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| author |   Biswakalyan Bhuyan <biswa@surgot.in>
| 2024-11-27 22:03:45 +0530 |
|---|---|---|
| committer | Biswakalyan Bhuyan <biswa@surgot.in>
| 2024-11-27 22:03:45 +0530 |
| commit | ab9359ae5b6fa18695df4bb3543672502452cd6f (patch) | |
| tree | 070fb44be77e45771c52f9a2eea1dabe31961ac3 | |
| parent | 5f85fe6603c6fbc68afa71d76c0b51ac8df6a41f (diff) | |
| download | autopredict-ab9359ae5b6fa18695df4bb3543672502452cd6f.tar.gz autopredict-ab9359ae5b6fa18695df4bb3543672502452cd6f.tar.bz2 autopredict-ab9359ae5b6fa18695df4bb3543672502452cd6f.zip | |
Added predict.py to predict from model
| -rw-r--r-- | predict.py | 9 |
1 files changed, 6 insertions, 3 deletions
@@ -4,6 +4,7 @@ import pandas as pd # Load the model mileage_model = joblib.load('mileage_predictor.pkl') price_model = joblib.load('price_predictor.pkl') +year_model = joblib.load('year_predictor.pkl') # Prepare input data for prediction def prepare_input(data_dict): @@ -20,12 +21,13 @@ def predict(input_data): prepared_data = prepare_input(input_data) mileage = mileage_model.predict(prepared_data)[0] price = price_model.predict(prepared_data)[0] + year = year_model.predict(prepared_data)[0] - return mileage, price + return mileage, price, int(year) # Sample data for prediction data = { - 'Year': 2018, + 'Year': 2022, 'Kilometers_Driven': 30000, 'Fuel_Type': 'Petrol', 'Transmission': 'Manual', @@ -38,7 +40,8 @@ data = { # Make prediction -predicted_mileage, predicted_price = predict(data) +predicted_mileage, predicted_price, predicted_year = predict(data) print(f"Predicted Mileage (Km/L): {predicted_mileage:.2f}") print(f"Predicted Price: ₹{predicted_price:,.2f} Lakhs") +print(f"Predicted Year: {predicted_year}") |