Commit 2b61135f by Rahim Rasool

Merge branch 'New-datasets' into 'master'

Added new datasets

See merge request datasciencedojo/datasets!1
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Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Beginner <br/>
**Recommended Use:** Classification <br/>
**Domain:** Health/Social Sciences <br/>
---
### Accidental Drug Related Deaths in Connecticut, US
---
![](IllegalDrugAbuse.jpg)
---
The Connecticut Deaths due to Drugs Dataset contains information about 5106 people who died due to drug overdose between 2012 and 2018 in Connecticut, US.
The dataset includes data related to the age, race, gender, place of residence of the victims as well as the drugs they overdosed on. This information can be used to understand if drug use is prevalent in a specific area or city, drug use by individuals of different age groups and races as well as the popularity of different types of drugs.
This dataset is recommended for exploring data visualization techniques, clustering techniques and implementing regression models to predict how drug use may increase over time.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ------------------- | ----------------------------------------------------------------- | --------- |
| 1 | ID | Row ID | Text |
| 2 | Date | Date | Date/Time |
| 3 | DateType | Type of Date in Column 2 <br>[Date of Reporting ot Date of Death] | Text |
| 4 | Age | Age of Patient | Numeric |
| 5 | Sex | Sex of Patient | Text |
| 6 | Race | Race of Patient | Text |
| 7 | ResidenceCity | City of Residence | Text |
| 8 | ResidenceCounty | County of Residence | Text |
| 9 | ResidenceState | State of Residence | Text |
| 10 | DeathCity | City of Death | Text |
| 11 | DeathCounty | County of Death | Text |
| 12 | Location | Location of Death [Hospital or Residence] | Text |
| 13 | LocationifOther | Location of Death if Not Hospital or Residence | Text |
| 14 | DescriptionofInjury | Cause of Death | Text |
| 15 | InjuryPlace | Place of Event that caused Death | Text |
| 16 | InjuryCity | City of Event that caused Death | Text |
| 17 | InjuryCounty | County of Event that caused Death | Text |
| 18 | InjuryState | State of Event that caused Death | Text |
| 19 | COD | Detailed Cause of Death | Text |
| 20 | OtherSignifican | Other Significant Injuries that may have lead to Death | Text |
| 21 | Heroin | Drug Found in Body [Y/N] | Text/Bool |
| 22 | Cocaine | Drug Found in Body [Y/N] | Text/Bool |
| 23 | Fentanyl | Drug Found in Body [Y/N] | Text/Bool |
| 24 | FentanylAnalogue | Drug Found in Body [Y/N] | Text/Bool |
| 25 | Oxycodone | Drug Found in Body [Y/N] | Text/Bool |
| 26 | Oxymorphone | Drug Found in Body [Y/N] | Text/Bool |
| 27 | Ethanol | Drug Found in Body [Y/N] | Text/Bool |
| 28 | Hydrocodone | Drug Found in Body [Y/N] | Text/Bool |
| 29 | Benzodiazepine | Drug Found in Body [Y/N] | Text/Bool |
| 30 | Methadone | Drug Found in Body [Y/N] | Text/Bool |
| 31 | Amphet | Drug Found in Body [Y/N] | Text/Bool |
| 32 | Tramad | Drug Found in Body [Y/N] | Text/Bool |
| 33 | Morphine_NotHeroin | Drug Found in Body [Y/N] | Text/Bool |
| 34 | Hydromorphone | Drug Found in Body [Y/N] | Text/Bool |
| 35 | Other | Drug Found in Body [Y/N] | Text/Bool |
| 36 | OpiateNOS | Drug Found in Body [Y/N] | Text/Bool |
| 37 | AnyOpioid | Drug Found in Body [Y/N] | Text/Bool |
| 38 | MannerofDeath | Manner of Death | Text |
| 39 | DeathCityGeo | City of Death | Text |
| 40 | ResidenceCityGeo | City of Residence | Text |
| 41 | InjuryCityGeo | City of Injury | Text |
---
### Acknowledgement
This data set has been sourced from the [US Government's
Open Data Initiative](https://data.gov) [Accidental Drug Related Deaths Dataset](https://catalog.data.gov/dataset/accidental-drug-related-deaths-january-2012-sept-2015).
The Open Data Initiative page mentions the following as the original source of the
data set:
*Local Government, Connecticut*
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Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Beginner <br/>
**Recommended Use:** Regression/Classification <br/>
**Domain:** Transportation and Mobility <br/>
---
## Birmingham Parking Dataset
![](ParkingBirmingham.jpg )
---
The Birmingham Parking Dataset is a simple beginner level dataset with 4 columns and 35718 rows. The dataset contains information about the number of cars parked in 30 parking areas around Birmingham at different times of the day between October to December 2016.
The information in the dataset can be used to understand driving patterns of Birmingham with respect to time and date and be used for efficient planning of new parking facilities.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
|---------------|------------------|---------------------------------------|-----------|
| 1 | SystemCodeNumber | Parking Lot ID | Text |
| 2 | Capacity | Maximum Capacity of the Parking Lot | Numeric |
| 3 | Occupancy | Number of Cars Parked at Time Instant | Numeric |
| 4 | LastUpdated | Time Stamp at which data was updated | Date/Time |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of
University of California, Irvine [Parking Birmingham Dataset (UC
Irvine)](https://archive.ics.uci.edu/ml/datasets/Parking+Birmingham). <br/><br/>
The UCI page mentions the following publication as the original source of the
data set:
*Stolfi, Daniel H. & Alba, Enrique & Yao, Xin. (2017). Predicting Car Park Occupancy Rates in Smart Cities. 107-117. 10.1007/978-3-319-59513-9_11.*
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Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Advanced <br/>
**Recommended Use:** Classification <br/>
**Domain:** Neuroscience/Healthcare <br/>
---
## EEG Eye State Dataset
![](BrainScanviaEEG.jpg)
---
The EEG Eye State Dataset is an advanced dataset of EEG (brainwave) Signals recorded to study the EEG activity of the brain with the eyes closed and open.
The data was recorded via the Emotiv Epoc+ EEG Headset with the subject having their eyes open and closed. The Emotiv Epoc+ EEG Headset has 14 electrodes placed at different areas of the scalp. Each row contains electrical signals generated by the brain gathered from the electrodes.
This data set can be used to explore which areas of the brain are active when a subject is receiving visual stimuli. This dataset is recommended for practicing classification models.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ---------------------------- | -------------------------------------------------------- | --------- |
| 1 | ATTRIBUTE AF3 NUMERIC | Data from EEG Sensor placed at AF3 Location on the Skull | Numeric |
| 2 | ATTRIBUTE F7 NUMERIC | Data from EEG Sensor placed at F7 Location on the Skull | Numeric |
| 3 | ATTRIBUTE F3 NUMERIC | Data from EEG Sensor placed at F3 Location on the Skull | Numeric |
| 4 | ATTRIBUTE FC5 NUMERIC | Data from EEG Sensor placed at FC5 Location on the Skull | Numeric |
| 5 | ATTRIBUTE T7 NUMERIC | Data from EEG Sensor placed at T7 Location on the Skull | Numeric |
| 6 | ATTRIBUTE P7 NUMERIC | Data from EEG Sensor placed at P7 Location on the Skull | Numeric |
| 7 | ATTRIBUTE O1 NUMERIC | Data from EEG Sensor placed at O1 Location on the Skull | Numeric |
| 8 | ATTRIBUTE O2 NUMERIC | Data from EEG Sensor placed at O2 Location on the Skull | Numeric |
| 9 | ATTRIBUTE P8 NUMERIC | Data from EEG Sensor placed at P8 Location on the Skull | Numeric |
| 10 | ATTRIBUTE T8 NUMERIC | Data from EEG Sensor placed at T8 Location on the Skull | Numeric |
| 11 | ATTRIBUTE FC6 NUMERIC | Data from EEG Sensor placed at FC6 Location on the Skull | Numeric |
| 12 | ATTRIBUTE F4 NUMERIC | Data from EEG Sensor placed at F4 Location on the Skull | Numeric |
| 13 | ATTRIBUTE F8 NUMERIC | Data from EEG Sensor placed at F8 Location on the Skull | Numeric |
| 14 | ATTRIBUTE AF4 NUMERIC | Data from EEG Sensor placed at AF4 Location on the Skull | Numeric |
| 15 | ATTRIBUTE eyeDetection [0/1] | State of Eye <br>[0: Eye is Open, 1: Eye is Closed] | Numeric |
---
#### EEG Headset Electrode Placement
![](EEGElectrodePlacement.png)
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of
University of California, Irvine [EEG Eye State Dataset (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State).
<br/>
The UCI page mentions the following as the original source of the
data set:
*Baden-Wuerttemberg Cooperative State University (DHBW), Stuttgart, Germany*
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2.999 19.006
23.011 38.008
42.015 55.022
59.03 68.027
72.034 88.037
\ No newline at end of file
2.999 10.997
15.003 26.008
30.015 44.012
48.018 57.016
61.022 73.021
\ No newline at end of file
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Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Advanced <br/>
**Recommended Use:** Dimensionality Reduction/Classification <br/>
**Domain:** Neuroscience/Healthcare <br/>
---
## EEG Steady State Evoked Potential Dataset
![](BrainScanviaEEG.jpg)
---
The EEG Steady State Evoked Potential dataset is a complex and advanced database of EEG (brainwave) Signals recorded from 30 participants to study Steady State Visually Evoked Potentials (SSVEP).
The data was recorded via the Emotiv Epoc+ EEG Headset while participants were exposed to different stimuli. The details of the participants and tests are explained in the Signals Database included in the dataset. The available CSV files contain raw time-series brainwave signals sampled at 128Hz while the participants were shown different stimuli.
The Emotiv Epoc+ EEG Headset has 14 electrodes placed at different areas of the scalp. Time-series data from each electrode is present in each column of the CSV data files.
This data set can be used to explore which areas of the brain are active during different types of stimuli (for example, visual stimuli and motor imagery will be processed by different regions of the brain and hence different electrodes will be activated).
This data set is recommended for exploring dimensionality reduction techniques and classification models.
---
### File Dictionary
| File Name | File Description | File Type |
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- |
| Signals Database | File containing information about;<br><br>1. The participants the data was collected from <br>2. Details about the experiments conducted <br>3. EEG datasets for each experiment | Microsoft Excel Worksheet (.xlsx) |
| Handshake Test | Explanation of the Handshake Motor Imagery Experiment | PDF File (.pdf) |
| A0xxxxx_x | EEG Dataset containing time-series brainwave data | Comma Separated Value File (.csv) |
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- | --------- |
| 1 | Counter | Time-Domain Sample Number<br>(Counter resets to 0 after every 128 samples which is exactly 1 second as the sampling rate of the EEG device is 128 Hz) | Numeric |
| 2 | Interpolated | Interpolated Signal | Numeric |
| 3 | AF3 | Data from EEG Sensor placed at AF3 Location on the Skull | Numeric |
| 4 | F7 | Data from EEG Sensor placed at F7 Location on the Skull | Numeric |
| 5 | F3 | Data from EEG Sensor placed at F3 Location on the Skull | Numeric |
| 6 | FC5 | Data from EEG Sensor placed at FC5 Location on the Skull | Numeric |
| 7 | T7 | Data from EEG Sensor placed at T7 Location on the Skull | Numeric |
| 8 | P7 | Data from EEG Sensor placed at P7 Location on the Skull | Numeric |
| 9 | O1 | Data from EEG Sensor placed at O1 Location on the Skull | Numeric |
| 10 | O2 | Data from EEG Sensor placed at O2 Location on the Skull | Numeric |
| 11 | P8 | Data from EEG Sensor placed at P8 Location on the Skull | Numeric |
| 12 | T8 | Data from EEG Sensor placed at T8 Location on the Skull | Numeric |
| 13 | FC6 | Data from EEG Sensor placed at FC6 Location on the Skull | Numeric |
| 14 | F4 | Data from EEG Sensor placed at F4 Location on the Skull | Numeric |
| 15 | F8 | Data from EEG Sensor placed at F8 Location on the Skull | Numeric |
| 16 | AF4 | Data from EEG Sensor placed at AF4 Location on the Skull | Numeric |
---
#### EEG Headset Electrode Placement
![](EEGElectrodePlacement.png)
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of
University of California, Irvine [EEG Steady-State Visual Evoked Potential Signals Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/EEG+Steady-State+Visual+Evoked+Potential+Signals).
