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stochastic-sisyphus authored Aug 6, 2024
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<section id="projects">
<h1>Data Science Projects</h1>
<div class="project-grid">
<div class="project-card">
<h2>Loan Approval Prediction</h2>
<p>Developed a machine learning model to predict loan approval status based on applicant information. This project showcases the application of various classification algorithms and feature engineering techniques.</p>
<div id="research-synthesizer" class="project-card">
<h2>AI-Powered Research Synthesizer</h2>
<p>Developed an AI system to automatically synthesize research papers, significantly enhancing the efficiency of literature reviews in academic and industrial research.</p>
<h3>Key Features:</h3>
<ul>
<li>Data preprocessing and cleaning</li>
<li>Exploratory Data Analysis (EDA)</li>
<li>Feature engineering and selection</li>
<li>Model training and evaluation</li>
<li>Hyperparameter tuning</li>
<li>Natural Language Processing for paper summarization</li>
<li>Topic modeling to identify key themes across multiple papers</li>
<li>Sentiment analysis to gauge research consensus</li>
<li>Automated citation network analysis</li>
</ul>
<p><strong>Technologies used:</strong> Python, Pandas, Scikit-learn, Matplotlib, Seaborn</p>
<a href="https://github.com/stochastic-sisyphus/Portfolio/tree/main/Loan%20Prediction" class="btn">View on GitHub</a>
<p><strong>Technologies used:</strong> Python, TensorFlow, BERT, Gensim, NetworkX</p>
<a href="#" class="btn">View Project Details</a>
</div>
<div class="project-card">
<h2>Titanic Survival Prediction</h2>
<p>Created a predictive model to determine the likelihood of survival for Titanic passengers. This project demonstrates the use of machine learning for binary classification problems and the importance of feature engineering.</p>
<div id="deep-learning-nlp" class="project-card">
<h2>Deep Learning for NLP</h2>
<p>Implemented advanced deep learning models for various natural language processing tasks, including sentiment analysis, named entity recognition, and machine translation.</p>
<h3>Key Features:</h3>
<ul>
<li>Data cleaning and imputation</li>
<li>Feature creation and encoding</li>
<li>Model comparison (Random Forest, Logistic Regression, SVM)</li>
<li>Cross-validation and model evaluation</li>
<li>BERT-based model for contextual word embeddings</li>
<li>Seq2Seq models with attention for machine translation</li>
<li>Custom loss functions for multi-task learning</li>
<li>Model deployment using TensorFlow Serving</li>
</ul>
<p><strong>Technologies used:</strong> Python, Pandas, Numpy, Scikit-learn, Matplotlib</p>
<a href="https://github.com/stochastic-sisyphus/Portfolio/tree/main/Titanic" class="btn">View on GitHub</a>
<p><strong>Technologies used:</strong> Python, PyTorch, Transformers, FastAPI</p>
<a href="#" class="btn">View Project Details</a>
</div>
<div class="project-card">
<h2>House Price Prediction</h2>
<p>Built a regression model to predict house prices based on various features. This project showcases advanced regression techniques and detailed feature analysis.</p>
<div id="web-scraping-econometrics" class="project-card">
<h2>Automated Web Scraping for Econometric Analysis</h2>
<p>Created a robust web scraping system to gather economic data from various sources, enabling comprehensive econometric analysis and forecasting.</p>
<h3>Key Features:</h3>
<ul>
<li>Extensive data preprocessing</li>
<li>Advanced feature engineering</li>
<li>Regularized regression models (Lasso, Ridge, Elastic Net)</li>
<li>Ensemble methods (Random Forest, Gradient Boosting)</li>
<li>Model stacking and blending</li>
<li>Distributed web crawling using Scrapy</li>
<li>Automated data cleaning and preprocessing</li>
<li>Integration with econometric modeling tools</li>
<li>Visualizations for time-series economic data</li>
</ul>
<p><strong>Technologies used:</strong> Python, Pandas, Numpy, Scikit-learn, XGBoost, Seaborn</p>
<a href="https://github.com/stochastic-sisyphus/Portfolio/tree/main/House%20Price%20Prediction" class="btn">View on GitHub</a>
<p><strong>Technologies used:</strong> Python, Scrapy, Pandas, Statsmodels, Matplotlib</p>
<a href="#" class="btn">View Project Details</a>
</div>
<div id="credit-risk-analysis" class="project-card">
<h2>Credit Risk Analysis Using Machine Learning</h2>
<p>Developed machine learning models to assess credit risk, improving lending decisions and reducing default rates for a financial institution.</p>
<h3>Key Features:</h3>
<ul>
<li>Ensemble methods (Random Forest, Gradient Boosting) for risk prediction</li>
<li>Feature importance analysis for interpretability</li>
<li>Handling imbalanced datasets using SMOTE</li>
<li>Model explainability using SHAP values</li>
</ul>
<p><strong>Technologies used:</strong> Python, Scikit-learn, XGBoost, SHAP, Pandas</p>
<a href="#" class="btn">View Project Details</a>
</div>
</div>
</section>
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