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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.
Glass Identification/glass.data 0 → 100644
1,1.52101,13.64,4.49,1.10,71.78,0.06,8.75,0.00,0.00,1
2,1.51761,13.89,3.60,1.36,72.73,0.48,7.83,0.00,0.00,1
3,1.51618,13.53,3.55,1.54,72.99,0.39,7.78,0.00,0.00,1
4,1.51766,13.21,3.69,1.29,72.61,0.57,8.22,0.00,0.00,1
5,1.51742,13.27,3.62,1.24,73.08,0.55,8.07,0.00,0.00,1
6,1.51596,12.79,3.61,1.62,72.97,0.64,8.07,0.00,0.26,1
7,1.51743,13.30,3.60,1.14,73.09,0.58,8.17,0.00,0.00,1
8,1.51756,13.15,3.61,1.05,73.24,0.57,8.24,0.00,0.00,1
9,1.51918,14.04,3.58,1.37,72.08,0.56,8.30,0.00,0.00,1
10,1.51755,13.00,3.60,1.36,72.99,0.57,8.40,0.00,0.11,1
11,1.51571,12.72,3.46,1.56,73.20,0.67,8.09,0.00,0.24,1
12,1.51763,12.80,3.66,1.27,73.01,0.60,8.56,0.00,0.00,1
13,1.51589,12.88,3.43,1.40,73.28,0.69,8.05,0.00,0.24,1
14,1.51748,12.86,3.56,1.27,73.21,0.54,8.38,0.00,0.17,1
15,1.51763,12.61,3.59,1.31,73.29,0.58,8.50,0.00,0.00,1
16,1.51761,12.81,3.54,1.23,73.24,0.58,8.39,0.00,0.00,1
17,1.51784,12.68,3.67,1.16,73.11,0.61,8.70,0.00,0.00,1
18,1.52196,14.36,3.85,0.89,71.36,0.15,9.15,0.00,0.00,1
19,1.51911,13.90,3.73,1.18,72.12,0.06,8.89,0.00,0.00,1
20,1.51735,13.02,3.54,1.69,72.73,0.54,8.44,0.00,0.07,1
21,1.51750,12.82,3.55,1.49,72.75,0.54,8.52,0.00,0.19,1
22,1.51966,14.77,3.75,0.29,72.02,0.03,9.00,0.00,0.00,1
23,1.51736,12.78,3.62,1.29,72.79,0.59,8.70,0.00,0.00,1
24,1.51751,12.81,3.57,1.35,73.02,0.62,8.59,0.00,0.00,1
25,1.51720,13.38,3.50,1.15,72.85,0.50,8.43,0.00,0.00,1
26,1.51764,12.98,3.54,1.21,73.00,0.65,8.53,0.00,0.00,1
27,1.51793,13.21,3.48,1.41,72.64,0.59,8.43,0.00,0.00,1
28,1.51721,12.87,3.48,1.33,73.04,0.56,8.43,0.00,0.00,1
29,1.51768,12.56,3.52,1.43,73.15,0.57,8.54,0.00,0.00,1
30,1.51784,13.08,3.49,1.28,72.86,0.60,8.49,0.00,0.00,1
31,1.51768,12.65,3.56,1.30,73.08,0.61,8.69,0.00,0.14,1
32,1.51747,12.84,3.50,1.14,73.27,0.56,8.55,0.00,0.00,1
33,1.51775,12.85,3.48,1.23,72.97,0.61,8.56,0.09,0.22,1
34,1.51753,12.57,3.47,1.38,73.39,0.60,8.55,0.00,0.06,1
35,1.51783,12.69,3.54,1.34,72.95,0.57,8.75,0.00,0.00,1
36,1.51567,13.29,3.45,1.21,72.74,0.56,8.57,0.00,0.00,1
37,1.51909,13.89,3.53,1.32,71.81,0.51,8.78,0.11,0.00,1
38,1.51797,12.74,3.48,1.35,72.96,0.64,8.68,0.00,0.00,1
39,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0.00,0.00,1
40,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0.00,0.00,1
41,1.51793,12.79,3.50,1.12,73.03,0.64,8.77,0.00,0.00,1
42,1.51755,12.71,3.42,1.20,73.20,0.59,8.64,0.00,0.00,1
