Results driven-data scientist with expertise in Data wrangling, Statistical analysis and Machine learning. With a strong background in Industrial Mathematics and Economics, I am an agile project leader with a passion for AI and a track record of delivering impactful solutions and achieving business objectives.
This project uses RFM analysis and clustering (K-Means & Gaussian Mixture Models) to segment customers based on their purchasing behavior. The goal is to identify key groups like loyal customers, at-risk users, and high spenders to support targeted marketing.
This project aimed to understand customer segments and reviews to provide actionable insights for improving the e-commerce platform's services and customer experience in Brazil. After a comprehensive analysis involving data cleaning and preprocessing, I utilized K-means clustering to segment customers based on their behaviors and interactions. Additionally, I employed Natural Language Processing (NLP) techniques to categorize and analyze sentiment from customer reviews.
A personal project whereby I researched and analyzed chunks of sentiment reviews of Twitter users. Machine Learning classification models were used to determine a user expression such as happy, angry, sad, neutral and so on through their tweets.
Built a deep learning model to detect and classify facial expressions such as happy, angry, disgust, etc using the Convolutional Neural Networks algorithm for integration in company's website
A personal project designed to statistically analyse based on some general chemical properties in winemaking, the effect of these properties on the quality of the wine.
Utilized python to curate 98% data from Suicide watch and depression posts, built and optimized NLP algorithms to demonstrate a remarkable 72.1% classification accuracy with Logistc regressions in order to give valuable insights to assist in mental health research and support initiaties.
Developed a predictive model to identify bank customers likely to churn using machine learning and deep learning techniques. Preprocessed data, conducted exploratory analysis, and addressed class imbalance using SMOTE. Achieved balanced F1-scores and improved accuracy, providing valuable insights for customer retention strategies.