• 1. General info
• 2. Datasets
• 3. Packages
• 4. Environment
• 5. Team Members

This repository serves as a centralized storage location for resources related to the Python sprint , which includes webinar notebooks, various exercises and supplementary materials. Its purpose is to provide learners with easy access to all relevant content needed to enhance their learning experience throughout their time at ExploreAI Academy.

Projects are categorized under machine learning and consequently adhere to a Data Science process guide, which encompasses the following steps:

• Data collection
• Data cleaning and preparation
• Exploratory Data Analysis
• Feature Engineering
• Model building, Evaluation and Deployment.

The datasets used in this repo can be found here.

To carry out all the objectives for this repo, the following necessary dependencies were loaded:

• Pandas 2.2.2 and Numpy 1.26
• Matplotlib 3.8.4

It's highly recommended to use a virtual environment for your projects, there are many ways to do this; we've outlined one such method below. Make sure to regularly update this section. This way, anyone who clones your repository will know exactly what steps to follow to prepare the necessary environment. The instructions provided here should enable a person to clone your repo and quickly get started.

# create the conda environment
conda create --name <env>

# activate the virtual environment
conda activate <env>
# install the pip package
conda install pip
# install the requirements for this project
pip install -r requirements.txt

• Make a README
• GitHub Docs
• FreeCodeCamp