43,1.51779,13.21,3.39,1.33,72.76,0.59,8.59,0.00,0.00,1
44,1.52210,13.73,3.84,0.72,71.76,0.17,9.74,0.00,0.00,1
45,1.51786,12.73,3.43,1.19,72.95,0.62,8.76,0.00,0.30,1
46,1.51900,13.49,3.48,1.35,71.95,0.55,9.00,0.00,0.00,1
47,1.51869,13.19,3.37,1.18,72.72,0.57,8.83,0.00,0.16,1
48,1.52667,13.99,3.70,0.71,71.57,0.02,9.82,0.00,0.10,1
49,1.52223,13.21,3.77,0.79,71.99,0.13,10.02,0.00,0.00,1
50,1.51898,13.58,3.35,1.23,72.08,0.59,8.91,0.00,0.00,1
51,1.52320,13.72,3.72,0.51,71.75,0.09,10.06,0.00,0.16,1
52,1.51926,13.20,3.33,1.28,72.36,0.60,9.14,0.00,0.11,1
53,1.51808,13.43,2.87,1.19,72.84,0.55,9.03,0.00,0.00,1
54,1.51837,13.14,2.84,1.28,72.85,0.55,9.07,0.00,0.00,1
55,1.51778,13.21,2.81,1.29,72.98,0.51,9.02,0.00,0.09,1
56,1.51769,12.45,2.71,1.29,73.70,0.56,9.06,0.00,0.24,1
57,1.51215,12.99,3.47,1.12,72.98,0.62,8.35,0.00,0.31,1
58,1.51824,12.87,3.48,1.29,72.95,0.60,8.43,0.00,0.00,1
59,1.51754,13.48,3.74,1.17,72.99,0.59,8.03,0.00,0.00,1
60,1.51754,13.39,3.66,1.19,72.79,0.57,8.27,0.00,0.11,1
61,1.51905,13.60,3.62,1.11,72.64,0.14,8.76,0.00,0.00,1
62,1.51977,13.81,3.58,1.32,71.72,0.12,8.67,0.69,0.00,1
63,1.52172,13.51,3.86,0.88,71.79,0.23,9.54,0.00,0.11,1
64,1.52227,14.17,3.81,0.78,71.35,0.00,9.69,0.00,0.00,1
65,1.52172,13.48,3.74,0.90,72.01,0.18,9.61,0.00,0.07,1
66,1.52099,13.69,3.59,1.12,71.96,0.09,9.40,0.00,0.00,1
67,1.52152,13.05,3.65,0.87,72.22,0.19,9.85,0.00,0.17,1
68,1.52152,13.05,3.65,0.87,72.32,0.19,9.85,0.00,0.17,1
69,1.52152,13.12,3.58,0.90,72.20,0.23,9.82,0.00,0.16,1
70,1.52300,13.31,3.58,0.82,71.99,0.12,10.17,0.00,0.03,1
71,1.51574,14.86,3.67,1.74,71.87,0.16,7.36,0.00,0.12,2
72,1.51848,13.64,3.87,1.27,71.96,0.54,8.32,0.00,0.32,2
73,1.51593,13.09,3.59,1.52,73.10,0.67,7.83,0.00,0.00,2
74,1.51631,13.34,3.57,1.57,72.87,0.61,7.89,0.00,0.00,2
75,1.51596,13.02,3.56,1.54,73.11,0.72,7.90,0.00,0.00,2
76,1.51590,13.02,3.58,1.51,73.12,0.69,7.96,0.00,0.00,2
77,1.51645,13.44,3.61,1.54,72.39,0.66,8.03,0.00,0.00,2
78,1.51627,13.00,3.58,1.54,72.83,0.61,8.04,0.00,0.00,2
79,1.51613,13.92,3.52,1.25,72.88,0.37,7.94,0.00,0.14,2
80,1.51590,12.82,3.52,1.90,72.86,0.69,7.97,0.00,0.00,2
81,1.51592,12.86,3.52,2.12,72.66,0.69,7.97,0.00,0.00,2
82,1.51593,13.25,3.45,1.43,73.17,0.61,7.86,0.00,0.00,2
83,1.51646,13.41,3.55,1.25,72.81,0.68,8.10,0.00,0.00,2
84,1.51594,13.09,3.52,1.55,72.87,0.68,8.05,0.00,0.09,2
85,1.51409,14.25,3.09,2.08,72.28,1.10,7.08,0.00,0.00,2
86,1.51625,13.36,3.58,1.49,72.72,0.45,8.21,0.00,0.00,2
87,1.51569,13.24,3.49,1.47,73.25,0.38,8.03,0.00,0.00,2
