Dataset ready for Time Series. Every record is a month, containing the milk production in that month, and the average production per day

Dataset title Monthly milk production: pounds per cow. Jan 62 – Dec 75
Last updated 1 Feb 2014, 19:52
Last updated by source 20 Jun 2012
Provider Time Series Data Library
Provider source Cryer (1986)
Source URL Milk Production
Units Pounds per cow
Dataset metrics 168 fact values in 1 timeseries.
Time granularity Month
Time range Jan 1962 – Dec 1975
Language English
License Default open license
License summary
This data release is licensed as follows: You may copy and redistribute the data. You may make derivative works from the data. You may use the data for commercial purposes. You may not sublicense the data when redistributing it. You may not redistribute the data under a different license. Source attribution on any use of this data: Must refer source.
Description
Agriculture, Source: Cryer (1986), in file: data/milk, Description: Monthly milk production: pounds per cow. Jan 62 – Dec 75

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant.

Source

Originally from the UCI repository

Madrid air pollution data, to predict pollution alerts 1 day in advance. Each row represents a day in central Madrid including features about: NO2 air levels, weather, traffic information with the objective field pollution "YES/NO". The objective field has been defined using Madrid local government pollution alerts definition. Contains data from 2013 to 2017.

Air: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=f3c0f7d512273410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

Weather: https://datosclima.es/Aemet2013/Datosporestacion.html

Traffic: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=33cb30c367e78410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

Context
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

Content
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)

Tips
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.

Source
Red Wine Quality at Kaggle based on UCI Wine Quality Data Set

Madrid air pollution data, to predict pollution alerts 4 days in advance. Each row represents a day in central Madrid including features about: NO2 air levels, weather, traffic information with the objective field pollution "YES/NO". The objective field has been defined using Madrid local government pollution alerts definition. Contains data from 2013 to 2017.

Air: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=f3c0f7d512273410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

Weather: https://datosclima.es/Aemet2013/Datosporestacion.html

Traffic: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=33cb30c367e78410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

This dataset contains data of protein localization sites in yeast bacteria, based on several bio-statistical tests. The data is taken from the UCI Machine Learning Repository at http://archive.ics.uci.edu/ml.

Madrid air pollution data, to predict pollution alerts 7 days in advance. Each row represents a day in central Madrid including features about: NO2 air levels, weather, traffic information with the objective field pollution "YES/NO". The objective field has been defined using Madrid local government pollution alerts definition. Contains data from 2013 to 2017.

Air: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=f3c0f7d512273410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

Weather: https://datosclima.es/Aemet2013/Datosporestacion.html

Traffic: https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=33cb30c367e78410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default

These data record the level of atmospheric ozone concentration from eight daily meteorological measurements made in the Los Angeles basin in 1976. Although measurements were made every day that year, some observations were missing; here we have the 330 complete cases. The response, referred to as ozone, is actually the log of the daily maximum of the hourly-average ozone concentrations in Upland, California.

Detailed variable names:

- ozone : Upland Maximum Ozone

vh : Vandenberg 500 mb Height - wind : Wind Speed (mph)

humidity : Humidity (%) - temp : Sandburg AFB Temperature

ibh : Inversion Base Height - dpg : Daggot Pressure Gradient

ibt : Inversion Base Temperature - vis : Visibility (miles)

doy : Day of the Year

Source: http://www-stat.stanford.edu/~tibs/ElemStatLearn/data.html

Context
Chocolate is one of the most popular candies in the world. Each year, residents of the United States collectively eat more than 2.8 billions pounds. However, not all chocolate bars are created equal! This dataset contains expert ratings of over 1,700 individual chocolate bars, along with information on their regional origin, percentage of cocoa, the variety of chocolate bean used and where the beans were grown.

Content
- 5 = Elite (Transcending beyond the ordinary limits)
- 4 = Premium (Superior flavor development, character and style)
- 3 = Satisfactory(3.0) to praiseworthy(3.75) (well made with special qualities)
- 2 = Disappointing (Passable but contains at least one significant flaw)
- 1 = Unpleasant (mostly unpalatable)

Each chocolate is evaluated from a combination of both objective qualities and subjective interpretation. A rating here only represents an experience with one bar from one batch. Batch numbers, vintages and review dates are included in the database when known.

The database is narrowly focused on plain dark chocolate with an aim of appreciating the flavors of the cacao when made into chocolate. The ratings do not reflect health benefits, social missions, or organic status.

Flavor is the most important component of the Flavors of Cacao ratings. Diversity, balance, intensity and purity of flavors are all considered. It is possible for a straight forward single note chocolate to rate as high as a complex flavor profile that changes throughout. Genetics, terroir, post harvest techniques, processing and storage can all be discussed when considering the flavor component.

Texture has a great impact on the overall experience and it is also possible for texture related issues to impact flavor. It is a good way to evaluate the makers vision, attention to detail and level of proficiency.

Aftermelt is the experience after the chocolate has melted. Higher quality chocolate will linger and be long lasting and enjoyable. Since the aftermelt is the last impression you get from the chocolate, it receives equal importance in the overall rating.

Overall Opinion is really where the ratings reflect a subjective opinion. Ideally it is my evaluation of whether or not the components above worked together and an opinion on the flavor development, character and style. It is also here where each chocolate can usually be summarized by the most prominent impressions that you would remember about each chocolate.

Acknowledgements
These ratings were compiled by Brady Brelinski, Founding Member of the Manhattan Chocolate Society. For up-to-date information, as well as additional content (including interviews with craft chocolate makers), please see his website: Flavors of Cacao

Source
Chocolate Bar Ratings at Kaggle