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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.pipeline import Pipeline
+from sklearn.linear_model import LinearRegression
+
+# Load pre-trained model
+with open("model.pkl", "rb") as file:
+    pipeline = pickle.load(file)
+
+# Define the feature columns
+feature_columns = [
+    "year",
+    "mileage",
+    "tax",
+    "mpg",
+    "engineSize",
+    "transmission",
+    "fuelType",
+    "Manufacturer",
+]
+
+
+def predict_price(
+    year, mileage, tax, mpg, engineSize, transmission, fuelType, Manufacturer
+):
+    input_df = pd.DataFrame(
+        [[year, mileage, tax, mpg, engineSize, transmission, fuelType, Manufacturer]],
+        columns=feature_columns,
+    )
+    prediction = pipeline.predict(input_df)
+    return prediction[0][0]
+
+
+# Streamlit app layout
+st.write("Enter the details of the car to predict its price:")
+
+# Input fields
+year = st.number_input("Year", min_value=1900, max_value=2100, value=2010)
+mileage = st.number_input("Mileage", min_value=0, value=50000)
+tax = st.number_input("Tax (£)", min_value=0, value=100)
+mpg = st.number_input("MPG", min_value=0, value=50)
+engineSize = st.number_input("Engine Size (L)", min_value=0.0, value=2.0)
+transmission = st.selectbox(
+    "Transmission", options=["Automatic", "Semi-Auto", "Manual"]
+)
+fuelType = st.selectbox("Fuel Type", options=["Petrol", "Diesel", "Electric", "Hybrid"])
+Manufacturer = st.selectbox(
+    "Manufacturer",
+    options=[
+        "toyota",
+        "hyundi",
+        "ford",
+        "BMW",
+        "Audi",
+        "merc",
+        "volkswagen",
+        "vauxhall",
+    ],
+)
+
+# Button to predict
+if st.button("🔮 Predict Price"):
+    price = predict_price(
+        year, mileage, tax, mpg, engineSize, transmission, fuelType, Manufacturer
+    )
+    st.write(f"The predicted price of the car is £{price:.2f}")
+
+# Developer Info
+
+st.sidebar.title("🚗 Car Price Predictor")
+st.sidebar.subheader("About the Developer")
+st.sidebar.markdown(
+    "Developed by [Tajeddine Bourhim](https://tajeddine-portfolio.netlify.app/)."
+)
+st.sidebar.markdown(
+    "[![GitHub](https://img.shields.io/badge/GitHub-Profile-blue?logo=github)](https://github.com/scorpionTaj)"
+)
+st.sidebar.markdown(
+    "[![LinkedIn](https://img.shields.io/badge/LinkedIn-Profile-blue?logo=linkedin)](https://www.linkedin.com/in/tajeddine-bourhim/)"
+)
+st.sidebar.subheader("📚 About This App")
+st.sidebar.markdown(
+    "This app uses a machine learning model to predict the price of a car based on various features."
+)
+st.sidebar.markdown(
+    "Model trained using historical car price data and includes features like year, mileage, and more."
+)

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:84816f05306c9974b8e19d920b5d529af65b63bbc522e18f952fa5f4fc2d2fd5
+size 2883

requirements.txt

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+streamlit==1.37.1
+pandas==2.2.2
+numpy==2.0.1
+scikit-learn==1.5.1