KUMBHA RAJESHBABU
Entry Level Graduate | Machine Learning Engineer • Hyderabad • r******************@gmail.com • +91*******654 • linkedin/•••••
Professional Summary
Entry-level Machine Learning Engineer with a B.Tech in Artificial Intelligence and Data Science (CGPA 8.96). Hands-on experience building and validating classification and regression models using Python, Pandas, NumPy and scikit-learn. Completed applied simulations and projects including customer churn prediction and sales forecasting; delivered model pipelines, visualizations and concise executive summaries for stakeholders. Seeking a data scientist / ML engineer role to apply feature engineering, model evaluation and cloud AI fundamentals to drive data-driven business outcomes.
Technical Skills
Programming Languages: Python
Frameworks and Libraries: NumPy,Pandas,scikit-learn,Jupyter Notebook,Matplotlib,Seaborn
Databases: SQL
Cloud and DevOps: Microsoft Azure,Azure AI
Data and Analytics: Random Forest,Logistic Regression,Decision Trees,Ensemble Methods,Hyperparameter Tuning,Model Evaluation,Exploratory Data Analysis
Tools and Methodologies: GitHub,Git
Methods & Practices: Feature Engineering,Cross-Validation,Classification,Regression
GenAI & APIs: OpenAI foundational knowledge
Work Experience
BCG (Forage) - Data Science Job Simulation
Remote / Hyderabad
Data Science Simulation Participant
January 2025 – January 2025
Tech Stack: Python, Pandas, NumPy, scikit-learn, Matplotlib, Seaborn, Jupyter Notebook, GitHub
- Led a customer churn analysis simulation using Python, Pandas and NumPy to ingest, clean and prepare client datasets for modeling, establishing a reproducible data pipeline for downstream analysis.
- Engineered features and built a Random Forest classifier with scikit-learn, applying hyperparameter tuning to achieve an 85% accuracy in churn prediction for the simulated dataset.
- Performed model validation using cross-validation and classification metrics (precision, recall, F1) to assess performance and reduce classification errors prior to stakeholder review.
- Created visualizations and trend analyses with Matplotlib and Seaborn to highlight top churn drivers and segment-level patterns for actionable insights.
- Documented experiments and versioned notebooks using Jupyter Notebook and GitHub to ensure reproducibility and handoff readiness for senior data teams.
- Produced a concise executive summary for the Associate Director with prioritized recommendations and proposed next steps (targeted interventions and A/B test ideas) based on model findings.
Analytics
Projects
Titanic Survival Prediction
Tools Used: Python, Pandas, NumPy, scikit-learn, Feature Engineering, Logistic Regression, Decision Trees, Ensemble Methods, Hyperparameter Tuning, Model Evaluation, Jupyter Notebook, Matplotlib, Seaborn
- Built classification models to predict passenger survival using exploratory data analysis and statistical methods to identify correlations with age, class and gender.
- Implemented feature selection and transformation pipelines that reduced dataset dimensionality by ~30%, improving model training efficiency and generalization.
- Applied logistic regression, decision trees and ensemble techniques, tuning hyperparameters to reduce false negatives and improve recall for minority classes.
- Documented modeling steps and presented results with visualizations and a summary that contextualized model insights for non-technical stakeholders.
Sales Prediction | July 2023 – August 2024
Tools Used: Python, Pandas, NumPy, scikit-learn, Regression, Feature Engineering, Matplotlib, Seaborn, Jupyter Notebook
- Developed a sales forecasting model using historical sales data and regression techniques to improve forecast accuracy for future planning.
- Improved preprocessing steps to handle missing values and categorical encoding, reducing model training time and improving stability.
- Executed and compared multiple regression models, implementing feature selection that resulted in a reported 92% metric accuracy on the provided evaluation set.
- Presented trend visualizations and actionable insights that linked forecast outputs to potential sales strategies and inventory decisions.
Education
KL University
Bachelor of Technology in Artificial Intelligence and Data Science • April 2020 – May 2024
Narayana Junior College
Board of Intermediate Education • June 2018 – March 2020
STZP High School
SSC [10th class] • June 2017 – April 2018
Certifications
Python for Data Science (Data Science Bootcamp)
Microsoft Azure AI Fundamentals — Microsoft
Microsoft Azure Fundamentals — Microsoft
APSSDC Machine Learning Certification — APSSDC
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