Data Science Jupyter Notebook Templates

This repository contains a collection of Jupyter Notebook templates for data science tasks such as hypothesis tests, regression models, and machine learning models. These templates are designed to help data scientists and analysts quickly and easily create and execute common data analysis tasks using Python.

Table of Contents

• Getting Started
• Notebook Templates
• Contributing

Getting Started

To use these notebook templates, you will need to have Python and Jupyter Notebook installed on your machine. You can download and install them from the following links:

• Python
• Jupyter Notebook

Once you have Python and Jupyter Notebook installed, you can clone this repository to your local machine using the following command:

git clone https://github.com/your_username/data-science-notebook-templates.git

Notebook Templates

This repository contains the following Jupyter Notebook templates:

• Exploratory Data Analysis (EDA) Template: This template provides a framework for the exploratory data analysis process. It includes code for Discovering, Structuring, Cleaning, Joining, Validating and Presenting data.

• Basic Hypothesis Testing Template: This template provides a starting point for conducting hypothesis tests on data using Python. It includes code for calculating statistical z-tests and t-tests and provides examples of how to interpret the results.

• Simple Linear Regression Template: This template provides a starting point for building simple linear regression models in Python. It includes code for importing data, preprocessing the data, building a regression model, and evaluating the model's performance.

• Multiple Regression Analysis Template: This template provides a starting point for building multiple regression models in Python. It includes code for importing data, preprocessing the data, building a regression model, and evaluating the model's performance.

• Binomial Logistic Regression Template: This template provides a starting point for the creation of a binomial logistic regression model in Python. It includes code for importing data, doing exploratory data analysis, constructing the regression model and evaluating the model performance.

• Naive Bayes Classification Model: This template provides a starting point for the construction of a Naive Bayes model in Python. The template includes code for loading relevant packages, importing data, doing EDA, constructing the model and evaluating the results.

• K-means Template: This template provides a starting point for the construction of a k-mean partitioning model in Python. The notebook includes code for importing libraries, importing data, doing exploratory data analysis, constructing the k-means model and evaluating the model performance.

• Decision Tree Template: This notebook template provides a starting point for the construction of a Decision Tree model in Python. The notebook includes code for loading packages, importing data, doing EDA, constructing and evaluating the model.

• Random Forest Template: This notebook template provides a starting point for the construction of a Random Forest classification model in Python. The notebook includes code for loading packages, importing data, doing EDA, constructing and evaluating the model.

• Gradient Boost Template: This notebook template provides a starting point for the construction of a Gradient Boost classification model in Python. The notebook includes code for loading packages, importing data, doing EDA, constructing and evaluating the model.

Contributing

If you have a notebook template that you would like to contribute, please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b new-template)
3. Add your template to the templates/ directory
4. Update the README.md file to include your new template in the list of templates
5. Commit your changes (git commit -am 'Add new template')
6. Push to the branch (git push origin new-template)
7. Create a new Pull Request