Statistical Analysis Projects Repository

Welcome to the Statistical Analysis Projects repository! This repository contains a collection of data science projects focused on statistical analysis.

Overview

This repository serves as a hub for various projects that delve into the intricate world of telecom data through statistical methodologies. Learning Statistics is one of the most Important step to get into the World of Data Science and Machine Learning. Statistics helps us to know data in a much better way and explains the behavior of the data based upon certain factors. It has many Elements which help us to understand the data better that includes Probability, Distributions, Descriptive Analysis, Inferential Analysis, Comparative Analysis, Chi-Square Test, T Test, Z test, AB Testing etc.

Project List

Project Name	Description
Project 1 - Telecom	Explore the dynamic trends in the telecom sector using statistical analysis techniques.

How to Use

Feel free to explore each project's directory for detailed documentation, code, and findings. The projects are designed to be informative, and you are encouraged to contribute, provide feedback, or use the insights for your own analyses.

Below are generic steps you can use as a starting point:

1. Clone the Repository:-

   • Open a terminal or command prompt.
   • Use the following command to clone the repository to your local machine:

     git clone https://github.com/yashksaini-coder/Statistics-For-Data-Science.git

2. Navigate to the Repository:-

   • Change your current directory to the cloned repository:

     cd Statistics-For-Data-Science

3. Create a Virtual Environment (Optional but Recommended):-

   • It's a good practice to use virtual environments to isolate project dependencies.

     # For Python
     python -m venv venv
     
     # Activate the virtual environment
     # On Windows
     .\venv\Scripts\activate
     # On macOS/Linux
     source venv/bin/activate

4. Install Dependencies:-

   • Depending on the programming language used, you need to install project dependencies.

     # For Python, using pip
     pip install -r requirements.txt

     Replace requirements.txt with the actual file name that contains the project dependencies.

5. Explore the Projects:-

   • Once the dependencies are installed, you can explore each project directory.
   • Follow the documentation within each project for specific instructions on running and analyzing the statistical analyses.

6. Contribute (Optional):-

   • If you plan to contribute or make changes, it's recommended to fork the repository and create a new branch for your modifications.
   • After making changes, submit a pull request to the original repository.

7. Run Jupyter Notebooks (If Applicable):-

   • If the projects include Jupyter notebooks, you can run them locally. Make sure to install Jupyter using:

     pip install jupyter

   • Then run the Jupyter notebook server:

     jupyter-notebook