RSK World - Housing Price Prediction Dataset - Project Files Browser | RSK World

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Housing Price Prediction Dataset - Demo Page
RSK World - Free Programming Resources & Source Code
Website: https://rskworld.in
Contact: help@rskworld.in, support@rskworld.in
Phone: +91 93305 39277
Founder: Molla Samser
Designer & Tester: Rima Khatun
Created: 2026
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            <h1>🏠 Housing Price Prediction Dataset</h1>
            <p>Real estate dataset for regression analysis and machine learning</p>
        </header>
        
        <div class="content">
            <div class="section">
                <h2>📊 Dataset Overview</h2>
                <p>This dataset includes property features like size, bedrooms, bathrooms, location coordinates, neighborhood data, and sale prices. Perfect for regression models, feature engineering, and real estate analytics.</p>
                
                <div class="info-grid">
                    <div class="info-card">
                        <h3>📈 Dataset Size</h3>
                        <p><strong>50 properties</strong> with <strong>20 features</strong></p>
                    </div>
                    <div class="info-card">
                        <h3>💰 Price Range</h3>
                        <p>$180,000 - $1,225,000</p>
                    </div>
                    <div class="info-card">
                        <h3>🎯 Difficulty Level</h3>
                        <p>Beginner</p>
                    </div>
                </div>
            </div>
            
            <div class="section">
                <h2>✨ Key Features</h2>
                <ul class="features-list">
                    <li>✅ Property features (bedrooms, bathrooms, square footage)</li>
                    <li>✅ Location data (coordinates, zipcode, neighborhood)</li>
                    <li>✅ Price information for regression models</li>
                    <li>✅ Neighborhood features and comparisons</li>
                    <li>✅ Ready for machine learning models</li>
                </ul>
            </div>
            
            <div class="section">
                <h2>🛠️ Technologies</h2>
                <div>
                    <span class="tech-badge">CSV</span>
                    <span class="tech-badge">JSON</span>
                    <span class="tech-badge">Pandas</span>
                    <span class="tech-badge">NumPy</span>
                    <span class="tech-badge">Scikit-learn</span>
                    <span class="tech-badge">Matplotlib</span>
                    <span class="tech-badge">Seaborn</span>
                    <span class="tech-badge">Jupyter</span>
                </div>
            </div>
            
            <div class="section">
                <h2>📁 Dataset Files</h2>
                <p>Download the dataset files:</p>
                <a href="housing_prices.csv" class="btn">Download CSV</a>
                <a href="housing_prices.json" class="btn">Download JSON</a>
                <a href="housing_prices.zip" class="btn">Download ZIP</a>
            </div>
            
            <div class="section">
                <h2>💻 Python Scripts</h2>
                <p>Use our provided Python scripts for analysis:</p>
                <ul class="features-list">
                    <li><code>data_analysis.py</code> - Data analysis and model training</li>
                    <li><code>data_visualization.py</code> - Create visualizations and plots</li>
                    <li><code>housing_price_prediction.ipynb</code> - Jupyter notebook for interactive analysis</li>
                </ul>
            </div>
            
            <div class="section">
                <h2>📊 Sample Data Preview</h2>
                <div class="data-preview">
                    <table>
                        <thead>
                            <tr>
                                <th>ID</th>
                                <th>Price</th>
                                <th>Bedrooms</th>
                                <th>Bathrooms</th>
                                <th>Sqft Living</th>
                                <th>Neighborhood</th>
                            </tr>
                        </thead>
                        <tbody>
                            <tr>
                                <td>1</td>
                                <td>$221,900</td>
                                <td>3</td>
                                <td>1</td>
                                <td>1,180</td>
                                <td>Greenwood</td>
                            </tr>
                            <tr>
                                <td>2</td>
                                <td>$538,000</td>
                                <td>3</td>
                                <td>2.25</td>
                                <td>2,570</td>
                                <td>Northgate</td>
                            </tr>
                            <tr>
                                <td>3</td>
                                <td>$180,000</td>
                                <td>2</td>
                                <td>1</td>
                                <td>770</td>
                                <td>Kenmore</td>
                            </tr>
                            <tr>
                                <td>4</td>
                                <td>$604,000</td>
                                <td>4</td>
                                <td>3</td>
                                <td>1,960</td>
                                <td>West Seattle</td>
                            </tr>
                            <tr>
                                <td>5</td>
                                <td>$510,000</td>
                                <td>3</td>
                                <td>2</td>
                                <td>1,680</td>
                                <td>Sammamish</td>
                            </tr>
                        </tbody>
                    </table>
                </div>
            </div>
            
            <div class="section">
                <h2>🚀 Getting Started</h2>
                <ol style="padding-left: 20px; line-height: 2;">
                    <li>Download the dataset files (CSV or JSON)</li>
                    <li>Install Python dependencies: <code>pip install -r requirements.txt</code></li>
                    <li>Run analysis scripts: <code>python data_analysis.py</code></li>
                    <li>Open Jupyter notebook for interactive analysis</li>
                    <li>Create visualizations: <code>python data_visualization.py</code></li>
                </ol>
            </div>
        </div>
        
        <footer>
            <p><strong>RSK World</strong> - Free Programming Resources & Source Code</p>
            <p>Website: <a href="https://rskworld.in">https://rskworld.in</a></p>
            <p>Contact: <a href="mailto:help@rskworld.in">help@rskworld.in</a> | <a href="mailto:support@rskworld.in">support@rskworld.in</a></p>
            <p>Phone: +91 93305 39277</p>
            <p>Founder: Molla Samser | Designer & Tester: Rima Khatun</p>
        </footer>
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