✨ Welcome to Dhrishya's GitHub Profile✨
Things about me
- 🌱 I’m a Graduate student in Data Science on the path of learning Python, R, Machine Learning, Tableau, and Power BI
- 📫 Find me on: LinkedIn Profile
- 😄 Pronouns: She/Her
- 🎈 Hobbies: Reading, Bird Watching, Gardening
- 📺 Favourite Series/Anime: Person of Interest, Hunter x Hunter, Saiki K
These are some of my academic projects:
- Data Science
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Predicting Customer Churn on Sparkify using Pyspark: Utilized Apache Spark's PySpark API and PySparkML to develop a predictive model for customer churn on a music streaming platform. Engineered and selected key features from user data, defining churn as 'Cancellation Confirmation.' Multiple classification algorithms (Naive Bayes, Logistic Regression, Linear SVC, Random Forest, Decision Tree) were evaluated for optimal performance. Conducted hyperparameter tuning to enhance model accuracy, and the best-performing model was identified, providing actionable insights for improving customer retention.
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Enhancing EEG-Based Classification of Obsessive-Compulsive Disorder Using Synthetic Data Generation: Developed generative models to create synthetic EEG data, addressing challenges of limited data and signal variability in neuroscientific applications. Real and synthetic data were combined, significantly improving the model accuracy for EEG signal analysis. Entropy and Fractal Dimension methods were used to classify drug-naive OCD patients, achieving 98% accuracy with Katz Fractal Dimension. Synthetic data augmentation was explored to enhance model performance in scenarios with limited real data availability.
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Wine Quality Analysis: The project consisted of applying machine learning algorithms to examine the quality of red and white wines. The main aim of the project was to understand and apply machine learning concepts, data analysis, and model evaluation techniques. The dataset was selected, and preprocessed, training was done using machine learning model(s), performance was evaluated and the findings were summarized.
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Data Science Book Recommender: The objective of the project was to develop a Shiny app for book recommendation based on the provided dataset which was obtained from Kaggle. The Book Recommender app offers users a curated list of the top 10 books based on their entered keyword(s). Users received a tailored list of the highest-rated books that match their search criteria based on the submitted keyword(s).
- Electronics and Communication
- Fall Detector for the Elderly: The main aim was to detect when a fall occurs using an accelerometer that detects and sends messages to the caretaker and ambulance services to provide emergency medication. The RFID reader and RFID card module present in the circuit helps to know the medical history of the patient. GSM module alerts the required authorities.
- Lexicon: A Smart Wearable Gadget for the disabled: The project's main aim was to produce recognition of hand gestures with accuracy. The MEMS sensor will detect hand motions. The data glove also enables impaired people to switch appliances On and Off as desired. Two main modules included were: One for identifying sign boards and the other for automatic recognition of predefined logos.
