Art of Stat: Machine Learning

Art of Stat: Machine Learning icon

ASO Keyword Dashboard

Tracking 2 keywords for Art of Stat: Machine Learning in Apple App Store

Developer: Bernhard Klingenberg Category: education

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

  • algorithms

    Opportunity: 72.0 • Difficulty: 37.3 • Rank 177

    Competitors: 51

    60.9
  • machine learning

    Opportunity: 71.0 • Difficulty: 34.0 • Rank 2

    Competitors: 15

    53.4

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 learning711003453

1,546 competing apps

Median installs: 350

Avg rating: 4.2

21

15

major competitor apps

algorithms721003761

4,366 competing apps

Median installs: 550

Avg rating: 4.1

177177

51

major competitor apps

2 keywords
1 of 1

App Description

The Art of Stat: Machine Learning app includes methods for supervised and unsupervised learning, lets you split data into training and test sets, visualizes all methods, including predictions and heatmaps, and lets you assess the accuracy of your algorithm by showing the confusion matrix and more.

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