Portfolio

Deep neural networks have been used to reach state-of-the-art performance on image classification tasks in the last decade. We will explore why it is a hard task and observe the limitations of traditional machine learning models for image classification.

Python
Machine Learning Deep Learning

Applying a standard linear regression model to predict the extent of fire damage to a forest. Our data comes from the Forest Fires dataset from the UCI Machine Learning Repository.

Python R
Machine Learning Model Selection Data Cleaning Cross Validation

Satisfying the huge global demand for garment products is mostly dependent on the production and delivery performance of the employees in the garment manufacturing companies.

Python
Machine Learning Classifier Desicion Trees Random Forest

Applying logistic regression model at a real-life dataset: the Heart Disease Data Set from the UCI Machine Learning Repository to predict heart disease, showing how machine learning can help solve problems that have a real impact on people’s lives.

Python
Machine Learning Classifier Logistic Regression Linear Regression

Creating a model that can predict how many people will be at the gym at a particular day and time. That way, I will be able to enjoy my excersise routine without waiting times.

Python
Machine Learning Linear Regression Stochastic Gradient Descent

This dataset contains information on individual medical insurance bills, which is associated with some demographic and personal characteristics of the person who received it.

Python
Machine Learning Linear Regression

Given a dataset containing information about the company’s clients and asked to help segment them into different groups in order to apply different business strategies for each type of customer.

Python
Machine Learning Clustering K-Mean Algorithm

Build a K-Nearest Neighbors classifier to accurately predict the likelihood of a patient having a heart disease in the future. It is imperative to identify these risk factors early on to prevent the onset of CVDs and reduce premature deaths.

Python
Machine Learning Classifier K-Nearest Neighbors

Jeopardy is a popular TV show in the US where participants answer questions to win money. I am going to work with a dataset of Jeopardy questions to figure out some patterns in the questions that could help to win.

Python R
Machine Learning Hypothesis Testing

Build a spam filter for SMS messages using the multinomial Naive Bayes algorithm. Our goal is to write a program that classifies new messages with an accuracy greater than 80%.

Python R
Machine Learning Classifier Naive Bayes Model Evaluation

By leveraging relevant data sources and analytical tools, we aim to provide actionable insights to inform the company’s advertising decision-making process.

Python R
Data Cleaning Data Visualization Data-driven Decision-making

In this project, we will focus on explanatory data visualization and practice how to use information design principles (familiarity and maximizing the data-ink ratio) to create better graphs for an audience.

Python
Data Cleaning Data Visualization Information Design Principles