Hello, this is Bhimanathini Sai Krishna.
I am a Data Scientist currently working at Callaway Digital Technologies. Over the past five years, I’ve gained extensive experience in Data Science, Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), and Asterisk.
I have significantly improved data warehousing solutions and led several machine learning projects within the organization. My work involves solving real-world classification, regression, and clustering problems using tools like Scikit-learn, XGBoost, Keras, and TensorFlow—applying both machine learning and deep learning techniques.
In the field of Computer Vision, I’ve worked with frameworks like YOLO (You Only Look Once) for real-time object detection, and applied OpenCV, TensorFlow, Keras, and PyTorch for various image and video processing tasks. On the NLP front, I have experience with spaCy, NLTK, and transformer models such as BERT.
During my free time, I enjoy working on Kaggle projects to sharpen my skills. I also write about my work, projects, and experiences to share knowledge with the broader community. It's been a great journey so far. I'm passionate about learning and solving complex technical problems.
My goal is to provide value to society.
Data is my pathway towards that goal
I have posted my Data Science projects here.
Build a recommendation system for apparel which gives the most similar products using amazon dataset. Techniques used Tfidf-W2Vec, VGG-16.
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Predict the movies for users with the help of surprise library and SVD. Trained different models and used the result of each model as feature to the next model with the final model as XgBoost to reduce RMSE.
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Exploratory Data Analysis of Cancer data from kaggle and running various models and plotting confusion matrix and return probability score to make model interpretable. Techniques used Tfidf, Naive Bayes, Logistic Regression, SVM, Random Forest.
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Predicting whether a question is duplicate or not and predict if the question is duplicate from quora dataset and reducing the loss using hyperparameter tuning. Techniques used : Avg-W2Vec, Logistic Regression, Linear SVM and XGBoost.
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Predict the pick-up density of cabs at a given particular time and a location in New York City using simple models such as Rolling Window and its variants and Regression Models such as Random Forest and XGBoost and using Time Series Forecasting and Fourier Features.
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Visualize the data set and apply various models and see which one performs better with respect to other on various performance metrics. Techniques used : KNN, Naive-Bayes, Logistic Regression, Decision Tree.
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Predict the Activity related to the datapoint Each datapoint corresponds one of the 6 Activities (Walking,WalkingUpstairs,WalkingDownstairs,Standing,Sitting,Lying) Performed hyperparameter tunning with LSTM.
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Predicted the tag related to the question and improved the micro-averaged-f1-score with hyperparameter tuning of Logistic Regression and SVM.
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