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Praapthi-yield-prediction
prathamrajbhar11/Praapthi-yield-prediction
Praapthi-yield-prediction is a machine learning model from prathamrajbhar11. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for sklearn. The card lists the license as mit.
Part of the KissanSahyog agritech platform.
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Public
Updated Apr 6, 2026
Repo size
631 MB
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Repo makeup
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.pkl631 MB · 99%
At a glance
- Library
- sklearn
- License
- mit
- Access
- Public
- Created
- Apr 6, 2026
- Updated
- Apr 6, 2026
- SHA
- 96f83eff
Model card
From the Hugging Face model README
🌾 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_yieldin 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")
- Library
- sklearn
- License
- mit
- Languages
- en, hi
- Created
- Apr 6, 2026
- Updated
- Apr 6, 2026