Predicting Google Play Store Apps installations with linear regression and XGBoost
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mobile phones and other portable electronic devices have taken an important role in our daily lives, and people’s expectations of the functionalities of such devices are constantly changing. For the mobile application marketplace to successfully meet the customer requirements, app developers must understand the market trends and users' interests. One way to evaluate the extent of success of an app is its amount of installations. With most existing model forecasts, app installs as a time series of past installation amounts. This article analyses the known features of applications such as category, rating, content rating, genre, etc., with linear regression and Extreme Gradient Boost to extract the relationship between app features and installations. The dataset used for training the models is ‘Google Play Store Apps’ from the world’s largest data science community, Kaggle. Furthermore, the performance of each model is demonstrated and compared with predictions on a testing set. The article describes the details in data processing, model training, and predicting. The results exhibit a strong relationship between several features, including date of last update, genre, and amount of reviews with app installations, and consequently provide a reference for app developers to understand the factors that impact the install amounts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it