Jupyter

This repository contains various Jupyter notebooks showcasing different data science and machine learning projects. Each notebook explores different datasets and techniques, providing detailed analyses and model implementations.

Features

• Various Jupyter notebooks for machine learning tasks.
• Exploratory data analysis and visualization.
• Model training and evaluation.

Usage

If pipenv is intalled, skip this step

pip install pipenv

To activate virtual environment

pipenv shell 

To run the notebooks:

jupyter notebook

If you want to have an interactive shell-like session in your .py file (In VS-Code):

Make sure you have two extensions installed: Python and Jupyter

# %%
print("This is the first code cell.")

with every # %% a new cell starts, and you can run debug them accordingly.

Description about various files:

• Jupyter_Basics.ipynb: An introduction to Jupyter notebooks, demonstrating basic features and functionalities.
• ML_Basics.ipynb: Covers fundamental machine learning concepts, including algorithms and evaluation metrics.
• R_Basics.ipynb: Provides an overview of R programming basics and its application in data analysis.
• Untitled.ipynb: Demonstrates the use of map, filter, lambda, and reduce functions in Python, along with a visualization of a binomial distribution using Seaborn.
• Pipfile and Pipfile.lock: Contains all the libraries required to be installed and activated in a virtual env.