+Copyright (c) 2016 - 2019 + +--- + +**Level:** Beginner
+**Recommended Use:** Classification Models
+**Domain:** Automobile
+ +## Car Evaluation Data Set + +### Predict acceptibility of a car + + +--- +![](1190.jpg) +--- + +This *intermediate* level data set was derived from a decision-making model which was originally developed for research on multi-attribute decision making. +Decision making involves selection between seemingly conflicting alternatives. + +The data set has 1728 rows and 7 columns in which car attributes such as price and technology are described across 6 attributes such as "Buying Price", "Maintenance", and "Safety" etc. There are multiple alternatives under each of the 6 attributes. Car's acceptability, +the seventh attribute, is the outcome variable. + +This data set is recommended for learning and practicing your skills in **classification modelling techniques**. Feel free to explore the data set with multiple +classification methods. The Following data dictionary gives more details on this data set: + +--- + +### Data Dictionary + +| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios | +|------------------- |---------------- |------------------------------------------------------------------------------------------- |------------- |------------------ |--------------- | +| 1 | buying | Buying price of the car (v-high, high, med, low) | Qualitative | low, med, high | 0 | +| 2 | maint | Price of the maintenance of car (v-high, high, med, low) | Qualitative | low, med, high | 0 | +| 3 | doors | Number of doors (2, 3, 4, 5-more) | Qualitative | 2, 3, 4 | 0 | +| 4 | persons | Capacity in terms of persons to carry (2, 4, more) | Qualitative | 2, 4, more | 0 | +| 5 | lug_boot | The size of luggage boot (small, med, big) | Qualitative | small, med, big | 0 | +| 6 | safety | Estimated safety of the car (low, med, high) | Qualitative | low, med, high | 0 | +| 7 | class | Car acceptability (unacc: unacceptible, acc: acceptible, good: good, v-good: very good) | Qualitative | unacc, acc, good | 0 | + +--- + +### Acknowledgement + +This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Car Evaluation Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Car+Evaluation). The UCI page mentions following as the donor of the dataset: + ++ Marko Bohanec (marko.bohanec '@' ijs.si) ++ Blaz Zupan (blaz.zupan '@' ijs.si) + + + + diff --git a/Car Evaluation/car.data b/Car Evaluation/car.data new file mode 100644 index 0000000..80fcc0d --- /dev/null +++ b/Car Evaluation/car.data @@ -0,0 +1,1728 @@ +vhigh,vhigh,2,2,small,low,unacc +vhigh,vhigh,2,2,small,med,unacc +vhigh,vhigh,2,2,small,high,unacc +vhigh,vhigh,2,2,med,low,unacc +vhigh,vhigh,2,2,med,med,unacc +vhigh,vhigh,2,2,med,high,unacc +vhigh,vhigh,2,2,big,low,unacc +vhigh,vhigh,2,2,big,med,unacc +vhigh,vhigh,2,2,big,high,unacc +vhigh,vhigh,2,4,small,low,unacc +vhigh,vhigh,2,4,small,med,unacc +vhigh,vhigh,2,4,small,high,unacc +vhigh,vhigh,2,4,med,low,unacc +vhigh,vhigh,2,4,med,med,unacc +vhigh,vhigh,2,4,med,high,unacc +vhigh,vhigh,2,4,big,low,unacc +vhigh,vhigh,2,4,big,med,unacc +vhigh,vhigh,2,4,big,high,unacc +vhigh,vhigh,2,more,small,low,unacc +vhigh,vhigh,2,more,small,med,unacc 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+Copyright (c) 2016 - 2019 + +--- + +**Level:** Intermediate
+**Recommended Use:** Regression Models
+**Domain:** Real Estate
+ +## Real Estate Valuation Data Set + +### Can you predict the price of a house? + + +--- +![](310.jpg) +--- + +This *intermediate* level data set has 414 rows and 7 columns. +It provides the market historical data set of real estate valuations which are collected from Sindian Dist., New Taipei City, Taiwan. +This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **regression 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 | X1 transaction date | The transaction date (for example, 2013.250=2013 March, 2013.500=2013 June, etc.) | Qualitative | 2013.500, 2013.500, 2013.333 | 0 | +| 2 | X2 house age | The house age (unit: year) | Quantitative | 19.5, 13.3, 5.0 | 0 | +| 3 | X3 distance to the nearest MRT station | The distance to the nearest MRT station (unit: meter) | Quantitative | 390.5684, 405.21340, 23.38284 | 0 | +| 4 | X4 number of convenience stores | The number of convenience stores in the living circle on foot | Quantitative | 6, 8, 1 | 0 | +| 5 | X5 latitude | The geographic coordinate, latitude (unit: degree) | Quantitative | 24.97937, 24.97544, 24.94925 | 0 | +| 6 | X6 longtitude | The geographic coordinate, longitude (unit: degree) | Quantitative | 121.54243, 121.49587, 121.51151 | 0 | +| 7 | Y house price of unit area | The house price of unit area (10000 New Taiwan Dollar/Ping, where Ping is a local unit, 1 Ping = 3.3 meter squared) for example, 29.3 = 293,000 New Taiwan Dollar/Ping | Quantitative | 29.3, 33.6, 47.7 | 0 | + +### Acknowledgement + + +This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Real Estate Valuation Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set).
