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https://github.com/ben519/MLPB
Machine Learning Problem Bible | Problem Set Here >>
https://github.com/ben519/MLPB
machine-learning python r
Last synced: about 2 months ago
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Machine Learning Problem Bible | Problem Set Here >>
- Host: GitHub
- URL: https://github.com/ben519/MLPB
- Owner: ben519
- Created: 2016-06-13T19:04:23.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-11-29T23:46:03.000Z (almost 5 years ago)
- Last Synced: 2024-05-03T06:37:57.483Z (5 months ago)
- Topics: machine-learning, python, r
- Language: R
- Homepage: https://github.com/ben519/MLPB/wiki/Problems
- Size: 326 KB
- Stars: 719
- Watchers: 91
- Forks: 275
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Problem Bible (MLPB)
MLPB is meant to become an organized collection of machine learning problems and solutions. In practice, machine learning often goes like this
> *I have this problem... I need to classify something as A, B or C using a combination of numeric and categorical features. If I could find a similar problem, maybe I could modify the solution to work for my needs.*
This is where MLPB steps in. Want to see machine learning problems with sparse data? Got it. Want to compare Scikit-learn’s RandomForestRegressor with R’s randomForest? Got it. Need an example of predicting a ranked target variable? Got it.
## How It Works
MLPB contains a directory of *Problems*. Within each problem is a designated *\_Data* directory and one or more scripts with a solution to the problem. This looks something like
```
Problems/
Classify Iris Species/
_Data/
iris.csv
train.csv
test.csv
predict_species_xgb.R
Predict NFL Game Winner/
_Data/
train.csv
test.csv
random_forest_model.py
random_forest_model.R
```
Most of these directories should include a *README.md* file providing details about the problem, data, and solution(s). You can browse all the problems in MLPB's [wiki](https://github.com/ben519/MLPB/wiki). You can also search for problems with specific tags like [mult-class classification], [sparse-data], [NLP], etc.
## Contact
If you'd like to contact me regarding bugs, questions, or general consulting, feel free to drop me a line - [email protected]
## Support
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