<br/><br/>
The UCI page mentions the following publications as the original source of the
data set: <br/><br/>
*Fernandez-Fraga, S. M., Aceves-Fernandez, M. A., Pedraza-Ortega, J. C. (2018). Feature Extraction of EEG Signal upon BCI Systems Based on Steady-State Visual Evoked Potentials Using the Ant Colony Optimization Algorithm. Discrete Dynamics in Nature and Society, 2018.*<br/><br/>
*S. M. Fernandez-Fraga, M. A. Aceves-Fernande, J. C. Pedraza-Ortega & J. M. Ramos-Arreguín (2018). Screen Task Experiments for EEG Signals Based on SSVEP Brain Computer Interface. International Journal of Advanced Research, 2018*
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"TOTAL,A_MD60,PC,ES",: ,7.5 ,7.4 ,7.2 ,5.8 ,4.2 ,5.2 b,5.3 ,4.7 ,6.6 ,6.1 ,7.5 ,7.0 ,6.3 ,4.9 ,5.9 ,:
"TOTAL,A_MD60,PC,EU",: ,: ,: ,: ,: ,: ,: ,: ,7.3 ,8.0 ,8.1 ,7.5 ,6.6 ,6.1 ,5.7 ,5.1 ,:
"TOTAL,A_MD60,PC,EU27_2007",: ,: ,: ,: ,8.5 ,7.9 ,7.2 ,7.2 ,7.3 ,8.1 ,8.1 ,7.5 ,6.6 ,6.1 ,5.7 ,5.1 ,:
"TOTAL,A_MD60,PC,EU27_2020",: ,: ,: ,: ,: ,: ,: ,7.5 e,7.6 e,8.3 e,8.0 e,7.6 e,6.7 e,6.3 e,5.8 e,5.3 e,:
"TOTAL,A_MD60,PC,EU28",: ,: ,: ,: ,: ,: ,: ,7.2 ,7.3 ,8.0 ,8.1 ,7.5 ,6.6 ,6.1 ,5.7 ,5.1 ,:
"TOTAL,A_MD60,PC,FI",: ,3.2 ,2.4 ,2.0 ,0.9 ,1.5 ,0.9 ,1.0 ,1.5 ,1.2 ,1.0 ,1.2 ,1.4 ,1.5 ,2.0 ,1.5 ,:
"TOTAL,A_MD60,PC,FR",: ,4.3 ,4.2 ,4.5 ,3.4 ,4.4 ,4.2 ,4.3 ,4.3 ,4.5 ,4.9 ,4.5 ,3.9 ,3.6 ,3.4 ,3.4 ,:
"TOTAL,A_MD60,PC,HR",: ,: ,: ,: ,: ,: ,: ,5.5 ,6.5 ,6.7 ,6.5 ,6.2 ,6.4 ,6.3 ,4.1 ,4.5 ,:
"TOTAL,A_MD60,PC,HU",: ,: ,15.2 ,12.7 ,9.0 ,8.1 ,7.7 ,9.0 ,9.4 ,11.7 ,11.1 ,8.5 ,7.0 ,6.9 ,5.6 ,4.1 ,:
"TOTAL,A_MD60,PC,IE",1.9 ,2.1 ,2.2 ,2.4 ,2.1 ,3.0 ,3.0 ,5.1 ,5.8 ,6.9 ,8.3 ,7.3 ,7.2 ,4.1 ,2.8 ,3.1 p,:
"TOTAL,A_MD60,PC,IS",: ,6.9 ,9.0 ,10.2 ,8.6 ,0.8 b,1.0 ,1.3 ,1.9 ,1.3 ,1.2 ,1.6 ,1.3 ,1.4 ,: ,: ,:
"TOTAL,A_MD60,PC,IT",: ,7.5 ,7.4 ,7.0 ,7.3 ,8.0 ,7.3 ,7.8 ,13.3 ,15.8 ,13.7 ,13.1 ,12.3 ,11.8 ,11.7 ,10.0 ,:
"TOTAL,A_MD60,PC,LT",: ,: ,32.1 ,25.3 ,19.7 ,20.4 ,21.9 ,22.9 ,35.3 ,33.2 ,28.0 ,24.5 ,28.7 ,29.1 ,26.8 ,25.6 ,:
"TOTAL,A_MD60,PC,LU",0.8 ,0.3 ,0.5 ,0.2 ,0.2 ,0.6 ,0.2 ,0.2 ,0.7 ,0.4 ,1.0 ,0.3 ,0.5 ,1.3 b,1.4 ,1.3 ,:
"TOTAL,A_MD60,PC,LV",: ,: ,26.3 ,21.6 ,15.7 ,11.1 ,11.9 ,15.3 ,18.2 ,16.3 ,17.7 ,13.0 ,10.3 ,7.3 ,6.7 ,5.1 ,5.7
"TOTAL,A_MD60,PC,ME",: ,: ,: ,: ,: ,: ,: ,: ,: ,: ,8.8 ,7.4 ,5.6 ,2.2 ,0.8 ,: ,:
"TOTAL,A_MD60,PC,MK",: ,: ,: ,: ,: ,: ,: ,21.3 ,18.6 ,20.0 ,21.7 ,19.0 ,17.7 ,21.9 ,21.2 ,21.3 ,:
"TOTAL,A_MD60,PC,MT",: ,: ,11.8 ,9.8 ,9.0 ,7.9 ,10.0 ,12.3 ,15.7 ,20.3 ,21.8 ,19.6 ,11.5 ,5.3 ,4.4 ,6.0 ,:
"TOTAL,A_MD60,PC,NL",: ,: ,2.3 ,1.7 ,1.3 ,1.4 ,1.0 ,1.5 ,0.9 ,1.5 ,2.5 ,1.8 ,2.2 ,1.8 b,1.6 ,1.6 ,:
"TOTAL,A_MD60,PC,NO",2.4 ,1.2 ,0.9 ,1.1 ,0.6 ,0.5 ,0.6 ,0.5 ,0.8 ,0.5 ,0.6 ,0.4 ,0.2 ,0.4 ,0.3 ,0.3 ,:
"TOTAL,A_MD60,PC,PL",: ,: ,29.1 ,24.2 ,19.2 ,17.2 b,12.9 ,11.3 ,10.4 ,10.3 ,8.8 ,6.7 ,5.2 ,5.1 ,4.5 ,3.6 ,2.9
"TOTAL,A_MD60,PC,PT",: ,31.0 ,34.6 ,35.3 ,36.8 ,30.1 ,25.1 ,25.8 ,22.9 ,23.5 ,24.1 ,23.7 ,19.1 ,17.8 ,16.3 ,15.8 ,:
"TOTAL,A_MD60,PC,RO",: ,: ,: ,: ,29.1 ,21.7 ,19.9 ,18.2 ,12.4 ,11.8 ,11.5 ,8.9 ,8.2 ,9.8 ,9.4 ,6.9 ,:
"TOTAL,A_MD60,PC,RS",: ,: ,: ,: ,: ,: ,: ,: ,: ,: ,14.5 ,13.7 ,11.6 ,10.4 ,10.1 ,6.9 ,:
"TOTAL,A_MD60,PC,SE",: ,1.3 ,1.3 ,2.2 ,1.6 ,1.4 b,1.2 ,1.6 ,1.5 ,1.3 ,0.4 ,0.8 ,0.9 ,2.2 ,1.6 ,1.8 ,:
"TOTAL,A_MD60,PC,SI",: ,: ,2.0 ,2.3 ,3.2 ,4.3 ,3.7 ,3.5 ,4.2 ,4.3 ,3.5 ,3.9 ,4.3 ,3.3 ,2.8 ,2.0 ,:
"TOTAL,A_MD60,PC,SK",: ,: ,12.8 ,8.5 ,3.4 ,5.0 ,2.6 ,2.9 ,3.4 ,4.3 ,3.8 ,3.8 ,4.2 ,3.4 ,2.4 ,3.3 ,:
"TOTAL,A_MD60,PC,TR",: ,: ,: ,31.3 ,30.6 ,34.3 ,31.7 ,: ,29.0 ,30.1 ,20.4 ,9.3 ,7.3 ,17.3 ,13.4 ,: ,:
"TOTAL,A_MD60,PC,UK",: ,: ,4.4 ,3.7 ,3.4 ,4.6 ,4.7 ,4.9 ,5.5 ,5.9 b,8.5 ,7.2 ,5.6 ,4.5 ,4.6 b,4.1 p,:
"TOTAL,B_MD60,PC,AT",7.0 ,5.2 ,6.0 ,7.7 ,9.1 ,10.0 b,7.8 ,9.0 ,8.6 ,7.7 ,8.3 ,7.7 ,8.0 ,8.7 ,9.5 ,4.8 ,:
"TOTAL,B_MD60,PC,BE",11.8 ,9.9 ,29.0 ,31.0 ,32.8 ,17.0 ,15.0 ,16.2 ,20.9 ,18.7 ,18.4 ,18.3 ,14.8 ,16.2 ,20.0 ,18.5 ,:
"TOTAL,B_MD60,PC,BG",: ,: ,79.0 ,79.0 ,82.1 ,81.4 ,80.2 ,83.3 ,68.9 ,70.0 ,69.7 ,66.0 b,66.8 ,61.9 b,59.5 ,56.0 ,:
"TOTAL,B_MD60,PC,CH",: ,: ,: ,: ,9.4 ,8.4 ,11.1 ,11.0 ,1.4 ,1.0 ,0.7 ,2.6 b,0.8 ,2.0 ,0.7 ,1.8 ,:
"TOTAL,B_MD60,PC,CY",: ,: ,51.3 ,56.3 ,63.0 ,48.1 b,37.8 ,40.1 ,46.3 ,50.6 ,51.0 ,47.5 ,49.2 ,49.0 ,46.8 ,45.4 ,:
"TOTAL,B_MD60,PC,CZ",: ,: ,19.5 ,20.2 ,17.3 ,16.8 ,12.7 ,11.2 ,13.4 ,15.3 ,14.6 ,15.6 ,13.5 ,13.0 ,9.2 ,8.9 ,:
"TOTAL,B_MD60,PC,DE",: ,: ,13.7 ,15.0 ,14.9 ,17.2 ,16.2 ,15.7 ,16.8 ,14.8 ,16.5 ,13.3 ,12.7 ,12.4 ,9.8 ,8.9 ,:
"TOTAL,B_MD60,PC,DK",16.2 ,14.2 ,14.5 ,16.4 ,18.2 ,6.0 ,2.8 ,4.9 ,7.4 ,8.4 ,10.2 ,5.8 ,12.7 ,7.9 ,6.6 ,7.8 ,8.4
"TOTAL,B_MD60,PC,EA18",: ,: ,20.2 ,20.8 ,19.8 ,18.8 ,19.0 ,20.0 ,21.7 ,24.1 ,23.7 ,23.7 ,22.8 ,21.6 ,19.3 ,19.0 ,:
"TOTAL,B_MD60,PC,EA19",: ,: ,20.5 ,21.0 ,20.0 ,19.0 ,19.1 ,20.2 ,21.9 ,24.2 ,23.8 ,23.8 ,23.0 ,21.6 ,19.5 ,19.2 ,:
"TOTAL,B_MD60,PC,EE",: ,10.5 ,5.9 ,5.0 ,8.3 ,3.0 ,4.7 ,9.0 ,8.1 ,9.6 ,5.7 ,3.7 b,6.1 ,6.1 ,8.1 ,4.2 ,:
"TOTAL,B_MD60,PC,EL",37.4 ,32.0 ,30.3 ,24.7 ,29.4 ,29.9 ,36.8 ,38.4 ,38.8 ,47.6 ,48.4 ,52.6 ,50.9 ,52.5 ,45.3 ,41.2 ,:
"TOTAL,B_MD60,PC,ES",: ,17.1 ,17.1 ,21.4 ,17.2 ,13.1 ,15.2 b,15.6 ,13.2 ,18.9 ,15.6 ,23.5 ,23.3 ,23.2 ,19.4 ,20.8 ,:
"TOTAL,B_MD60,PC,EU",: ,: ,: ,: ,: ,: ,: ,: ,22.0 ,24.5 ,24.1 ,23.5 ,22.7 ,21.0 ,18.4 ,17.9 ,:
"TOTAL,B_MD60,PC,EU27_2007",: ,: ,: ,: ,22.9 ,20.9 ,20.5 ,21.1 ,22.0 ,24.5 ,24.1 ,23.5 ,22.7 ,21.0 ,18.4 ,17.8 ,:
"TOTAL,B_MD60,PC,EU27_2020",: ,: ,: ,: ,: ,: ,: ,22.4 e,23.4 e,25.2 e,24.5 e,23.9 e,23.3 e,21.8 e,19.3 e,19.0 e,:
"TOTAL,B_MD60,PC,EU28",: ,: ,: ,: ,: ,: ,: ,21.1 ,22.0 ,24.5 ,24.1 ,23.5 ,22.7 ,21.0 ,18.4 ,17.9 ,:
"TOTAL,B_MD60,PC,FI",: ,3.6 ,4.0 ,4.8 ,2.6 ,4.3 ,3.5 ,3.5 ,3.8 ,3.8 ,2.8 ,3.3 ,3.7 ,3.8 ,2.3 ,3.1 ,:
"TOTAL,B_MD60,PC,FR",: ,16.5 ,12.9 ,15.7 ,12.8 ,11.5 ,15.0 ,15.3 ,16.9 ,15.2 ,17.7 ,15.0 ,16.3 ,14.0 ,15.0 ,15.6 ,:
"TOTAL,B_MD60,PC,HR",: ,: ,: ,: ,: ,: ,: ,18.9 ,22.5 ,23.9 ,24.0 ,24.3 ,23.7 ,21.7 ,20.3 ,21.2 ,:
"TOTAL,B_MD60,PC,HU",: ,: ,33.9 ,26.0 ,23.7 ,21.1 ,16.8 ,23.2 ,29.4 ,35.1 ,34.0 ,29.4 ,24.7 ,22.7 ,15.0 ,19.9 ,:
"TOTAL,B_MD60,PC,IE",8.2 ,7.8 ,11.4 ,10.4 ,9.9 ,7.6 ,10.3 ,16.0 ,12.5 ,16.2 ,19.5 ,17.0 ,18.6 ,14.8 ,12.7 ,11.6 p,:
"TOTAL,B_MD60,PC,IS",: ,11.5 ,12.6 ,13.4 ,13.2 ,2.6 b,1.5 ,2.2 ,3.7 ,3.5 ,2.7 ,4.4 ,2.9 ,3.6 ,: ,: ,:
"TOTAL,B_MD60,PC,IT",: ,25.5 ,26.5 ,24.4 ,25.0 ,26.1 ,26.3 ,28.0 ,36.1 ,44.0 ,40.4 ,38.3 ,35.9 ,32.4 ,29.1 ,30.0 ,:
"TOTAL,B_MD60,PC,LT",: ,: ,45.1 ,36.9 ,33.9 ,30.9 ,32.4 ,34.1 ,40.1 ,38.2 ,34.0 ,34.7 ,39.4 ,29.8 ,35.6 ,35.5 ,:
"TOTAL,B_MD60,PC,LU",3.3 ,3.9 ,3.2 ,2.9 ,2.3 ,3.0 ,1.1 ,1.7 ,2.2 ,2.2 ,4.5 ,2.0 ,3.3 ,4.0 b,3.9 ,6.2 ,:
"TOTAL,B_MD60,PC,LV",: ,: ,44.7 ,38.9 ,40.1 ,33.0 ,28.9 ,33.7 ,40.8 ,35.1 ,35.5 ,31.0 ,29.1 ,22.7 ,20.3 ,15.4 ,15.9
"TOTAL,B_MD60,PC,ME",: ,: ,: ,: ,: ,: ,: ,: ,: ,: ,23.1 ,12.5 ,17.9 ,19.2 ,16.5 ,: ,:
"TOTAL,B_MD60,PC,MK",: ,: ,: ,: ,: ,: ,: ,49.0 ,48.9 ,45.9 ,41.2 ,51.0 ,44.2 ,39.0 ,33.8 ,37.7 ,:
"TOTAL,B_MD60,PC,MT",: ,: ,17.2 ,16.8 ,16.5 ,13.9 ,17.5 ,25.1 ,28.1 ,32.1 ,35.5 ,36.4 ,27.4 ,13.3 ,15.8 ,15.8 ,:
"TOTAL,B_MD60,PC,NL",: ,: ,9.6 ,7.2 ,4.6 ,4.7 ,4.3 ,9.6 ,6.6 ,8.7 ,6.3 ,9.0 ,8.2 ,7.9 b,7.8 ,6.3 ,:
"TOTAL,B_MD60,PC,NO",6.3 ,7.0 ,4.4 ,4.4 ,2.3 ,3.3 ,2.4 ,2.8 ,4.3 ,2.3 ,3.6 ,2.3 ,2.4 ,4.5 ,3.9 ,4.9 ,:
"TOTAL,B_MD60,PC,PL",: ,: ,51.2 ,46.1 ,39.3 ,34.4 b,33.2 ,30.7 ,28.7 ,27.6 ,23.8 ,20.7 ,18.7 ,16.7 ,15.1 ,13.7 ,11.5
"TOTAL,B_MD60,PC,PT",: ,56.9 ,62.1 ,60.6 ,64.9 ,56.0 ,44.3 ,49.7 ,44.8 ,43.1 ,44.6 ,47.5 ,43.3 ,42.7 ,38.9 ,37.0 ,:
"TOTAL,B_MD60,PC,RO",: ,: ,: ,: ,46.0 ,33.3 ,29.8 ,26.9 ,26.7 ,25.8 ,25.6 ,24.6 ,27.3 ,25.6 ,17.4 ,18.2 ,:
"TOTAL,B_MD60,PC,RS",: ,: ,: ,: ,: ,: ,: ,: ,: ,: ,30.0 ,27.2 ,25.2 ,21.6 ,21.7 ,19.4 ,:
"TOTAL,B_MD60,PC,SE",: ,3.9 ,2.3 ,5.0 ,3.4 ,3.5 b,4.6 ,5.3 ,4.3 ,4.0 ,3.9 ,2.9 ,2.5 ,4.6 ,5.3 ,4.6 ,:
"TOTAL,B_MD60,PC,SI",: ,: ,6.6 ,8.7 ,11.4 ,14.3 ,11.5 ,13.1 ,12.4 ,17.3 ,13.1 ,15.4 ,13.6 ,14.2 ,11.5 ,11.4 ,:
"TOTAL,B_MD60,PC,SK",: ,: ,18.9 ,19.3 ,14.7 ,13.8 ,12.1 ,15.6 ,10.4 ,13.6 ,16.1 ,22.4 ,17.8 ,17.0 ,17.3 ,15.8 ,:
"TOTAL,B_MD60,PC,TR",: ,: ,: ,61.2 ,64.8 ,61.4 ,55.9 ,: ,56.3 ,59.9 ,58.7 ,36.1 ,45.8 ,47.6 ,46.4 ,: ,:
"TOTAL,B_MD60,PC,UK",: ,: ,11.0 ,9.2 ,9.1 ,11.5 ,11.0 ,11.9 ,11.4 ,19.2 b,21.7 ,20.2 ,18.6 ,14.2 ,12.4 b,11.3 p,:
"TOTAL,TOTAL,PC,AT",2.9 ,2.3 ,3.2 ,3.8 ,2.6 ,3.9 b,2.9 ,3.8 ,2.7 ,3.2 ,2.7 ,3.2 ,2.6 ,2.7 ,2.4 ,1.6 ,:
"TOTAL,TOTAL,PC,BE",6.0 ,6.4 ,14.1 ,14.5 ,14.6 ,6.4 ,5.1 ,5.6 ,7.1 ,6.6 ,5.8 ,5.4 ,5.2 ,4.8 ,5.7 ,5.2 ,3.9 b
"TOTAL,TOTAL,PC,BG",: ,: ,69.5 ,69.5 ,67.4 ,66.3 ,64.2 ,66.5 ,46.3 ,46.5 ,44.9 ,40.5 b,39.2 ,39.2 b,36.5 ,33.7 ,:
"TOTAL,TOTAL,PC,CH",: ,: ,: ,: ,6.9 ,6.9 ,7.6 ,7.3 ,0.7 ,0.4 ,0.4 ,0.7 b,0.6 ,0.6 ,0.4 ,0.6 ,:
"TOTAL,TOTAL,PC,CY",: ,: ,33.7 ,33.8 ,34.6 ,29.2 b,21.7 ,27.3 ,26.6 ,30.7 ,30.5 ,27.5 ,28.3 ,24.3 ,22.9 ,21.9 ,:
"TOTAL,TOTAL,PC,CZ",: ,: ,9.3 ,8.9 ,6.1 ,6.0 ,5.2 ,5.2 ,6.4 ,6.7 ,6.2 ,6.1 ,5.0 ,3.8 ,3.1 ,2.7 ,:
"TOTAL,TOTAL,PC,DE",: ,: ,4.6 ,5.5 ,5.4 ,5.9 ,5.5 ,5.0 ,5.2 ,4.7 ,5.3 ,4.9 ,4.1 ,3.7 ,3.3 ,2.7 ,:
"TOTAL,TOTAL,PC,DK",10.5 ,10.1 ,8.9 ,9.4 ,10.3 ,1.7 ,1.5 ,1.9 ,2.3 ,2.5 ,3.8 ,2.9 ,3.6 ,2.7 ,2.7 ,3.0 ,2.8
"TOTAL,TOTAL,PC,EA18",: ,: ,8.6 ,8.7 ,8.1 ,7.8 ,7.5 ,7.8 ,8.9 ,10.1 ,10.0 ,10.1 ,9.3 ,8.6 ,7.8 ,7.4 ,:
"TOTAL,TOTAL,PC,EA19",: ,: ,8.9 ,8.9 ,8.3 ,8.0 ,7.7 ,8.0 ,9.2 ,10.4 ,10.1 ,10.2 ,9.4 ,8.8 ,8.0 ,7.6 ,:
"TOTAL,TOTAL,PC,EE",: ,5.8 ,2.6 ,2.3 ,3.6 ,1.1 ,1.7 ,3.1 ,3.0 ,4.2 ,2.9 ,1.7 b,2.0 ,2.7 ,2.9 ,2.3 ,2.5 p
"TOTAL,TOTAL,PC,EL",17.4 ,16.8 ,15.7 ,12.0 ,13.8 ,15.4 ,15.7 ,15.4 ,18.6 ,26.1 ,29.5 ,32.9 ,29.2 ,29.1 ,25.7 ,22.7 ,17.9 p
"TOTAL,TOTAL,PC,ES",: ,9.5 ,9.4 ,10.1 ,8.0 ,5.9 ,7.2 b,7.5 ,6.5 ,9.1 ,8.0 ,11.1 ,10.6 ,10.1 ,8.0 ,9.1 ,:
"TOTAL,TOTAL,PC,EU",: ,: ,: ,: ,: ,: ,: ,: ,9.8 ,10.8 ,10.7 ,10.2 ,9.4 ,8.7 ,7.8 ,7.3 ,:
"TOTAL,TOTAL,PC,EU27_2007",: ,: ,: ,: ,10.9 ,10.1 ,9.3 ,9.5 ,9.8 ,10.8 ,10.8 ,10.3 ,9.4 ,8.7 ,7.8 ,7.3 ,:
"TOTAL,TOTAL,PC,EU27_2020",: ,: ,: ,: ,: ,: ,: ,9.9 e,10.3 e,11.2 e,10.8 e,10.4 e,9.6 e,9.0 e,8.1 e,7.6 e,:
"TOTAL,TOTAL,PC,EU28",: ,: ,: ,: ,: ,: ,: ,9.5 ,9.8 ,10.8 ,10.7 ,10.3 ,9.4 ,8.7 ,7.8 ,7.3 ,:
"TOTAL,TOTAL,PC,FI",: ,3.3 ,2.6 ,2.4 ,1.1 ,1.9 ,1.3 ,1.4 ,1.8 ,1.5 ,1.2 ,1.5 ,1.7 ,1.7 ,2.0 ,1.7 ,:
"TOTAL,TOTAL,PC,FR",: ,5.9 ,5.3 ,5.9 ,4.6 ,5.3 ,5.5 ,5.7 ,6.0 ,6.0 ,6.6 ,5.9 ,5.5 ,5.0 ,4.9 ,5.0 ,:
"TOTAL,TOTAL,PC,HR",: ,: ,: ,: ,: ,: ,: ,8.3 ,9.8 ,10.2 ,9.9 ,9.7 ,9.9 ,9.3 ,7.4 ,7.7 ,6.6 p
"TOTAL,TOTAL,PC,HU",: ,: ,17.7 ,14.8 ,10.8 ,9.7 ,8.9 ,10.7 ,12.2 ,15.0 ,14.6 ,11.6 ,9.6 ,9.2 ,6.8 ,6.1 ,:
"TOTAL,TOTAL,PC,IE",3.2 ,3.3 ,4.0 ,3.8 ,3.5 ,3.7 ,4.1 ,6.8 ,6.8 ,8.4 ,10.0 ,8.9 ,9.0 ,5.9 ,4.4 ,4.4 p,:
"TOTAL,TOTAL,PC,IS",: ,7.4 ,9.3 ,10.5 ,9.1 ,1.0 b,1.0 ,1.4 ,2.0 ,1.5 ,1.4 ,1.8 ,1.4 ,1.6 ,: ,: ,:
"TOTAL,TOTAL,PC,IT",: ,10.9 ,11.0 ,10.4 ,10.7 ,11.4 ,10.8 ,11.6 ,17.8 ,21.3 ,18.8 ,18.0 ,17.0 ,16.1 ,15.2 ,14.1 ,:
"TOTAL,TOTAL,PC,LT",: ,: ,34.8 ,27.6 ,22.4 ,22.6 ,24.1 ,25.2 ,36.2 ,34.1 ,29.2 ,26.5 ,31.1 ,29.3 ,28.9 ,27.9 ,:
"TOTAL,TOTAL,PC,LU",1.1 ,0.7 ,0.9 ,0.6 ,0.5 ,0.9 ,0.3 ,0.5 ,0.9 ,0.6 ,1.6 ,0.6 ,0.9 ,1.7 b,1.9 ,2.1 ,:
"TOTAL,TOTAL,PC,LV",: ,: ,29.8 ,25.7 ,20.9 ,16.8 ,16.4 ,19.1 ,22.5 ,19.9 ,21.1 ,16.8 ,14.5 ,10.6 ,9.7 ,7.5 ,8.0
"TOTAL,TOTAL,PC,ME",: ,: ,: ,: ,: ,: ,: ,: ,: ,: ,12.4 ,8.6 ,8.6 ,6.3 ,4.5 ,: ,:
"TOTAL,TOTAL,PC,MK",: ,: ,: ,: ,: ,: ,: ,28.8 ,26.7 ,26.8 ,26.4 ,26.1 ,23.4 ,25.7 ,24.0 ,24.9 ,:
"TOTAL,TOTAL,PC,MT",: ,: ,12.6 ,10.8 ,10.2 ,8.8 ,11.1 ,14.3 ,17.6 ,22.1 ,23.9 ,22.3 ,14.1 ,6.6 ,6.3 ,7.6 ,8.1 p
"TOTAL,TOTAL,PC,NL",: ,: ,3.1 ,2.2 ,1.6 ,1.8 ,1.3 ,2.3 ,1.6 ,2.2 ,2.9 ,2.6 ,2.9 ,2.6 b,2.4 ,2.2 ,:
"TOTAL,TOTAL,PC,NO",2.8 ,1.8 ,1.3 ,1.5 ,0.8 ,0.8 ,0.8 ,0.7 ,1.2 ,0.7 ,0.9 ,0.6 ,0.5 ,0.9 ,0.8 ,0.9 ,1.0 p
"TOTAL,TOTAL,PC,PL",: ,: ,33.6 ,28.4 ,22.7 ,20.1 b,16.3 ,14.8 ,13.6 ,13.2 ,11.4 ,9.0 ,7.5 ,7.1 ,6.0 ,5.1 ,4.2
"TOTAL,TOTAL,PC,PT",: ,36.3 ,40.0 ,39.9 ,41.9 ,34.9 ,28.5 ,30.1 ,26.8 ,27.0 ,27.9 ,28.3 ,23.8 ,22.5 ,20.4 ,19.4 ,:
"TOTAL,TOTAL,PC,RO",: ,: ,: ,: ,33.3 ,24.4 ,22.1 ,20.1 ,15.6 ,15.0 ,14.7 ,12.9 ,13.1 ,13.8 ,11.3 ,9.6 ,:
"TOTAL,TOTAL,PC,RS",: ,: ,: ,: ,: ,: ,: ,: ,: ,: ,18.3 ,17.1 ,15.2 ,13.3 ,13.1 ,10.0 ,:
"TOTAL,TOTAL,PC,SE",: ,1.6 ,1.4 ,2.5 ,1.8 ,1.6 b,1.7 ,2.1 ,1.9 ,1.7 ,0.9 ,1.1 ,1.2 ,2.6 ,2.1 ,2.3 ,:
"TOTAL,TOTAL,PC,SI",: ,: ,2.6 ,3.0 ,4.2 ,5.6 ,4.6 ,4.7 ,5.4 ,6.1 ,4.9 ,5.6 ,5.6 ,4.8 ,3.9 ,3.3 ,2.3 p
"TOTAL,TOTAL,PC,SK",: ,: ,13.6 ,9.7 ,4.6 ,6.0 ,3.6 ,4.4 ,4.3 ,5.5 ,5.4 ,6.1 ,5.8 ,5.1 ,4.3 ,4.8 ,7.8 p
"TOTAL,TOTAL,PC,TR",: ,: ,: ,39.3 ,39.1 ,41.0 ,37.8 ,: ,35.4 ,37.2 ,29.3 ,15.5 ,15.9 ,24.2 ,20.7 ,: ,:
"TOTAL,TOTAL,PC,UK",: ,: ,5.7 ,4.8 ,4.5 ,6.0 ,5.8 ,6.1 ,6.5 ,8.1 b,10.6 ,9.4 ,7.8 ,6.1 ,5.9 b,5.4 p,:
Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Intermediate <br/>
**Recommended Use:** Regression <br/>
**Domain:** Social Sciences <br/>
---
## EU Population Poverty Status Dataset
### Population unable to keep home adequately warm by poverty status
---
![](EUPoverty.jpg)
---
This dataset provides information about the percentage of population in each EU Country that cannot afford basic indoor heating from 2003 to 2019. It is a good indicator of the percentage of population in the EU that is living close to or below the poverty line.
The dataset can be used to understand if the poor population in the EU has grown over the last 15 years and can be used to predict how the population will change in the years to come.
This data set is recommended for exploring data visualization techniques and implementing regression models for prediction tasks.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ----------- | --------------------------------------------------------------------------------------------------------------- | --------- |
