Here's my third project in Machine Learning for classification problems. This project utilizes the advancement in technology to help the farmer community.
The objectives are:
- The aim of this project is to develop a robust and accurate Machine Learning model that can predict the most suitable crop(s) to plant in a given geographical area based on a set of environmental and soil conditions.
- This prediction aims to assist farmers and agricultural stakeholders in making informed decisions to maximize yield, optimize resource use, and promote sustainable farming practices.
STEPS INVOLVED: Data Collection Preprocessing EDA Model Selection and Training Model Evaluation Hyperparameter Tuning Testing with outside values
Model was build using KNN, Decision tree, Random Forest, Gradient boosting, Adaboost, SVC. With highest accuracy score of 0.9367 Support vector machine SVC has been finalized as the best model for this project.
Data-driven decision-making is the cornerstone of effective crop recommendation strategies, paving the way for a more efficient, resilient, and sustainable agricultural future.