Coursera lesson for using cleaning functions in R

David Barón Breiner Mejía

contains variable data that has been organized by files. data has been uploaded and ran in rstudio, and cleaned by the end.

Group Members: Saad Alsulaiman Abdulrahman Alsuhaibani Najd Alkhalifah Musab Askar Saleh Alhussaini Submitted for: Dr. Mahmoud Masoud Problem Statement: The restaurant industry is a challenging and competitive field where every establishment competes to maintain existing customers and attract new ones. In this context, ascertaining the underlying variables determining the restaurant's success and development is crucial for its staying power and further progression. The TripAdvisor Restaurant survival data dataset, which includes many details, is useful for focusing on these comprehensive issues. Many factors lead to a restaurant's success or close down, from food and service quality to a restaurant's environment and location. This report will analyze the TopAdvisor Restaurant survival data to identify key driving forces and risks the restaurant may face. By examining patterns and trends within the data, we aim to address the fundamental question: "How do ratings, visibility, and placement of a new restaurant influence its location rating and continued existence in a continuously changing market?" This knowledge is necessary for people or businesspeople striving to outdo hospitality industry competitors. For restaurant owners, the challenge of meeting and surpassing the customers' expectations never ceases. This is because online ratings and social media posts can lead to reputation-breaking or makings. Consequently, this analysis is designed to identify the determinants of restaurant survival and ratings based on the TripAdvisor Restaurant survival data. By analyzing different sets, including attributes like location, environment, and existence status, we will utilize advanced analytical tools and methodologies to discover new statistics and correlations. The result of our efforts is to equip leaders with analytical tools through which they make proper decisions and attain excellence in operations, higher customer satisfaction, and competitiveness over time as the market keeps on evolving.

The company X Education, which offers consumers online education services, is represented in the dataset used in this project. Lead generation analytics is essential for businesses to analyze and monitor the Lead generation funnel as the ED-tech market is growing daily. It provides the sales and marketing teams with practical information that enable them to take calculated risks when investing in brand development and advertising. The plan involves cleansing and analyzing the dataset to understand the factors affecting customer retention. Through exploratory data analysis and segmentation, key drivers influencing conversion rates, such as website engagement and lead origin, will be identified.

Women data. In this weight is explained by height variable