Analytical and detail-oriented Computer Science Engineering graduate with hands-on experience in data analysis, reporting, and insight generation through internships and project work. Strong foundation in Python, SQL, Ad…
Analytical and detail-oriented Computer Science Engineering graduate with hands-on experience in data analysis, reporting, and insight generation through internships and project work. Strong foundation in Python, SQL, Advanced Excel, and data visualization tools, with experience in data cleaning, transformation, exploratory analysis, and dashboard creation. Proven ability to translate complex datasets into actionable insights to support business and operational decision-making. Comfortable working with stakeholders, understanding requirements, and delivering structured analytical outputs. A motivated fresher with internship exposure, strong problem-solving skills, and readiness to contribute in an analyst role.
Developed a CNN-based Facial Recognition System using Python and OpenCV, achieving ~75% recognition accuracy
Performed data preprocessing, image augmentation, and feature extraction to support model training and evaluation
Validated and optimized model performance for real-time detection under varying conditions
Developed a CNN-based Facial Recognition System using Python and OpenCV, achieving ~75% recognition accuracy, Performed data preprocessing, image augmentation, and feature extraction to support model training and evaluation, Validated and optimized model performance for real-time detection under varying conditions
Built and validated a data-driven recommendation system using Python and web scraping
Performed data preprocessing, feature selection, and model evaluation
Improved recommendation accuracy and user engagement by 25% through structured data analysis
Built and validated a data-driven recommendation system using Python and web scraping, Performed data preprocessing, feature selection, and model evaluation, Improved recommendation accuracy and user engagement by 25% through structured data analysis
Built a regression-based machine learning model to predict solar panel efficiency. Performed exploratory data analysis, feature engineering, and model optimization using ensemble techniques, achieving approximately 80 percent prediction accuracy.
Developed a CNN-based real-time facial recognition system using Python and OpenCV. Implemented data preprocessing, image augmentation, and model validation, achieving around 75 percent recognition accuracy under varying conditions.
Developed a collaborative filtering–based recommendation system using Python and Pandas. Cleaned and transformed user interaction data, applied similarity-based techniques, and evaluated model performance, resulting in a 25 percent improvement in user engagement.
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