Machine Learning / Agriculture
Crop Recommendation System (Machine Learning)
Project Overview
An AI-driven predictive model that recommends the most suitable crops for cultivation based on soil metrics and environmental factors.
Detailed Case Study
This project leverages machine learning to assist in precision agriculture by recommending the best crop to plant based on specific environmental and soil parameters. By analyzing features such as Nitrogen (N), Phosphorous (P), Potassium (K) levels, temperature, humidity, pH value, and rainfall, the classification model accurately predicts the optimal crop for maximum yield. Built using Python, Scikit-Learn, and Pandas, this project demonstrates data preprocessing, exploratory data analysis (EDA), and the training of a multi-class classification algorithm to solve a real-world agricultural challenge.
Technologies Used
Python
Machine Learning
Data Science
Classification
Scikit-Learn
Precision Agriculture
AI