Art of Stat: Machine Learning

Developer: Bernhard Klingenberg, Art

Category: Education

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35 installs

Ratings not yet available

+6.00 weekly installs

trend steady

+24 monthly installs

trend steady

ASO Keyword Dashboard

Tracking 62 keywords for Art of Stat: Machine Learning in Google Play

Developer: Bernhard Klingenberg, Art of Stat Category: education

Art of Stat: Machine Learning tracks 62 keywords (1 keyword ranks; 61 need traction). Key metrics: 0% top-10 coverage, opportunity 70.8, difficulty 43.9, best rank 12.

Supervised & Unsupervised Learning with Real Data

Tracked keywords

1 ranked • 61 not ranking yet

Top 10 coverage

Best rank 12 • Latest leader —

Avg opportunity

70.8

Top keyword: decision

Avg difficulty

43.9

Lower scores indicate easier wins

Opportunity leaders

decision

Opportunity: 74.0 • Difficulty: 41.3 • Rank —

Competitors: 541
64.8
nearest

Opportunity: 74.0 • Difficulty: 43.0 • Rank —

Competitors: 831
65.8
algorithm

Opportunity: 74.0 • Difficulty: 39.9 • Rank —

Competitors: 311
60.4
split

Opportunity: 74.0 • Difficulty: 49.7 • Rank —

Competitors: 572
60.8
credit card

Opportunity: 74.0 • Difficulty: 44.9 • Rank —

Competitors: 858
65.2

Unranked opportunities

decision

Opportunity: 74.0 • Difficulty: 41.3 • Competitors: 541
nearest

Opportunity: 74.0 • Difficulty: 43.0 • Competitors: 831
algorithm

Opportunity: 74.0 • Difficulty: 39.9 • Competitors: 311
split

Opportunity: 74.0 • Difficulty: 49.7 • Competitors: 572
credit card

Opportunity: 74.0 • Difficulty: 44.9 • Competitors: 858

High competition keywords

new

Total apps: 370,270 • Major competitors: 21,630

Latest rank: — • Difficulty: 59.9
make

Total apps: 328,331 • Major competitors: 18,397

Latest rank: — • Difficulty: 59.0
features

Total apps: 252,920 • Major competitors: 13,775

Latest rank: — • Difficulty: 59.3
data

Total apps: 180,122 • Major competitors: 6,587

Latest rank: — • Difficulty: 57.6
real

Total apps: 178,133 • Major competitors: 11,974

Latest rank: — • Difficulty: 58.0

All tracked keywords

Includes opportunity, difficulty, rankings and competitor benchmarks

							Major Competitors
machine learning	72	100	54	51 1,575 competing apps Median installs: 1,211 Avg rating: 1.9	18	12	63 major competitor apps
new	65	100	60	89 370,270 competing apps Median installs: 2,549 Avg rating: 2.0	—	—	21,630 major competitor apps
card	69	100	51	77 69,238 competing apps Median installs: 3,586 Avg rating: 2.1	—	—	4,671 major competitor apps
art	70	100	51	75 50,171 competing apps Median installs: 2,063 Avg rating: 1.9	—	—	2,977 major competitor apps
make	65	100	59	88 328,331 competing apps Median installs: 2,059 Avg rating: 2.0	—	—	18,397 major competitor apps
heart	71	100	47	74 42,373 competing apps Median installs: 813 Avg rating: 1.7	—	—	1,452 major competitor apps
multiple	67	100	56	81 129,574 competing apps Median installs: 2,393 Avg rating: 2.0	—	—	8,197 major competitor apps
including	67	100	57	81 130,038 competing apps Median installs: 2,121 Avg rating: 2.0	—	—	7,024 major competitor apps
various	67	100	54	82 140,205 competing apps Median installs: 2,629 Avg rating: 1.9	—	—	7,888 major competitor apps
test	68	100	52	79 96,461 competing apps Median installs: 2,011 Avg rating: 1.9	—	—	4,876 major competitor apps
continuous	73	100	43	69 20,069 competing apps Median installs: 1,223 Avg rating: 1.7	—	—	657 major competitor apps
provides	67	100	55	83 166,176 competing apps Median installs: 1,227 Avg rating: 1.8	—	—	5,004 major competitor apps
real	67	100	58	84 178,133 competing apps Median installs: 1,935 Avg rating: 2.0	—	—	11,974 major competitor apps
user	67	100	56	82 143,402 competing apps Median installs: 1,605 Avg rating: 1.8	—	—	5,128 major competitor apps
includes	69	100	50	77 66,636 competing apps Median installs: 2,092 Avg rating: 1.9	—	—	2,571 major competitor apps
random	73	100	47	69 20,563 competing apps Median installs: 2,978 Avg rating: 2.0	—	—	1,283 major competitor apps
accuracy	73	100	43	67 15,870 competing apps Median installs: 1,343 Avg rating: 1.9	—	—	609 major competitor apps
features	66	100	59	86 252,920 competing apps Median installs: 2,198 Avg rating: 2.0	—	—	13,775 major competitor apps
decision	74	100	41	65 11,706 competing apps Median installs: 1,760 Avg rating: 1.9	—	—	541 major competitor apps
training	70	100	48	76 55,850 competing apps Median installs: 629 Avg rating: 1.8	—	—	1,643 major competitor apps
credit	71	100	48	73 40,026 competing apps Median installs: 2,085 Avg rating: 2.1	—	—	2,418 major competitor apps
data	67	100	58	84 180,122 competing apps Median installs: 1,181 Avg rating: 1.8	—	—	6,587 major competitor apps
nearest	74	100	43	66 13,580 competing apps Median installs: 4,210 Avg rating: 2.1	—	—	831 major competitor apps
select	68	100	58	80 99,496 competing apps Median installs: 2,334 Avg rating: 2.0	—	—	5,606 major competitor apps
algorithm	74	100	40	60 6,179 competing apps Median installs: 2,689 Avg rating: 2.1	—	—	311 major competitor apps

App Description

Supervised & Unsupervised Learning with Real Data

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