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| import streamlit as st | |
| import numpy as np | |
| # Model | |
| import seaborn as sns | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import mean_squared_error, r2_score | |
| # Load dataset | |
| df = sns.load_dataset('mpg') | |
| df.dropna(inplace=True) # Dropping missing values | |
| # Selecting relevant features for simplicity | |
| features = df[['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model_year']] | |
| target = df['mpg'] | |
| # Splitting the dataset into training and testing sets | |
| X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42) | |
| # Create and train the model | |
| model = LinearRegression() | |
| model.fit(X_train, y_train) | |
| # Write a function to take user inputs | |
| def user_input_features(): | |
| cylinders = st.sidebar.slider('Cylinders', 3, 8, 4) | |
| displacement = st.sidebar.number_input('Displacement') | |
| horsepower = st.sidebar.number_input('Horsepower') | |
| weight = st.sidebar.number_input('Weight') | |
| acceleration = st.sidebar.number_input('Acceleration') | |
| model_year = st.sidebar.slider('Model Year', 70, 82, 76) | |
| data = {'cylinders': cylinders, | |
| 'displacement': displacement, | |
| 'horsepower': horsepower, | |
| 'weight': weight, | |
| 'acceleration': acceleration, | |
| 'model_year': model_year} | |
| features = pd.DataFrame(data, index=[0]) | |
| return features | |
| # Main | |
| st.write(""" | |
| # Simple MPG Prediction App | |
| This app predicts the **Miles Per Gallon (MPG)** of your car! | |
| """) | |
| # User input features | |
| input_df = user_input_features() | |
| # Display the user input features | |
| st.subheader('User Input features') | |
| st.write(input_df) | |
| # Predict and display the output | |
| st.subheader('Prediction') | |
| prediction = model.predict(input_df) | |
| st.write(prediction) | |