BHADRA V RAJA

Enthusiastic, highly motivated Computer Science student with proven leadership capabilities and a strong passion for finance, machine learning and deep learning who likes to take initiative and seek out new challenges.

Stock Sentimental Analysis using news headlines through machine learning

• Implemented natural language processing techniques to preprocess and analyze news headline data for sentiment classification.
• Developed a machine learning model to analyze sentiment from news headlines and its impact on stock market fluctuations.

MNIST digit classification using neural networks

• Utilized deep learning frameworks such as TensorFlow or PyTorch to design and train convolutional neural networks (CNNs) for feature extraction and classification of digit images
• Conducted extensive experimentation with various network architectures, hyperparameters, and optimization techniques to enhance model performance and generalization capabilities

Automating Bankruptcy prediction using financial accounting ratios.

• Engineered predictive models, including logistic regression, decision trees, and ensemble methods, to analyze financial data and classify companies into bankruptcy risk categories
• Applied statistical analysis techniques to identify key financial indicators and ratios predictive of bankruptcy risk, leveraging datasets from diverse industry sectors