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Add household_electric, glass, daily_demand & concrete dataset

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Concrete Compressive Strength/README.md 0 → 100644
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**Level:** Intermediate <br/>
**Recommended Use:** Regression Models<br/>
**Domain:** Civil Engineering/Construction<br/>
## Concrete Compressive Strength Data Set
### Estimate compressive strength of concrete
---
![](123.jpg)
---
This *intermediate* level data set has 1030 rows and 9 columns.
Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients
The actual concrete compressive strength (MPa) for a given mixture under a specific age (days) was determined from laboratory. Data is in raw form (not scaled).
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **regression modelling techniques**.
It also allows you to practice with non-linear functions. Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |-------------------------------------------------------- |------------------------------------------------------------------------------- |-------------- |-------------------------------------- |--------------- |
| 1 | Cement (component 1)(kg in a m^3 mixture) | Cement (component 1) -- Kilogram in a meter-cube mixture -- Input Variable | Quantitative | 194.68, 379.5, 167.95 | 0 |
| 2 | Blast Furnace Slag (component 2)(kg in a m^3 mixture) | Blast Furnace Slag (component 2) -- kg in a m3 mixture -- Input Variable | Quantitative | 0, 151.2, 42.08 | 0 |
| 3 | Fly Ash (component 3)(kg in a m^3 mixture) | Fly Ash (component 3) -- kg in a m3 mixture -- Input Variable | Quantitative | 100.52, 0, 163.83 | 0 |
| 4 | Water (component 4)(kg in a m^3 mixture) | Water (component 4) -- kg in a m3 mixture -- Input Variable | Quantitative | 165.62, 153.9, 121.75 | 0 |
| 5 | Superplasticizer (component 5)(kg in a m^3 mixture) | Superplasticizer (component 5) -- kg in a m3 mixture -- Input Variable | Quantitative | 7.48, 15.9, 5.72 | 0 |
| 6 | Coarse Aggregate (component 6)(kg in a m^3 mixture) | Coarse Aggregate (component 6) -- kg in a m3 mixture -- Input Variable | Quantitative | 1006.4, 1134.3, 1058.7 | 0 |
| 7 | Fine Aggregate (component 7)(kg in a m^3 mixture) | Fine Aggregate (component 7) -- kg in a m3 mixture -- Input Variable | Quantitative | 905.9, 605, 780.11 | 0 |
| 8 | Age (day) | Age -- Day (1-365) -- Input Variable | Quantitative | 56, 91, 28 | 0 |
| 9 | Concrete compressive strength(MPa, megapascals) | Concrete compressive strength -- MegaPascals -- Output Variable | Quantitative | 33.96358776, 56.49566344, 32.8535314 | 0 |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Concrete Compressive Strength Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength).
The UCI page mentions the following publication as the original source of the data set:
*I-Cheng Yeh, "Modeling of strength of high performance concrete using artificial neural networks," Cement and Concrete Research, Vol. 28, No. 12, pp. 1797-1808 (1998)*
Daily Demand Forecasting Orders/Daily_Demand_Forecasting_Orders.csv 0 → 100644
Week of the month (first week, second, third, fourth or fifth week;Day of the week (Monday to Friday);Non-urgent order;Urgent order;Order type A;Order type B;Order type C;Fiscal sector orders;Orders from the traffic controller sector;Banking orders (1);Banking orders (2);Banking orders (3);Target (Total orders)
1;4;316.307;223.270;61.543;175.586;302.448;0;65556;44914;188411;14793;539.577
1;5;128.633;96.042;38.058;56.037;130.580;0;40419;21399;89461;7679;224.675
1;6;43.651;84.375;21.826;25.125;82.461;1.386;11992;3452;21305;14947;129.412
2;2;171.297;127.667;41.542;113.294;162.284;18.156;49971;33703;69054;18423;317.120
2;3;90.532;113.526;37.679;56.618;116.220;6.459;48534;19646;16411;20257;210.517
