Mayowa Ekundayo's repositories
Carbon-Footprint-Prediction
This Project analyses the carbon footprint of the U.S. commercial sector using three machine learning models. A combination of energy consumption data and carbon dioxide emission data was used to achieve the carbon footprint variable.
Car-Review-Classification
This project covers the steps carried out in the analysis and cleaning of an unstructured Car review dataset. The dataset is made up of reviews of 10,678 customers for four different car models (Toyota, Hyundai, Ford, and Kia), with their respective car recommendation choices to other people, over a period. The aim is to develop a suitable model that will utilize the information in the dataset and classify new/future customer reviews appropriately. This information would help the car manufacturers/industry’s to make informed decisions based on predicted positive and negative car recommendation reviews to others.
Newspaper-Digital-Subscribers
This project covers a critical analysis of existing subscribers in a daily newspaper company. The dataset adopted for use in this report, comprises of personal information of the company’s digital subscribers. The newspaper company is perceived to be a market leader but has been faced with the challenge of customer retention. The company is therefore interested in developing strategies to manage subscriber churn rate. A proposed initiative is to offer 25% discount to inactive subscribers. In a bid to achieve this, the company is looking to develop a model that can predict possible customer/subscriber churn, in order to minimize financial loss and cost incurred in gaining new market entrance/confidence. Upon achievement of this goal, the company will be able to minimize subscriber churn and achieve overall business success.
Restaurant-Reservation-System-Design
This project focuses on the software process for the development of an application whose functionality is to successfully make reservation at a selected restaurant. To effectively design and implement an online reservation system for Xylo.
Superstore-Visualization-and-Analysis
This project covers the visualisation techniques adopted for the analysis of a publicly available Superstore dataset. The anonymous Superstore is a perceived market leader in the retail distribution industry in the United States (US). This Superstore seeks to enhance its sustainable competitive advantage by understanding business areas that need to be developed for increased sales, demand, and profit. Also, the business is interested in identifying operations, products, and locations that contribute significantly to financial loss and decreased sales. The developed visualisations aim to reflect the business performance, enabling key drivers draw insights, and make informed business decisions.
Telecommunication-Churn-Prediction
The selected dataset provided in this project is gathered from a telecommunications company in line with its customer retention program. The focus of the program is to examine the total number of existing and churned customers of the company. The resultant information will further be used to predict the number of customers that are most likely to churn. The company is therefore interested in building a classification model for churn prediction, which will help to achieve the objective of minimizing the churn rate and consequently enhance customer retention. Identifying the factors that would likely cause a customer to churn, will therefore be of key importance.