Welcome to the Fruit-Price-Forecast repository! In this project, I have developed a model to forecast the prices of fruits and vegetables in India. The data used for this project is sourced from Kaggle's Vegetable and Fruits Price in India dataset. I have reprocessed the data to prepare it for building artificial intelligence models.

I have utilized a Random Forest model, which has demonstrated remarkable performance. Here are the details of the model's accuracy:

• Training Accuracy: 99.9%
• Test Accuracy: 99.1%

To use this model and predict the price of a fruit or vegetable, follow these steps:

• Download the encoder.pkl file attached to this repository. This file contains the list of all fruits and vegetables the model was trained on.

• Use the following code snippet to load the encoder in your Python environment:

  import joblib
  
  file_path = 'encoder.pkl'
  loaded_encoder = joblib.load(file_path)

• Fruit or Vegetable Name: Ensure the name of the fruit or vegetable you want to predict the price for is in the list of classes from the encoder.
• Date: Specify the date for which you want to forecast the price.

Utilize the loaded encoder and the specified date to predict the price using the trained model.

• The pre-trained Random Forest model can be downloaded from RF Model.
• Download the data after reprocessing it to be ready to train models Pre-Processed Data.

If you have any questions or need further assistance, feel free to reach out to me:

• Telegram: Mustafa Mohammad
• Facebook: Mustafa Mohammadhttps://www.facebook.com/profile.php?id=100049592914479)

Happy predicting!