README.md

metadata

license: apache-2.0
language:
  - en
metrics:
  - accuracy
base_model:
  - distilbert/distilbert-base-uncased-finetuned-sst-2-english
library_name: sklearn

Potato Price Prediction Model

This model predicts potato prices based on various features such as arrival quantity, temperature, humidity, and historical price data.

Input Features

• Date: Date of prediction (format: YYYY-MM-DD)
• ArrivalQuantity: Quantity of potatoes arriving at the market
• Temperature: Temperature on the given date
• Humidity: Humidity on the given date
• Wind direction: Wind direction on the given date
• Events: Any significant events on the given date
• Impacts: Any significant impacts on the given date
• PriceLag1: Previous day's price
• PriceLag7: Price from 7 days ago
• PriceRollingMean7: 7-day rolling mean price
• PriceRollingStd7: 7-day rolling standard deviation of price
• PrevWeekAvgPrice: Average price of the previous week

Output

The model returns a predicted potato price for the given input features.

Usage

from potato_price_model import predictor

input_data = {
    'Date': '2024-09-14',
    'ArrivalQuantity': 1000,
    'Temperature': 25,
    'Humidity': 60,
    'Wind direction': 180,
    'Events': 'Normal day',
    'Impacts': 'No significant impacts',
    'PriceLag1': 50,
    'PriceLag7': 48,
    'PriceRollingMean7': 49,
    'PriceRollingStd7': 2,
    'PrevWeekAvgPrice': 49
}

result = predictor.predict(input_data)
print(f"Predicted potato price: {result['predicted_price']}")