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Welcome to the Data Science Introduction repository! This repository is designed to provide an introduction to the field of data science, covering various topics and techniques commonly used in the industry.

License: MIT License

Jupyter Notebook 100.00%
data-science data-visualization exploratory-data-analysis knn knn-classification python simple-linear-regression classification-algorithm decision-tree-classifier

data-science-introduction's Introduction

๐Ÿ“Š Data Science Introduction

Welcome to the Data Science Introduction repository! This repository is designed to provide an introduction to the field of data science, covering various topics and techniques commonly used in the industry.

๐Ÿ“š Introduction

Data Science is a multidisciplinary field that combines statistics, mathematics, programming, and domain knowledge to extract insights and knowledge from data. It involves various stages, such as data collection, data cleaning, data analysis, modeling, and visualization, with the goal of making data-driven decisions and solving complex problems.

This repository serves as a starting point for individuals interested in learning about data science. It provides code examples, tutorials, and resources to help you get started and gain a solid foundation in the field.

๐Ÿš€ Getting Started

To get started with the content in this repository, you can follow these steps:

  1. Clone the repository to your local machine using the following command:
git clone https://github.com/Ruban2205/Data-Science-Introduction.git
  1. Navigate to the cloned directory:
cd Data-Science-Introduction
  1. Explore the various folders and files in the repository to access code examples, tutorials, and other learning resources.

๐Ÿ“ Topics Coverd

The repository covers a wide range of topics related to data science. Some of the main topics covered include:

  1. Introduction with Python Dictionary
  2. Basic Preprocessing
  3. Data Visualization
  4. Exploratory Data Analysis
  5. Statistical Inference
  6. Simple Linear Regression
  7. KNN
  8. Decision Tree Classification
  9. K_Means Clustering
  10. Recommender System

Each topic includes code examples, and Jupyter notebooks to help you understand the concepts and apply them in practice.

๐Ÿ™Œ Contributng

Contributions to this repository are welcome! If you have any improvements, additional examples, or new topics you would like to add, please follow these steps:

  1. Fork the repository in GitHub.
  2. Create a new branch wth a descriptive name for your changes.
  3. Make you modifications, additions, or improvements.
  4. Commit and push your changes to your forked repository.
  5. Submit a pull request to the original repository.

Please ensure your contributions adhere to the coding style and guidelines used in the repository.

๐Ÿ“ƒ License

This repository is licenced under the MIT LICENSE. You are free to use, modify, and distribute the code and content within this repository for personal or commercial purposes. However, please provide attribution to the original repository by linking back to it.

๐Ÿ“ง Contact

For any questions or inquiries, please contact the project owner:

Blog Link: rubangino.hashnode.dev

Website Mail LinkedIn

Feel free to report any issues or suggest improvements by creating an issue in the GitHub repository.

Star โญ this repository for Future use ๐Ÿ˜Š

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Thank You!!


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