| 1 | hhtyp | Total | Text |
| | incgrp | Income Group<br>[A_MD60: Above 60% of Median Equalized Income,<br>B_MD60: Below 60% of Median Equalized Income] | Text |
| | unit | Data Type of Values in the table <br>[PC: Percentage] | Text |
| | geo | Country in EU. | Text |
| 2-18 | 2003 - 2019 | Data Points (in percentage) for years between 2003 to 2019 | Numeric |
| Special Character | Description |
| ----------------- | ----------------------------- |
| : | Data not Available |
| e | Data Point has been Estimated |
| b | Break in Time Series |
| p | Provisional |
---
### Acknowledgement
This data set has been sourced from the [EU Open Data Portal](https://data.europa.eu/euodp/data/dataset/2yoXfAzeAb5AoFO7BSvX2g).
The EU Open Data Portal mentions the following survery as the original source of the
data set:
*European Union Statistics on Income and Living Conditions (EU-SILC)*
Feature Names,Feature Values,Discretization (Items)
Age,32:61,"[0; 32], ]32; 37], ]37; 42],]42; 47], ]47; 52], ]52; 57],]57; 62]"
Gender,"Male,Female","[Male], [Female]"
BMI(Body Mass Index),22:35,"[0; 18:5[ [18:5; 25[, [25; 30[, [30; 35[, [35; 40["
Fever,"Absent, Present","[Absent], [Present] -"
Nausea/Vomiting,"Absent, Present","[Absent], [Present] -"
Headache,"Absent, Present","[Absent], [Present] -"
Diarrhea,"Absent, Present","[Absent], [Present] -"
Fatigue,"Absent, Present","[Absent], [Present] -"
Bone ache,"Absent, Present","[Absent], [Present] -"
Jaundice,"Absent, Present","[Absent], [Present] -"
Epigastria pain,"Absent, Present","[Absent], [Present] -"
WBC(White Blood Cells),2991:12101,"[0; 4000[, [4000; 11000[, [11000; 12101]"
RBC(Red Blood Cells),3816422:5018451,"[0; 3000000[, [3000000; 5000000[,[5000000; 5018451]"
HGB(Hemoglobin),2:20,"If (Gender==[Male]):[2; 14[, [14; 17:5], ]17:5; 20]If(Gender==[Female]):[2; 12:3[, [12:3; 15:3], ]15:3; 20]"
Plat(Platelet),93013:226464,"[93013; 100000[, [100000; 255000[,[255000; 226465["
AST1(1 week),0.088888889,"[0; 20[, [20; 40], ]40; 128]"
ALT1(1 week),0.088888889,"[0; 20[, [20; 40], ]40; 128]"
ALT4(4 weeks),0.088888889,"[0; 20[, [20; 40], ]40; 128]"
ALT12(12 weeks),0.088888889,"[0; 20[, [20; 40], ]40; 128]"
ALT24(24 weeks),0.088888889,"[0; 20[, [20; 40], ]40; 128]"
ALT36(36 weeks),0.088888889,"[0; 20[, [20; 40], ]40; 128]"
ALT48(48 weeks),0.088888889,"[0; 20[, [20; 40], ]40; 128]"
RNA Base,0:1201086,"[0; 5], ]5; 1201086]"
RNA 4,0:1201715,"[0; 5], ]5; 1201715]"
RNA 12,0:3731527,"[0; 5], ]5; 3731527]"
RNA EOT,0:808450,"[0; 5], ]5; 808450]"
RNA EF(Elongation Factor),0:808450,"[0; 5], ]5; 808450]"
Baseline Histological Grading,1:16,[1]; [2]; [3]; :::[16]
Baseline Histological,F0:F4,"[No Fibrosis], [Portal Fibrosis],Staging (Class Label) [Few Septa], [Many Septa], [Cirrhosis]"
This source diff could not be displayed because it is too large. You can view the blob instead.
Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Intermediate <br/>
**Recommended Use:** Classification <br/>
**Domain:** Healthcare <br/>
---
### Hepatitis C Virus (HCV) Classification Dataset
---
![](HepatitusCVirus.jpg)
---
The Hepatitis C Virus (HCV) Classification Dataset contains information about 1385 patients who underwent treatment for HCV. The dataset provides information about the symptoms the patients were experiencing as well as the results of their Liver Function Tests (LFTs) and Blood Panels before the start of treatment and at the end of the treatment.
This data set can be used to determine if it is possible to reach a diagnosis regarding HCV through only blood tests. It can also be used to predict the condition of a patients liver solely based on their bloodwork.
This data set is recommended for practicing classification modelling techniques.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ----------------------------- | ----------------------------------------------------------------------------------------------------- | ------------ |
| 1 | Age | Age of Patient | Numeric |
| 2 | Gender | Gender of Patient [1: Male, 2: Female] | Numeric/Bool |
| 3 | BMI(Body Mass Index) | Body Mass Index of Patient | Numeric |
| 4 | Fever | Presence of Fever <br>[1: Absent, 2: Present] | Numeric/Bool |
| 5 | Nausea/Vomiting | Presence of Nausea/Vomiting<br>[1: Absent, 2: Present] | Numeric/Bool |
| 6 | Headache | Presence of Headache<br>[1: Absent, 2: Present] | Numeric/Bool |
| 7 | Diarrhea | Presence of Diarrhea<br>[1: Absent, 2: Present] | Numeric/Bool |
| 8 | Fatigue & Boneaches | Presence of Fatigue<br>[1: Absent, 2: Present] | Numeric/Bool |
| 9 | Jaundice | Presence of Jaundice<br>[1: Absent, 2: Present] | Numeric/Bool |
| 10 | Epigastria pain | Presence of Epigastric Pain<br>[1: Absent, 2: Present] | Numeric/Bool |
| 11 | WBC | White Blood Cell Count<br>(per μl of blood) | Numeric |
| 12 | RBC | Red Blood Cell Count<br>(per μl of blood) | Numeric |
| 13 | HGB | Haemoglobin (g/dL) | Numeric |
| 14 | Plat | Platelet Count (per μl of blood) | Numeric |
| 15 | AST1(1 week) | Aspartate Aminotransferase Enzyme level at 1 week (units per litre) | Numeric |
| 16 | ALT1(1 week) | Alanine Aminotransferase Enzyme level at 1 week (units per litre) | Numeric |
| 17 | ALT4(4 weeks) | Alanine Aminotransferase Enzyme level at 4 weeks (units per litre) | Numeric |
| 18 | ALT12(12 weeks) | Alanine Aminotransferase Enzyme level at 12 weeks (units per litre) | Numeric |
| 19 | ALT24(24 weeks) | Alanine Aminotransferase Enzyme level at 24 weeks (units per litre) | Numeric |
| 20 | ALT36(36 weeks) | Alanine Aminotransferase Enzyme level at 36 weeks (units per litre) | Numeric |
| 21 | ALT48(48 weeks) | Alanine Aminotransferase Enzyme level at 48 weeks (units per litre) | Numeric |
| 22 | ALT after 24 w | Alanine Aminotransferase Enzyme level after 24 weeks (units per litre) | Numeric |
| 23 | RNA Base | RNA at Start of Treatment | Numeric |
| 24 | RNA 4 | RNA at 4 Weeks | Numeric |
| 25 | RNA 12 | RNA at 12 Weeks | Numeric |
| 26 | RNA EOT | RNA at End of Treatment | Numeric |
| 27 | RNA EF(Elongation Factor) | RNA Elongation Factor | Numeric |
| 28 | Baseline Histological Grading | Baseline Histological Grading Score <br>[0-16] | Numeric |
| 29 | Baseline Histological | Condition of Liver<br>[0: No Fibrosis, 1: Portal Fibrosis, 2: Few Septa, 3: Many Septa, 4: Cirrhosis] | Numeric |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of
University of California, Irvine [Hepatitis C Virus (HCV) for Egyptian patients Data Set (UC
Irvine)](https://archive.ics.uci.edu/ml/datasets/Hepatitis+C+Virus+%28HCV%29+for+Egyptian+patients#).
