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This model predicts one out of three Iris species, based on petal length and width and sepal length and width.
The data is taken from The UCI Machine Learning Repository and created by R.A. Fisher.
Fisher,R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950).
Fires in Spain
Predicting incidence of thyroid disease based on demographic data and the outcomes of several medical tests.
New York Air Quality Measurements
Enigma EU government farm subsidies
The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).
These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).
This dataset is also available from the UCI machine learning repository, https://archive.ics.uci.edu/ml/datasets/wine+quality
For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests):
1 - fixed acidity
2 - volatile acidity
3 - citric acid
4 - residual sugar
5 - chlorides
6 - free sulfur dioxide
7 - total sulfur dioxide
8 - density
9 - pH
10 - sulphates
11 - alcohol
Output variable (based on sensory data):
12 - quality (score between 0 and 10)
What might be an interesting thing to do, is aside from using regression modelling, is to set an arbitrary cutoff for your dependent variable (wine quality) at e.g. 7 or higher getting classified as 'good/1' and the remainder as 'not good/0'. This allows you to practice with hyper parameter tuning on e.g. decision tree algorithms looking at the ROC curve and the AUC value. Without doing any kind of feature engineering or overfitting you should be able to get an AUC of .88 (without even using random forest algorithm)
Please include this citation if you plan to use this database: P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
Ozone day forecasting based on ozone level dataset. Original source and fields description in: UCI Repository of Machine Learning Databases and Domain Theories
Fires in Spain
Historical daily snowfall data in Whistler, BC, Canada over the period July 1 1972 to December 31 2009.
The goal is to get the total snow on ground based on the month, Max & Min temp and the total rain.