Chaithra Angadi

Detail-oriented and highly motivated recent graduate with a strong foundation in data engineering principles and technologies. Proficient in SQL, Python, and data modeling, with hands-on experience in data analysis and visualization through academic projects. Eager to leverage analytical skills and problem-solving abilities to contribute to data-driven decision-making. Committed to continuous learning and professional development in the field of data engineering.

1. Apple and Paddy Crop Disease Detection Using CNN : Observation and Development Of Models, Leaf disease can be detected by CNN algorithm which analyses the input image and compares the grey level values with data sets by the TensorFlow models.

2. AI Solving for New York Times Connections Game : The project involves developing an AI-powered solution to automate word grouping in the New York Times Connection Game using semantic relationships. The team utilized Word2Vec for word embeddings and cosine similarity to form and evaluate logical word groups. A web-based interface was created to visualize and interact with grouped words, and success metrics demonstrated alignment with human-like comprehension. The model achieved high accuracy in replicating original game groupings and provided recommendations for further refinement.

3.The Heart Disease Prediction : This project aims to develop a machine-learning model to forecast heart disease risk using algorithms such as Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and Artificial Neural Networks (ANN). The dataset was preprocessed by handling missing values, normalizing numerical features, and encoding categorical variables. Feature engineering emphasized age groups and critical risk factors, enhancing predictive accuracy. Among the models, the Random Forest classifier delivered the highest accuracy, validated through hyperparameter tuning, confusion matrices, and classification reports.