< Description />
The Crop Yield Enhancement Model is a machine learning-based system designed to optimize farming practices and improve agricultural productivity. By analyzing key factors such as soil health, weather conditions, irrigation patterns, fertilizer application, and pest control measures, the model provides data-driven recommendations to maximize crop yield.
Key Features:
Data Processing & Feature Engineering: Cleans and structures agricultural data, considering soil composition, climate trends, and farming techniques.
Machine Learning Algorithms: Utilizes Random Forest, XGBoost, Deep Learning, and Reinforcement Learning to optimize farming decisions.
Model Evaluation: Measures effectiveness using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and crop yield improvement rates.
Deployment: Integrated with Flask, FastAPI, or IoT-based smart farming systems for real-time decision-making.
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My passion for data science and machine learning stems from my curiosity about extracting meaningful insights from data to solve real-world challenges. Over the past two years, I have actively engaged in research projects, competitions, and internships, continuously developing m…