<br/>
The UCI page mentions the following publication as the original source of the
data set:
*M. Nasr, K. El-Bahnasy, M. Hamdy and S. M. Kamal, "A novel model based on non invasive methods for prediction
of liver fibrosis," 2017 13th International Computer Engineering Conference (ICENCO), Cairo, 2017, pp. 276-281.*
This source diff could not be displayed because it is too large. You can view the blob instead.
Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Beginner <br/>
**Recommended Use:** Regression <br/>
**Domain:** Transportation and Mobility <br/>
---
### Interstate-94 (I-94) Traffic Volume Dataset
---
![](Interstate94.jpg)
---
The Interstate Traffic Volume Dataset contains information about the hourly traffic volume on the West-bound lane of Interstate-94 (I-94) in the US. The dataset includes hourly weather and temperature reports from 2012 to 2018.
The information in the dataset can be used to understand the flow of traffic on the interstate with respect to time and date and can be helpful in prediction of rush hours, weather forecasting as well as planning expansions of interstates and highways in the US.
This data set is recommended for exploring data visualization techniques and implementing regression models for prediction tasks.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ------------------- | ------------------------------------------------ | --------- |
| 1 | holiday | Categorical US Holidays | Text |
| 2 | temp | Average Temperature (Kelvin) | Numeric |
| 3 | rain_1h | Amount of rain (in mm) in the last hour | Numeric |
| 4 | snow_1h | Amount of snow (in mm) in the last hour | Numeric |
| 5 | clouds_all | Percentage of Cloud Cover | Numeric |
| 6 | weather_main | Short Description of Weather in the last hour | Text |
| 7 | weather_description | Detailed Description of Weather in the last hour | Text |
| 8 | date_time | Date and Time of Data Point | Date/Time |
| 9 | traffic_volume | Traffic Volume reported on I-94 in the last hour | Numeric |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of
University of California, Irvine [Metro Interstate Traffic Volume Dataset (UC
Irvine)](https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume).
The UCI page mentions the following individual as the original source of the
data set:
*John Hogue, john.d.hogue '@' live.com, Social Data Science & General Mills*
This source diff could not be displayed because it is too large. You can view the blob instead.
Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Advanced <br/>
**Recommended Use:** Regression <br/>
**Domain:** Social Sciences <br/>
---
## Portugal 2019 Election Dataset
![](PortugalElection2019.png)
---
This advanced dataset contains real-time election results of the 2019 Portuguese Parliamentary Election.
The data contains information about the results of the 27 parties involved in the election. The results were updated at an interval of 5 minutes and the entire process spanned a total of 4 hours and 25 minutes.
This dataset can be used for predictive modelling and numerical forecasting tasks.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ------------------------ | ---------------------------------------------------------------------------------- | --------- |
| 1 | TimeElapsed | Time (in minutes) since first Data Acquisition | Numeric |
| 2 | time | Date and Time of Acquisition of Record | Date/Time |
| 3 | territoryName | Name of Territory/Location | Text |
| 4 | totalMandates | Number of Currently elected MPs | Numeric |
| 5 | availableMandates | Number Electable MPs | Numeric |
| 6 | numParishes | Number of Parishes in the Territory/Location | Numeric |
| 7 | numParishesApproved | Number of approved Parishes in the Territory/Location | Numeric |
| 8 | blankVotes | Number of blank votes | Numeric |
| 9 | blankVotesPercentage | Percentage of blank votes | Numeric |
| 10 | nullVotes | Number of null votes | Numeric |
| 11 | nullVotesPercentage | Percentage of null votes | Numeric |
| 12 | votersPercentage | Percentage of voters from total population | Numeric |
| 13 | subscribedVoters | Number of subscribed voters in the Territory/Location | Numeric |
| 14 | totalVoters | Percentage of blank votes | Numeric |
| 15 | pre.blankVotes | Number of blank votes in previous election | Numeric |
| 16 | pre.blankVotesPercentage | Percentage of blank votes in previous election | Numeric |
| 17 | pre.nullVotes | Number of null votes in previous election | Numeric |
| 18 | pre.nullVotesPercentage | Percentage of null votes in previous election | Numeric |
| 19 | pre.votersPercentage | Percentage of voters in previous election | Numeric |
| 20 | pre.subscribedVoters | Number of subscribed voters in the Territory/Location during the previous election | Numeric |
| 21 | pre.totalVoters | Percentage of blank votes in the previous election | Numeric |
| 22 | Party | Political Party | Text |
| 23 | Mandates | MP's elected at the moment for the party in a given district | Numeric |
| 24 | Percentage | Percentage of votes in a party | Numeric |
| 25 | validVotesPercentage | Percentage of valid votes in a party | Numeric |
| 26 | Votes | Percentage of party votes | Numeric |
| 27 | Hondt | Number of MP's according to the distribution of votes now | Numeric |
| 28 | FinalMandates | Target: final number of elected MP's in a district/national-level | Numeric |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of
University of California, Irvine [Real-time Election Results: Portugal 2019 Data Set (UC
Irvine)](https://archive.ics.uci.edu/ml/datasets/Real-time+Election+Results%3A+Portugal+2019).
<br/>
The UCI page mentions the following publication as the original source of the
data set:
*[Nuno Moniz (2019) Real-time 2019 Portuguese Parliament Election Results Dataset](https://arxiv.org/abs/1912.08922)*
Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Intermediate <br/>
**Recommended Use:** Classification <br/>
**Domain:** Healthcare <br/>
---
## Risk Factors for Cervical Cancer
![](CervicalCancerCell.jpg)
---
The Risk Factor for Cervical Cancer Dataset contains demographic information, habits, and historic medical records of 858 patients. Some of these patients were diagnosed with Cervical Cancer, Cervical Intraepithelial Neoplasia or HPV.
This data set can be used to determine if smoking, invasive birth control methods and a history of STDs can lead to Cervical Cancer. This information can also be used to determine if a patient is susceptible to cancer by looking at their medical history.
This data set is recommended for exploring data visualization techniques and implementing classification models.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ---------------------------------- | ---------------------------------------------------------------- | ------------ |
| 1 | Age | Current Age | Numeric |
| 2 | Number of sexual partners | Number of Sexual Partners | Numeric |
| 3 | First sexual intercourse | Age of First Sexual Intercourse | Numeric |
| 4 | Num of pregnancies | Number of Pregnancies | Numeric |
| 5 | Smokes | Is the Patient a Smoker? <br>[0: No, 1: Yes] | Numeric/Bool |
| 6 | Smokes (years) | Number of Years the Patient has been Smoking | Numeric |
| 7 | Smokes (packs/year) | Number of Packs/year | Numeric |
| 8 | Hormonal Contraceptives | Does the Patient use Hormonal Contraceptives? [0: No, 1: Yes] | Numeric/Bool |
| 9 | Hormonal Contraceptives (years) | Number of Years Hormonal Contraceptives have been used for | Numeric |
| 10 | IUD | Does the Patient use IUD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 11 | IUD (years) | Number of Years IUD has been used for | Numeric |
| 12 | STDs | History of STDs? <br>[0: No, 1: Yes] | Numeric/Bool |
| 13 | STDs (number) | Number of STDs | Numeric |
| 14 | STDs:condylomatosis | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 15 | STDs:cervical condylomatosis | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 16 | STDs:vaginal condylomatosis | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 17 | STDs:vulvo-perineal condylomatosis | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 18 | STDs:syphilis | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 19 | STDs:pelvic inflammatory disease | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 20 | STDs:genital herpes | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 21 | STDs:molluscum contagiosum | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 22 | STDs:AIDS | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 23 | STDs:HIV | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 24 | STDs:Hepatitis B | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 25 | STDs:HPV | Suffered from this specific STD? <br>[0: No, 1: Yes] | Numeric/Bool |
| 26 | STDs: Number of diagnosis | Number of STD Diagnosis | Numeric |
| 27 | STDs: Time since first diagnosis | Years since First Diagnosis | Numeric |
| 28 | STDs: Time since last diagnosis | Years since Last Diagnosis | Numeric |
| 29 | Dx:Cancer | Cancer Diagnosis <br>[0: No, 1: Yes] | Numeric/Bool |
| 30 | Dx:CIN | Cervical Intraepithelial Neoplasia Diagnosis <br>[0: No, 1: Yes] | Numeric/Bool |
| 31 | Dx:HPV | Human Papilloma Virus Diagnosis <br>[0: No, 1: Yes] | Numeric/Bool |
| 32 | Dx | Other Diagnosis <br>[0: No, 1: Yes] | Numeric/Bool |
| 33 | Hinselmann | Hinselmann Test Recommended<br>[0: No, 1: Yes] | Numeric/Bool |
| 34 | Schiller | Schiller Test Recommended<br>[0: No, 1: Yes] | Numeric/Bool |
| 35 | Citology | Citology Recommended<br>[0: No, 1: Yes] | Numeric/Bool |
| 36 | Biopsy | Biopsy Recommended<br>[0: No, 1: Yes] | Numeric/Bool |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of
University of California, Irvine [Risk Factors for Cervical Cancer Dataset (UC
Irvine)](https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29).
<br/>
The UCI page mentions the following publication as the original source of the
data set:
*Kelwin Fernandes, Jaime S. Cardoso, and Jessica Fernandes. 'Transfer Learning with Partial Observability Applied to Cervical Cancer Screening.' Iberian Conference on Pattern Recognition and Image Analysis. Springer International Publishing, 2017*
This source diff could not be displayed because it is too large. You can view the blob instead.
Data Science Dojo <br/>
Copyright (c) 2019 - 2020
---
**Level:** Intermediate <br/>
**Recommended Use:** Regression <br/>
**Domain:** Healthcare <br/>
---
## US Tuberculosis Dataset
![](Tuberculosis-USA.png)
---
The US Tuberculosis Dataset contains information about the number of reported cases of Tuberculosis (TB) in each state of the US in 2018.
The information in the dataset can be used to understand how TB has spread in each state over the course of 4 quarters and to predict how the disease may spread in the future. The dataset can be used to identify states under threat from the disease so that appropriate countermeasures can be taken.
This data set is recommended for exploring data visualization techniques (for example, making a heatmap as shown in the image) and implementing regression models for prediction tasks.
---
### Data Dictionary
| Column Number | Attribute | Attribute Description | Data Type |
| ------------- | ------------------------------------------- | ---------------------------------------------- | --------- |
| 1 | Reporting Area | Area under consideration | Text |
| 2 | MMWR Year | Year under consideration | Year |
| 3 | MMWR Quarter | Quarter under consideration | Numeric |
| 4 | Tuberculosis, Current quarter | Number of TB Patients in the Current Quarter | Numeric |
| 5 | Tuberculosis, Current quarter, flag | Data point present/absent | Text |
| 6 | Tuberculosis, Previous 4 quarters Min | Number of minimum TB Patients in the last year | Numeric |
| 7 | Tuberculosis, Previous 4 quarters Min, flag | Data point present/absent | Text |
| 8 | Tuberculosis, Previous 4 quarters Max | Number of maximum TB Patients in the last year | Numeric |
| 9 | Tuberculosis, Previous 4 quarters Max, flag | Data point present/absent | Text |
| 10 | Tuberculosis, Cum 2018 | Total number of TB Patients in 2018 | Numeric |
| 11 | Tuberculosis, Cum 2018, flag | Data point present/absent | Text |
| 12 | Tuberculosis, Cum 2017 | Total number of TB Patients in 2017 | Numeric |
| 13 | Tuberculosis, Cum 2017, flag | Data point present/absent | Text |
---
### Acknowledgement
This data set has been sourced from the [US Government's
Open Data Initiative](https://catalog.data.gov/dataset/nndss-table-iii-tuberculosis-93e65).
