Crop Recommendation System Project Machine Learning | AI-Powered Crop Selection | ML Projects
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Updated Source Code: https://github.com/611noorsaeed/Crop-...
Dataset: https://www.kaggle.com/datasets/athar...
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In this project, we built a crop recommendation system using machine learning algorithms and integrated it into a web application using the Flask framework. The system takes in input data such as soil type, rainfall, temperature, and humidity, and recommends the best crop that can be grown in that particular region based on the machine learning model trained on historical data.
The crop recommendation system is useful for farmers who want to maximize their crop yield and minimize crop failure by selecting the best crop based on the conditions of their land. The system uses machine learning algorithms such as decision trees, random forests, and support vector machines to make predictions and recommend the best crop for the given input data.
Timestamps:
00:00:00 - Introduction To Website and Crop system
00:03:14 - Loading Dataset and Jupyter notebook code
00:11:18 - Exploring Crop Dataset Features
00:16:46 - Encoding Crops Classes
00:21:06 - Train Test split
00:24:56 - Scaling and Normalization
00:27:54 - Training Different ML Classifiers
00:34:33 - Recommendations (predictions)
00:45:06 - Deployment: App.py file
00:45:06 - UI Design and Creation, Integrations, Results Etc
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