The Crop Recommendation Flask Web App is a web application that recommends the best crop to grow based on soil and climate conditions. The project involves building a machine learning model that can predict the crop yield based on several parameters such as soil pH, temperature, rainfall, humidity, and crop type. The machine learning model is then integrated into a Flask web application to provide farmers with a simple and easy-to-use tool for crop selection.

Here’s a general overview of the project:

1. Data collection: Collect soil and climate data from reliable sources such as the National Soil Information System and the National Oceanic and Atmospheric Administration (NOAA).
2. Data preprocessing: Clean and prepare the data for use in the machine learning model.
3. Feature selection: Select the most important features that can affect the crop yield, such as soil pH, temperature, rainfall, humidity, and crop type.
4. Model training: Train a machine learning model using the preprocessed data and the selected features.
5. Model evaluation: Evaluate the performance of the machine learning model to ensure it can accurately predict the crop yield.
6. Flask app development: Develop a Flask web application that allows users to input soil and climate parameters and get a recommendation for the best crop to grow.
7. Deployment: Deploy the web application to a server so that it can be accessed by users.

Overall, the Crop Recommendation Flask Web App project can be a valuable tool for farmers to increase their crop yield and improve their farming practices.

 Algorithm :

1. *Random Forest Classifier* is used for development of model.
2. Only three algorithms are used to predict the output. They are *Logistic Regression*, *XGBoost* and *Random Forest*.\
   1. Accuracy of the model using Logistic Regression is 95%.
   2. Accuracy of the model using Random Forest Classifier is 99%.
   3. Accuracy of the model using XGBoost Classifier is 99%.