The Open Data Initiative page mentions the following as the original source of the
data set:
*Centers for Disease Control and Prevention*
Reporting area,MMWR Year,MMWR Quarter,"Tuberculosis†, Current quarter","Tuberculosis†, Current quarter, flag","Tuberculosis†, Previous 4 quarters Min","Tuberculosis†, Previous 4 quarters Min, flag","Tuberculosis†, Previous 4 quarters Max","Tuberculosis†, Previous 4 quarters Max, flag","Tuberculosis†, Cum 2018","Tuberculosis†, Cum 2018, flag","Tuberculosis†, Cum 2017","Tuberculosis†, Cum 2017, flag",Location 1,Location 2
UNITED STATES,2018,1,1216,,1216,,2475,,1216,,1268,,,
NEW ENGLAND,2018,1,57,,57,,97,,57,,39,,,
MID. ATLANTIC,2018,1,271,,271,,358,,271,,176,,,
NEW YORK CITY,2018,1,157,,150,,168,,157,,95,,,
E.N. CENTRAL,2018,1,125,,125,,247,,125,,107,,,
W.N. CENTRAL,2018,1,39,,39,,120,,39,,49,,,
S. ATLANTIC,2018,1,218,,218,,477,,218,,189,,,
E.S. CENTRAL,2018,1,106,,77,,106,,106,,59,,,
W.S. CENTRAL,2018,1,44,,44,,379,,44,,225,,,
MOUNTAIN,2018,1,36,,36,,132,,36,,66,,,
PACIFIC,2018,1,320,,320,,681,,320,,358,,,
AMER. SAMOA,2018,1,,-,0,,2,,,-,,-,,
INDIANA,2018,1,19,,19,,33,,19,,16,,"INDIANA
(39.76845, -86.156212)",
ILLINOIS,2018,1,54,,52,,111,,54,,58,,"ILLINOIS
(40.477092, -88.993221)",
TEXAS,2018,1,6,,6,,307,,6,,176,,"TEXAS
(31.827233, -99.423022)",
GEORGIA,2018,1,46,,46,,82,,46,,50,,"GEORGIA
(32.836038, -83.631709)",
OHIO,2018,1,24,,24,,56,,24,,14,,"OHIO
(40.056807, -82.405897)",
ALASKA,2018,1,5,,5,,19,,5,,8,,"ALASKA
(64.836661, -147.738891)",
WISCONSIN,2018,1,14,,10,,15,,14,,7,,"WISCONSIN
(44.395526, -89.834513)",
MISSISSIPPI,2018,1,23,,13,,23,,23,,2,,"MISSISSIPPI
(32.738663, -89.537312)",
VIRGINIA,2018,1,28,,28,,90,,28,,10,,"VIRGINIA
(37.542699, -78.45822)",
NEBRASKA,2018,1,2,,2,,6,,2,,5,,"NEBRASKA
(41.640503, -99.367122)",
PENNSYLVANIA,2018,1,33,,33,,56,,33,,34,,"PENNSYLVANIA
(40.789676, -77.858055)",
ARIZONA,2018,1,7,,7,,55,,7,,33,,"ARIZONA
(34.869712, -111.760902)",
RHODE ISLAND,2018,1,9,,2,,9,,9,,2,,"RHODE ISLAND
(41.707447, -71.522488)",
NEW YORK,2018,1,51,,47,,51,,51,,25,,"NEW YORK
(42.826892, -75.544286)",
MARYLAND,2018,1,36,,36,,65,,36,,11,,"MARYLAND
(39.290443, -76.612333)",
PUERTO RICO,2018,1,3,,3,,14,,3,,6,,"PUERTO RICO
(18.215692, -66.414655)",
MAINE,2018,1,1,,1,,6,,1,,4,,"MAINE
(45.252223, -68.986647)",
ALABAMA,2018,1,23,,23,,31,,23,,22,,"ALABAMA
(32.839182, -86.631125)",
VERMONT,2018,1,,-,0,,1,,,-,,-,"VERMONT
(43.622479, -72.51927)",
NEW HAMPSHIRE,2018,1,1,,1,,6,,1,,2,,"NEW HAMPSHIRE
(43.657736, -71.500736)",
WASHINGTON,2018,1,10,,10,,59,,10,,26,,"WASHINGTON
(47.517368, -120.467672)",
NORTH CAROLINA,2018,1,21,,21,,86,,21,,17,,"NORTH CAROLINA
(35.47995, -79.180571)",
WYOMING,2018,1,,-,0,,0,,,-,2,,"WYOMING
(43.23558, -108.110518)",
CONNECTICUT,2018,1,10,,10,,21,,10,,8,,"CONNECTICUT
(41.544654, -72.651713)",
SOUTH DAKOTA,2018,1,1,,1,,4,,1,,5,,"SOUTH DAKOTA
(44.35371, -100.373709)",
NEW MEXICO,2018,1,2,,2,,12,,2,,6,,"NEW MEXICO
(34.520396, -106.244402)",
NEVADA,2018,1,10,,10,,29,,10,,4,,"NEVADA
(39.491833, -117.070159)",
DELAWARE,2018,1,,-,0,,5,,,-,2,,"DELAWARE
(39.008676, -75.574561)",
OKLAHOMA,2018,1,18,,11,,18,,18,,7,,"OKLAHOMA
(35.468494, -97.521264)",
FLORIDA,2018,1,79,,79,,155,,79,,86,,"FLORIDA
(28.915325, -81.922409)",
VIRGIN ISL.,2018,1,,-,0,,0,,,-,,-,"VIRGIN ISLANDS
(18.060868, -64.840836)",
SOUTH CAROLINA,2018,1,4,,4,,40,,4,,6,,"SOUTH CAROLINA
(34.009286, -81.037094)",
LOUISIANA,2018,1,12,,12,,36,,12,,30,,"LOUISIANA
(31.2931, -92.463284)",
MASSACHUSETTS,2018,1,36,,36,,65,,36,,23,,"MASSACHUSETTS
(42.268546, -72.085064)",
KENTUCKY,2018,1,13,,13,,17,,13,,11,,"KENTUCKY
(37.64591, -84.774043)",
MONTANA,2018,1,,-,0,,2,,,-,1,,"MONTANA
(47.062617, -109.430168)",
MISSOURI,2018,1,2,,2,,23,,2,,7,,"MISSOURI
(38.636168, -92.566292)",
UTAH,2018,1,8,,2,,8,,8,,7,,"UTAH
(39.359772, -111.584173)",
KANSAS,2018,1,2,,2,,11,,2,,6,,"KANSAS
(38.345528, -98.201847)",
CALIFORNIA,2018,1,264,,264,,550,,264,,292,,"CALIFORNIA
(37.640735, -121.002435)",
GUAM,2018,1,1,,1,,23,,1,,15,,"GUAM
(13.3532, 144.653198)",
IDAHO,2018,1,2,,0,,4,,2,,3,,"IDAHO
(43.680635, -114.364237)",
HAWAII,2018,1,24,,21,,33,,24,,23,,"HAWAII
(21.30992, -157.858158)",
C.N.M.I.,2018,1,6,,6,,12,,6,,7,,"NORTHERN MARIANA ISLANDS
(15.1834, 145.725693)",
DIST. OF COL.,2018,1,4,,4,,10,,4,,6,,"DISTRICT OF COLUMBIA
(38.892062, -77.019912)",
TENNESSEE,2018,1,47,,28,,47,,47,,24,,"TENNESSEE
(35.681402, -85.774444)",
MINNESOTA,2018,1,28,,28,,77,,28,,14,,"MINNESOTA
(46.357661, -94.793397)",
COLORADO,2018,1,7,,7,,28,,7,,10,,"COLORADO
(38.841772, -106.132562)",
NORTH DAKOTA,2018,1,4,,0,,9,,4,,4,,"NORTH DAKOTA
(47.475879, -100.121011)",
MICHIGAN,2018,1,14,,14,,37,,14,,12,,"MICHIGAN
(44.66399, -84.711206)",
IOWA,2018,1,,-,0,,17,,,-,8,,"IOWA
(42.46942, -93.815856)",
ARKANSAS,2018,1,8,,8,,29,,8,,12,,"ARKANSAS
(34.748745, -92.275105)",
WEST VIRGINIA,2018,1,,-,0,,7,,,-,1,,"WEST VIRGINIA
(38.668501, -80.709421)",
NEW JERSEY,2018,1,30,,30,,107,,30,,22,,"NEW JERSEY
(40.104655, -74.386686)",
OREGON,2018,1,17,,10,,22,,17,,9,,"OREGON
(44.567912, -120.156945)",
OKLAHOMA,2018,2,12,,12,,19,,31,,24,,"OKLAHOMA
(35.468494, -97.521264)",
NEW JERSEY,2018,2,73,,30,,108,,103,,95,,"NEW JERSEY
(40.104655, -74.386686)",
INDIANA,2018,2,32,,19,,33,,51,,44,,"INDIANA
(39.76845, -86.156212)",
IOWA,2018,2,4,,4,,17,,14,,23,,"IOWA
(42.46942, -93.815856)",
MARYLAND,2018,2,47,,37,,65,,84,,83,,"MARYLAND
(39.290443, -76.612333)",
WISCONSIN,2018,2,5,,5,,14,,19,,28,,"WISCONSIN
(44.395526, -89.834513)",
NORTH DAKOTA,2018,2,1,,0,,9,,5,,4,,"NORTH DAKOTA
(47.475879, -100.121011)",
E.S. CENTRAL,2018,2,107,,89,,107,,212,,173,,,
W.S. CENTRAL,2018,2,48,,48,,337,,192,,732,,,
MICHIGAN,2018,2,2,,2,,37,,29,,53,,"MICHIGAN
(44.66399, -84.711206)",
MID. ATLANTIC,2018,2,307,,268,,354,,575,,578,,,
NEW ENGLAND,2018,2,53,,53,,97,,127,,134,,,
OREGON,2018,2,16,,10,,21,,34,,38,,"OREGON
(44.567912, -120.156945)",
GUAM,2018,2,,-,0,,23,,1,,41,,"GUAM
(13.3532, 144.653198)",
SOUTH DAKOTA,2018,2,4,,1,,4,,6,,9,,"SOUTH DAKOTA
(44.35371, -100.373709)",
SOUTH CAROLINA,2018,2,27,,4,,40,,31,,24,,"SOUTH CAROLINA
(34.009286, -81.037094)",
RHODE ISLAND,2018,2,2,,2,,8,,10,,5,,"RHODE ISLAND
(41.707447, -71.522488)",
S. ATLANTIC,2018,2,349,,293,,478,,642,,698,,,
WASHINGTON,2018,2,4,,4,,55,,47,,100,,"WASHINGTON
(47.517368, -120.467672)",
ALASKA,2018,2,17,,8,,19,,27,,26,,"ALASKA
(64.836661, -147.738891)",
NEW YORK,2018,2,44,,44,,49,,93,,86,,"NEW YORK
(42.826892, -75.544286)",
VERMONT,2018,2,,-,0,,1,,,-,1,,"VERMONT
(43.622479, -72.51927)",
WEST VIRGINIA,2018,2,2,,0,,7,,2,,7,,"WEST VIRGINIA
(38.668501, -80.709421)",
KANSAS,2018,2,,-,0,,11,,2,,13,,"KANSAS
(38.345528, -98.201847)",
ARIZONA,2018,2,11,,11,,55,,47,,93,,"ARIZONA
(34.869712, -111.760902)",
KENTUCKY,2018,2,21,,13,,21,,34,,32,,"KENTUCKY
(37.64591, -84.774043)",
MISSOURI,2018,2,,-,0,,23,,2,,40,,"MISSOURI
(38.636168, -92.566292)",
PACIFIC,2018,2,389,,389,,635,,898,,1234,,,
LOUISIANA,2018,2,7,,7,,36,,38,,76,,"LOUISIANA
(31.2931, -92.463284)",
GEORGIA,2018,2,49,,49,,82,,108,,144,,"GEORGIA
(32.836038, -83.631709)",
AMER. SAMOA,2018,2,1,,0,,2,,1,,,-,,
W.N. CENTRAL,2018,2,60,,52,,120,,112,,158,,,
TEXAS,2018,2,21,,21,,270,,102,,593,,"TEXAS
(31.827233, -99.423022)",
IDAHO,2018,2,2,,2,,5,,7,,4,,"IDAHO
(43.680635, -114.364237)",
VIRGINIA,2018,2,43,,29,,90,,72,,69,,"VIRGINIA
(37.542699, -78.45822)",
MINNESOTA,2018,2,51,,28,,77,,79,,58,,"MINNESOTA
(46.357661, -94.793397)",
C.N.M.I.,2018,2,,-,0,,11,,7,,21,,"NORTHERN MARIANA ISLANDS
(15.1834, 145.725693)",
NEVADA,2018,2,22,,10,,29,,32,,31,,"NEVADA
(39.491833, -117.070159)",
ILLINOIS,2018,2,92,,52,,110,,147,,174,,"ILLINOIS
(40.477092, -88.993221)",
E.N. CENTRAL,2018,2,169,,139,,246,,308,,368,,,
CONNECTICUT,2018,2,10,,10,,21,,25,,27,,"CONNECTICUT
(41.544654, -72.651713)",
MAINE,2018,2,9,,1,,9,,10,,7,,"MAINE
(45.252223, -68.986647)",
