Commit 726a60b1 by Rahim Rasool

### Add household_electric, glass, daily_demand & concrete dataset

parent f79a29c3
 Data Science Dojo
Copyright (c) 2016 - 2019 --- **Level:** Intermediate
**Recommended Use:** Regression Models
**Domain:** Civil Engineering/Construction
## 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)*

111 KB

 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
 Data Science Dojo
Copyright (c) 2016 - 2019 --- **Level:** Intermediate
**Recommended Use:** Regression Models
## 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*

339 KB

 Data Science Dojo
Copyright (c) 2016 - 2019 --- **Level:** Intermediate
**Recommended Use:** Classification Models
**Domain:** Physical
## 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.
This diff is collapsed. Click to expand it.

139 KB

 Data Science Dojo
Copyright (c) 2016 - 2019 --- **Level:** Intermediate
**Recommended Use:** Regression/Clustering Models
**Domain:** Electricity
## 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*