NDV_Code_By_KowsheekK_Real Estate Dashboard

NDV_Code_By_KowsheekKella_Capstone_Project/Model_train.py

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+# train_model.py
+
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+import joblib
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load dataset
+df = pd.read_csv("real_estate.csv")
+
+# Check and fill missing values
+df.fillna(df.mean(numeric_only=True), inplace=True)
+
+# EDA Plots
+plt.figure(figsize=(8, 6))
+sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
+plt.title("Feature Correlation Heatmap")
+plt.savefig("eda_heatmap.png")
+
+# Selected features for simplicity
+features = ["RM", "LSTAT", "PTRATIO", "TAX", "DIS"]
+target = "MEDV"
+X = df[features]
+y = df[target]
+
+# Scale features
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+# Split data
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+
+# Train model
+model = RandomForestRegressor(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+
+# Predict & evaluate
+y_pred = model.predict(X_test)
+print("RMSE:", np.sqrt(mean_squared_error(y_test, y_pred)))
+print("MAE:", mean_absolute_error(y_test, y_pred))
+print("R² Score:", r2_score(y_test, y_pred))
+
+# Save model and scaler
+joblib.dump(model, "real_estate_model.pkl")
+joblib.dump(scaler, "real_estate_scaler.pkl")

NDV_Code_By_KowsheekKella_Capstone_Project/app.py

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+import os
+import streamlit as st
+import numpy as np
+import pandas as pd
+import joblib
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+
+# -----------------------------
+# Auto-train model if not found
+# -----------------------------
+model_path = "real_estate_model.pkl"
+scaler_path = "real_estate_scaler.pkl"
+
+if not os.path.exists(model_path) or not os.path.exists(scaler_path):
+    st.warning("Training model... Please wait ⏳")
+
+    # Load dataset (assumed to be in the same folder)
+    csv_path = os.path.join(os.path.dirname(__file__), "real_estate.csv")
+    df = pd.read_csv(csv_path)
+
+    # Use selected features from dataset
+    features = ['RM', 'LSTAT', 'PTRATIO', 'TAX', 'DIS']
+    X = df[features]
+    y = df['MEDV']  # Target is median house price in $1000s
+
+    # Preprocess
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)
+
+    # Train model
+    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X_train, y_train)
+
+    # Save model and scaler
+    joblib.dump(model, model_path)
+    joblib.dump(scaler, scaler_path)
+
+    st.success("Model trained and ready ✅")
+else:
+    # Load saved model and scaler
+    model = joblib.load(model_path)
+    scaler = joblib.load(scaler_path)
+
+# -----------------------------
+# Streamlit UI
+# -----------------------------
+
+st.set_page_config(page_title="🏠 Real Estate Price Dashboard", layout="centered")
+st.title("🏠 Real Estate Price Prediction Dashboard")
+st.markdown("Enter the property details below to predict the house price (in ₹).")
+
+# Input sliders
+rm = st.slider("How many rooms?", min_value=1.0, max_value=10.0, value=6.0)
+lstat = st.slider("% of low-income people nearby (0 = rich, 40 = poor)", min_value=0.0, max_value=40.0, value=12.0)
+ptratio = st.slider("Students per teacher (10 = good, 30 = crowded)", min_value=10.0, max_value=30.0, value=18.0)
+tax = st.slider("Property tax (100 = low, 800 = high)", min_value=100, max_value=800, value=300)
+dis = st.slider("Distance to major services (0 = near, 15 = far)", min_value=0.0, max_value=15.0, value=4.0)
+
+input_data = np.array([[rm, lstat, ptratio, tax, dis]])
+
+# Predict
+if st.button("💰 Predict Price"):
+    scaled_input = scaler.transform(input_data)
+    prediction = model.predict(scaled_input)[0]
+    price_inr = prediction * 1000 * 83  # Convert to INR
+    st.success(f"💸 Predicted House Price: ₹ {price_inr:,.2f}")

NDV_Code_By_KowsheekKella_Capstone_Project/requirements.txt

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+pandas
+numpy
+matplotlib
+seaborn
+scikit-learn
+joblib
+streamlit