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Data Scientist with 1+ years in Machine Learning & Data Analytics
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Results-driven Data Scientist with hands-on experience building end-to-end Machine Learning and Deep Learning solutions across forecasting, classification, and NLP domains. Boosted model accuracy by 15% and reduced preprocessing effort by 25% through feature engineering, EDA, and hyperparameter tuning. Proficient in Python, SQL, TensorFlow, Scikit-learn, Power BI, and Tableau. Published researcher with a peer-reviewed conference paper (ICACTEA 2025) on deep learning applications for environmental monitoring.
Lakireddy Balireddy College of Engineering
B.Tech · Computer Science and Engineering
N/A – June 30, 2025
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Detection of Microplastics in Soil using CNN
January 1, 2025 – January 1, 2025
Built and compared multiple deep learning models Faster R-CNN (ResNet-50), EfficientNetB7, and YOLOv8 for classifying microplastics in soil into four categories: fiber, film, fragment, and pallet. Applied advanced image preprocessing using OpenCV and NumPy for noise reduction and feature enhancement, and trained models on a custom-annotated soil image dataset achieving 92% classification accuracy. Selected Faster R-CNN with ResNet-50 as the best-performing model based on superior Precision, Recall, and F1-Score across all classes, reducing manual microscopic analysis time by 40%. Published findings as a peer-reviewed paper at ICACTEA-2025, Aditya University, demonstrating real-world applicability of CNNs for environmental monitoring and soil quality assessment.
View ProjectApple Stock Price Forecasting
January 1, 2025 – January 1, 2025
Analyzed 100,000+ records of Apple stock data (2010-2021), performing time-series alignment, weekend/after-hours filtering, and missing value handling to build a clean, market-ready dataset. Applied and compared multiple forecasting models ARIMA, SARIMA, Hybrid ARIMA, XGBoost, and LSTM evaluating performance using MAE, MSE, and RMSE to identify the best-performing approach. Selected SARIMA as the final model for its superior accuracy, successfully capturing long-term upward trends and short-term market volatility in Apple stock prices. Deployed an interactive Streamlit dashboard for real-time prediction visualization, enabling non-technical users to explore forecasts and historical trends intuitively.
View ProjectData Science Programme
EXCELR
November 1, 2025 – Present
peer-reviewed conference paper at ICACTEA-2025
Aditya University
January 1, 2025 – Present
Big Data, Hadoop and Spark Basics
EDX
January 1, 2024 – Present
Participant, AWS Cloud Computation Event
Braino Vision
January 1, 2023 – Present
Winner, 24-Hour Hackathon on Data Science using Python – Braino Vision
Braino Vision
January 1, 2022 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, including environmental monitoring and financial forecasting, shows a broad interest in applying data science to different domains. Their participation in hackathons and virtual internships, alongside academic projects, indicates a proactive and continuous learning mindset. The listed skills align well with a data scientist role, suggesting a good fit for a team focused on innovation and practical application of ML/DL.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their project work, particularly in selecting optimal models and improving efficiency. Their ability to deploy interactive dashboards suggests good communication and user-centric design. The hackathon win indicates a competitive spirit and ability to perform under pressure. The peer-reviewed publication highlights attention to detail and a structured approach to problem-solving.