Machine Learning

Projects in supervised and unsupervised learning completed in R (various packages) and Python (Scikit Learn).

Flavours of Sentiment: Leveraging NLP for Restaurant Review Analysis

Machine Learning, Python /

Introduction In today’s digital age, where opinions and experiences are shared at the click of a button, understanding customer sentiment has become more crucial than ever. As businesses strive to provide exceptional products and services, they turn to the wealth of customer feedback available online to gain insights into customer satisfaction, preferences, and areas for […]

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A Machine Learning approach to predicting the likelihood of an accident insurance claim

Machine Learning, Python /

Project Introduction Insurance companies face the challenge of accurately predicting the likelihood of policyholders filing claims, as it directly affects policy pricing and risk assessment. Understanding the factors that influence the possibility of an insurance claim is crucial for insurers to establish fair and competitive pricing structures. However, predicting claims is not a straightforward task,

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Predicting wine types using Machine Learning in R: A comparison of algorithms

Machine Learning, R /

In this project, we delve into the world of wine classification using machine learning algorithms. We explore the wine quality dataset, aiming to predict the type of wine (red or white) based on several key factors. Through a comparative analysis of three popular machine learning algorithms, we uncover the strengths and weaknesses of each approach,

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Lights on: Electricity price prediction using Decision tree and SVM in Python

Data Analytics, Data Visualisation, Machine Learning, Python /

The price of electricity depends on many factors. Predicting the price of electricity helps many businesses understand how much electricity they must pay each year. The Electricity Price Prediction task is based on a case study where a data analyst would need to predict the daily price of electricity based on the daily consumption of

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Which employees are most frequently absent from work? Clustering the employee absenteeism dataset in R.

Data Analytics, Machine Learning, R /

In today’s fast-paced work environments, organizations face numerous challenges, and one persistent issue that can hinder productivity and disrupt operations is absenteeism. High rates of absenteeism can lead to reduced efficiency, increased costs, and decreased employee morale. Understanding the underlying patterns and factors contributing to absenteeism is crucial for organizations to proactively address this issue.

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