2;4;110.925;96.360;30.792;50.704;125.868;79;52042;8773;47522;24966;207.364
2;5;144.124;118.919;43.304;66.371;153.368;0;46573;33597;48269;20973;263.043
2;6;119.379;113.870;38.584;85.961;124.413;15.709;35033;26278;56665;18502;248.958
3;2;218.856;124.381;33.973;148.274;162.044;1.054;66612;19461;103376;10458;344.291
3;3;146.518;101.045;36.399;43.306;168.723;865;58224;7742;82395;11948;248.428
3;4;178.433;102.793;45.706;111.036;124.678;194;47046;17299;108719;15560;281.420
3;5;145.865;91.180;43.851;66.277;133.440;6.523;66910;17768;36693;29046;243.568
3;6;170.566;114.412;43.339;136.434;128.405;23.200;32529;34002;78153;31949;308.178
4;2;220.343;141.406;46.241;120.865;196.296;1.653;34878;32905;117137;29188;363.402
4;3;193.768;141.854;56.519;136.709;143.644;1.250;57858;23956;101048;30134;336.872
4;4;122.736;124.256;56.167;78.101;112.724;0;52321;10046;62799;24233;246.992
4;5;144.051;158.408;51.660;92.272;164.948;6.421;47167;6440;91784;15973;308.880
4;6;105.415;108.688;47.717;71.474;113.935;19.023;42737;26020;27873;17600;233.126
5;2;240.660;163.720;59.135;157.681;187.564;0;39273;32917;155617;9203;404.380
1;3;131.067;166.649;90.476;80.509;127.575;844;60543;19141;78378;73839;298.560
1;4;130.129;98.927;42.904;43.962;142.383;193;54760;9163;29874;46992;229.249
1;5;123.286;103.551;47.331;72.444;116.529;9.467;48732;21196;47793;47574;236.304
1;6;190.816;87.629;32.077;127.358;137.739;18.729;46368;36798;92701;31098;297.174
2;2;266.741;141.437;58.721;139.034;211.646;1.223;58081;43333;135314;29716;409.401
2;3;123.143;106.083;36.017;75.813;119.205;1.809;45340;22109;55584;29803;231.035
2;4;148.139;85.310;35.576;79.997;123.253;5.377;59686;14188;67617;32319;238.826
2;5;118.552;100.417;54.401;75.613;105.584;16.629;40423;24682;47563;35314;235.598
2;6;146.959;95.153;37.656;59.907;144.549;0;50908;45733;43930;28998;242.112
3;2;299.770;133.375;57.810;236.248;196.732;57.645;71772;57756;159373;29160;490.790
3;3;151.341;131.788;43.359;89.382;156.916;6.528;53573;42638;62732;32386;289.657
3;4;206.206;92.160;45.555;148.718;104.186;93;49110;36904;126632;33237;298.459
3;5;170.868;131.463;45.550;120.548;157.505;21.272;42534;79556;50433;36483;323.603
4;3;435.304;181.149;67.884;267.342;281.227;0;64867;210508;177229;30514;616.453
4;4;235.106;110.874;70.376;154.242;121.417;55;23257;163452;63699;33805;346.035
4;5;168.179;125.119;71.068;100.544;136.033;14.347;28072;95989;50763;55445;307.645
4;6;172.783;77.371;64.137;109.062;80.648;3.693;46321;66498;61593;31625;253.847
5;2;381.768;140.041;118.178;260.632;152.134;9.135;34236;194216;136035;47601;530.944
5;3;221.438;111.392;51.199;124.660;157.500;529;39964;136119;66745;31031;333.359
5;4;193.957;111.859;47.002;99.892;159.462;540;59179;94460;54772;34616;306.356
1;6;275.076;121.697;109.888;131.165;175.777;20.057;37906;138536;85378;14020;416.830
2;2;252.298;150.708;77.388;154.863;182.936;12.181;32133;69093;169088;12516;415.187
2;3;165.472;102.530;46.295;96.870;124.837;0;48458;43112;72840;11304;268.002
2;4;126.030;108.055;53.366;69.150;111.987;418;42201;13736;70191;16710;234.503
2;5;112.246;106.641;47.399;77.610;109.715;15.837;35316;25876;38646;13989;234.724
2;6;123.302;94.315;48.081;72.826;109.157;12.447;43284;30138;52112;12632;230.064
3;2;187.810;167.455;59.042;130.098;168.254;2.129;37817;36445;103567;10443;357.394
3;3;119.863;139.383;44.809;99.072;115.365;0;54584;17242;59231;12543;259.246
3;4;127.805;114.813;39.025;110.740;94.470;1.617;33366;21103;84558;16683;244.235
3;5;120.629;112.703;39.600;240.922;122.085;169.275;37387;20246;63778;13886;402.607
3;6;130.465;105.273;57.467;88.462;109.132;19.323;27200;41713;59513;12260;255.061
4;2;222.282;120.324;41.418;135.189;165.999;0;39446;29290;154144;10811;342.606
4;3;150.257;116.959;34.193;115.536;118.911;1.424;51346;19782;89704;12182;268.640
4;4;96.494;87.294;32.653;81.576;74.372;4.813;34631;22420;49644;15390;188.601
