apple-price-predictor

Apple Price Prediction Model — Indian Market

Hybrid Prophet + ARIMA ensemble for forecasting Indian wholesale apple prices (₹/kg) and generating SELL / STORE recommendations for farmers and traders.

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Overview

This model forecasts apple mandi/wholesale prices 7 days ahead and recommends whether farmers or cold-storage traders should SELL immediately or STORE for better returns, accounting for Indian cold-storage costs (₹0.75/kg/day).

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Quickstart

from model.predict import predict_price

result = predict_price({
    "date":              "2026-03-07",
    "current_price":     120.0,       # ₹/kg (current wholesale price)
    "storage_time_days": 15,
    "apple_variety":     "Kinnauri",
    "region":            "Himachal Pradesh",
})

print(result)
# {
#   "predicted_price_7d": 127.50,
#   "recommendation":     "STORE",
#   "current_price":      120.0,
#   "storage_cost_7d":    5.25,
#   "breakeven_price":    125.25,
#   "currency":           "INR",
#   "confidence":         "hybrid Prophet+ARIMA (0.6/0.4)"
# }

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Repository Structure

apple-price-predictor/
├── data/
│   └── apple_price_dataset.csv     # 12,000-sample Indian market dataset
├── models/
│   ├── prophet_model.pkl           # Trained Prophet model
│   ├── arima_model.pkl             # Trained ARIMA model
│   ├── scaler.pkl                  # MinMaxScaler for feature normalization
│   └── metrics.json                # MAE / RMSE evaluation metrics (in ₹)
├── model/
│   └── predict.py                  # Inference wrapper
├── train.py                        # Full training pipeline
├── requirements.txt
└── README.md

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Indian Market Dataset

Synthetic dataset simulating realistic Indian apple market dynamics:

Price Drivers Modelled

Typical wholesale price range: ₹40 – ₹165/kg

• Maharaji (local): ₹40 – ₹80/kg
• Royal Delicious: ₹55 – ₹110/kg
• Golden Delicious: ₹60 – ₹115/kg
• Shimla: ₹65 – ₹130/kg
• Kinnauri (premium): ₹85 – ₹165/kg

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Feature Engineering

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Model Architecture

Prophet

• Yearly + weekly seasonality (multiplicative mode)
• Custom festival seasonality component (Fourier order 3)
• Changepoint prior scale: 0.05

ARIMA

• Auto parameter search via pmdarima
• Best order selected by AIC minimisation
• Stepwise search: p, d, q ∈ {0…3}

Hybrid Ensemble

final_prediction = (0.6 × prophet_prediction) + (0.4 × arima_prediction)

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Sell / Store Decision Engine

storage_cost_7d  = ₹0.75 × 7  =  ₹5.25/kg
breakeven_price  = current_price + storage_cost_7d

if predicted_price_7d > breakeven_price:
    recommendation = "STORE"
else:
    recommendation = "SELL"

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Training

pip install -r requirements.txt
python train.py

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Evaluation Metrics (₹/kg)

(Exact values saved in models/metrics.json)

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License

MIT License — free for research and commercial use.