ndvtechsyssolutions · Munisree · Jul 11, 2025
diff --git a/Code_Data/CodeData b/Code_Data/CodeData
diff --git a/NDV_Code_By_KishoreK_TaxCalculator /TaxCalculator.py b/NDV_Code_By_KishoreK_TaxCalculator /TaxCalculator.py
diff --git a/NDV_RealEstate_PricePrediction/Bengaluru_House_Data.csv b/NDV_RealEstate_PricePrediction/Bengaluru_House_Data.csv
diff --git a/NDV_RealEstate_PricePrediction/README.md b/NDV_RealEstate_PricePrediction/README.md
@@ -0,0 +1,12 @@
+# Real Estate Price Prediction - Flask Dashboard
+
+## 🔍 Project Overview
+This project is a Flask-based web application that predicts real estate prices in Bangalore based on:
+- Location
+- Area (in square feet)
+- Number of Bedrooms (BHK)
+- Number of Bathrooms
+
+The app uses a machine learning model trained on the **Bengaluru House Price Dataset** from Kaggle.
+
+---
diff --git a/NDV_RealEstate_PricePrediction/app.py b/NDV_RealEstate_PricePrediction/app.py
@@ -0,0 +1,34 @@
+from flask import Flask, render_template, request
+import joblib
+import pandas as pd
+
+app = Flask(__name__)
+model = joblib.load('real_estate_model.pkl')
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    location = request.form['location']
+    area = float(request.form['area'])
+    bath = int(request.form['bath'])
+    bhk = int(request.form['bhk'])
+
+    # Create a DataFrame with the same columns as during training
+    input_df = pd.DataFrame([{
+        'location': location,
+        'total_sqft': area,
+        'bath': bath,
+        'bhk': bhk
+    }])
+
+    # Predict using the trained pipeline
+    prediction = model.predict(input_df)
+    output = round(prediction[0], 2)
+
+    return render_template('index.html', prediction_text=f'Estimated Price: ₹{output} Lakhs')
+
+if __name__ == '__main__':
+    app.run(debug=True)
diff --git a/NDV_RealEstate_PricePrediction/model.py b/NDV_RealEstate_PricePrediction/model.py
@@ -0,0 +1,52 @@
+import pandas as pd
+import joblib
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+
+def convert_sqft_to_num(x):
+    try:
+        if '-' in str(x):
+            tokens = x.split('-')
+            return (float(tokens[0]) + float(tokens[1])) / 2
+        return float(x)
+    except:
+        return None
+
+# Load the dataset
+data = pd.read_csv('Bengaluru_House_Data.csv')
+
+# Create bhk column
+data['bhk'] = data['size'].str.extract('(\d+)').astype(float)
+
+# Clean total_sqft
+data['total_sqft'] = data['total_sqft'].apply(convert_sqft_to_num)
+
+# Drop rows with missing/invalid data
+data = data.dropna(subset=['location', 'total_sqft', 'bath', 'bhk', 'price'])
+
+# Select features and target
+X = data[['location', 'total_sqft', 'bath', 'bhk']]
+y = data['price']
+
+# Preprocess location
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('loc', OneHotEncoder(handle_unknown='ignore'), ['location'])
+    ],
+    remainder='passthrough'
+)
+
+# Build and train the pipeline
+pipeline = Pipeline(steps=[
+    ('preprocessor', preprocessor),
+    ('model', RandomForestRegressor(n_estimators=100, random_state=42))
+])
+
+pipeline.fit(X, y)
+
+# Save the trained model
+joblib.dump(pipeline, 'real_estate_model.pkl')
+print("✅ Model trained and saved as real_estate_model.pkl")
diff --git a/NDV_RealEstate_PricePrediction/real_estate_model.pkl b/NDV_RealEstate_PricePrediction/real_estate_model.pkl
diff --git a/NDV_RealEstate_PricePrediction/requirements.txt b/NDV_RealEstate_PricePrediction/requirements.txt
@@ -0,0 +1,4 @@
+Flask
+pandas
+scikit-learn
+joblib
diff --git a/NDV_RealEstate_PricePrediction/templates/index.html b/NDV_RealEstate_PricePrediction/templates/index.html
@@ -0,0 +1,64 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Real Estate Price Predictor</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background-color: #f2f2f2;
+            padding: 30px;
+        }
+        .container {
+            background-color: #fff;
+            border-radius: 8px;
+            padding: 20px;
+            width: 400px;
+            margin: auto;
+            box-shadow: 0 0 10px rgba(0,0,0,0.1);
+        }
+        input[type="text"], input[type="number"], select {
+            width: 100%;
+            padding: 8px;
+            margin: 8px 0 16px;
+            border: 1px solid #ccc;
+            border-radius: 4px;
+        }
+        input[type="submit"] {
+            background-color: #4CAF50;
+            color: white;
+            border: none;
+            padding: 10px;
+            width: 100%;
+            border-radius: 4px;
+            cursor: pointer;
+        }
+        input[type="submit"]:hover {
+            background-color: #45a049;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <h2>Real Estate Price Prediction</h2>
+        <form action="/predict" method="post">
+            <label>Location:</label>
+            <input type="text" name="location" placeholder="e.g., Whitefield" required>
+
+            <label>Area (sq ft):</label>
+            <input type="number" name="area" required>
+
+            <label>Bedrooms (BHK):</label>
+            <input type="number" name="bhk" required>
+
+            <label>Bathrooms:</label>
+            <input type="number" name="bath" required>
+
+            <input type="submit" value="Predict Price">
+        </form>
+
+        {% if prediction_text %}
+            <h3>{{ prediction_text }}</h3>
+        {% endif %}
+    </div>
+</body>
+</html>
diff --git a/README.md b/README.md
diff --git a/Test/Test b/Test/Test
diff --git a/first.html b/first.html
diff --git a/notes b/notes
diff --git a/tags_types.html b/tags_types.html