+The UCI page mentions the following as the original source of the data set:
+*Yeh, I. C., & Hsu, T. K. (2018). Building real estate valuation models with comparative approach through case-based reasoning. Applied Soft Computing, 65, 260-271* \ No newline at end of file diff --git a/Real Estate Valuation/Real estate valuation data set.xlsx b/Real Estate Valuation/Real estate valuation data set.xlsx new file mode 100644 index 0000000..f9c80c6 Binary files /dev/null and b/Real Estate Valuation/Real estate valuation data set.xlsx differ diff --git a/User Knowledge Modeling/342309-PA9PNI-658.jpg b/User Knowledge Modeling/342309-PA9PNI-658.jpg new file mode 100644 index 0000000..57d25f8 Binary files /dev/null and b/User Knowledge Modeling/342309-PA9PNI-658.jpg differ diff --git a/User Knowledge Modeling/Data_User_Modeling_Dataset_Hamdi Tolga KAHRAMAN.xls b/User Knowledge Modeling/Data_User_Modeling_Dataset_Hamdi Tolga KAHRAMAN.xls new file mode 100644 index 0000000..0b19e65 Binary files /dev/null and b/User Knowledge Modeling/Data_User_Modeling_Dataset_Hamdi Tolga KAHRAMAN.xls differ diff --git a/User Knowledge Modeling/README.md b/User Knowledge Modeling/README.md new file mode 100644 index 0000000..2fc4811 --- /dev/null +++ b/User Knowledge Modeling/README.md @@ -0,0 +1,45 @@ +Data Science Dojo
+Copyright (c) 2016 - 2019 + +--- + +**Level:** Intermediate
+**Recommended Use:** Classification/Clustering
+**Domain:** Education/Web
+ +## User Knowledge Modeling Data Set + +### Predict student's knowledge level + + +--- +![](342309-PA9PNI-658.jpg) +--- + +This *intermediate* level data set has 403 rows and 6 columns. +The data set has been divided into training and testing (training: 258, testing: 145). +It is a real dataset about the students' knowledge status about the subject of Electrical DC Machines. +This data set is recommended for learning and practicing your skills in **exploratory data analysis**, **data visualization**, and **classification** and **clustering** 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 | STG | The degree of study time for goal object materials | Quantitative | 0.060, 0.100, 0.080 | 0 | +| 2 | SCG | The degree of repetition number of user for goal object materials | Quantitative | 0.000, 0.100, 0.250 | 0 | +| 3 | STR | The degree of study time of user for related objects with goal object | Quantitative | 0.10, 0.15, 0.05 | 0 | +| 4 | LPR | The exam performance of user for related objects with goal object | Quantitative | 0.98, 0.10, 0.01 | 0 | +| 5 | PEG | The exam performance of user for goal objects | Quantitative | 0.66, 0.56, 0.33 | 0 | +| 6 | UNS | The knowledge level of user (Very Low, Low, Middle, High) | Qualitative | "High", "Middle", "Low" | 0 | + +--- + +### Acknowledgement + + +This data set has been sourced from the Machine Learning Repository of University of California, Irvine [User Knowledge Modeling Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/User+Knowledge+Modeling).
