prathamrajbhar11/Praapthi-yield-prediction

🌾 Praapthi — Crop Yield Prediction Model

Part of the KissanSahyog agritech platform.

Praapthi (Sanskrit: प्राप्ति) means "that which is obtained / harvest".

Model Files

File	Description
yield_pipeline.pkl	Full sklearn pipeline (preprocessor + model)
rf_model.pkl	Random Forest base model artifact
le_crop.pkl	Label encoder for crop names
le_state.pkl	Label encoder for Indian states
le_dist.pkl	Label encoder for districts
cleaned_dataset.csv	Training dataset reference

Inputs

• Crop, State, District (categorical)
• Area (ha), Temperature, Humidity, Rainfall, N, P, K (numeric)

Output

• predicted_yield in kg/ha

Usage

import joblib
import pandas as pd

model = joblib.load("yield_pipeline.pkl")

# Inputs: Crop, State, District, Area, Temp, Humid, Rain, N, P, K
data = pd.DataFrame([[
    "Rice", "West Bengal", "PURBA BARDHAMAN", 
    500, 25.5, 80, 1500, 80, 40, 40
]], columns=[
    'Crop', 'State', 'District', 'Area', 'Temperature', 
    'Humidity', 'Rainfall', 'N', 'P', 'K'
])

prediction = model.predict(data)
print(f"Predicted Yield: {prediction[0]:.2f} kg/ha")