From ab9359ae5b6fa18695df4bb3543672502452cd6f Mon Sep 17 00:00:00 2001
From: Biswakalyan Bhuyan <biswa@surgot.in>
Date: Wed, 27 Nov 2024 22:03:45 +0530
Subject: Added predict.py to predict from model

---
 predict.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/predict.py b/predict.py
index d7f3a9b..5d470b6 100644
--- a/predict.py
+++ b/predict.py
@@ -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}")
-- 
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