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Data Analyst with less than a year in Data Science & Machine Learning
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Aspiring Data Analyst with a strong background in Data Science and Statistics, passionate about building predictive models and using data-driven insights to support better business decisions and real-world outcomes.
Kristu Jayanti (Deemed to be) University
M.Sc. · Data Science
August 1, 2024 – Present
Bhavan’s Vivekananda College, Hyderabad.
B.Sc. (Hons) · Data Science
August 1, 2021 – June 30, 2024
Apollo 24/7
Data Analyst Intern
February 1, 2026 – May 31, 2026
India
Startup Bankruptcy Prediction (end to - end)
June 1, 2025 – July 31, 2025
The Startup Bankruptcy Prediction system built in this project demonstrates how Big Data Analytics and Machine Learning can be applied to assess startup financial risk. After rigorous preprocessing, encoding, feature selection, and model comparison, the system provides reliable bankruptcy predictions. Predicted startup bankruptcy using financial and operational data. Developed and evaluated machine learning models (Logistic Regression, Random Forest, XGBoost) to provide actionable insights for risk assessment and investment decisions. SVM proved to be the best model, achieving near-perfect accuracy, precision, recall, and F1-score, making it highly reliable for predicting startup bankruptcy. Other models like Logistic Regression, Naive Bayes, and XGBoost performed well but were slightly less accurate.
Breast Cancer Analysis
January 1, 2025 – June 30, 2025
Developed a predictive model to detect breast cancer using patient data. Applied data preprocessing, feature selection, and machine learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost) to achieve high accuracy and support early diagnosis and treatment decisions. XGBoost is the best-performing model with an approximate accuracy of 89% indicating it can reliably predict breast cancer classification with minimal errors.
Red Wine Quality & Hepatitis Classification Projects (Capstone project)
August 1, 2023 – January 31, 2024
Built regression and classification models to assess wine quality and hepatitis condition severity compared algorithms (Logistic Regression, SVM, Decision Trees) based on accuracy and performance metrics. Focused on achieving near-perfect accuracy through data cleaning and feature selection.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying data science to diverse domains like finance, healthcare, and consumer goods (wine quality). The internship at Apollo 24/7 aligns well with a practical, business-oriented application of data analysis. The breadth of skills and project diversity indicate a willingness to explore different problem spaces, which is a positive for cultural fit in a dynamic data analyst role. The candidate's profile suggests a learning-oriented individual, currently pursuing a Master's degree, which indicates a proactive approach to skill development.
Soft Skills & Operational Fit
The candidate lists teamwork, collaboration, communication, creativity, and adaptability as soft skills. The project descriptions and internship experience suggest an ability to work on analytical tasks and contribute to data-driven decision-making. However, the depth of these soft skills in a professional setting is not extensively detailed beyond self-assessment.