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Data Scientist with less than a year in Machine Learning & Data Science
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Passionate and detail-oriented Data Scientist with a strong foundation in machine learning, deep learning, natural language processing, data visualization, and statistical modeling. Proficient in Python and libraries like Scikit-learn, TensorFlow, and Hugging Face, with hands-on experience in building predictive models, neural networks, and interactive dashboards. Eager to apply my skills in data analysis, AI, and model development to real-world problems and contribute to impactful, data-driven solutions.
Sri Venkateswara University College of Sciences
Master of Sciences · Applied Statistics
August 1, 2021 – June 30, 2023
Sri Govinda Raja Swamy Arts College
Bachelor of Arts · Mathematics, Economics, Statistics
August 1, 2018 – June 30, 2021
A.P.S.W.R.E.I.S (Center of Excellence) School & Jr. College
Intermediate · Mathematics, Physics, Chemistry
June 1, 2016 – May 31, 2018
A.P.S.W.R.E.I.S School & Jr. College for Boys
Secondary School Education
N/A – May 31, 2016
CSRC, DEPT. OF HUMANITIES AND SOCIAL SCIENCES, IIT TIRUPATI
FIELD INVESIGATOR
January 1, 2025 – April 1, 2025
India
Data Analytics Internship Training – Centum Foundation (Under Accenture Software Training Program)
June 1, 2026 – June 1, 2026
Gained technical proficiency in Python, MySQL, R, and data visualization tools such as Tableau and MS Excel. Completed various projects in technical skills, data visualization, and soft skills: Python and MySQL: Established database connections and performed data manipulation using Python and MySQL. Multiple Disease Predictive System: Created a disease prediction system using machine learning models with data from Kaggle (Diabetes, Parkinson's, Heart Disease), deployed via Streamlit. HR Data Analytics Dashboard: Designed an HR analytics dashboard using Tableau. Visualized datasets using Python libraries (Seaborn, Plotly, Matplotlib) and R libraries (ggplot, Lattice, Plotly). In soft skills training, participated in creating and presenting a team project on 'Interview Readiness.'
1. Machine Learning – House Price Prediction
June 1, 2026 – June 1, 2026
Built a Regression model using scikit-learn to predict house prices. Performed data cleaning, feature engineering, and trained models using Random Forest & XGBoost. Developed a Streamlit web app for real-time predictions.
4. Data Wrangling – Credit Card Fraud Detection
June 1, 2026 – June 1, 2026
Used imbalanced data handling techniques (SMOTE, Under sampling). Applied Random Forest, Logistic Regression, and XGBoost for fraud detection. Achieved high accuracy and precision in identifying fraudulent transactions.
5. Credit Card Fraud Detection (Self-Project)
June 1, 2026 – June 1, 2026
Used imbalanced data handling techniques (SMOTE, Under sampling). Applied Random Forest, Logistic Regression, and XGBoost for fraud detection. Achieved high accuracy and precision in identifying fraudulent transactions.
Data Science Online Internship – Oasis Info byte
June 1, 2026 – June 1, 2026
Completed three data science tasks using Python and machine learning: Iris Flower Classification: Classified iris flowers based on sepal and petal dimensions using linear regression. Unemployment Data Analysis: Analysed unemployment trends during COVID-19 to observe improvements over the years. Sales Prediction: Trained a machine learning model to forecast future sales based on advertising data.
2. Customer Churn Prediction
June 1, 2026 – June 1, 2026
Analysed customer behaviour and developed a classification model using Random Forest & XGBoost. Achieved 85% accuracy in predicting customer churn for telecom data.
3. NLP – Sentiment Analysis on Movie Reviews
June 1, 2026 – June 1, 2026
Collected IMDB reviews and applied NLP techniques (TF-IDF, Word2Vec, BOW). Built a classification model using Logistic Regression & LSTM for sentiment prediction.
Data Visualization (Academic Project)
January 1, 2021 – January 1, 2023
Developed a project on "Data Visualization using Statistical Methods and Techniques." Utilized MS Excel, SPSS, and R for data analysis and visualization. Worked with datasets such as sex ratio data of various states and population growth over the years. Created a variety of visualizations, including bar charts, line charts, pie charts, box plots, radar charts, and scatter plots, using data collected from census and online repositories like Kaggle.
R-Language for Data Science, R Essentials badge
Cognitiveclass.ai
June 1, 2026 – Present
Introduction to Data Concepts
IBM
June 1, 2026 – Present
Intro to Programming, Python, Intro to Machine learning, Data Visualization
Kaggle
June 1, 2026 – Present
AI/ML for Geo Data analysis
IIRS under ISRO
June 1, 2026 – Present
Oasisinfobyte Internship Completed Certification
Oasisinfobyte
June 1, 2026 – Present
Communication Skills, Email Etiquette Certificate
TCSion
June 1, 2026 – Present
Introduction to Python Basic, Intermediate, Data analysis and data Visualization python basics Data Science Concepts, Introduction to Power BI
ai. Planet
June 1, 2026 – Present
Cultural Fit Analysis
The candidate shows a strong inclination towards continuous learning through various certifications from IBM, Kaggle, IIRS (ISRO), and ai.Planet, which aligns with a culture of growth and self-improvement. The diversity of projects, from disease prediction to fraud detection and sentiment analysis, indicates a broad interest in applying data science across different domains. The academic background in statistics and economics, combined with practical project experience, suggests a well-rounded individual who can contribute to diverse teams. However, the lack of extensive professional experience means their adaptability to specific company cultures and team dynamics is yet to be fully assessed.
Soft Skills & Operational Fit
The candidate's resume mentions participation in soft skills training and a team project on 'Interview Readiness,' indicating an awareness of professional communication and collaboration. The 'FIELD INVESIGATOR' role involved data collection, cleaning, and report preparation, suggesting attention to detail and structured work. However, the overall experience is limited to internships and academic projects, which might require more guidance in a fast-paced, senior-level operational environment. The candidate's ability to work independently on complex, ambiguous problems, typical for a senior role, is yet to be fully demonstrated.