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authorLibravatarLibravatar Biswakalyan Bhuyan <biswa@surgot.in> 2024-11-27 20:03:53 +0530
committerLibravatarLibravatar Biswakalyan Bhuyan <biswa@surgot.in> 2024-11-27 20:03:53 +0530
commit6670a8dfc419cf3d1f60427774de99e7010987e5 (patch)
treef554360fe4cb7dfcc091643979e8538eb4e17a03 /predict.py
parent7947a931d66697bca3af1003703296ee0edcdfd0 (diff)
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Added the Feature to predict price
Diffstat (limited to 'predict.py')
-rw-r--r--predict.py48
1 files changed, 33 insertions, 15 deletions
diff --git a/predict.py b/predict.py
index 707ef92..d7f3a9b 100644
--- a/predict.py
+++ b/predict.py
@@ -2,25 +2,43 @@ import joblib
import pandas as pd
# Load the model
-model = joblib.load('model.pkl')
+mileage_model = joblib.load('mileage_predictor.pkl')
+price_model = joblib.load('price_predictor.pkl')
+
+# Prepare input data for prediction
+def prepare_input(data_dict):
+ # Prepare a DataFrame from a dictionary of input data
+ input_df = pd.DataFrame([data_dict])
+ input_df['Car_Age'] = 2024 - input_df['Year']
+ input_df.drop(columns=['Year'], inplace=True)
+
+ return input_df
+
+# Make prediction
+def predict(input_data):
+ # Predict mileage and price for a given input.
+ prepared_data = prepare_input(input_data)
+ mileage = mileage_model.predict(prepared_data)[0]
+ price = price_model.predict(prepared_data)[0]
+
+ return mileage, price
# Sample data for prediction
data = {
- 'Kilometers_Driven': [50000],
- 'Fuel_Type': ['Petrol'],
- 'Transmission': ['Manual'],
- 'Owner_Type': ['First'],
- 'Engine CC': [1197],
- 'Power': [82],
- 'Seats': [5],
- 'Car_Age': [6],
- 'Location': ['Mumbai']
+ 'Year': 2018,
+ 'Kilometers_Driven': 30000,
+ 'Fuel_Type': 'Petrol',
+ 'Transmission': 'Manual',
+ 'Owner_Type': 'First',
+ 'Location': 'Mumbai',
+ 'Engine CC': 1200,
+ 'Power': 85,
+ 'Seats': 5
}
-# Convert to DataFrame
-input_data = pd.DataFrame(data)
+# Make prediction
-# Predict
-predicted_mileage = model.predict(input_data)
+predicted_mileage, predicted_price = predict(data)
-print(f"Predicted Mileage: {predicted_mileage[0]:.2f} Km/L")
+print(f"Predicted Mileage (Km/L): {predicted_mileage:.2f}")
+print(f"Predicted Price: ₹{predicted_price:,.2f} Lakhs")