88,1.51645,13.40,3.49,1.52,72.65,0.67,8.08,0.00,0.10,2
89,1.51618,13.01,3.50,1.48,72.89,0.60,8.12,0.00,0.00,2
90,1.51640,12.55,3.48,1.87,73.23,0.63,8.08,0.00,0.09,2
91,1.51841,12.93,3.74,1.11,72.28,0.64,8.96,0.00,0.22,2
92,1.51605,12.90,3.44,1.45,73.06,0.44,8.27,0.00,0.00,2
93,1.51588,13.12,3.41,1.58,73.26,0.07,8.39,0.00,0.19,2
94,1.51590,13.24,3.34,1.47,73.10,0.39,8.22,0.00,0.00,2
95,1.51629,12.71,3.33,1.49,73.28,0.67,8.24,0.00,0.00,2
96,1.51860,13.36,3.43,1.43,72.26,0.51,8.60,0.00,0.00,2
97,1.51841,13.02,3.62,1.06,72.34,0.64,9.13,0.00,0.15,2
98,1.51743,12.20,3.25,1.16,73.55,0.62,8.90,0.00,0.24,2
99,1.51689,12.67,2.88,1.71,73.21,0.73,8.54,0.00,0.00,2
100,1.51811,12.96,2.96,1.43,72.92,0.60,8.79,0.14,0.00,2
101,1.51655,12.75,2.85,1.44,73.27,0.57,8.79,0.11,0.22,2
102,1.51730,12.35,2.72,1.63,72.87,0.70,9.23,0.00,0.00,2
103,1.51820,12.62,2.76,0.83,73.81,0.35,9.42,0.00,0.20,2
104,1.52725,13.80,3.15,0.66,70.57,0.08,11.64,0.00,0.00,2
105,1.52410,13.83,2.90,1.17,71.15,0.08,10.79,0.00,0.00,2
106,1.52475,11.45,0.00,1.88,72.19,0.81,13.24,0.00,0.34,2
107,1.53125,10.73,0.00,2.10,69.81,0.58,13.30,3.15,0.28,2
108,1.53393,12.30,0.00,1.00,70.16,0.12,16.19,0.00,0.24,2
109,1.52222,14.43,0.00,1.00,72.67,0.10,11.52,0.00,0.08,2
110,1.51818,13.72,0.00,0.56,74.45,0.00,10.99,0.00,0.00,2
111,1.52664,11.23,0.00,0.77,73.21,0.00,14.68,0.00,0.00,2
112,1.52739,11.02,0.00,0.75,73.08,0.00,14.96,0.00,0.00,2
113,1.52777,12.64,0.00,0.67,72.02,0.06,14.40,0.00,0.00,2
114,1.51892,13.46,3.83,1.26,72.55,0.57,8.21,0.00,0.14,2
115,1.51847,13.10,3.97,1.19,72.44,0.60,8.43,0.00,0.00,2
116,1.51846,13.41,3.89,1.33,72.38,0.51,8.28,0.00,0.00,2
117,1.51829,13.24,3.90,1.41,72.33,0.55,8.31,0.00,0.10,2
118,1.51708,13.72,3.68,1.81,72.06,0.64,7.88,0.00,0.00,2
119,1.51673,13.30,3.64,1.53,72.53,0.65,8.03,0.00,0.29,2
120,1.51652,13.56,3.57,1.47,72.45,0.64,7.96,0.00,0.00,2
121,1.51844,13.25,3.76,1.32,72.40,0.58,8.42,0.00,0.00,2
122,1.51663,12.93,3.54,1.62,72.96,0.64,8.03,0.00,0.21,2
123,1.51687,13.23,3.54,1.48,72.84,0.56,8.10,0.00,0.00,2
124,1.51707,13.48,3.48,1.71,72.52,0.62,7.99,0.00,0.00,2
125,1.52177,13.20,3.68,1.15,72.75,0.54,8.52,0.00,0.00,2
126,1.51872,12.93,3.66,1.56,72.51,0.58,8.55,0.00,0.12,2
127,1.51667,12.94,3.61,1.26,72.75,0.56,8.60,0.00,0.00,2
128,1.52081,13.78,2.28,1.43,71.99,0.49,9.85,0.00,0.17,2
129,1.52068,13.55,2.09,1.67,72.18,0.53,9.57,0.27,0.17,2
130,1.52020,13.98,1.35,1.63,71.76,0.39,10.56,0.00,0.18,2
131,1.52177,13.75,1.01,1.36,72.19,0.33,11.14,0.00,0.00,2
