Krupa Sagar Gella
Junior Data Scientist | Data Analyst (Entry-level) • Konidena, Andhra Pradesh, India • k****************@gmail.com • +91*******348 • drivetube.ai/•••••
Professional Summary
Junior Data Scientist with 1 year of experience building Python-based ML models, data pipelines, and analytical dashboards for finance, retail, and healthcare use cases. Skilled in data cleaning, feature engineering, model evaluation, and presenting insights to non-technical stakeholders; seeking an entry-level Data Scientist or Data Analyst role to deliver actionable, data-driven decisions.
Technical Skills
Programming Languages: Python
Frameworks and Libraries: Pandas,NumPy,Scikit-learn,TensorFlow,Keras,Matplotlib,Seaborn
Databases: SQL
Cloud and DevOps: AWS
Data and Analytics: Regression,Classification,Feature Engineering,Cross-Validation,Model Evaluation,Neural Networks,Word Embeddings,Tokenization,Power BI,Tableau,ETL,Data Cleaning,Data Pipelines,Exploratory Data Analysis,Data Visualization
Tools and Methodologies: Git,Jupyter Notebook,VS Code
Core Competencies: Statistical Analysis,Predictive Analytics,Model Benchmarking
Work Experience
URCHOICE Computer Education
Narasaraopet, Andhra Pradesh, India
Python Programming Intern
Intern at a computer education/training provider; developed Python-based data pipelines, predictive models, and visual dashboards for hands-on projects in finance, retail, and healthcare domains.
Tech Stack: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebook, Git, AWS
- Designed and implemented end-to-end ETL pipelines in Python using Pandas and NumPy to ingest, clean, and transform diverse training datasets, reducing data preparation time by ~30%.
- Built regression and classification models with Scikit-learn for pricing, demand forecasting and healthcare prediction scenarios; applied feature engineering and cross-validation to improve model stability and generalization.
- Developed modular preprocessing workflows (missing-value imputation, encoding, scaling) and reusable feature sets to standardize inputs across finance, retail, and healthcare projects, enabling faster model iteration.
- Created interpretable visualizations and analytical reports with Matplotlib and Seaborn to translate model outputs and performance metrics for instructors and non-technical stakeholders.
- Prepared reproducible, notebook-driven prototypes in Jupyter Notebook, managed code with Git, and leveraged AWS for dataset storage and lightweight prototype execution to support student project deployments.
- Evaluated models using RMSE, R2, precision and recall; benchmarked Linear Regression, Ridge and Lasso models to recommend model selection and parameter settings for improved predictive performance.
Projects
Boston House Price Prediction System
Tools Used: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
- Engineered a regression model on the Boston Housing dataset to predict residential prices, applying feature engineering (price-per-sqft ratio, proximity encoding) and outlier detection to improve signal.
- Achieved R2 > 0.85 through iterative feature selection and hyperparameter tuning; benchmarked Linear Regression, Ridge and Lasso using cross-validation and RMSE to choose the best performer.
- Designed interpretable visualizations (heatmaps, residual plots, correlation matrices) to communicate pricing patterns and model behavior to technical and non-technical audiences.
Education
Acharya Nagarjuna University | Vasavi Degree College
Bachelor of Science – Mathematics, Statistics & Computer Science (MSCS) • Narasaraopet, Andhra Pradesh, India • 2021 – 2024
Royal Junior College
Intermediate – Mathematics, Physics & Chemistry (MPC) • Narasaraopet, Andhra Pradesh, India • 2019 – 2021
Certifications
Python Programming Certification — URCHOICE Computer Education
Full Stack Data Science and Gen AI — Naresh I Technologies
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