---
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']}")
```