I am a Master of Science in Computer Science student majoring in Data Science from Northeastern University, Boston.

👨🏻‍💻 Hello! I'm Aswath, an enthusiastic software professional focused mainly on Cloud Computing, Data Engineering and Data Science. Here's a glimpse into what defines me:

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• 🔬 As a Research Enthusiast, I have contributed to various Research Publications and collaborated with multiple Research Institutes
• 🔧 As a Data Engineer, I design and build robust data pipelines, ensuring efficient data ingestion, storage, and processing.
• 📊 On the Data Science side, I love exploring complex datasets, deriving meaningful insights, and developing predictive models.
• 🌐 Bridging the gap between engineering and science, I bring a holistic approach to problem-solving and data-driven decision making.
• 🚀 With a passion for innovation, I constantly seek ways to optimize processes, enhance data quality, and drive actionable results.
• 🤝 Collaborating with cross-functional teams, I leverage my technical expertise to deliver end-to-end data solutions.
• 📈 Data visualization is my creative outlet, where I transform raw data into compelling visual narratives that empower stakeholders.
• 🌱 Lifelong learning is my mantra, as I stay up-to-date with the latest advancements in software technologies and industry best practice.
• 🎤 As a Toastmaster, I embrace the art of public speaking, effectively communicating data-driven insights to diverse audiences.
• ⚡ Fun fact: I love finding patterns not only in data but also in the world around me, from spotting hidden connections to discovering new hobbies!

1. Container Orchestrator: This project focused on development of a cloud-based web application on AWS with a microservice architecture. Executed a custom Container Orchestration capable of load balancing incoming HTTP requests and handling fault tolerance.

Technologies used: AWS, Docker, Kubernetes, Flask, HTML, CSS, Javascript, VMWare

2. Analysing Conflicts in Online Football Communities of Reddit: In this work, I chose football communities in Reddit to understand inter-community conflicts. I first plotted various graphs to have a macro level understanding of such Reddit communities i.e. number of comments and unique users over months. Then to explore the Subreddits that are more involved in conflicts, I developed a framework for detecting magnitude as well as direction of conflicts and plot an intercommunity conflict graph with directed weighted edges where weights denote the intensity of conflicts. The conflict graphs then help us identify Subreddits that are most conflict-prone and also help us understand the timeline events when conflict occurs. The framework developed in this work can be used and extended for studying conflict in various online forums.

Technologies used: Python, NetworkX, Plotly, Gephi

3. Classifying Skin Cancer Images Based on Machine Learning Algorithms and a CNN Model: My current research involves the proposition of an efficient approach to detect the kind of skin disease on the human body. In this paper I am recognizing and classifying seven type of skin diseases such as Basal cell carcinoma, Melanocytic Nevi, Actinic keratoses, Melanoma, Dermatofibroma, Benign keratosis-like lesions, Vascular lesions. Extraction of features have been performed by deploying five different algorithms such as Gray-Level Co-Occurrence Matrix(GLCM), Histogram of Oriented Gradient(HOG) and Local Binary Pattern(LBP), Color Histogram and an extended version of Xception. Classification is achieved by utilizing Machine learning algorithms such as Random Forest Classifier, XGBoost Classifier, Support Vector Machine and CAT Boost. Apart from deploying an extended version of a Xception model for the best results we also compare it with other algorithms based on Recall, Precision and F1-score.

Technologies used: Python, Keras, Tensorflow, Seaborn, Numpy, Mtplotlib, sklearn

4. Facial Image Indexing using Locally Extracted Sparse Vectors: I proposed a methodology in which preprocessed images are passed onto Logically Adaptive Regression Kernel for coherent facial feature extraction. The patterns recorded from these feature vectors are condensed using LDA to reduce the computational load. These sparse vectors are quantized and aggregated using VLAD with an intention to classify the descriptors in the later stages of the pipeline. Classification is achieved using CAT Boost and Multi-Layered Perceptron to demonstrate the results using a comparative paradigm. The proposed system has been tested on three benchmark datasets namely Faces95, Faces96, and Grimace. Evaluation of these datasets has been done considering the precision, recall, and F1-Score with an intention to perceive the best one among the proposed alternatives.

Technologies used: Python, Keras, Tensorflow, Deep Learning, Machine Learning, Image Processing, LARK

5. Flight Price Predictor: Exploring the flight price dataset in Kaggle, developed a price prediction tool which finds what influences the demand for the rides and how the costs change depending on factors such as ticket class, days left for travel, airline, etc by applying Multivariable Linear Regression (90.39%), Random Forest Regressor (95.4%) and Extra Trees Regressor (97.2%)

Technologies used: Python, Plotly, Scikit learn, Pandas, Seaborn, Matplotlib

📫 How to reach me:

Feel free to connect with me on LinkedIn, and let's explore the fascinating world of data together!

You can also reach me at: 📞 Phone: +1 8576929678 📧 Email: senthilkumar.as@northeastern.edu