+The UCI page mentions the following publication as the original source of the data set:
+*H. T. Kahraman, Sagiroglu, S., Colak, I., Developing intuitive knowledge classifier and modeling of users' domain dependent data in web, Knowledge Based Systems, vol. 37, pp. 283-295, 2013* \ No newline at end of file diff --git a/Wireless Indoor Localization/.gitkeep b/Wireless Indoor Localization/.gitkeep new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/Wireless Indoor Localization/.gitkeep diff --git a/Wireless Indoor Localization/181.jpg b/Wireless Indoor Localization/181.jpg new file mode 100644 index 0000000..4153e80 Binary files /dev/null and b/Wireless Indoor Localization/181.jpg differ diff --git a/Wireless Indoor Localization/README.md b/Wireless Indoor Localization/README.md new file mode 100644 index 0000000..6dcee57 --- /dev/null +++ b/Wireless Indoor Localization/README.md @@ -0,0 +1,50 @@ +Data Science Dojo
+Copyright (c) 2016 - 2019 + +--- + +**Level:** Beginner
+**Recommended Use:** Classification Models
+**Domain:** Mobile/Location
+ +## Wireless Indoor Localization Data Set + +### Predict location from wifi signal strength + + +--- +![](181.jpg) +--- + +This *beginner* level data set has 2000 rows and 8 columns. +The data set contains wifi signal strength observed from 7 wifi devices on a smartphone collected in indoor space. +The data could be used to estimate the location in one of the four rooms. + +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 | Wifi 1 | Signal strength of wifi 1 | Quantitative | -42, -67, -50 | 0 | +| 2 | Wifi 2 | Signal strength of wifi 2 | Quantitative | -58, -64, -40 | 0 | +| 3 | Wifi 3 | Signal strength of wifi 3 | Quantitative | -65, -50, -70 | 0 | +| 4 | Wifi 4 | Signal strength of wifi 4 | Quantitative | -63, -59, -37 | 0 | +| 5 | Wifi 5 | Signal strength of wifi 5 | Quantitative | -80, -73, -56 | 0 | +| 6 | Wifi 6 | Signal strength of wifi 6 | Quantitative | -90, -75, -64 | 0 | +| 7 | Wifi 7 | Signal strength of wifi 7 | Quantitative | -91, -65, -83 | 0 | +| 8 | Room | One of the four rooms (1, 2, 3, 4) | Quantitative | 1, 2, 3 | 0 | + +--- + +### Acknowledgement + +This data set has been sourced from the Machine Learning Repository of University of California, Irvine [Wireless Indoor Localization Data Set (UC Irvine)](https://archive.ics.uci.edu/ml/datasets/Wireless+Indoor+Localization). +The UCI page mentions the following 2 publications as the original source of the data set: + +*1. Rajen Bhatt, 'Fuzzy-Rough Approaches for Pattern Classification: Hybrid measures, Mathematical analysis, Feature selection algorithms, Decision tree algorithms, Neural learning, and Applications', Amazon Books*
+*2. Jayant G Rohra, Boominathan Perumal, Swathi Jamjala Narayanan, Priya Thakur, and Rajen B Bhatt, 'User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization & Gravitational Search Algorithm with Neural Networks', in Proceedings of Sixth International Conference on Soft Computing for Problem Solving,2017, pp. 286-295.* + diff --git a/Wireless Indoor Localization/wifi_localization.txt b/Wireless Indoor Localization/wifi_localization.txt new file mode 100644 index 0000000..98e71ea --- /dev/null +++ b/Wireless Indoor Localization/wifi_localization.txt @@ -0,0 +1,2000 @@ +-64 -56 -61 -66 -71 -82 -81 1 +-68 -57 -61 -65 -71 -85 -85 1 +-63 -60 -60 -67 -76 -85 -84 1 +-61 -60 -68 -62 -77 -90 -80 1 +-63 -65 -60 -63 -77 -81 -87 1 +-64 -55 -63 -66 -76 -88 -83 1 +-65 -61 -65 -67 -69 -87 -84 1 +-61 -63 -58 -66 -74 -87 -82 1 +-65 -60 -59 -63 -76 -86 -82 1 +-62 -60 -66 -68 -80 -86 -91 1 +-67 -61 -62 -67 -77 -83 -91 1 +-65 -59 -61 -67 -72 -86 -81 1 +-63 -57 -61 -65 -73 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