datafrank.github.io/housing_details.html at main · Frank-Ellingsen/datafrank.github.io · GitHub

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  <title>Housing Price Predictor | Frank Ellingsen</title>

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            <h2 style="border-bottom: none; font-size: 2.5em; margin-bottom: 0.5rem;">Housing Price Predictor</h2>
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              <span class="tag-badge primary">Python ·</span>
              <span class="tag-badge primary">Machine Learning ·</span>
              <span class="tag-badge primary">Regression · </span>
              <span class="tag-badge primary">Streamlit</span></span>
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            <img src="images/housing-price.png" alt="Housing Price Chart" class="top" style="width: 100%;">

            <h3>Business Problem</h3>
            <p>
              Accurately pricing residential properties is critical for buyers, sellers, and real estate professionals.
              Manual pricing methods are often subjective and fail to capture complex interactions between location, size, and quality.
            </p>
            <p>
              The goal of this project is to support <strong>data-driven pricing decisions</strong> by building a predictive model
              that estimates housing prices and highlights the key factors influencing value.
            </p>

            <h3>The Process</h3>
            <ul>
              <li><strong>Data Cleaning:</strong> Standardized raw housing datasets and handled missing values.</li>
              <li><strong>Feature Engineering:</strong> Created location-based bins and price tiers to improve model accuracy.</li>
              <li><strong>Model Selection:</strong> Evaluated multiple algorithms, selecting Random Forest for its balance of performance and interpretability.</li>
              <li><strong>Validation:</strong> Compared predictions against actual market prices to ensure reliability.</li>
            </ul>

            <h3>Insights & Business Value</h3>
            <p>
              The model demonstrates strong predictive performance with an <strong>R² Score of 0.82</strong> and an <strong>18% reduction in RMSE</strong>
              compared to baseline estimates. Beyond raw predictions, the analysis identified that location and living area remain the primary price drivers.
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            <h3>Tools Used</h3>
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              <span class="tag-badge">Pandas ·</span>
              <span class="tag-badge">scikit-learn ·</span>
              <span class="tag-badge">Matplotlib ·</span>
              <span class="tag-badge">Seaborn ·</span>
              <span class="tag-badge">Streamlit </span>
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            <h3>Key Metrics</h3>
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              <div class="metric-item">R²: 0.82</div>
              <div class="metric-item">RMSE ↓ 18%</div>
              <div class="metric-item">8k+ Data Points</div>
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            <h3 style="margin-top: 1.5rem;">Project Links</h3>
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              <li><a href="https://github.com/Frank-Ellingsen/Housing-Price-Predictor" target="_blank" rel="noopener">GitHub Repository →</a></li>
              <li><a href="https://housing-price-predictor-8u16.onrender.com/" target="_blank" rel="noopener">Live Streamlit App →</a></li>
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