Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.datasets import fetch_openml
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error, r2_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Page setup
+st.set_page_config(page_title="Explore Linear Regression", layout="wide")
+st.title("🏡 Linear Regression with the Boston Housing Dataset")
+
+# Intro
+st.markdown("""
+## 📘 What is Linear Regression?
+
+Linear Regression models the relationship between a continuous outcome and one or more input variables (features).
+
+**Equation:**
+\[
+\\hat{y} = w_1x_1 + w_2x_2 + ... + w_nx_n + b
+\]
+
+It tries to find the line (or hyperplane) that best fits the data.
+
+---  
+""")
+
+# Load dataset from OpenML (Boston housing)
+@st.cache_data
+def load_data():
+    boston = fetch_openml(name="boston", version=1, as_frame=True)
+    df = boston.frame
+    return df
+
+df = load_data()
+
+st.subheader("🏠 Dataset: Boston Housing Prices")
+st.markdown("This dataset contains information about houses in Boston suburbs and aims to predict the **median value of owner-occupied homes**.")
+st.dataframe(df.head(), use_container_width=True)
+
+# Feature selection
+target_col = "MEDV"
+X = df.drop(columns=target_col)
+y = df[target_col]
+
+# Feature scaling
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+
+# Model training
+model = LinearRegression()
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+
+# Evaluation
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+
+st.success(f"📏 Model Performance: R² = {r2:.2f}, MSE = {mse:.2f}")
+
+# Feature coefficients
+st.markdown("### 🔍 Coefficients (Feature Importance)")
+coef_df = pd.DataFrame({
+    "Feature": X.columns,
+    "Coefficient": model.coef_
+}).sort_values(by="Coefficient", key=abs, ascending=False)
+
+st.dataframe(coef_df, use_container_width=True)
+
+# Actual vs Predicted Plot
+st.markdown("### 📈 Actual vs Predicted Home Prices")
+fig1, ax1 = plt.subplots(figsize=(8, 5))
+sns.scatterplot(x=y_test, y=y_pred, ax=ax1, alpha=0.7)
+ax1.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], '--r')
+ax1.set_xlabel("Actual MEDV")
+ax1.set_ylabel("Predicted MEDV")
+ax1.set_title("Actual vs Predicted Home Values")
+st.pyplot(fig1)
+
+# Residuals Plot
+st.markdown("### 🔧 Residual Plot (Errors)")
+residuals = y_test - y_pred
+fig2, ax2 = plt.subplots(figsize=(8, 5))
+sns.histplot(residuals, kde=True, ax=ax2, color="purple")
+ax2.set_title("Distribution of Residuals")
+ax2.set_xlabel("Error (Actual - Predicted)")
+st.pyplot(fig2)
+
+# Summary
+st.markdown("""
+---
+## 📚 Key Takeaways
+
+- **Linear Regression** is great for understanding relationships and making simple predictions.
+- **Coefficients** show how each feature affects the target.
+- **Residuals** help assess how well the model fits the data.
+
+### ✅ Use Linear Regression when:
+- The outcome is **continuous**
+- There’s a **linear trend**
+- You need **interpretability** over complexity
+
+🎯 *Pro Tip:* Try removing or combining features and observe how it affects accuracy and residuals!
+""")