Crop Yield Prediction Model

The Crops Recommendation Model is a machine learning-based system designed to suggest the most suitable crops for cultivation based on environmental and soil conditions. By analyzing factors such as soil type, temperature, rainfall, pH levels, and nutrient composition, the model helps farmers optimize crop selection for higher yield and sustainability.

Key Features:
Data Processing & Feature Engineering: Cleans and normalizes agricultural data, handling missing values and categorical variables.
Machine Learning Algorithms: Uses Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks for crop prediction.
Model Evaluation: Measures accuracy using precision, recall, confusion matrix, and F1-score.
Deployment: Integrated with Flask, FastAPI, or a mobile app for real-time recommendations to farmers.
This model supports precision agriculture, enhances productivity, and promotes sustainable farming practices by recommending the best cro…

The Crops Recommendation Model is a machine learning-based system designed to suggest the most suitable crops for cultivation based on environmental and soil conditions. By analyzing factors such as soil type, temperature, rainfall, pH levels, and nutrient composition, the model helps farmers optimize crop selection for higher yield and sustainability.

Key Features:
Data Processing & Feature Engineering: Cleans and normalizes agricultural data, handling missing values and categorical variables.
Machine Learning Algorithms: Uses Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks for crop prediction.
Model Evaluation: Measures accuracy using precision, recall, confusion matrix, and F1-score.
Deployment: Integrated with Flask, FastAPI, or a mobile app for real-time recommendations to farmers.
This model supports precision agriculture, enhances productivity, and promotes sustainable farming practices by recommending the best crops for specific environmental conditions.