Art of Stat: Machine Learning
ASO Keyword Dashboard
Tracking 2 keywords for Art of Stat: Machine Learning in Apple App Store
Art of Stat: Machine Learning tracks 2 keywords (2 keywords rank; full coverage across the tracked set). Key metrics: 50% top-10 coverage, opportunity 71.5, difficulty 35.6, best rank 1.
Tracked keywords
2
2 ranked • 0 not ranking yet
Top 10 coverage
50%
Best rank 1 • Latest leader 177
Avg opportunity
71.5
Top keyword: algorithms
Avg difficulty
35.6
Lower scores indicate easier wins
Opportunity leaders
- 60.9
algorithms
Opportunity: 72.0 • Difficulty: 37.3 • Rank 177
Competitors: 51
- 53.4
machine learning
Opportunity: 71.0 • Difficulty: 34.0 • Rank 2
Competitors: 15
Unranked opportunities
Every tracked keyword currently has some ranking data.
High competition keywords
algorithms
Total apps: 4,366 • Major competitors: 51
Latest rank: 177 • Difficulty: 37.3
machine learning
Total apps: 1,546 • Major competitors: 15
Latest rank: 2 • Difficulty: 34.0
All tracked keywords
Includes opportunity, difficulty, rankings and competitor benchmarks
| Major Competitors | |||||||
|---|---|---|---|---|---|---|---|
| machine learning | 71 | 100 | 34 | 53 1,546 competing apps Median installs: 350 Avg rating: 4.2 | 2 | 1 | 15 major competitor apps |
| algorithms | 72 | 100 | 37 | 61 4,366 competing apps Median installs: 550 Avg rating: 4.1 | 177 | 177 | 51 major competitor apps |
App Description
Included ML algorithms so far include:
- Multiple Linear Regression (including categorical predictors and interactions interactions)
- Multiple Logistic Regression (including categorical predictors and interactions interactions)
- Discriminant Analysis (Linear & Quadratic)
- Naive Bayes
- K-Means Clustering
Functionality:
- Provides various datasets (Palmer Penguins, Wine Quality, Heart Disease, Iris Flowers, Credit Card Defaults, ...) or lets user upload their own CSV file
- Split data into training and test sets
- Standardize Features
- Select continuous and/or categorical features (where appropriate)
- Visualize all methods (Scatterplots, Heatmaps), predicted labels or posterior probabilities
- Assess accuracy by displaying Confusion Matrix and accuracy statistics (including precision and recall) in multiple ways
- Make predictions for new observations
Modules in preparation:
- Decision Trees & Random Forests
- Nearest Neighbor