This repository is designed for collaborative learning and hands-on data science experimentation through column-wise feature engineering. The primary goal is to foster a collaborative environment where participants can enhance their skills in data preprocessing, feature engineering, and collaborative coding practices. 💻🤝
The main objective of this repository is to facilitate collaborative learning by dividing a dataset among participants and assigning specific columns to each individual or team. Participants will be responsible for performing feature engineering on their assigned columns, contributing to the overall data preparation process. 📊🧠 By splitting the work across participants, this project aims to provide a practical learning experience in data preprocessing, feature engineering, and collaborative coding practices while also promoting knowledge sharing and teamwork. 🌐👥
The data used in this project has been collected from the following sources:
1.ICRISAT (International Crops Research Institute for the Semi-Arid Tropics)
2.HMIS (Health Management Information System)
The data includes information on fertilizer usage, rainfall, crop production and health records from various regions over the past 20 years. 🌻💨💊📆
1.Participants will be assigned specific columns from the dataset for feature engineering. 📝
2.Each participant or team will create a new branch in the repository to work on their assigned columns. 🌳
3.Participants will perform data exploration, cleaning, and feature engineering on their assigned columns using Jupyter Notebooks or Python scripts. 📂💻
4.Once the feature engineering is complete, participants will create a pull request to merge their changes into the main branch. 🔃
5.Code reviews and discussions will be conducted to ensure code quality, collaborative learning, and knowledge sharing. 🔍💬
6.After all feature engineering tasks are completed, the team can proceed with further data analysis and modeling. ⚙️🧪