Chameleon-company/MOP-Code
Dec 2024 – Dec 2024
1 Month active
Languages Used
CSSHTMLJavaScriptPython
Technical Skills
Data AnalysisData CleaningData VisualizationDavies-Bouldin IndexExploratory Data Analysis (EDA)K-Means Clustering
Dhruvilmehtaa
PROFILE
Dhruvilmehtaa
Dhruvil Mehta developed the Melbourne Employment and Housing Price Analysis Pipeline for the Chameleon-company/MOP-Code repository, delivering an end-to-end workflow for analyzing the relationship between employment and housing prices in Melbourne. He designed the pipeline to handle data loading, cleaning, preprocessing, and exploratory data analysis, incorporating correlation analysis and clustering techniques such as K-Means. Using Python, Pandas, and Scikit-learn, Dhruvil implemented robust data visualization with Matplotlib and Seaborn to communicate findings to business stakeholders. The project provided actionable insights for business decisions, demonstrating depth in data engineering and analysis within a focused, single-feature delivery over a one-month period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
11,489
Activity Months1
Your Network
98 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month 2024-12: Delivered the Melbourne Employment and Housing Price Analysis Pipeline for Chameleon-company/MOP-Code, enabling end-to-end data handling from loading and cleaning to preprocessing, analysis, and visualization. The pipeline supports exploratory data analysis, correlation analysis, clustering, and visualization to derive actionable business insights for decisions on employment impact on housing prices in Melbourne.
1 Commits • 1 Features
Dec 1, 2024Month 2024-12: Delivered the Melbourne Employment and Housing Price Analysis Pipeline for Chameleon-company/MOP-Code, enabling end-to-end data handling from loading and cleaning to preprocessing, analysis, and visualization. The pipeline supports exploratory data analysis, correlation analysis, clustering, and visualization to derive actionable business insights for decisions on employment impact on housing prices in Melbourne.
December 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
CSSHTMLJavaScriptPython
Technical Skills
Data AnalysisData CleaningData VisualizationDavies-Bouldin IndexExploratory Data Analysis (EDA)K-Means ClusteringMatplotlibMinMaxScalerNumPyPCA (Principal Component Analysis)PandasScikit-learnSeabornSilhouette Score
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