This portfolio showcases a comprehensive collection of data science and analytics projects which were completed during my graduate studies, covering the entire data pipeline and advanced analytical techniques. Each folder represents a key step or methodology, including:
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Data Acquisition & Cleaning: Methods to gather and preprocess raw data for analysis.
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Exploratory Data Analysis (EDA): Initial data investigation techniques to identify patterns, trends, and anomalies.
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Predictive Modeling: Implementation of statistical and machine learning models, such as Linear Regression, Logistic Regression, Naive Bayes, and Random Forests.
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Clustering & Dimensionality Reduction: Insights from unsupervised learning methods like K-Means and Principal Component Analysis.
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Time-Series & Sentiment Analysis: Advanced modeling approaches for sequential data and text-based sentiment extraction.
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Market Basket Analysis: Techniques for understanding associations and consumer behavior.
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Reporting & Communication: Comprehensive reporting to translate technical results into actionable insights.
This portfolio is designed to demonstrate technical proficiency, analytical thinking, and hands-on expertise in solving real-world problems using Python, R, SQL, and Tableau.