From 37e3dda489a2371882d1a1a900a9664350bb6a37 Mon Sep 17 00:00:00 2001
From: Rishika S <142384026+rishika712@users.noreply.github.com>
Date: Fri, 11 Jul 2025 18:37:11 +0530
Subject: [PATCH 1/4] NDV_Code_By_RishikaS_House_Price_Prediction
capstone project
---
.../app.py | 134 ++++++++++++++++++
1 file changed, 134 insertions(+)
create mode 100644 NDV_Code_By_RishikaS_House_Price_Prediction/app.py
diff --git a/NDV_Code_By_RishikaS_House_Price_Prediction/app.py b/NDV_Code_By_RishikaS_House_Price_Prediction/app.py
new file mode 100644
index 000000000..9be1045ac
--- /dev/null
+++ b/NDV_Code_By_RishikaS_House_Price_Prediction/app.py
@@ -0,0 +1,134 @@
+import streamlit as st
+import pandas as pd
+import joblib
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load model and encoders
+model = joblib.load(r"C:\Users\RISHIKA\Downloads\model.pkl")
+encoders = joblib.load(r"C:\Users\RISHIKA\Downloads\encoders.pkl")
+
+# Set page config and background styling
+st.set_page_config(page_title="🏡 Real Estate Price Predictor", layout="centered")
+
+# Custom background and header using updated Streamlit selector
+st.markdown(
+ """
+
+ """,
+ unsafe_allow_html=True
+)
+
+# Title
+st.title("🏠 Real Estate Price Prediction App")
+st.markdown("Use the form below to get an estimated price for your dream home.")
+
+# Form layout
+with st.form("prediction_form"):
+ st.subheader("🔍 Property Details")
+ col1, col2 = st.columns(2)
+
+ with col1:
+ area = st.number_input("📐 Area (sq. ft)", min_value=100)
+ bedrooms = st.slider("🛏️ Bedrooms", 1, 10)
+ bathrooms = st.slider("🛁 Bathrooms", 1, 10)
+ stories = st.selectbox("🏢 Stories", [1, 2, 3, 4])
+ parking = st.slider("🚗 Parking Spaces", 0, 4)
+ mainroad = st.selectbox("🛣️ Main Road", ["Yes", "No"])
+
+ with col2:
+ guestroom = st.selectbox("🧳 Guest Room", ["Yes", "No"])
+ basement = st.selectbox("🏚️ Basement", ["Yes", "No"])
+ hotwaterheating = st.selectbox("🔥 Hot Water Heating", ["Yes", "No"])
+ airconditioning = st.selectbox("❄️ Air Conditioning", ["Yes", "No"])
+ prefarea = st.selectbox("📍 Preferred Area", ["Yes", "No"])
+ furnishingstatus = st.selectbox("🛋️ Furnishing Status", ["Unfurnished", "Semi-Furnished", "Furnished"])
+ location = st.selectbox("🌆 Location", ["downtown", "suburb", "countryside"])
+
+ submitted = st.form_submit_button("🔮 Predict Price")
+
+ if submitted:
+ # Encode input using saved encoders
+ input_dict = {
+ "area": area,
+ "bedrooms": bedrooms,
+ "bathrooms": bathrooms,
+ "stories": stories,
+ "mainroad": encoders["mainroad"].transform([mainroad.lower()])[0],
+ "guestroom": encoders["guestroom"].transform([guestroom.lower()])[0],
+ "basement": encoders["basement"].transform([basement.lower()])[0],
+ "hotwaterheating": encoders["hotwaterheating"].transform([hotwaterheating.lower()])[0],
+ "airconditioning": encoders["airconditioning"].transform([airconditioning.lower()])[0],
+ "parking": parking,
+ "prefarea": encoders["prefarea"].transform([prefarea.lower()])[0],
+ "furnishingstatus": encoders["furnishingstatus"].transform([furnishingstatus.lower()])[0],
+ "location": encoders["location"].transform([location.lower()])[0]
+ }
+
+ input_df = pd.DataFrame([input_dict])
+ prediction = model.predict(input_df)[0]
+
+ st.success(f"💰 **Estimated Property Price:** ₹ {int(prediction):,}")
+
+ # Feature importance chart
+ st.subheader("📊 Feature Importance")
+ feat_imp = pd.Series(model.feature_importances_, index=input_df.columns)
+ st.bar_chart(feat_imp)
+
+# Divider
+st.markdown("---")
+st.header("📂 Batch Prediction (Upload CSV)")
+
+uploaded_file = st.file_uploader("Upload a CSV file with property details", type=["csv"])
+if uploaded_file is not None:
+ try:
+ batch_data = pd.read_csv(uploaded_file)
+ for col in encoders:
+ if col in batch_data.columns:
+ batch_data[col] = batch_data[col].astype(str).str.lower()
+ batch_data[col] = encoders[col].transform(batch_data[col])
+ predictions = model.predict(batch_data)
+ batch_data["Predicted Price"] = [f"₹ {int(p):,}" for p in predictions]
+ st.success("✅ Batch Prediction Complete!")
+ st.dataframe(batch_data)
+ except Exception as e:
+ st.error(f"❌ Error: {e}")
From 27ff36c47a82b8c443d767f945169c2711b1e2e2 Mon Sep 17 00:00:00 2001
From: Rishika S <142384026+rishika712@users.noreply.github.com>
Date: Fri, 11 Jul 2025 18:41:48 +0530
Subject: [PATCH 2/4] Add files via upload
---
.../Housing.csv | 546 ++++++++++++++++++
.../encoders.pkl | Bin 0 -> 2194 bytes
.../hpppic.jpg | Bin 0 -> 2738045 bytes
.../model.pkl | Bin 0 -> 4828849 bytes
.../requirements.txt | 7 +
.../sample_input.csv | 6 +
6 files changed, 559 insertions(+)
create mode 100644 NDV_Code_By_RishikaS_House_Price_Prediction/Housing.csv
create mode 100644 NDV_Code_By_RishikaS_House_Price_Prediction/encoders.pkl
create mode 100644 NDV_Code_By_RishikaS_House_Price_Prediction/hpppic.jpg
create mode 100644 NDV_Code_By_RishikaS_House_Price_Prediction/model.pkl
create mode 100644 NDV_Code_By_RishikaS_House_Price_Prediction/requirements.txt
create mode 100644 NDV_Code_By_RishikaS_House_Price_Prediction/sample_input.csv
diff --git a/NDV_Code_By_RishikaS_House_Price_Prediction/Housing.csv b/NDV_Code_By_RishikaS_House_Price_Prediction/Housing.csv
new file mode 100644
index 000000000..21a75e0db
--- /dev/null
+++ b/NDV_Code_By_RishikaS_House_Price_Prediction/Housing.csv
@@ -0,0 +1,546 @@
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diff --git a/NDV_Code_By_RishikaS_House_Price_Prediction/encoders.pkl b/NDV_Code_By_RishikaS_House_Price_Prediction/encoders.pkl
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