Created ModeL

app.py

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+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
+
+# Load dataset
+@st.cache_data
+def load_data():
+    df = pd.read_csv("car_data.csv")  # Replace with your dataset
+    return df
+
+def preprocess_data(df):
+    label_encoders = {}
+    for column in ["brand", "model", "fuel_type"]:  # Encode categorical features
+        le = LabelEncoder()
+        df[column] = le.fit_transform(df[column])
+        label_encoders[column] = le
+    return df, label_encoders
+
+# Train the model
+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)
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+    return model
+
+df = load_data()
+df, label_encoders = preprocess_data(df)
+model = train_model(df)
+
+# Streamlit UI
+st.title("Car Price Prediction App")
+st.sidebar.header("Enter Car Details")
+
+year = st.sidebar.slider("Year", int(df["year"].min()), int(df["year"].max()), 2015)
+mileage = st.sidebar.number_input("Mileage (in km)", min_value=0, max_value=500000, value=50000)
+
+brand = st.sidebar.selectbox("Brand", label_encoders["brand"].classes_)
+filtered_models = df[df["brand"] == label_encoders["brand"].transform([brand])[0]]["model"].unique()
+model_name = st.sidebar.selectbox("Model", label_encoders["model"].inverse_transform(filtered_models))
+fuel_type = st.sidebar.selectbox("Fuel Type", label_encoders["fuel_type"].classes_)
+
+if st.sidebar.button("Predict Price"):
+    brand_encoded = label_encoders["brand"].transform([brand])[0]
+    model_encoded = label_encoders["model"].transform([model_name])[0]
+    fuel_encoded = label_encoders["fuel_type"].transform([fuel_type])[0]
+    
+    input_data = np.array([[year, mileage, brand_encoded, model_encoded, fuel_encoded]])
+    predicted_price = model.predict(input_data)[0]
+    
+    st.write(f"### Predicted Car Price: ${predicted_price:,.2f}")

