Predicting Google Play Store Apps installations with linear regression and XGBoost
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".