132,1.52614,13.70,0.00,1.36,71.24,0.19,13.44,0.00,0.10,2
133,1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0.00,0.00,2
134,1.51800,13.71,3.93,1.54,71.81,0.54,8.21,0.00,0.15,2
135,1.51811,13.33,3.85,1.25,72.78,0.52,8.12,0.00,0.00,2
136,1.51789,13.19,3.90,1.30,72.33,0.55,8.44,0.00,0.28,2
137,1.51806,13.00,3.80,1.08,73.07,0.56,8.38,0.00,0.12,2
138,1.51711,12.89,3.62,1.57,72.96,0.61,8.11,0.00,0.00,2
139,1.51674,12.79,3.52,1.54,73.36,0.66,7.90,0.00,0.00,2
140,1.51674,12.87,3.56,1.64,73.14,0.65,7.99,0.00,0.00,2
141,1.51690,13.33,3.54,1.61,72.54,0.68,8.11,0.00,0.00,2
142,1.51851,13.20,3.63,1.07,72.83,0.57,8.41,0.09,0.17,2
143,1.51662,12.85,3.51,1.44,73.01,0.68,8.23,0.06,0.25,2
144,1.51709,13.00,3.47,1.79,72.72,0.66,8.18,0.00,0.00,2
145,1.51660,12.99,3.18,1.23,72.97,0.58,8.81,0.00,0.24,2
146,1.51839,12.85,3.67,1.24,72.57,0.62,8.68,0.00,0.35,2
147,1.51769,13.65,3.66,1.11,72.77,0.11,8.60,0.00,0.00,3
148,1.51610,13.33,3.53,1.34,72.67,0.56,8.33,0.00,0.00,3
149,1.51670,13.24,3.57,1.38,72.70,0.56,8.44,0.00,0.10,3
150,1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0.00,0.00,3
151,1.51665,13.14,3.45,1.76,72.48,0.60,8.38,0.00,0.17,3
152,1.52127,14.32,3.90,0.83,71.50,0.00,9.49,0.00,0.00,3
153,1.51779,13.64,3.65,0.65,73.00,0.06,8.93,0.00,0.00,3
154,1.51610,13.42,3.40,1.22,72.69,0.59,8.32,0.00,0.00,3
155,1.51694,12.86,3.58,1.31,72.61,0.61,8.79,0.00,0.00,3
156,1.51646,13.04,3.40,1.26,73.01,0.52,8.58,0.00,0.00,3
157,1.51655,13.41,3.39,1.28,72.64,0.52,8.65,0.00,0.00,3
158,1.52121,14.03,3.76,0.58,71.79,0.11,9.65,0.00,0.00,3
159,1.51776,13.53,3.41,1.52,72.04,0.58,8.79,0.00,0.00,3
160,1.51796,13.50,3.36,1.63,71.94,0.57,8.81,0.00,0.09,3
161,1.51832,13.33,3.34,1.54,72.14,0.56,8.99,0.00,0.00,3
162,1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,3
163,1.52211,14.19,3.78,0.91,71.36,0.23,9.14,0.00,0.37,3
164,1.51514,14.01,2.68,3.50,69.89,1.68,5.87,2.20,0.00,5
165,1.51915,12.73,1.85,1.86,72.69,0.60,10.09,0.00,0.00,5
166,1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0.00,0.00,5
167,1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0.00,0.00,5
168,1.51969,12.64,0.00,1.65,73.75,0.38,11.53,0.00,0.00,5
169,1.51666,12.86,0.00,1.83,73.88,0.97,10.17,0.00,0.00,5
170,1.51994,13.27,0.00,1.76,73.03,0.47,11.32,0.00,0.00,5
171,1.52369,13.44,0.00,1.58,72.22,0.32,12.24,0.00,0.00,5
172,1.51316,13.02,0.00,3.04,70.48,6.21,6.96,0.00,0.00,5
173,1.51321,13.00,0.00,3.02,70.70,6.21,6.93,0.00,0.00,5
