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Predicting Work Trends Across Industries with Naive Bayes: Remote or On-Site
Probability theory and random variables, Naive Bayes classifier for multivariate Bernoulli models
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As we navigate through the evolving landscape of the 2023 job market, we are witnessing a significant shift in work methods. More…
Exploring Roanoke Workers’ Residential Choices: A Data-Driven Analysis into Roanoke’s Metropolitan Living Choices
Random Forest Classifier
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Deciding where to live for Workers on-site (WOS) is a big decision for everyone, and for the people of Roanoke, it’s no different. We all have our own checklist when it comes to choosing our neighborhoods – some of us…
Exploring the Relationship Between Income and Living Location Preference
A Comparative Analysis of Linear and Non-Linear Regression Models
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Living in Roanoke: Does Your Income Decide Your Address? We’ve dived into the world of Roanoke’s remote workers to see how much their earnings influence where they live. Using linear regression, we’ve connected the dots between income and neighborhood choices in this vibrant…
Beyond the Cluster: Spotting the Outliers in Roanoke and Salem
Uncovering Hidden Residential Gems with DBSCAN Anomaly Detection
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Discovering anomalies can reveal much about a city’s living patterns that conventional clustering might miss. In Roanoke and Salem cities, we dive into the uncommon, the…
Urban Clustering: Decoding Roanoke and Salem’s Living Patterns
Insights from K-Means Clustering on Residential Concentrations
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In the bustling urban landscapes and the serene outskirts of Roanoke and Salem cities, every homeplace tells a story. Geographic clustering, a powerful analytical tool…
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URBAN Insights: K-Means vs. DBSCAN in Decoding Roanoke and Salem’s Residential Landscapes
K-Means and DBSCAN Side by Side
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In the quest to decode the living patterns of Roanoke and Salem, our exploration has evolved. We’ve delved into the fabric of these cities with a dual approach: the…
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