Update app.py

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import pickle
-from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import LabelEncoder
 
@@ -20,11 +19,16 @@ def preprocess_data(df):
         label_encoders[column] = le
     return df, label_encoders
 
-# Train the model
+# Train the model (Manual Data Split)
 def train_model(df):
-    X = df[["year", "mileage", "brand", "model", "fuel_type"]]
-    y = df["price"]
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    X = df[["year", "mileage", "brand", "model", "fuel_type"]].values
+    y = df["price"].values
+
+    # Manual 80-20 split
+    split_index = int(0.8 * len(X))  # 80% for training, 20% for testing
+    X_train, X_test = X[:split_index], X[split_index:]
+    y_train, y_test = y[:split_index], y[split_index:]
+
     model = LinearRegression()
     model.fit(X_train, y_train)
     return model