Introduction The ability to extract meaningful insights from raw data is more crucial now than ever. Among the many datasets available for analysis, the “Adult Census Income” data can be used to understand the socio-economic factors that influence income levels. This dataset, collected from the 1994 U.S. Census, includes a variety of demographic information, such…
Introduction Heart disease remains one of the leading causes of morbidity and mortality worldwide. Hence, understanding the factors that contribute to heart disease and the treatments that can mitigate its effects is crucial for improving patient outcomes. This project will explore a dataset related to heart attack patients and analyse various demographic, lifestyle, and clinical…
Introduction Customer service quality can make or break a company’s reputation. Call-centres are often at the frontline of customer interactions, making them a crucial component of customer satisfaction strategies. To ensure that these interactions meet customer expectations, it’s essential to analyse and understand the factors that contribute to positive or negative experiences. In this project,…
Introduction The vast amounts of retail data that accumulate from day-to-day transactions can provide valuable insights into product performance, customer segmentation, and market trends. In this project, we will carry out a detailed analysis of a retail dataset, exploring various aspects such as sales trends, geographical performance, and customer segmentation using RFM (Recency, Frequency, Monetary)…
Introduction The automotive industry is undergoing a profound transformation, with electric vehicles (EVs) at the forefront of this revolution. As governments, consumers, and manufacturers alike recognise the environmental and economic benefits of EVs, adoption rates are soaring, especially in forward-thinking regions like Washington State. This project delves into a comprehensive analysis of EV adoption trends…
Introduction Fraudulent activities have become increasingly sophisticated, posing significant risks to financial institutions and consumers alike. Detecting and preventing fraud has become a critical focus for businesses as they strive to protect their assets and customer trust. In this beginner’s data analysis project, we will carry out an exploratory data analysis (EDA) of financial transaction…
Efficient warehouse management is essential for maintaining a smooth supply chain and ensuring customer satisfaction. A well-organised warehouse not only reduces operational costs but also enhances the ability to meet customer demand promptly. In this project, we will carry out a comprehensive analysis of warehouse stock trends, utilising Python programming to provide insights into product…
Introduction In the world of data science and analytics, the quality of your analysis is only as good as the quality of your data. Unfortunately, real-world datasets are often messy, requiring extensive cleaning before you can extract meaningful insights. In this tutorial, we’ll walk through a step-by-step process to clean a typical messy IMDB dataset…
Introduction Data cleaning is a crucial step in the data analysis process, especially when dealing with real-world datasets, which are often incomplete, inconsistent, and filled with errors. In this tutorial, we’ll walk through the process of cleaning-up a ‘messy’ healthcare dataset using Python. This guide will be helpful for data analysts, data scientists, or anyone…
Introduction Dealing with messy datasets is a common challenge when working with real-world data as a Data Scientist or Data Analyst. In this exercise, we’ll walk through the process of cleaning a typical messy Human Resources (HR) dataset using Python programming.