Python Popular Libraries for Data Science and Machine Learning Basics and sample datasets!!

This repository is a collection of the Python notebooks which uses popular python libraries for Data Science and Machine Learning.

Objective of this repository is to provide a collection of notebooks which can be used as a reference for learning and practicing the concepts of Data Science and Machine Learning.

Popular Python Libraries for Data Science and Machine Learning

1. Numpy
2. Pandas
3. Matplotlib
4. Seaborn
5. Scikit-Learn
6. Tensorflow
7. Keras
8. PyTorch
9. XGBoost
10. LightGBM
11. CatBoost
12. NLTK
13. Spacy
14. Gensim
15. Scrapy

How to Contribute

You can contribute to this repository by adding new notebooks or improving the existing notebooks. You can also add new datasets or improve the existing datasets. New features and improvements are always welcome.

How to use this repository

You can use this repository in two ways:

1. You can clone this repository and run the notebooks in your local machine.
2. You can use Google Colab to run the notebooks in the cloud.

How to run the notebooks in Google Colab

1. Open the notebook in GitHub.
2. Click on the "Open in Colab" button.
3. The notebook will open in Google Colab.
4. You can run the notebook in Google Colab.

How to run the notebooks in your local machine

1. Clone this repository.
2. Install the required libraries.
3. Run the notebooks.

How to install the required libraries

1. Open the terminal.
2. Run the following command:

pip install -r requirements.txt

How to run the notebooks

1. Open the terminal.
2. Run the following command:

jupyter notebook

3. The Jupyter Notebook will open in your browser.
4. Open the notebook.
5. Run the cells.

How to add new notebooks/datasets

1. Create a new branch.
2. Add the new notebook.
3. Commit the changes.
4. Push the changes..
5. Create a pull request.

Thanks for reading this far. If you like this repository, please give it a star. If you have any suggestions, please create an issue.

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Contribuers