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Consquently, *no* Neural Network will be used and so far the following Models are implemented:\n\n- [Ridge-Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html)\n- [Gradient-Boosting-Trees](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html)\n- [Random-Forest-Regressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)\n\nFurthermore, for a first analysis, the cluster- and *aprori*-pair-plots can be easily generated for checking dependencies in the data.\n\nThe **CSV-First-Insights**-application can be installed like this:\n\n    python setup.py install\n \nThe options of the Command Line Interface of **CSV-First-Insights** are:\n\n    python -m pyinsights --help\n    usage: __main__.py [-h] [--fname FNAME FNAME] [--mode MODE] [--export]\n\n    Analyzer for small (# \u003c 10,000) csv-Databases with binary content via scikit-learn! \n    Training-Set and Test-Set is separately stored in two databases.\n\n    optional arguments:\n    -h, --help           show this help message and exit\n    --fname FNAME FNAME  Two filenames have to be defined for the train- and test-set. \n                         Default names are: train-data.csv','test-data.csv'\n    --mode MODE          Please chose the model for the forecaset: \n                          *Ridge-Regression as a Variation of Linear-Regressions -\u003e rig(deafault) \n                          *Gradient-Boosting-Trees -\u003e grad \n                          *Random-Forest -\u003e fors \n                          *All three models, please choose -\u003e all\n    --export             Export the Apriori-Analysis, Cluster-Maps, and Predictions as png- and txt-file\n\nThe **CSV-First-Insights** can be also loaded as packages like this:\n```python\nimport pyinsights\nimport pyinsights.dataread as dr\nimport pyinsights.mlmodels as ml\nimport pyinsights.sklsetups as skl\n```\n\nThe Ridge-Regression-Prediction of **CSV-First-Insights** for the [The Ultimate Halloween Candy Power Ranking](https://www.kaggle.com/fivethirtyeight/the-ultimate-halloween-candy-power-ranking) of kaggle:\n\n![](https://github.com/Anselmoo/csv_first_insight/blob/master/docs/DecissionBar_ridge_reg_prediction.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanselmoo%2Fcsv_first_insight","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanselmoo%2Fcsv_first_insight","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanselmoo%2Fcsv_first_insight/lists"}