MASSACHUSETTS,2018,2,32,,32,,64,,77,,86,,"MASSACHUSETTS
(42.268546, -72.085064)",
MISSISSIPPI,2018,2,28,,13,,28,,50,,20,,"MISSISSIPPI
(32.738663, -89.537312)",
NEW HAMPSHIRE,2018,2,,-,0,,6,,5,,8,,"NEW HAMPSHIRE
(43.657736, -71.500736)",
MONTANA,2018,2,,-,0,,0,,,-,3,,"MONTANA
(47.062617, -109.430168)",
NORTH CAROLINA,2018,2,52,,21,,86,,73,,81,,"NORTH CAROLINA
(35.47995, -79.180571)",
OHIO,2018,2,38,,24,,56,,62,,69,,"OHIO
(40.056807, -82.405897)",
CALIFORNIA,2018,2,335,,335,,517,,743,,1004,,"CALIFORNIA
(37.640735, -121.002435)",
NEW YORK CITY,2018,2,126,,126,,168,,275,,290,,,
UTAH,2018,2,2,,2,,8,,10,,19,,"UTAH
(39.359772, -111.584173)",
MOUNTAIN,2018,2,68,,68,,133,,142,,211,,,
HAWAII,2018,2,17,,17,,30,,47,,66,,"HAWAII
(21.30992, -157.858158)",
ALABAMA,2018,2,24,,23,,31,,47,,60,,"ALABAMA
(32.839182, -86.631125)",
ARKANSAS,2018,2,8,,8,,29,,21,,39,,"ARKANSAS
(34.748745, -92.275105)",
TENNESSEE,2018,2,34,,30,,47,,81,,61,,"TENNESSEE
(35.681402, -85.774444)",
COLORADO,2018,2,21,,9,,27,,30,,42,,"COLORADO
(38.841772, -106.132562)",
NEW MEXICO,2018,2,10,,6,,12,,16,,17,,"NEW MEXICO
(34.520396, -106.244402)",
PUERTO RICO,2018,2,6,,3,,14,,9,,15,,"PUERTO RICO
(18.215692, -66.414655)",
DELAWARE,2018,2,4,,0,,4,,4,,9,,"DELAWARE
(39.008676, -75.574561)",
PENNSYLVANIA,2018,2,64,,37,,64,,104,,107,,"PENNSYLVANIA
(40.789676, -77.858055)",
UNITED STATES,2018,2,1550,,1550,,2480,,3208,,4286,,,
NEBRASKA,2018,2,,-,0,,6,,4,,11,,"NEBRASKA
(41.640503, -99.367122)",
DIST. OF COL.,2018,2,14,,6,,14,,20,,16,,"DISTRICT OF COLUMBIA
(38.892062, -77.019912)",
VIRGIN ISL.,2018,2,,-,0,,0,,,-,,-,"VIRGIN ISLANDS
(18.060868, -64.840836)",
FLORIDA,2018,2,111,,111,,155,,248,,265,,"FLORIDA
(28.915325, -81.922409)",
WYOMING,2018,2,,-,0,,0,,,-,2,,"WYOMING
(43.23558, -108.110518)",
NORTH DAKOTA,2018,3,1,,1,,5,,8,,13,,"NORTH DAKOTA
(47.475879, -100.121011)",
E.S. CENTRAL,2018,3,82,,82,,111,,298,,264,,,
ALASKA,2018,3,6,,6,,19,,35,,34,,"ALASKA
(64.836661, -147.738891)",
UNITED STATES,2018,3,1419,,1419,,2481,,5271,,6547,,,
W.N. CENTRAL,2018,3,40,,40,,121,,179,,265,,,
PUERTO RICO,2018,3,5,,3,,14,,14,,26,,"PUERTO RICO
(18.215692, -66.414655)",
GUAM,2018,3,10,,10,,23,,40,,61,,"GUAM
(13.3532, 144.653198)",
ALABAMA,2018,3,29,,23,,29,,76,,91,,"ALABAMA
(32.839182, -86.631125)",
LOUISIANA,2018,3,20,,20,,36,,79,,105,,"LOUISIANA
(31.2931, -92.463284)",
PENNSYLVANIA,2018,3,55,,41,,65,,161,,144,,"PENNSYLVANIA
(40.789676, -77.858055)",
KANSAS,2018,3,,-,0,,12,,18,,24,,"KANSAS
(38.345528, -98.201847)",
NORTH CAROLINA,2018,3,46,,21,,86,,125,,127,,"NORTH CAROLINA
(35.47995, -79.180571)",
MOUNTAIN,2018,3,44,,44,,133,,209,,300,,,
VIRGINIA,2018,3,32,,29,,90,,107,,114,,"VIRGINIA
(37.542699, -78.45822)",
MISSOURI,2018,3,,-,0,,24,,3,,61,,"MISSOURI
(38.636168, -92.566292)",
NEW JERSEY,2018,3,75,,30,,108,,180,,175,,"NEW JERSEY
(40.104655, -74.386686)",
VIRGIN ISL.,2018,3,,-,0,,0,,,-,,-,"VIRGIN ISLANDS
(18.060868, -64.840836)",
MARYLAND,2018,3,39,,39,,65,,134,,142,,"MARYLAND
(39.290443, -76.612333)",
RHODE ISLAND,2018,3,5,,2,,8,,16,,11,,"RHODE ISLAND
(41.707447, -71.522488)",
OHIO,2018,3,33,,24,,56,,100,,96,,"OHIO
(40.056807, -82.405897)",
PACIFIC,2018,3,356,,356,,635,,1480,,1845,,,
NEBRASKA,2018,3,4,,1,,6,,10,,15,,"NEBRASKA
(41.640503, -99.367122)",
MICHIGAN,2018,3,13,,13,,37,,59,,85,,"MICHIGAN
(44.66399, -84.711206)",
VERMONT,2018,3,2,,0,,2,,3,,2,,"VERMONT
(43.622479, -72.51927)",
MAINE,2018,3,1,,1,,10,,12,,13,,"MAINE
(45.252223, -68.986647)",
S. ATLANTIC,2018,3,303,,276,,477,,1006,,1145,,,
TENNESSEE,2018,3,32,,30,,47,,115,,92,,"TENNESSEE
(35.681402, -85.774444)",
NEW MEXICO,2018,3,5,,5,,12,,21,,25,,"NEW MEXICO
(34.520396, -106.244402)",
COLORADO,2018,3,11,,9,,27,,44,,57,,"COLORADO
(38.841772, -106.132562)",
FLORIDA,2018,3,107,,107,,151,,395,,420,,"FLORIDA
(28.915325, -81.922409)",
MID. ATLANTIC,2018,3,297,,272,,354,,884,,910,,,
E.N. CENTRAL,2018,3,152,,140,,246,,482,,513,,,
IDAHO,2018,3,4,,2,,5,,11,,8,,"IDAHO
(43.680635, -114.364237)",
IOWA,2018,3,,-,0,,10,,18,,41,,"IOWA
(42.46942, -93.815856)",
KENTUCKY,2018,3,14,,13,,22,,49,,48,,"KENTUCKY
(37.64591, -84.774043)",
HAWAII,2018,3,17,,17,,30,,77,,93,,"HAWAII
(21.30992, -157.858158)",
UTAH,2018,3,5,,2,,8,,15,,21,,"UTAH
(39.359772, -111.584173)",
ARIZONA,2018,3,3,,3,,55,,67,,133,,"ARIZONA
(34.869712, -111.760902)",
MASSACHUSETTS,2018,3,34,,34,,63,,122,,145,,"MASSACHUSETTS
(42.268546, -72.085064)",
SOUTH DAKOTA,2018,3,1,,1,,5,,8,,10,,"SOUTH DAKOTA
(44.35371, -100.373709)",
ARKANSAS,2018,3,16,,13,,17,,45,,68,,"ARKANSAS
(34.748745, -92.275105)",
NEW YORK CITY,2018,3,127,,126,,151,,404,,458,,,
OKLAHOMA,2018,3,19,,13,,21,,53,,38,,"OKLAHOMA
(35.468494, -97.521264)",
AMER. SAMOA,2018,3,,-,0,,2,,1,,,-,,
C.N.M.I.,2018,3,3,,3,,16,,27,,27,,"NORTHERN MARIANA ISLANDS
(15.1834, 145.725693)",
WEST VIRGINIA,2018,3,1,,0,,2,,3,,14,,"WEST VIRGINIA
(38.668501, -80.709421)",
WASHINGTON,2018,3,,-,0,,55,,86,,150,,"WASHINGTON
(47.517368, -120.467672)",
DIST. OF COL.,2018,3,6,,6,,14,,26,,26,,"DISTRICT OF COLUMBIA
(38.892062, -77.019912)",
SOUTH CAROLINA,2018,3,16,,3,,37,,46,,64,,"SOUTH CAROLINA
(34.009286, -81.037094)",
NEW YORK,2018,3,40,,40,,50,,139,,133,,"NEW YORK
(42.826892, -75.544286)",
W.S. CENTRAL,2018,3,98,,98,,338,,544,,1073,,,
ILLINOIS,2018,3,74,,55,,110,,221,,226,,"ILLINOIS
(40.477092, -88.993221)",
WISCONSIN,2018,3,8,,5,,14,,27,,39,,"WISCONSIN
(44.395526, -89.834513)",
MONTANA,2018,3,1,,0,,2,,4,,3,,"MONTANA
(47.062617, -109.430168)",
TEXAS,2018,3,43,,43,,271,,367,,862,,"TEXAS
(31.827233, -99.423022)",
MINNESOTA,2018,3,34,,28,,77,,114,,101,,"MINNESOTA
(46.357661, -94.793397)",
NEW ENGLAND,2018,3,47,,47,,88,,189,,232,,,
MISSISSIPPI,2018,3,7,,7,,29,,58,,33,,"MISSISSIPPI
(32.738663, -89.537312)",
CONNECTICUT,2018,3,5,,5,,15,,31,,48,,"CONNECTICUT
(41.544654, -72.651713)",
NEW HAMPSHIRE,2018,3,,-,0,,6,,5,,13,,"NEW HAMPSHIRE
(43.657736, -71.500736)",
CALIFORNIA,2018,3,319,,319,,517,,1234,,1520,,"CALIFORNIA
(37.640735, -121.002435)",
WYOMING,2018,3,,-,0,,0,,,-,2,,"WYOMING
(43.23558, -108.110518)",
NEVADA,2018,3,15,,10,,29,,47,,51,,"NEVADA
(39.491833, -117.070159)",
OREGON,2018,3,14,,14,,21,,48,,48,,"OREGON
(44.567912, -120.156945)",
GEORGIA,2018,3,56,,41,,66,,163,,226,,"GEORGIA
(32.836038, -83.631709)",
DELAWARE,2018,3,,-,0,,7,,7,,12,,"DELAWARE
(39.008676, -75.574561)",
INDIANA,2018,3,24,,19,,33,,75,,67,,"INDIANA
(39.76845, -86.156212)",
NEW MEXICO,2018,4,8,,6,,9,,32,,37,,"NEW MEXICO
(34.520396, -106.244402)",
COLORADO,2018,4,15,,9,,23,,66,,84,,"COLORADO
(38.841772, -106.132562)",
VIRGIN ISL.,2018,4,,-,0,,0,,,-,,-,"VIRGIN ISLANDS
(18.060868, -64.840836)",
TENNESSEE,2018,4,30,,30,,47,,144,,122,,"TENNESSEE
(35.681402, -85.774444)",
MISSISSIPPI,2018,4,16,,13,,29,,80,,46,,"MISSISSIPPI
(32.738663, -89.537312)",
W.S. CENTRAL,2018,4,172,,172,,320,,1079,,1413,,,
MICHIGAN,2018,4,21,,19,,28,,92,,122,,"MICHIGAN
(44.66399, -84.711206)",
PACIFIC,2018,4,563,,556,,639,,2364,,2481,,,
NORTH DAKOTA,2018,4,3,,2,,5,,12,,14,,"NORTH DAKOTA
(47.475879, -100.121011)",
SOUTH CAROLINA,2018,4,25,,3,,27,,71,,101,,"SOUTH CAROLINA
(34.009286, -81.037094)",
NEW YORK,2018,4,54,,40,,54,,193,,182,,"NEW YORK
(42.826892, -75.544286)",
MASSACHUSETTS,2018,4,44,,44,,49,,187,,208,,"MASSACHUSETTS
(42.268546, -72.085064)",
E.N. CENTRAL,2018,4,206,,139,,206,,708,,759,,,
KENTUCKY,2018,4,12,,12,,22,,60,,65,,"KENTUCKY
(37.64591, -84.774043)",
C.N.M.I.,2018,4,,-,0,,16,,33,,39,,"NORTHERN MARIANA ISLANDS
(15.1834, 145.725693)",
MONTANA,2018,4,,-,0,,2,,4,,3,,"MONTANA
(47.062617, -109.430168)",
MOUNTAIN,2018,4,68,,68,,101,,358,,433,,,
HAWAII,2018,4,15,,15,,33,,102,,116,,"HAWAII
(21.30992, -157.858158)",
NORTH CAROLINA,2018,4,33,,21,,58,,158,,213,,"NORTH CAROLINA
(35.47995, -79.180571)",
ILLINOIS,2018,4,79,,54,,92,,309,,336,,"ILLINOIS
(40.477092, -88.993221)",
ARIZONA,2018,4,22,,22,,49,,158,,188,,"ARIZONA
