Data Science

• I perform extensive data analysis and interpretation to understand patterns and trends.

• Uncovering Hidden Patterns and Automating Insights.

• I provide actionable insights based on data analysis.

Deep Learning

• Developed a high-accuracy, efficient CNN model for pneumonia detection.

• Optimized a CNN architecture for dog-cat classification, achieving high accuracy with improved efficiency.

• I'm currently working on a project inspired by Yoon Kim's 2014 paper on CNNs for sentence classification. This project delves into the comparative performance of Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for sentence classification tasks.

Machine Learning

• Leveraged hyperparameter tuning techniques to maximize the effectiveness of machine learning models for regression and classification problems.

• Extensively tuned various machine learning models, including Random Forest, SVM, and K-Nearest Neighbors, to achieve peak performance for regression and classification tasks.

Web Develop

• Utilized NLTK for text pre-processing in a Fake News Detection project.

• Explored various word embedding techniques (e.g., Bag-of-Words, TF-IDF, one-hot encoding, etc).