174,1.52043,13.38,0.00,1.40,72.25,0.33,12.50,0.00,0.00,5
175,1.52058,12.85,1.61,2.17,72.18,0.76,9.70,0.24,0.51,5
176,1.52119,12.97,0.33,1.51,73.39,0.13,11.27,0.00,0.28,5
177,1.51905,14.00,2.39,1.56,72.37,0.00,9.57,0.00,0.00,6
178,1.51937,13.79,2.41,1.19,72.76,0.00,9.77,0.00,0.00,6
179,1.51829,14.46,2.24,1.62,72.38,0.00,9.26,0.00,0.00,6
180,1.51852,14.09,2.19,1.66,72.67,0.00,9.32,0.00,0.00,6
181,1.51299,14.40,1.74,1.54,74.55,0.00,7.59,0.00,0.00,6
182,1.51888,14.99,0.78,1.74,72.50,0.00,9.95,0.00,0.00,6
183,1.51916,14.15,0.00,2.09,72.74,0.00,10.88,0.00,0.00,6
184,1.51969,14.56,0.00,0.56,73.48,0.00,11.22,0.00,0.00,6
185,1.51115,17.38,0.00,0.34,75.41,0.00,6.65,0.00,0.00,6
186,1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,7
187,1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,7
188,1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,7
189,1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,7
190,1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,7
191,1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,7
192,1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,7
193,1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,7
194,1.51719,14.75,0.00,2.00,73.02,0.00,8.53,1.59,0.08,7
195,1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,7
196,1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,7
197,1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,7
198,1.51727,14.70,0.00,2.34,73.28,0.00,8.95,0.66,0.00,7
199,1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,7
200,1.51609,15.01,0.00,2.51,73.05,0.05,8.83,0.53,0.00,7
201,1.51508,15.15,0.00,2.25,73.50,0.00,8.34,0.63,0.00,7
202,1.51653,11.95,0.00,1.19,75.18,2.70,8.93,0.00,0.00,7
203,1.51514,14.85,0.00,2.42,73.72,0.00,8.39,0.56,0.00,7
204,1.51658,14.80,0.00,1.99,73.11,0.00,8.28,1.71,0.00,7
205,1.51617,14.95,0.00,2.27,73.30,0.00,8.71,0.67,0.00,7
206,1.51732,14.95,0.00,1.80,72.99,0.00,8.61,1.55,0.00,7
207,1.51645,14.94,0.00,1.87,73.11,0.00,8.67,1.38,0.00,7
208,1.51831,14.39,0.00,1.82,72.86,1.41,6.47,2.88,0.00,7
209,1.51640,14.37,0.00,2.74,72.85,0.00,9.45,0.54,0.00,7
210,1.51623,14.14,0.00,2.88,72.61,0.08,9.18,1.06,0.00,7
211,1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,7
212,1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,7
213,1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,7
214,1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7
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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