app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Function to load data
@st.cache_data
def load_data_from_url(url):
    data = pd.read_csv(url)
    return data

# App title
st.title("House Price Prediction")

# Sidebar for user inputs
st.sidebar.header("Upload Your Data")
uploaded_file = st.sidebar.file_uploader("Upload a CSV file", type=["csv"])
data_url = st.sidebar.text_input("Or enter a URL to a CSV file")

# Load data
if uploaded_file:
    data = pd.read_csv(uploaded_file)
elif data_url:
    data = load_data_from_url(data_url)
else:
    st.write("Please upload a CSV file or enter a URL to a CSV file.")
    st.stop()

st.write("Dataset", data)

# Feature selection
st.sidebar.header("Configure Input Features")
selected_features = st.sidebar.multiselect("Select features", data.columns[:-1])

if selected_features:
    X = data[selected_features]
    y = data.iloc[:, -1]  # Assuming the last column is the target

    # Split data
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    # Train model
    model = LinearRegression()
    model.fit(X_train, y_train)

    # Prediction
    y_pred = model.predict(X_test)
    
    # Plot
    fig, ax = plt.subplots()
    ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
    ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=4)
    ax.set_xlabel('Actual')
    ax.set_ylabel('Predicted')
    ax.set_title('Actual vs Predicted')
    st.pyplot(fig)

    # Model performance
    st.write("Mean Squared Error", mean_squared_error(y_test, y_pred))
    st.write("R-squared Score", r2_score(y_test, y_pred))

    # Display results
    st.write("Selected Features", selected_features)
    st.write("Model Coefficients", model.coef_)
    st.write("Predictions", y_pred)
    st.write("Actual Values", y_test.values)