📊 Internshala Data Science Projects

Welcome to the Internshala Data Science Projects repository! This collection showcases my skills in data manipulation, analysis, and visualization using tools like Excel, SQL, Python, and Tableau. Each project reflects the knowledge gained from the Internshala Data Science course, demonstrating practical applications in real-world scenarios.

Table of Contents

• About the Projects
• Technologies Used
• Project List
• Installation Instructions
• Usage
• Contributing
• License
• Contact

About the Projects

This repository contains a variety of projects that highlight my proficiency in data science. Each project is designed to solve specific problems, showcasing the ability to analyze data and extract valuable insights. The focus is on practical applications, ensuring that each project is not only educational but also relevant to industry standards.

Technologies Used

The following technologies are used throughout the projects:

• Excel: For data manipulation and basic analysis.
• SQL: For database management and querying.
• Python: For advanced data analysis and machine learning.
• Tableau: For data visualization and dashboard creation.
• PostgreSQL: For managing and querying databases.

Project List

1. Sales Data Analysis

   • Description: Analyzed sales data to identify trends and patterns.
   • Tools: Excel, SQL
   • Key Insights: Monthly sales trends, top-selling products.

2. Customer Segmentation

   • Description: Segmented customers based on purchasing behavior.
   • Tools: Python, SQL
   • Key Insights: Identified key customer groups for targeted marketing.

3. Stock Price Prediction

   • Description: Developed a predictive model for stock prices using historical data.
   • Tools: Python, SQL
   • Key Insights: Forecasting accuracy and potential investment strategies.

4. Employee Performance Dashboard

   • Description: Created a dashboard to visualize employee performance metrics.
   • Tools: Tableau, Excel
   • Key Insights: Performance trends and areas for improvement.

5. E-commerce Website Analysis

   • Description: Analyzed user behavior on an e-commerce platform.
   • Tools: SQL, Python
   • Key Insights: User engagement metrics and conversion rates.

Installation Instructions

To run the projects locally, follow these steps:

1. Clone the Repository

   git clone https://github.com/abdelazizfouad/internshala-ds-projects.git

2. Navigate to the Project Directory

   cd internshala-ds-projects

3. Install Required Packages For Python projects, ensure you have the required libraries installed. You can use pip:

   pip install -r requirements.txt

4. Database Setup (if applicable) For projects that require a database, ensure you have PostgreSQL installed. Create a database and import the necessary SQL files provided in the project folder.

5. Run the Projects Follow the instructions in each project's README file to execute the code and visualize the results.

Usage

Each project comes with its own set of instructions for usage. After setting up the environment, you can run the scripts or open the notebooks to explore the analyses.

For data visualization projects, you can view the dashboards in Tableau or similar tools. The insights derived from each project can be utilized for various applications in business and research.

Contributing

Contributions are welcome! If you have suggestions for improvements or new projects, feel free to fork the repository and submit a pull request.

How to Contribute

1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Make your changes and commit them.
4. Push to your forked repository.
5. Submit a pull request with a description of your changes.

License

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

Contact

For any inquiries or feedback, please reach out to me at [your.email@example.com].

You can also check the Releases section for the latest updates and downloadable files.

Thank you for visiting my repository! Your support and feedback are greatly appreciated.