Varalakshmi Vundru

Results-driven assistant professor with four years of experience in mathematics, now equipped with a comprehensive Global Certificate in Data Science and hands-on project experience. Passionate about using analytical skills to uncover actionable insights from data and support informed decision-making. Skilled at translating complex concepts into clear, practical solutions, with a strong foundation in statistical analysis, data visualization, and model building. Eager to transition into a Data Analyst role to make a meaningful impact through data-driven strategies.

Employee Attrition Model:

• Collaborated with a Team of Professionals to develop machine learning model to predict employee attrition.
• Used SQL, Python packages including pandas, NumPy, Matplotlib, seaborn to transform, and visualize raw HR data.
• Designed a classification model for the HR department support the HR department in identifying employees at risk of leaving.
• Conducted in-depth analyses of departmental data, employee details, administrative processes, workload distribution, and mutual information among employees. Developed Classifier models, evaluation metrics and applied cross validation techniques.

Exploratory Data Analysis: Telecom Industry

• Collaborated with a Team of Professionals to integrate ideas and provide insights on dataset having 14 features and 32lac+ entries.
• Used SQL, Python packages including pandas, NumPy, Matplotlib, seaborn to transform raw data to understandable format to find useful Insight for the company. Performed EDA and identified:Niche customer segments, Their usage patterns based on demographic details,Device feature preferences.
• Proposed actionable plan to increase market share and visibility by strategic and critical thinking.

Data Analysis of Facebook utilization:

• Extensive analysis on the Facebook Utilization data to uncover user behavior patterns and platform engagement trends. Transformed Raw data into easily understandable format by using python to draw meaningful Insight for the company.
• Performed EDA on dataset having 15 features and 99,003 entries to explore:

o Age-based segmentation to understand usage

frequency among younger vs. older adults

o Gender-based trend analysis through classification and

comparison

o Connectivity preferences, distinguishing between web

and mobile users.

• Proposed data-driven recommendations to enhance Facebook usage by leveraging insights from user behavior patterns.