Download Constituents Dataset

Google Colab Notebook

You can access and run the project in Google Colab by following this link:

Google Colab Notebook for Constituents EDA

Topics Covered

1. Data Transformation and Pre-Processing:
   Cleaning and preparing data for accurate analysis.

2. Merging Data Frames:
   Combining multiple data sources into a unified dataset.

3. Statistical Analysis:
   Using descriptive statistics to understand attributes and relationships.

4. Univariate Plots:
   Visualizing individual variables to assess distributions and identify outliers.

5. Bivariate Analysis:
   Examining relationships between pairs of variables.

6. Multivariate Analysis:
   Analyzing complex interrelationships among multiple variables.

7. Time Series Analysis:
   Identifying trends over time for key stakeholder groups.

8. K-Means Clustering:
   Segmenting stakeholders based on shared characteristics.

9. Hierarchical Clustering:
   Uncovering deeper relationships among stakeholders.

10. Principal Component Analysis (PCA):
    Simplifying data to highlight core insights.

11. Linear Regression:
    Building predictive models to estimate trends in stakeholder data.

Insights and Visualizations

Using Python libraries like Pandas, Matplotlib, and Seaborn, I conducted visualizations and discovered patterns that provide a data-driven perspective on each stakeholder’s role and influence. This project showcases advanced EDA techniques in Google Colab, making it an ideal resource for analysts interested in understanding company dynamics through detailed data exploration.

Installation

Prerequisites

• Python 3.x
• Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn

Clone the Repository

git clone https://github.com/Rahulaggl/EDA.git

Install Dependencies

cd EDA
pip install -r requirements.txt

Usage

You can explore the project directly in the Google Colab Notebook linked above. It provides step-by-step instructions to perform EDA on the constituents of a company.

Contributing

1. Fork this repository.
2. Create a feature branch.
3. Commit your changes.
4. Push your branch.
5. Open a pull request.

License

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

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