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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)
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