Hello I'm
Hello I'm
Hello! I’m Dhanush M S, a dedicated Data Analyst with expertise in business analytics, data science, and machine learning. Currently, I’m pursuing an MSc in Business Analytics at Queen’s University Belfast, where I am on track to graduate with distinction. I also hold a B.E in Computer Science Engineering from Visvesvaraya Technology University, India.
I specialize in using Python, R, SQL, and various data analysis tools to solve complex business problems. My key skills include predictive modeling, statistical analysis, and data visualization. I have a proven track record in applying advanced analytical techniques to derive actionable insights and drive business growth.
I am passionate about leveraging data to make informed decisions and improve processes. Here you will find a collection of my work and projects that showcase my analytical skills and technical expertise.
Aim: To perform comprehensive data management tasks using SQL.
This project involved data extraction, transformation, and loading (ETL) processes. The primary focus was on ensuring data integrity and consistency across multiple databases. Key tasks included writing complex SQL queries to manipulate and retrieve data efficiently.
Aim: To analyze HR data and derive insights to enhance workforce management.
The project focused on exploring employee demographics, performance metrics, and turnover rates using descriptive statistics, regression analysis, and visualization techniques. Predictions included identifying high-risk employees for turnover and forecasting performance trends.
Aim: To apply advanced statistical techniques using R for data analysis.
Various datasets were analyzed to perform hypothesis testing, regression analysis, and ANOVA. Predictions included identifying significant factors influencing the data and making data-driven recommendations.
Aim: To analyze marketing data and derive actionable insights for optimizing marketing strategies.
The project included customer segmentation, campaign analysis, and ROI calculation. Predictions focused on customer behavior, campaign effectiveness, and identifying high-value customer segments.
Aim: To tackle complex analytical tasks using advanced methods.
Techniques such as predictive modeling, clustering, and optimization were applied to real-world data problems. The project required using R for data manipulation, model building, and result visualization.
Aim: To forecast and analyze the performance ratings of online firms using Generalized Additive Models (GAM) and Decision Trees.
The project involved building models to predict business performance scores, identifying key factors influencing ratings, and providing actionable insights for performance improvement.
Aim: To enhance credit risk management in Indian banks using advanced machine learning models.
Description: This dissertation applied various machine learning models, including Multinomial Logistic Regression, Random Forest, Support Vector Machine (SVM), and Deep Neural Networks (DNN), to predict credit risk. Random Forest was the best-performing model, achieving 93.3% accuracy. SHapley Additive exPlanations (SHAP) explained the model’s predictions. Key factors included monthly income, credit card utilization, and credit inquiries.
Technologies Used: R, Python, SHAP, Random Forest, Support Vector Machine (SVM), Deep Neural Networks (DNN), SQL
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