NDV_Code_By_RishikaS_House_Price_Prediction

LICENSE

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+MIT License
+
+Copyright (c) 2025 Rishika S
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NDV_Code_By_RishikaS_House_Price_Prediction/app.py

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+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(
+    """
+    <style>
+    .stApp {
+        background: url("https://images.unsplash.com/photo-1600585154340-be6161a56a0c") no-repeat center center fixed;
+        background-size: cover;
+    }
+
+    /* ❌ Removed blurry overlay */
+    /* .stApp::before {
+        content: "";
+        position: fixed;
+        top: 0;
+        left: 0;
+        width: 100%;
+        height: 100%;
+        background: rgba(255, 255, 255, 0.75);
+        z-index: 0;
+    } */
+
+    .main, .block-container {
+        position: relative;
+        z-index: 1;
+        background-color: rgba(255, 255, 255, 0.95); /* You can make this more transparent if needed */
+        padding: 2rem;
+        border-radius: 12px;
+        box-shadow: 0 4px 12px rgba(0, 0, 0, 0.1);
+    }
+
+    h1 {
+        color: #004D40;
+        text-align: center;
+    }
+
+    .stButton button {
+        background-color: #26A69A;
+        color: white;
+        font-weight: bold;
+        border-radius: 8px;
+        padding: 0.5rem 1rem;
+    }
+    </style>
+    """,
+    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}")

NDV_Code_By_RishikaS_House_Price_Prediction/readme.md

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+
+# 🏠 Real Estate Price Prediction Dashboard
+
+An interactive web application built using **Streamlit** that predicts the price of a house based on various features such as area, bedrooms, amenities, and location. The app uses a machine learning model trained on a real estate dataset to provide accurate price predictions in real time.
+
+---
+
+## 📌 Project Features
+
+- 🔮 Predict property price based on user inputs
+
+- 🧠 Trained Random Forest Regressor model  
+
+- 📂 Batch prediction from uploaded CSV files  
+
+- 📊 Feature importance visualized using charts  
+
+- 🎨 Styled UI with custom background and layout
+
+---
+
+## 🧰 Tech Stack
+
+| Tool             | Purpose                     |
+|------------------|-----------------------------|
+| Python           | Programming Language        |
+| Pandas, NumPy    | Data manipulation           |
+| Scikit-learn     | Model training & evaluation |
+| Matplotlib, Seaborn | Data visualization       |
+| Joblib           | Model serialization         |
+| Streamlit        | Web app development         |
+
+---
+
+## 🗂️ Dataset
+
+- 📥 Source: [Housing Price Prediction – Kaggle](https://www.kaggle.com/datasets/harishkumardatalab/housing-price-prediction)
+
+- 🔎 Features include:
+
+   - Area (sq. ft)
+
+  - Bedrooms, Bathrooms, Stories
+
+  - Main road access, Guest room, Basement
+
+  - Hot water heating, Air conditioning
+
+  - Parking availability
+
+  - Furnishing status, Preferred area
+
+  - Location
+
+---
+
+## 🚀 How to Run the Project
+
+### 🛠️ Local Setup
+
+1. **Clone the repo**
+
+2. **Install dependencies**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Run the app**
+   ```bash
+   streamlit run app.py
+   ```
+
+4. ✅ The app will open in your browser at `http://localhost:8501`either automatically or when you click on the link in your terminal.
+
+---
+
+## 📦 File Structure
+
+```
+├── app.py                   # Streamlit app script
+
+├── model.pkl                # Trained model
+
+├── encoders.pkl             # Encoded label mappings
+
+├── sample_input.csv         # Sample input for batch prediction
+
+├── requirements.txt         # Project dependencies
+
+└── README.md                # Project documentation
+```
+
+---
+
+## 📄 License
+
+This project is under the MIT License.

NDV_Code_By_RishikaS_House_Price_Prediction/requirements.txt

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+streamlit==1.35.0
+pandas==2.2.2
+numpy==1.26.4
+scikit-learn==1.3.2
+matplotlib==3.8.4
+seaborn==0.13.2
+joblib==1.4.2

NDV_Code_By_RishikaS_House_Price_Prediction/sample_input.csv

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+area,bedrooms,bathrooms,stories,mainroad,guestroom,basement,hotwaterheating,airconditioning,parking,prefarea,furnishingstatus,location
+3450,3,1,1,yes,no,yes,no,no,2,no,unfurnished,countryside
+4960,2,1,1,yes,no,no,no,no,0,no,unfurnished,downtown
+1950,3,2,2,yes,no,yes,no,no,0,yes,unfurnished,downtown
+1950,3,1,1,no,no,no,yes,no,0,no,unfurnished,suburb
+2135,3,2,2,no,no,no,no,no,0,no,unfurnished,suburb