In this project, I dive into the practical world of time series analysis to forecast sales and demand—a crucial skill in today’s data-driven business environment. By working with real sales data from Nielsen BookScan, I have the opportunity to bridge the gap between theory and real-world application. My goal is to turn historical data into
Student retention is critical for educational institutions, impacting financial sustainability andacademic success. High dropout rates can lead to revenue losses and reputational damage.Study Group, a global education provider, aims to enhance student success by identifying atrisk students early and implementing proactive interventions. This study applies supervisedmachine learning techniques to predict dropout risks, enabling Study Group
Customer segmentation enables a business to group customers based on demographics (e.g., age, gender, education, occupation, marital status, and family size), geographics (e.g. country, time zone, language, and location), psychographics (e.g. lifestyle, values, personality, and attitudes), behaviour (e.g. purchase history, brand loyalty, and response to marketing activities), technographics (e.g. device type, browser type, and original source),
A poorly maintained ship engine in the supply chain industry can lead to inefficiencies, increased fuel consumption, higher risks of malfunctions, and potential safety hazards. Issues with engines could lead to engine malfunctions, potential safety hazards, and downtime causing delayed deliveries, resulting in the breakdown of a ship’s overall functionality, consequently impacting the business, such
Coles is a prominent supermarket, retail, and customer service brand in Australia, with over 800 stores nationwide and a 27% share of the market. As a key player in the Australian retail landscape, it caters to millions of customers by providing a wide range of products and services. Its extensive presence and significant market influence
In this project, I performed a comprehensive analysis of a loan application dataset to create a model predicting the probability of loan default. The dataset includes applicant information such as age, income, employment background, and loan-specific details like loan amount, interest rate, and purpose. The goal was to develop a model that would enable financial
In this project, The aim is to build a predictive model to estimate used vehicle prices based on various attributes such as brand, model, mileage, engine type, transmission, etc. I am utilizing a dataset containing vehicle information that is readily available on Kaggle. This dataset comprises 188,532 data points, each representing a unique vehicle listing,