(34.869712, -111.760902)",
RHODE ISLAND,2018,4,2,,2,,8,,19,,13,,"RHODE ISLAND
(41.707447, -71.522488)",
CALIFORNIA,2018,4,485,,452,,518,,1948,,2037,,"CALIFORNIA
(37.640735, -121.002435)",
NEW HAMPSHIRE,2018,4,,-,0,,5,,5,,19,,"NEW HAMPSHIRE
(43.657736, -71.500736)",
IOWA,2018,4,8,,8,,13,,39,,47,,"IOWA
(42.46942, -93.815856)",
ALABAMA,2018,4,15,,15,,29,,91,,120,,"ALABAMA
(32.839182, -86.631125)",
NEBRASKA,2018,4,1,,1,,5,,11,,21,,"NEBRASKA
(41.640503, -99.367122)",
DIST. OF COL.,2018,4,7,,6,,15,,36,,36,,"DISTRICT OF COLUMBIA
(38.892062, -77.019912)",
INDIANA,2018,4,44,,19,,44,,118,,100,,"INDIANA
(39.76845, -86.156212)",
NEW YORK CITY,2018,4,145,,126,,152,,558,,607,,,
NEVADA,2018,4,16,,10,,22,,65,,80,,"NEVADA
(39.491833, -117.070159)",
VIRGINIA,2018,4,53,,29,,53,,167,,204,,"VIRGINIA
(37.542699, -78.45822)",
ALASKA,2018,4,10,,10,,19,,55,,53,,"ALASKA
(64.836661, -147.738891)",
WEST VIRGINIA,2018,4,3,,0,,3,,6,,16,,"WEST VIRGINIA
(38.668501, -80.709421)",
S. ATLANTIC,2018,4,325,,276,,432,,1415,,1622,,,
VERMONT,2018,4,,-,0,,2,,5,,3,,"VERMONT
(43.622479, -72.51927)",
SOUTH DAKOTA,2018,4,2,,1,,5,,10,,14,,"SOUTH DAKOTA
(44.35371, -100.373709)",
AMER. SAMOA,2018,4,,-,0,,1,,1,,2,,,
MARYLAND,2018,4,41,,39,,57,,177,,207,,"MARYLAND
(39.290443, -76.612333)",
OREGON,2018,4,20,,16,,20,,74,,69,,"OREGON
(44.567912, -120.156945)",
WASHINGTON,2018,4,33,,33,,56,,185,,206,,"WASHINGTON
(47.517368, -120.467672)",
OHIO,2018,4,62,,24,,62,,162,,152,,"OHIO
(40.056807, -82.405897)",
E.S. CENTRAL,2018,4,73,,73,,111,,375,,353,,,
FLORIDA,2018,4,101,,101,,156,,546,,546,,"FLORIDA
(28.915325, -81.922409)",
GEORGIA,2018,4,60,,41,,72,,239,,285,,"GEORGIA
(32.836038, -83.631709)",
MAINE,2018,4,3,,1,,10,,15,,14,,"MAINE
(45.252223, -68.986647)",
ARKANSAS,2018,4,6,,6,,24,,60,,85,,"ARKANSAS
(34.748745, -92.275105)",
DELAWARE,2018,4,2,,0,,9,,15,,14,,"DELAWARE
(39.008676, -75.574561)",
CONNECTICUT,2018,4,12,,8,,15,,48,,63,,"CONNECTICUT
(41.544654, -72.651713)",
UNITED STATES,2018,4,1828,,1828,,2275,,8062,,9031,,,
PENNSYLVANIA,2018,4,47,,42,,64,,209,,192,,"PENNSYLVANIA
(40.789676, -77.858055)",
WISCONSIN,2018,4,,-,0,,14,,27,,49,,"WISCONSIN
(44.395526, -89.834513)",
NEW ENGLAND,2018,4,61,,61,,80,,279,,320,,,
WYOMING,2018,4,,-,0,,0,,,-,2,,"WYOMING
(43.23558, -108.110518)",
MISSOURI,2018,4,1,,1,,11,,33,,85,,"MISSOURI
(38.636168, -92.566292)",
W.N. CENTRAL,2018,4,60,,60,,90,,290,,386,,,
PUERTO RICO,2018,4,9,,3,,9,,23,,40,,"PUERTO RICO
(18.215692, -66.414655)",
OKLAHOMA,2018,4,20,,13,,21,,73,,52,,"OKLAHOMA
(35.468494, -97.521264)",
IDAHO,2018,4,4,,2,,5,,15,,10,,"IDAHO
(43.680635, -114.364237)",
KANSAS,2018,4,,-,0,,12,,18,,27,,"KANSAS
(38.345528, -98.201847)",
GUAM,2018,4,18,,14,,20,,68,,84,,"GUAM
(13.3532, 144.653198)",
MID. ATLANTIC,2018,4,300,,275,,313,,1194,,1264,,,
NEW JERSEY,2018,4,54,,30,,75,,234,,283,,"NEW JERSEY
(40.104655, -74.386686)",
TEXAS,2018,4,122,,122,,264,,843,,1135,,"TEXAS
(31.827233, -99.423022)",
MINNESOTA,2018,4,45,,28,,52,,167,,178,,"MINNESOTA
(46.357661, -94.793397)",
LOUISIANA,2018,4,24,,20,,33,,103,,141,,"LOUISIANA
(31.2931, -92.463284)",
UTAH,2018,4,3,,2,,8,,18,,29,,"UTAH
(39.359772, -111.584173)",
NEW MEXICO,2018,4,8,,6,,9,,32,,37,,"NEW MEXICO
(34.520396, -106.244402)",
COLORADO,2018,4,15,,9,,23,,66,,84,,"COLORADO
(38.841772, -106.132562)",
VIRGIN ISL.,2018,4,,-,0,,0,,,-,,-,"VIRGIN ISLANDS
(18.060868, -64.840836)",
TENNESSEE,2018,4,30,,30,,47,,144,,122,,"TENNESSEE
(35.681402, -85.774444)",
MISSISSIPPI,2018,4,16,,13,,29,,80,,46,,"MISSISSIPPI
(32.738663, -89.537312)",
W.S. CENTRAL,2018,4,172,,172,,320,,1079,,1413,,,
MICHIGAN,2018,4,21,,19,,28,,92,,122,,"MICHIGAN
(44.66399, -84.711206)",
PACIFIC,2018,4,563,,556,,639,,2364,,2481,,,
NORTH DAKOTA,2018,4,3,,2,,5,,12,,14,,"NORTH DAKOTA
(47.475879, -100.121011)",
SOUTH CAROLINA,2018,4,25,,3,,27,,71,,101,,"SOUTH CAROLINA
(34.009286, -81.037094)",
NEW YORK,2018,4,54,,40,,54,,193,,182,,"NEW YORK
(42.826892, -75.544286)",
MASSACHUSETTS,2018,4,44,,44,,49,,187,,208,,"MASSACHUSETTS
(42.268546, -72.085064)",
E.N. CENTRAL,2018,4,206,,139,,206,,708,,759,,,
KENTUCKY,2018,4,12,,12,,22,,60,,65,,"KENTUCKY
(37.64591, -84.774043)",
C.N.M.I.,2018,4,,-,0,,16,,33,,39,,"NORTHERN MARIANA ISLANDS
(15.1834, 145.725693)",
MONTANA,2018,4,,-,0,,2,,4,,3,,"MONTANA
(47.062617, -109.430168)",
MOUNTAIN,2018,4,68,,68,,101,,358,,433,,,
HAWAII,2018,4,15,,15,,33,,102,,116,,"HAWAII
(21.30992, -157.858158)",
NORTH CAROLINA,2018,4,33,,21,,58,,158,,213,,"NORTH CAROLINA
(35.47995, -79.180571)",
ILLINOIS,2018,4,79,,54,,92,,309,,336,,"ILLINOIS
(40.477092, -88.993221)",
ARIZONA,2018,4,22,,22,,49,,158,,188,,"ARIZONA
(34.869712, -111.760902)",
RHODE ISLAND,2018,4,2,,2,,8,,19,,13,,"RHODE ISLAND
(41.707447, -71.522488)",
CALIFORNIA,2018,4,485,,452,,518,,1948,,2037,,"CALIFORNIA
(37.640735, -121.002435)",
NEW HAMPSHIRE,2018,4,,-,0,,5,,5,,19,,"NEW HAMPSHIRE
(43.657736, -71.500736)",
IOWA,2018,4,8,,8,,13,,39,,47,,"IOWA
(42.46942, -93.815856)",
ALABAMA,2018,4,15,,15,,29,,91,,120,,"ALABAMA
(32.839182, -86.631125)",
NEBRASKA,2018,4,1,,1,,5,,11,,21,,"NEBRASKA
(41.640503, -99.367122)",
DIST. OF COL.,2018,4,7,,6,,15,,36,,36,,"DISTRICT OF COLUMBIA
(38.892062, -77.019912)",
INDIANA,2018,4,44,,19,,44,,118,,100,,"INDIANA
(39.76845, -86.156212)",
NEW YORK CITY,2018,4,145,,126,,152,,558,,607,,,
NEVADA,2018,4,16,,10,,22,,65,,80,,"NEVADA
(39.491833, -117.070159)",
VIRGINIA,2018,4,53,,29,,53,,167,,204,,"VIRGINIA
(37.542699, -78.45822)",
ALASKA,2018,4,10,,10,,19,,55,,53,,"ALASKA
(64.836661, -147.738891)",
WEST VIRGINIA,2018,4,3,,0,,3,,6,,16,,"WEST VIRGINIA
(38.668501, -80.709421)",
S. ATLANTIC,2018,4,325,,276,,432,,1415,,1622,,,
VERMONT,2018,4,,-,0,,2,,5,,3,,"VERMONT
(43.622479, -72.51927)",
SOUTH DAKOTA,2018,4,2,,1,,5,,10,,14,,"SOUTH DAKOTA
(44.35371, -100.373709)",
AMER. SAMOA,2018,4,,-,0,,1,,1,,2,,,
MARYLAND,2018,4,41,,39,,57,,177,,207,,"MARYLAND
(39.290443, -76.612333)",
OREGON,2018,4,20,,16,,20,,74,,69,,"OREGON
(44.567912, -120.156945)",
WASHINGTON,2018,4,33,,33,,56,,185,,206,,"WASHINGTON
(47.517368, -120.467672)",
OHIO,2018,4,62,,24,,62,,162,,152,,"OHIO
(40.056807, -82.405897)",
E.S. CENTRAL,2018,4,73,,73,,111,,375,,353,,,
FLORIDA,2018,4,101,,101,,156,,546,,546,,"FLORIDA
(28.915325, -81.922409)",
GEORGIA,2018,4,60,,41,,72,,239,,285,,"GEORGIA
(32.836038, -83.631709)",
MAINE,2018,4,3,,1,,10,,15,,14,,"MAINE
(45.252223, -68.986647)",
ARKANSAS,2018,4,6,,6,,24,,60,,85,,"ARKANSAS
(34.748745, -92.275105)",
DELAWARE,2018,4,2,,0,,9,,15,,14,,"DELAWARE
(39.008676, -75.574561)",
CONNECTICUT,2018,4,12,,8,,15,,48,,63,,"CONNECTICUT
(41.544654, -72.651713)",
UNITED STATES,2018,4,1828,,1828,,2275,,8062,,9031,,,
PENNSYLVANIA,2018,4,47,,42,,64,,209,,192,,"PENNSYLVANIA
(40.789676, -77.858055)",
WISCONSIN,2018,4,,-,0,,14,,27,,49,,"WISCONSIN
(44.395526, -89.834513)",
NEW ENGLAND,2018,4,61,,61,,80,,279,,320,,,
WYOMING,2018,4,,-,0,,0,,,-,2,,"WYOMING
(43.23558, -108.110518)",
MISSOURI,2018,4,1,,1,,11,,33,,85,,"MISSOURI
(38.636168, -92.566292)",
W.N. CENTRAL,2018,4,60,,60,,90,,290,,386,,,
PUERTO RICO,2018,4,9,,3,,9,,23,,40,,"PUERTO RICO
(18.215692, -66.414655)",
OKLAHOMA,2018,4,20,,13,,21,,73,,52,,"OKLAHOMA
(35.468494, -97.521264)",
IDAHO,2018,4,4,,2,,5,,15,,10,,"IDAHO
(43.680635, -114.364237)",
KANSAS,2018,4,,-,0,,12,,18,,27,,"KANSAS
(38.345528, -98.201847)",
GUAM,2018,4,18,,14,,20,,68,,84,,"GUAM
(13.3532, 144.653198)",
MID. ATLANTIC,2018,4,300,,275,,313,,1194,,1264,,,
NEW JERSEY,2018,4,54,,30,,75,,234,,283,,"NEW JERSEY
(40.104655, -74.386686)",
TEXAS,2018,4,122,,122,,264,,843,,1135,,"TEXAS
(31.827233, -99.423022)",
MINNESOTA,2018,4,45,,28,,52,,167,,178,,"MINNESOTA
(46.357661, -94.793397)",
LOUISIANA,2018,4,24,,20,,33,,103,,141,,"LOUISIANA
(31.2931, -92.463284)",
UTAH,2018,4,3,,2,,8,,18,,29,,"UTAH
(39.359772, -111.584173)",
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