crop price prediction using machinelearning | agriculture machinelearning |ieee machinelearning 2024
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🌾 Crop Price Prediction Using Machine Learning 🌾
In this video, we walk you through our complete project on predicting crop prices using machine learning. From data preprocessing to building and evaluating different models, we cover everything step by step to give you a comprehensive understanding of the process.
What's Covered:
Dataset Preparation: Acquiring and cleaning the crop price dataset.
Data Preprocessing: Handling missing values, normalizing data, and preparing it for modeling.
Feature Selection: Using wrapper techniques and Lasso regression to select the best features.
Model Building: Implementing multiple machine learning models like Random Forest, XGBoost, SVR, AdaBoost, and Linear Regression. We achieved a 99% accuracy with the Random Forest model!
Frontend & Backend Integration: Demonstrating how we built a user-friendly interface using HTML, CSS, and Flask to interact with our prediction model.
Why Watch? Whether you're a student, researcher, or someone interested in machine learning applications in agriculture, this video provides valuable insights and practical knowledge.
📩 Interested in the full code, detailed documentation, or mentorship? Contact us for more information! We're here to help you bring your own machine learning projects to life.
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