4;5;89.526;99.756;51.985;51.930;98.107;12.740;31850;32150;21573;13807;202.022
4;6;134.425;79.084;36.748;71.353;105.408;0;33970;28701;65199;11023;213.509
5;2;158.716;158.133;59.131;92.639;165.079;0;32027;33282;128269;9287;316.849
5;3;150.784;133.069;54.224;115.746;116.442;2.559;51235;34421;87708;11354;286.412
5;4;193.534;109.639;58.378;142.382;102.687;274;28364;88404;91367;15003;303.447
5;5;196.555;108.395;76.763;96.478;131.709;0;37011;109931;50112;12957;304.950
5;6;192.116;121.106;107.568;121.152;103.180;18.678;27328;108072;56015;10690;331.900
Daily Demand Forecasting Orders/README.md 0 → 100644
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**Level:** Intermediate <br/>
**Recommended Use:** Regression Models<br/>
**Domain:** Business<br/>
## Daily Demand Forecasting Orders Data Set
### Predict total number of demand of orders
---
![](294036-P6YS7U-202.jpg)
---
This *intermediate* level data set has 60 rows and 13 columns.
The dataset was collected during 60 days, this is a real database of a brazilian logistics company.
The dataset has twelve predictive attributes and a target that is the total of orders for daily treatment.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **regression modelling techniques**.
It also allows you to practice with large number of features. Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |--------------------------------------------- |------------------------------------------------------------------------------- |-------------- |--------------------------- |--------------- |
| 1 | Week of the month | Week of the month (1: first, 2: second, 3: third, 4: fourth, 5:fifth) | Quantitative | 1, 2, 3 | 0 |
| 2 | Day of the week | Day of the week (2: Monday, 3: Tuesday, 4: Wednesday, 5:Thursday, 6:Friday) | Quantitative | 2, 3, 4 | 0 |
| 3 | Non-urgent order | Non-urgent order | Quantitative | 171.297, 220.343, 127.805 | 0 |
| 4 | Urgent order | Urgent order | Quantitative | 127.667, 141.406, 114.813 | 0 |
| 5 | Order type A | Order type A | Quantitative | 41.542, 46.241, 39.025 | 0 |
| 6 | Order type B | Order type B | Quantitative | 113.294, 120.865, 110.74 | 0 |
| 7 | Order type C | Order type C | Quantitative | 162.284, 196.296, 94.47 | 0 |
| 8 | Fiscal sector orders | Fiscal sector orders | Quantitative | 18.156, 1.653, 1.617 | 0 |
| 9 | Orders from the traffic controller sector | Orders from the traffic controller sector | Quantitative | 49971, 34878, 33366 | |
| 10 | Banking orders (1) | Banking orders (1) | Quantitative | 33703, 32905, 21103 | 0 |
| 11 | Banking orders (2) | Banking orders (2) | Quantitative | 69054, 117137, 84558 | 0 |
| 12 | Banking orders (3) | Banking orders (3) | Quantitative | 18423, 29188, 16683 | 0 |
| 13 | Target (Total orders) | Target (Total orders) | Quantitative | 317.12, 363.402, 244.235 | 0 |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Daily Demand Forecasting Orders Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Daily+Demand+Forecasting+Orders).
The UCI page mentions the following publication as the original source of the data set:
*Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on daily demand forecasting orders using artificial neural network. IEEE Latin America Transactions, 14(3), 1519-1525*
Glass Identification/README.md 0 → 100644
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**Level:** Intermediate<br/>
**Recommended Use:** Classification Models<br/>
**Domain:** Physical<br/>
## Glass Identification Data Set
### Predict the type of glass
---
![](211.jpg)
---
This *intermediate* level data set has 214 rows and 10 columns.
The data set provides details about 6 types of glass, defined in terms of their oxide content (i.e. Na, Fe, K, etc).
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **classification modelling techniques**.
Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |---------------- |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |-------------- |--------------------------- |--------------- |
| 1 | Id number | Id number from 1 to 214 | Quantitative | 16, 75, 211 | 0 |
| 2 | RI | RI: Refractive Index | Quantitative | 1.51755, 1.51613, 1.51844 | 0 |
| 3 | Na | NA: Sodium (unit measurement: weight percent in corresponding oxide) | Quantitative | 13.19, 12.79, 14.21 | 0 |
| 4 | Mg | Mg: Magnesium (unit measurement: weight percent in corresponding oxide) | Quantitative | 3.82, 2.87, 3.59 | 0 |
| 5 | Al | Al: Aluminum (unit measurement: weight percent in corresponding oxide) | Quantitative | 1.56, 1.43, | 0 |
| 6 | Si | Si: Silicon (unit measurement: weight percent in corresponding oxide) | Quantitative | 73.20, 71.77, 72.95 | 0 |
| 7 | K | K: Potassium (unit measurement: weight percent in corresponding oxide) | Quantitative | 0.67, 0.57, 0.11 | 0 |
| 8 | Ca | Ca: Calcium (unit measurement: weight percent in corresponding oxide) | Quantitative | 8.09, 7.83, 9.57 | 0 |
| 9 | Ba | Ba: Barium (unit measurement: weight percent in corresponding oxide) | Quantitative | 0.00, 0.11, 0.27 | 0 |
| 10 | Fe | Fe: Iron (unit measurement: weight percent in corresponding oxide) | Quantitative | 0.11, 0.14, 0.00 | 0 |
| 11 | Type of Glass | Glas Type (1: building_windows_float_processed, 2: building_windows_non_float_processed, 3: vehicle_windows_float_processed, 4: vehicle_windows_non_float_processed, 5: containers, 6: tableware, 7: headlamps) | Quantitative | 2, 5, 7 | 0 |
---
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Glass Identification Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Glass+Identification).
The UCI page mentions USA Forensic Science Service as the following as the original source of the data set.
Individual Household Electric Power Consumption/README.md 0 → 100644
Data Science Dojo <br/>
Copyright (c) 2016 - 2019
---
**Level:** Intermediate <br/>
**Recommended Use:** Regression/Clustering Models<br/>
**Domain:** Electricity<br/>
## Individual household electric power consumption Data Set
### Find a short term forecast on electricity consumption of a single home
---
![](230088-P1ZDYV-666.jpg)
---
This *intermediate* level data set has 2075259 rows and 9 columns.
This dataset provides measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years.
Different electrical quantities and some sub-metering values are available.
This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, **clustering** and **regression modelling techniques**.
It also allows you to practice with large number of features. Feel free to explore the data set with multiple **supervised** and **unsupervised** learning techniques. The Following data dictionary gives more details on this data set:
---
### Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|------------------- |----------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |-------------- |---------------------------------- |--------------- |
| 1 | Date | Date: Date in format dd/mm/yyyy | Quantitative | 16/12/2006, 10/5/2007, 24/9/2007 | ? |
| 2 | Time | Time: time in format hh:mm:ss | Quantitative | 17:27:00, 6:56:00, 10:00:00 | ? |
| 3 | Global_Active_Power | Global_active_power: Household global minute-averaged active power (in kilowatt) | Quantitative | 4.216, 5.412, 3.488 | ? |
| 4 | Global_Reactive_Power | Global_reactive_power: Household global minute-averaged reactive power (in kilowatt) | Quantitative | 0.418, 0.47, 0.228 | ? |
| 5 | Voltage | Voltage: Minute-averaged voltage (in volt) | Quantitative | 234.84, 232.78, 233.06 | ? |
| 6 | Global_Intensity | Global_intensity: Household global minute-averaged current intensity (in ampere) | Quantitative | 18.4, 23.2, 15 | ? |
| 7 | Sub_Metering_1 | Sub_metering_1: Energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). | Quantitative | 1, 38, 17 | ? |
| 8 | Sub_Metering_2 | Sub_metering_2: Energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. | Quantitative | 1, 36, 5 | ? |
| 9 | Sub_Metering_3 | Sub_metering_3: Energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner | Quantitative | 17, 0, 18 | ? |
---
Note:
(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3
### Acknowledgement
This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Individual household electric power consumption Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption).
The UCI page mentions the following as the source of the data set:
*Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France*
*Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France*
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