Data Science & Analysis
Song Recommender & Web scraping Project
The Song Recommender project is a powerful tool that utilizes web scraping, machine learning, and unsupervised learning techniques to provide users with personalized and accurate music recommendations. Whether you're looking for the latest chart-toppers or hidden gems from a specific genre, this application has you covered.
The Arctic Ice Extent Predictions Project
Arctic Ice Extent Predictions project is an impressive application that showcases the power of machine learning for predicting important environmental phenomena. The project's open-source nature and user-friendly interface make it an important resource for researchers and environmentalists who are interested in studying Arctic sea ice extent and its implications for the planet.
The Discrimination Detector project is an initiative that addresses the problem of discrimination on social media platforms. Using natural language processing (NLP) to analyze text from social media, the project provides users with feedback and suggestions for how to rewrite text that contains discriminatory language. The dataset for this project includes 210,000 discriminatory tweets and 100,000 random tweets from a control group that do not contain specific discriminatory words. The analysis includes discrimination category detection and discrimination level detection to identify the specific types of discrimination present in social media text and provide insights into the severity of discrimination.
PFC Neurons While Learning: The Role of Pyramidal Neurons in Outcome-Dependent Synchronization (Masters Thesis)
PFC Neurons While Learning is a research project that explores the fascinating and complex world of pyramidal neurons in PFC. With its interdisciplinary approach and use of machine learning tools, the project is a valuable resource for researchers and students who are interested in neuroscience, machine learning, and the intersection of the two fields.
Loan Default Risk Classification
This classification project aims to analyze and predict loan default risk using machine learning algorithms such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Logistic Regression.
With a dataset of 346 customers containing historical loan information, the project categorizes borrowers into risk groups based on the likelihood of loan default. By comparing the performance of each algorithm, the project seeks to identify the most accurate and reliable method for loan default risk classification.
The insights gained from this project can help financial institutions enhance their risk management strategies, make informed lending decisions, and optimize their overall loan portfolio.
NeighbourLink is a valuable project for people who are planning to move from Hamburg to Berlin and are looking for guidance on which neighborhood to move to. By analyzing neighborhood characteristics and providing recommendations based on the user's current neighborhood, NeighbourLink makes it easier for users to make an informed decision about where to move.
AcciPredict is a machine learning project hosted on GitHub that was developed as part of the IBM Data Science Professional Certificate program.