Not all app categories can boast the same degree of user retention on day 30. While news apps were reported in the third quarter of 2024 to have a retention rate of almost 10 percent, social media apps presented less than two percent retention rate after 30 days from install. Entertainment apps presented a three percent installation rate, while a shopping apps had a retention rate of around four percent one month after installation. Before retention: user acquisition Gaining new users is fundamental for the healthy growth of a mobile application, and app developers have an array of tools that can be used to expand their audience. As of the second quarter of 2022, CPI, or cost per install, was the most used pricing model for user acquisition campaigns according to app developers worldwide. The cost of acquiring one new install in North America was of 5.28 U.S. dollars, but driving in-app purchases in the region was more pricey, with a cost of roughly 75 U.S. dollars per user. The future of in-app advertising In recent years, subscriptions and in-app purchases have become more popular app monetization practices, with users finally willing to pay for app premium functionalities and services. In 2020, video ads were reportedly the most expensive type of ads to drive conversions on both iOS and Android apps, while banner ads had a cost per action (CPA) of 36.77 U.S. dollars on iOS, and 10.28 U.S. dollars on Android.

Summary

dataset created from a higher education institution (acquired from several disjoint databases) related to students enrolled in different undergraduate degrees, such as agronomy, design, education, nursing, journalism, management, social service, and technologies. The dataset includes information known at the time of student enrollment (academic path, demographics, and social-economic factors) and the students' academic performance at the end of the first and second semesters. The data is used to build classification models to predict students' dropout and academic sucess. The problem is formulated as a three category classification task, in which there is a strong imbalance towards one of the classes.

Introduction

This dataset delves into the correlation between dropout rates and student success in various educational settings. It includes comprehensive information on student demographics, academic performance, and factors contributing to dropout incidents. The dataset aims to provide valuable insights for educators, policymakers, and researchers to enhance strategies for fostering student retention and academic achievement.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17474923%2Fc00e9ef81fed562fd0f70e620fef80f7%2Fcollege-dropouts1.jpg?generation=1704037747011701&alt=media" alt="">

Dataset

The dataset includes information known at the time of student enrollment – academic path, demographics, and social-economic factors.

- Marital status: Categorical variable indicating the marital status of the individual. (1 – single 2 – married 3 – widower 4 – divorced 5 – facto union 6 – legally separated)

- Application mode: Categorical variable indicating the mode of application. (1 - 1st phase - general contingent 2 - Ordinance No. 612/93 5 - 1st phase - special contingent (Azores Island) 7 - Holders of other higher courses 10 - Ordinance No. 854-B/99 15 - International student (bachelor) 16 - 1st phase - special contingent (Madeira Island) 17 - 2nd phase - general contingent 18 - 3rd phase - general contingent 26 - Ordinance No. 533-A/99, item b2) (Different Plan) 27 - Ordinance No. 533-A/99, item b3 (Other Institution) 39 - Over 23 years old 42 - Transfer 43 - Change of course 44 - Technological specialization diploma holders 51 - Change of institution/course 53 - Short cycle diploma holders 57 - Change of institution/course (International)).

- Application order: Numeric variable indicating the order of application. (between 0 - first choice; and 9 last choice).

- Course: Categorical variable indicating the chosen course. (33 - Biofuel Production Technologies 171 - Animation and Multimedia Design 8014 - Social Service (evening attendance) 9003 - Agronomy 9070 - Communication Design 9085 - Veterinary Nursing 9119 - Informatics Engineering 9130 - Equinculture 9147 - Management 9238 - Social Service 9254 - Tourism 9500 - Nursing 9556 - Oral Hygiene 9670 - Advertising and Marketing Management 9773 - Journalism and Communication 9853 - Basic Education 9991 - Management (evening attendance)).

- evening attendance: Binary variable indicating whether the individual attends classes during the daytime or evening. (1 for daytime, 0 for evening).

- Previous qualification: Numeric variable indicating the level of the previous qualification. (1 - Secondary education 2 - Higher education - bachelor's degree 3 - Higher education - degree 4 - Higher education - master's 5 - Higher education - doctorate 6 - Frequency of higher education 9 - 12th year of schooling - not completed 10 - 11th year of schooling - not completed 12 - Other - 11th year of schooling 14 - 10th year of schooling 15 - 10th year of schooling - not completed 19 - Basic education 3rd cycle (9th/10th/11th year) or equiv. 38 - Basic education 2nd cycle (6th/7th/8th year) or equiv. 39 - Technological specialization course 40 - Higher education - degree (1st cycle) 42 - Professional higher technical course 43 - Higher education - master (2nd cycle)).

- Nationality: Categorical variable indicating the nationality of the individual. (1 - Portuguese; 2 - German; 6 - Spanish; 11 - Italian; 13 - Dutch; 14 - English; 17 - Lithuanian; 21 - Angolan; 22 - Cape Verdean; 24 - Guinean; 25 - Mozambican; 26 - Santomean; 32 - Turkish; 41 - Brazilian; 62 - Romanian; 100 - Moldova (Republic of); 101 - Mexican; 103 - Ukrainian; 105 - Russian; 108 - Cuban; 109 - Colombian).

- Mother's qualification: Numeric variable indicating the level of the mother's qualification.
(1 - Secondary Education - 12th Year of Schooling or Eq. 2 - Higher Education - Bachelor's Degree 3 - Higher Education - Degree 4 - Higher Education - Master's 5 - Higher Education - Doctorate 6 - Frequency of Higher Education 9 - 12th Year of Schooling - Not Completed 10 - 11th Year of Schooling - Not Completed 11 - 7th Year (...

Portobello Tech is an app innovator that has devised an intelligent way of predicting employee turnover within the company. It periodically evaluates employees' work details including the number of projects they worked upon, average monthly working hours, time spent in the company, promotions in the last 5 years, and salary level. Data from prior evaluations show the employee’s satisfaction at the workplace. The data could be used to identify patterns in work style and their interest to continue to work in the company. The HR Department owns the data and uses it to predict employee turnover. Employee turnover refers to the total number of workers who leave a company over a certain time period.