Data Scientist skilled in Microsoft Excel, SQL, Tableau, Python, and Machine Learning @Gogo Harrison
In this project, I developed a comprehensive sentiment analysis solution capable of extracting, preprocessing, and analyzing customer reviews to uncover actionable insights. This solution enables business stakeholders to make data-driven decisions, driving improved customer experience and satisfaction.
In this project, I crafted a powerful predictive model that accurately forecasts loan application approval status based on customer details. By harnessing the power of supervised learning on historical data, this innovative system delivers reliable predictions, transforming it into an essential decision-support tool for lenders.
In this project, I engineered a powerful SMS spam detection model designed to accurately classify messages as either legitimate or spam. Utilizing advanced Natural Language Processing (NLP) techniques and the Naive Bayes algorithm, I analyzed a dataset of 5,574 labeled SMS messages ('ham' or 'spam'). This project contributes to the development of effective spam filtering solutions, enhancing user experience by mitigating the impact of unsolicited messages.
In this project, I developed a machine learning model to predict the likelihood of heart disease in patients based on specific health-related features. The project involved data preprocessing, model training, and the evaluation of multiple machine learning algorithms to identify the best-performing model.
Leveraging the K-Means Clustering Algorithm, I conducted customer segmentation to uncover five distinct customer groups based on annual income and spending behavior. The optimal number of clusters was determined using the elbow method, and the clustering performance was validated with the silhouette score.
In this project, I analyzed customer churn data for a telecom firm and developed machine learning models to predict customer churn. The model identifies key indicators of churn and suggests effective retention strategies that the company can implement.
In this project, I developed a predictive model to estimate employee monthly income based on factors like age, education, and job role. Utilizing Linear Regression, the model’s performance was rigorously evaluated through metrics such as MAE, MSE, RMSE, and R-squared. To enhance accuracy, I also performed hyperparameter tuning with GradientBoostingRegressor using GridSearchCV.
In this project, I developed a predictive model for employee attrition using logistic regression. This model accurately forecasts the likelihood of an employee leaving the company by analyzing key factors such as job satisfaction, work-life balance, and demographic data, providing valuable insights to improve employee retention strategies.
In this project, I conducted a comprehensive analysis of e-commerce sales data to uncover key performance insights for the past year. By identifying trends and patterns, I provided actionable, data-driven recommendations to enhance the company's sales strategy and overall performance.
In this project, I leveraged historical sales data from XYZ Company to uncover the critical drivers behind sales performance. Through in-depth analysis, I delivered key insights and actionable strategies that significantly boost revenue and profitability.
In this project, I developed a robust regression model to accurately predict California house prices by analyzing key demographic and geographical factors. The model reveals how variables such as median income, house age, and proximity to the ocean drive housing prices across diverse regions in California..
In this project, I embarked on an in-depth analysis and visualization project using Tableau, diving into complex suicide datasets to reveal hidden patterns, correlations, and trends. The powerful insights gained from this project are poised to drive evidence-based public health strategies and interventions, making a significant impact on global mental health initiatives.
In this project, I leveraged data on Redbull energy drinks to uncover the startling effects of energy drinks on blood pressure. By diving deep into the numbers, I revealed critical insights that highlight the health implications of these popular beverages.