Data Analysis

Below are some of my data analysis projects, showcasing my skills in both R and Python.

Each project highlights my ability to derive insights from data and present them in a meaningful way.

Customer Insights and Marketing Optimization

This project is an EDA of customer and marketing campaign data. The objective is to understand the spending patterns of different customer segments, identify distinct customer groups based on their purchasing behaviors, and tailor marketing strategies to target specific segments effectively. By uncovering these insights, the business can optimize marketing efforts, improve customer satisfaction, and ultimately drive sales and revenue growth.

Cryptocurrency Data Aggregator

Golf Course Strategy Optimization

Rate My Professor Coffee

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This project involves developing a Python script that scrapes cryptocurrency data from CoinMarketCap and enhances it with detailed metrics from the CoinGecko API. The script extracts key information such as price, market cap, volume (24h), and circulating supply for various cryptocurrencies. By integrating real-time data from multiple sources, the project provides a comprehensive view of the cryptocurrency market, enabling users to track and analyze market trends and metrics effectively.

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This project provides consulting advice for Municipal Golf of Seattle (MGS), analyzing whether MGS should adjust its green fee structure during winter months and prioritize funding for cart paths or driving ranges to increase play. The analysis uses data from multiple golf courses to derive actionable insights aimed at enhancing the operational strategies of MGS.

This project involves analyzing coffee ratings data to understand the quality and characteristics of Arabica coffee. By examining factors such as processing methods and regions of origin, the analysis aims to uncover insights that can help in identifying top-quality coffee beans and improving coffee production processes.

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Global Workout Trends

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Student Mental Health

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#R #MachineLearning #CustomerSegmentation

#Python #WebScraping #RealTimeData

#R #Econometrics #AnalysisReport

#R #MachineLearning #RegressionModels

#Python #NPS

#SQL

#Python

The product analysis project aims to analyze Net Promoter Score (NPS) data from different sources (email, web, and mobile) to assess customer satisfaction. The project involves combining multiple CSV files into a single DataFrame, calculating NPS for each data source, and addressing a deprecation warning related to the grouping columns in pandas. This analysis provides insights into customer feedback across various platforms, helping to identify areas for improvement and track the overall customer experience.

Music Genre Classification

Multi-Source NPS Analysis

#Python #MachineLearning #PCA

The product analysis project aims to analyze Net Promoter Score (NPS) data from different sources (email, web, and mobile) to assess customer satisfaction. The project involves combining multiple CSV files into a single DataFrame, calculating NPS for each data source, and addressing a deprecation warning related to the grouping columns in pandas. This analysis provides insights into customer feedback across various platforms, helping to identify areas for improvement and track the overall customer experience.

This project involves building and evaluating machine learning models to classify music tracks as either rock or hip-hop based on audio features. The workflow includes data preparation, normalization, dimensionality reduction using PCA, and training decision tree and logistic regression models. The project also explores data balancing and cross-validation to ensure robust model performance.