Retail-Feature-Image

SQL exercises on retail shopping: Single table queries

Introduction In this module, we will explore how to use Structured Query Language (SQL) to solve problems in real-world business scenarios. The questions and their solutions will involve querying single tables, requiring only a basic understanding of SQL commands. We will focus on business scenarios related to human resources (HR) and product orders within a…

Bank deposit - feature image

Predicting bank deposit subscriptions using machine learning

Introduction The ability to predict customer behaviour has become increasingly important for businesses, especially in the financial sector. For banks, understanding which clients are likely to subscribe to a term deposit can lead to more effective marketing strategies and better resource allocation. This analysis explores a beginner’s approach to predicting whether a client will subscribe…

Income-levels-Feature-Image

How to predict income levels using machine learning

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…

Heart-disease-feature-image

How to predict heart disease treatment using Python

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…

Call centre Feature Image

How to analyse call-centre-sentiments data using Python

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,…

Retail Insights-Feature Image

How to analyse and unlock retail insights from a superstore data using Python

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)…