Skip to content

Data Science and Machine Learning Notebook Welcome to the Data Science and Machine Learning Notebook repository! This repository contains a comprehensive collection of essential concepts and techniques in data science and machine learning, designed to help you understand and apply these methods effectively.

Notifications You must be signed in to change notification settings

MuhammadRuby/code-for-some-statistical-ML-model

Folders and files

NameName
Last commit message
Last commit date

Latest commit

ย 

History

7 Commits
ย 
ย 
ย 
ย 
ย 
ย 

Repository files navigation

Data Science and Machine Learning Notebook Welcome to the Data Science and Machine Learning Notebook repository! This repository contains a comprehensive collection of essential concepts and techniques in data science and machine learning, designed to help you understand and apply these methods effectively. <<<<<<< HEAD

๐Ÿ“š Contents

  1. Mean, Median, and Mode
    • Mean
    • Median
    • Mode
  2. Percentiles
  3. Data Distribution
  4. Normal Distribution
  5. Multiple Regression
  6. Scaling
  7. Train and Test Data
  8. Confusion Matrix
  9. Hierarchical Clustering
  10. Categorical Data
  11. Cross Validation

๐Ÿ› ๏ธ Features Clear Explanations: Each topic is explained in detail with practical examples. Structured Layout: Organized sections for easy navigation. Interactive Notebooks: Jupyter notebooks to run and modify code. ๐Ÿš€ Getting Started To get started with this repository, clone it to your local machine using the following command:

git clone This is Repo Link

Navigate to the repository directory:

cd code-for-some-statistical-ML-model

Open the Jupyter notebook:

jupyter notebook

๐Ÿค Contributing Contributions are welcome! If you have any suggestions or improvements, please feel free to open an issue or submit a pull request.

About

Data Science and Machine Learning Notebook Welcome to the Data Science and Machine Learning Notebook repository! This repository contains a comprehensive collection of essential concepts and techniques in data science and machine learning, designed to help you understand and apply these methods effectively.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published