shola-ayeotan Goto Github PK

aws---etl-pipeline-with-youtube-data

climate-change-sentiment-tracker

In this project, I analyzed public opinion on climate change using a text-mining approach on Twitter data, uncovering prevalent sentiments and viewpoints.

clinical-trial

This project sought to provide valuable insights into the world of clinical research by combining clinical trial data with information on pharmaceutical companies.

customer-segmentation-using-rfm-modeling

This project focused on segmenting customers of a UK-based, online non-store retail company using RFM (Recency, Frequency, Monetary) analysis. The company specializes in unique, all-occasion gifts, and has a broad customer base, including many wholesalers.

ensemble-ml-insurance-claims-severity-analysis

This project leveraged ensemble machine learning algorithms to analyze and predict the impact of unforeseen events on insurance claims for All-State, a leading personal insurance company in the US. It encompassed everything from data cleaning to model deployment.

global-economic-scorecard---time-series-analysis

An in-depth and thorough exploration of global economic trends using data obtained from the World Bank's World Development Indicators. Using a combination of statistical and visual analysis, patterns and relationships in economic indicators across countries and continents were meticulously uncovered.

household-energy-consumption

This project explored patterns in household electricity consumption to inform sustainable energy practices, using K-Means and Agglomerative Hierarchical Clustering techniques on an energy usage dataset compiled from observing a residential building in Sceaux, France.

library-database

Built on a foundation of data efficiency, integrity, and security, this database system empowered a library to streamline operations and enhance their member experience.

loan-eligbility-prediction

Implemented H20.ai and deep learning models to predict loan eligibility for individuals applying for loans.

predicting-customer-outcomes

This project used ensemble machine learning algorithms to predict customer subscriptions to financial products offered by a Portuguese Bank. The goal was to conduct a comprehensive analysis from end to end, extracting valuable insights into customer behavior and preferences.

predictive-maintenance-with-machine-learning

This project developed a predictive maintenance model using vibration sensor data to classify faults, optimize maintenance operations, and improve machinery reliability.

shola-ayeotan

Config files for my GitHub profile.