I'm proficient in using several Python libraries to build machine learning algorithm ranging from simple algorithm to complex deep neural networks. I have experience working with supervised learning algorithm, unsupervised learning algorithm, and deep learning models.
I developed a predictive ridge regression model using building features and country macroeconomic data to accurately forecast real estate prices. This project demonstrates my proficiency in merging diverse datasets and applying advanced machine learning techniques, showcasing my expertise in data-driven analysis for the real estate market.
I utilized Pandas, scikit-learn, Matplotlib, and SciPy to analyze past loan application data, constructing a logistic regression model for predicting loan application statuses. This project exemplifies my proficiency in data analysis and machine learning.
In this project, I employed ARCH models to forecast stock volatility using historical data sourced from Yahoo Finance. Leveraging the power of ARCH models, renowned for their ability to capture volatility clustering in financial data, I delved into intricate patterns within stock market dynamics.
I utilized Python to train a K-Nearest Neighbors (KNN) algorithm using data from 200 customers, focusing on income and spending score. This project highlights my expertise in machine learning and predictive modeling, enabling businesses to discern patterns in customer behavior and enhance their decision-making processes.
In this project, I used Pytorch libraries to transform 9000 images of 9 different species of fishes. In addition to this, I created a classification model using ResNext50 and the optimized the performance using intuitive fuzzy technique and extended learning machine.