car_data.csv

@@ -0,0 +1,101 @@
+year,mileage,brand,model,fuel_type,price
+2022,114370,Ford,Escape,Hybrid,6674.87
+2005,218398,Ford,Focus,Petrol,48657.79
+2015,217329,Honda,Civic,Hybrid,55673.83
+2022,30173,Toyota,Camry,Hybrid,29420.31
+2014,21973,Toyota,Camry,Petrol,41318.21
+2014,132154,Toyota,Camry,Diesel,57053.43
+2017,65727,Nissan,Rogue,Diesel,51498.18
+2005,122186,Honda,Accord,Hybrid,58685.49
+2017,202461,Nissan,Rogue,Hybrid,16044.29
+2014,44157,Toyota,Corolla,Petrol,50544.38
+2013,230461,Toyota,RAV4,Electric,8595.83
+2012,49377,Ford,Escape,Electric,10107.73
+2023,10407,Nissan,Altima,Electric,32341.96
+2009,218313,Honda,Civic,Hybrid,7101.26
+2023,49655,Ford,Escape,Electric,50828.85
+2014,158137,Nissan,Sentra,Petrol,38753.23
+2008,85780,Honda,Accord,Petrol,50089.18
+2013,218923,Honda,CR-V,Electric,16676.43
+2019,169648,Nissan,Sentra,Petrol,57087.33
+2023,93206,Honda,CR-V,Electric,26363.71
+2008,237357,Toyota,Camry,Hybrid,32794.84
+2014,228055,Toyota,Camry,Petrol,18061.17
+2009,205737,Honda,Accord,Petrol,56591.04
+2023,21161,BMW,X5,Diesel,46022.34
+2014,236502,Ford,Fusion,Electric,33802.91
+2011,106454,BMW,X5,Hybrid,18870.85
+2021,149049,BMW,3 Series,Petrol,42588.23
+2020,161145,Toyota,RAV4,Petrol,17435.75
+2016,147931,Honda,CR-V,Hybrid,13650.18
+2014,228821,BMW,X5,Electric,54729.7
+2023,234720,Ford,Fusion,Diesel,45337.72
+2012,173181,Nissan,Altima,Diesel,8236.32
+2020,211188,Toyota,RAV4,Petrol,22071.91
+2008,162549,BMW,3 Series,Petrol,11114.1
+2017,97557,Toyota,Camry,Hybrid,42633.37
+2011,55172,BMW,X3,Hybrid,14257.55
+2021,188935,Honda,Accord,Diesel,26893.15
+2010,131149,Toyota,Camry,Petrol,16139.29
+2010,196634,Toyota,Corolla,Hybrid,53997.08
+2023,32603,BMW,X3,Hybrid,8230.34
+2012,232201,BMW,X5,Hybrid,48270.97
+2017,126971,Nissan,Sentra,Electric,43073.84
+2017,157109,Honda,Civic,Petrol,9965.82
+2016,137246,Ford,Focus,Hybrid,12194.14
+2013,97282,Nissan,Rogue,Petrol,14321.09
+2022,26755,Toyota,RAV4,Diesel,18314.11
+2015,195123,Ford,Focus,Electric,7668.99
+2006,83255,Ford,Focus,Diesel,43519.67
+2015,148711,Toyota,Corolla,Petrol,49230.81
+2005,188059,Honda,Civic,Diesel,17717.2
+2018,48615,Nissan,Altima,Electric,4836.54
+2007,147590,Toyota,Camry,Diesel,21724.47
+2018,45428,Nissan,Altima,Petrol,49927.27
+2019,215070,Ford,Fusion,Petrol,28441.83
+2014,228261,Nissan,Altima,Petrol,44328.07
+2016,184600,Honda,Civic,Petrol,34245.7
+2006,88461,BMW,3 Series,Diesel,19569.62
+2014,133896,Ford,Escape,Diesel,57019.9
+2017,61302,Nissan,Sentra,Petrol,26287.24
+2014,144269,Nissan,Altima,Electric,43487.28
+2022,244580,Honda,Civic,Diesel,28236.3
+2005,24100,Toyota,Camry,Electric,12674.1
+2007,118456,Nissan,Rogue,Hybrid,41507.96
+2020,20359,BMW,3 Series,Hybrid,46188.46
+2007,26897,BMW,3 Series,Petrol,29756.59
+2022,103680,Honda,CR-V,Hybrid,42218.02
+2022,207698,Toyota,Camry,Electric,23484.79
+2021,216329,Honda,Accord,Petrol,11671.72
+2020,142876,BMW,X3,Diesel,52032.58
+2005,191069,Toyota,Camry,Petrol,37422.52
+2014,60179,Ford,Focus,Hybrid,33384.69
+2016,202737,Toyota,Corolla,Electric,54489.42
+2007,71761,Nissan,Altima,Hybrid,5589.71
+2013,217915,Nissan,Sentra,Petrol,19861.67
+2020,66157,Nissan,Rogue,Petrol,31263.52
+2011,47636,Ford,Focus,Diesel,54826.2
+2016,239017,Toyota,RAV4,Electric,42639.12
+2008,85245,Honda,Civic,Diesel,36393.84
+2008,7924,Nissan,Sentra,Petrol,57417.32
+2018,80220,Honda,Civic,Hybrid,58809.73
+2014,117704,Honda,CR-V,Electric,49856.84
+2015,177025,Honda,CR-V,Hybrid,10376.35
+2005,35885,BMW,X3,Petrol,51045.28
+2016,196392,BMW,X5,Electric,41524.89
+2005,130887,Nissan,Altima,Hybrid,43061.41
+2023,73929,Ford,Focus,Diesel,5308.57
+2023,136653,Ford,Focus,Hybrid,25862.99
+2015,173398,Toyota,Camry,Diesel,37219.05
+2011,69238,Honda,CR-V,Diesel,4867.25
+2005,87947,Honda,CR-V,Diesel,11732.04
+2013,133510,Honda,Civic,Electric,12632.23
+2018,9968,Toyota,RAV4,Hybrid,28529.03
+2017,197988,Ford,Focus,Hybrid,39786.83
+2012,86434,Nissan,Sentra,Diesel,23234.51
+2008,222832,Honda,Civic,Diesel,38588.24
+2008,100769,Ford,Fusion,Diesel,42574.31
+2013,22411,Nissan,Rogue,Electric,43318.02
+2014,113829,Toyota,Camry,Electric,7845.3
+2007,202200,Nissan,Rogue,Diesel,14169.5
+2016,7334,Ford,Fusion,Petrol,39685.37