| --- |
| 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 |
|
|
| ```python |
| 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']}") |
| ``` |
