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.

Data Preprocessing for Numeric features (Jupyter Notebook)

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

Medicinal Plants Detection

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.

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…

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.

This is the Data Mining Project for predicting the student's grade before the final and Mid-2 examination. I use Python and Jupyter Notebook for this Project.

Cardiovascular Risk Prediction

BoomBikes, a U.S. bike-sharing provider, faces revenue drops due to COVID-19. To boost revenue post-lockdown, they aim to develop a business plan by analyzing factors influencing bike demand. Using a dataset on daily demand, including weather and user habits, they seek to understand key predictors and optimize bike-sharing operations.

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

Interconnect : Clients Churn Prediction using ML

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

Machine Learning Project

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).

This is a supervised machine learning project using telecom customer data to predict customers that would churn based on customer Age Group, Relationship Status, Subscribed Services, Charges, and Financial Responsibilities, etc.