Suhas S

Data Science, AI & ML Enthusiast | MERN Developer

About Me

Hello! I'm currently navigating my third year at ATME College Of Engineering, where I'm majoring in Computer Science Engineering with a focus on Data Science. This journey has not only fueled my passion for AI and Machine Learning but has also equipped me with a robust skill set in Python, SQL, Excel, and Power BI. I am keen on expanding my professional network with fellow data enthusiasts, tech professionals, and potential employers. Feel free to connect with me to exchange ideas, opportunities, or simply to say hello!

Skills

Excel | Power BI | Tableau | SQL | Python

  • Data Visualization

  • Data Analysis

  • EDA

  • ML Algorithms

Latest Projects

Credit Card Approval
Data Analysis

utilized data science techniques to analyze and predict credit card approval decisions. The goal was to build a predictive model that helps financial institutions make more informed decisions while reducing the risk of credit default.

Certifications

  • BCG - Data Science Job Simulation

  • British Airways - Data Science Job Simulation

  • Quantium - Data Analytics Job Simulation

Credit Card Approval
Data Analysis

This project focuses on analyzing credit card approval data to identify patterns and factors that significantly impact the approval process. Using a comprehensive dataset, I performed data cleaning, preprocessing, and exploratory data analysis (EDA) to understand the relationships between various attributes like income, credit history, and demographics.Leveraging machine learning techniques, including logistic regression, decision trees, and support vector machines, I built predictive models to classify whether a credit card application would be approved or denied. The analysis also includes feature importance ranking to highlight which variables play a crucial role in decision-making.This project not only showcases my proficiency in data manipulation and machine learning but also highlights my ability to derive meaningful business insights from data. It's an example of how data-driven approaches can optimize credit risk assessment and improve the efficiency of financial services.