Agriculture Yield Prediction Using Data Analytics Techniques
Plag ProAgritech & Agricultural Data AnalyticsProject Objectives:
Develop an agricultural analytics system that predicts crop yield based on soil conditions, weather patterns, and historical production data. The platform will assist farmers and policymakers in improving agricultural planning and productivity.
Project Tasks:
Collect crop yield, soil, and weather datasets.
Clean and preprocess agricultural data.
Perform correlation analysis between yield and environmental factors.
Engineer predictive features.
Implement regression models for yield prediction.
Evaluate performance using R-squared and RMSE.
Visualize yield trends geographically.
Develop dashboards for farmers.
Optimize prediction accuracy.
Document findings and agricultural insights.
Educational Qualifications
B.TechB.EBCAMCA
Required Skills
Data Cleaning And Preprocessing Using Python (Pandas, Numpy)Statistical Correlation And Feature Engineering TechniquesRegression Modeling Using Libraries Such As Scikit-LearnModel Evaluation Using R-Squared, Rmse, And Cross-ValidationDashboard Development Using Tools Like Tableau Or Microsoft Power Bi
