Banumathi M

Skilled Data Scientist with a solid foundation in statistical analysis, machine learning, and data engineering. Adept at transforming complex data sets into actionable insights and developing data-driven solutions to address real-world problems. Proven track record in implementing advanced analytical techniques and leveraging programming expertise to create efficient models. Strong analytical skills and experience in various projects, coupled with a detail-oriented and organized approach, make me well-equipped to contribute effectively to dynamic teams.

Capstone Project

Diabetic Patient Readmission Prediction

• Objective: Develop a machine learning model to predict diabetic patient readmission within 30 days.
• Approach: Analyzed and preprocessed a 10-year dataset, selected, and trained multiple models. Evaluated performance to categorize readmission as less than 30 days, beyond 30 days, or no readmission.
• Tools: Python, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn (Linear Regression, KNN Regressor, Random Forest Regressor, Gradient Boosting).

Course Projects

Machine Learning Model for Continuous Variable Prediction

• Objective: Develop a linear model to predict the population of US cities in 1930 based on 1920 data.
• Approach: Built a linear regression model, analyzing the relationship between 1920 and 1930 populations. The model achieved an R-squared value of 0.85.
• Tools: Python, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn (Linear Regression, KNN Regressor, Random Forest Regressor, Gradient Boosting).

Online Shopping Process Analytics

• Objective: Analyze online customer purchasing behavior to understand seasonality, business patterns, product performance, and user behavior.
• Approach: Conducted a decade-long data analysis to reveal trends and insights, informing marketing strategies and inventory management.
• Tools: MySQL for database management and querying.

Titanic - Machine Learning from Disaster (Kaggle Dataset)

• Objective: Predict which passengers survived the Titanic shipwreck using various features.
• Approach: Developed a model achieving 74% accuracy, demonstrating data preprocessing, feature engineering, and classification techniques.
• Tools: Python, Pandas, Matplotlib, Seaborn, SciPy, Scikit-learn (Linear Regression, KNN Regressor, Random Forest Regressor, Gradient Boosting, Decision Tree).

UG Project

Asset Management System

• Division: UG Project
• Objective: Develop a system to manage all of a company's assets, both tangible and intangible, across the business.
• Approach: Implemented an asset management system to manage personnel, buildings, software, hardware, inventory, and monetary assets crucial to business operations.
• Tools: HTML, CSS, JavaScript, MySQL.

Achievements

• Outstanding Achievement Awards:National level MAT Lab, July 2017.
  National level Big Data Analytics (Big Data privacy and security challenges), October 2017.
  National level workshop in Android app security, January 2018.
  International conference on Data Science Analytics (RF Based Node locations and mobility tracking in IoT), February 2018.
  Workshop on Digital marketing & job opportunities, April 2018.
  

Certifications

• Completed Cyber Security Certificate Course.