Passionate about blending cognitive science with data analytics to uncover unique insights and solve complex problems. Continuously looking to expand my knowledge and experiences in data science and cognitive science. I am open to opportunities that challenge me and allow me to contribute meaningfully while growing professionally.
Programming Languages Intermediate Experience: Python & R Advanced Experience: SQL & Snap!
Data Techniques Intermediate Experience: Pandas, sk-learn, matplotlib, seaborn, EDA, PCA
Machine Learning Intermediate Experience: Regression, Classification, Clustering, Neural Networks
Organization Microsoft Excel & Google Sheets
Communication
Qualitative Research
Report Writing
Teamwork & Collaboration
Language Learning Model: Designed an Elman-based recurrent neural network for next-letter prediction, improving accuracy with Bayesian analysis
Perceptron Training: Created a binary classifier using the Perceptron algorithm, featuring a robust training system and exploring its limitations in non-linearly separable problem classification
Email Classifier: Developed an sk-learn-based email classifier, enhancing accuracy through advanced feature engineering and regression model optimization
Housing Price Predictor: Created a Bay Area housing price predictor by aggregating datasets into a comprehensive dataframe, using RMSE-based predictive modeling and feature engineering for estimation of property values
Multi-Layer Neural Network: Trained multi-layer neural networks using both supervised and unsupervised learning techniques, designed and tested various frameworks for complex data analysis