A machine learning exercise using the Spotify "hit predictor" dataset, with data analysis of past "hits" by decade. Deployment using Flask via Heroku.

The project involves performing clustering analysis (K-Means, Hierarchical clustering, visualization post PCA) to segregate stocks based on similar characteristics or with minimum correlation. Having a diversified portfolio tends to yield higher returns and face lower risk by tempering potential losses when the market is down.

With the Student Alcohol Consumption data set, we predict high or low alcohol consumption of students.

Data Preprocessing for Numeric features (Jupyter Notebook)

Medicinal Plants Detection

I am on the Advisory Services Team of a financial consultancy. One of MY clients, a prominent investment bank, is interested in offering a new cryptocurrency investment portfolio for its customers. The company, however, is lost in the vast universe of cryptocurrencies. They’ve asked me to create a report that includes what cryptocurrencies are o…

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

ML / DL Algorithms implemented from scratch. Developed with only numpy as dependency. Machine Learning Algorithms such as Support Vector Machine, Linear Regression, Artificial Neural Networks and other data transformation algorithms are implemented. Project is released as a python package and can be download from Python Package Installer.

Cardiovascular Risk Prediction

An analysis, interpretation, and presentation of what cryptocurrencies are available on the trading market and how they can be grouped using classification. In this project, there are unsupervised learning and Amazon SageMaker skills exhibited by clustering cryptocurrencies and creating plots to present results.

Using unsupervised learning to predict if crypto currencies are affected by 24-hour or 7-day price changes.

The principal component analysis is a technique that can transform higher dimensional data into lower dimensional data while keeping the essence of the data Benefits: i) fast execution of the algorithm ii) visualization is easy

Interconnect : Clients Churn Prediction using ML

Using LightGBM and Other Models for Car Prices' Prediction – Study Project for Yandex Practicum

In this project we built a model to predict whether a person will remain in a hypothetical trade union called the United Data Scientists Union (UDSU).

Apply unsupervised learning techniques to identify customers segments.

Prediction of the sale price of a vehicle using predictive models using gradient boosting