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How to download Dataset from UCI Repository
 
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The video has sound issues. please bare with us. This video will help in demonstrating the step-by-step approach to download Datasets from the UCI repository.
Views: 10222 Santhosh Shanmugam
How to download iris dataset from UCI dataset and preparing data
 
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Hi Today, I will shows how to download datasets from UCI dataset and prepare data Let GO 1. Go to web site UCI dataset https://archive.ics.uci.edu/ml/datasets.html 2. Choose the dataset, iris dataset 3. Click Data Folder 4. Click iris.data 5. Copy all text 6. Paste to Notepad++ 7. Replace following Iris-setosa 1,-1,-1 Iris-versicolor -1,1,-1 Iris-virginica -1,-1,1 Thank you ^^
Views: 5574 COMSCI Channel
The Best Way to Prepare a Dataset Easily
 
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In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 174517 Siraj Raval
Data Mining Project - Analysis on Car Dataset
 
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In this video, I have demonstrated the analysis performed on the car dataset (dataset source: UCI repository) by using SAS Enterprise Miner.
KNN using UCI machine learning repository datasets, by S. Han
 
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A short Python tutorial for KNN, by Sooan Han (Creative Technology Management, Underwood International College, Yonsei University, South Korea)
Views: 523 Kee Heon Lee
Datasets : How to Download?
 
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Datasets : How to Download?
Views: 6523 Social Networks
Project: Data resource for training Neural Network. Part: 5/10
 
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Project: Data resource for training Neural Network and modification. Part: 5/1 UCI Machine Learning Repository link: http://archive.ics.uci.edu/ml/index.php Follow me on LinkedIn: https://www.linkedin.com/in/palash-mondal-4b3370aa/ Pdf link : https://files.acrobat.com/a/preview/bb969b7b-5c55-42e4-8ce4-9339fb8c6456 To take a project of this contact with: [email protected]
Views: 258 SMART PALASH
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 376991 APMonitor.com
5 Data Sets You Must Work On | Machine Learning Data Set || Stephen Simon
 
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This video tells top 5 machine learning data sets you must work on if you are working in the field of Machine Learning! Also sorry, in the intro I've said algorithm instead of Data sets! Thanks for your support! Data sets details: Data set 1 : https://www.kaggle.com/camnugent/california-housing-prices Data set 2 : https://www.kaggle.com/uciml/german-credit Data set 3 : https://archive.ics.uci.edu/ml/datasets/iris Data set 4 : https://www.kaggle.com/francksylla/titanic-machine-learning-from-disaster Data set 5 : http://yann.lecun.com/exdb/mnist/ #dataset #mldataset #machinelearning ******************************************** Insta : https://www.instagram.com/codewithsimon Facebook : https://www.fb.com/codewithsimonpage Twitter : https://www.twitter.com/codewithsimon LinkedIn : https://www.linkedin.com/in/codewithsimon *************************************************** Song: Ikson - Do It (Vlog No Copyright Music) Music promoted by Vlog No Copyright Music. Video Link: https://youtu.be/3uARld40fpE
Views: 237 Stephen Simon
Data analytics CRISP-DM Project
 
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Data Analytics Classification Bank Marketing Dataset source UCI machine learning: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing
Views: 306 PARTH PANDYA
Gaurang Panchal - Data Mining/Machine Learning Project
 
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Dataset: https://archive.ics.uci.edu/ml/datasets/Bank+Marketing# Overview: The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. This dataset consists of client information of a bank; 41188 records with 20 inputs, ordered by date (from May 2008 to November 2010). Aim: The classification goal is to predict if the client will subscribe (yes/no) a term deposit. The data includes information about the clients and marketing calls. Together with this data there is a record of whether the clients are currently enrolled for a term deposit. All of the variables should be considered and modeled to produce classification to accurately predict an entry for a client. Attribute Information: Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 5 - default: has credit in default? (categorical: 'no','yes','unknown') 6 - housing: has housing loan? (categorical: 'no','yes','unknown') 7 - loan: has personal loan? (categorical: 'no','yes','unknown') # related with the last contact of the current campaign: 8 - contact: contact communication type (categorical: 'cellular','telephone') 9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. # other attributes: 12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 14 - previous: number of contacts performed before this campaign and for this client (numeric) 15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') # social and economic context attributes 16 - emp.var.rate: employment variation rate - quarterly indicator (numeric) 17 - cons.price.idx: consumer price index - monthly indicator (numeric) 18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric) 19 - euribor3m: euribor 3 month rate - daily indicator (numeric) 20 - nr.employed: number of employees - quarterly indicator (numeric) Output variable (desired target): 21 - y - has the client subscribed a term deposit? (binary: 'yes','no')
Views: 423 Gaurang Panchal
persiapan dataset data mining
 
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Dataset yang digunakan berupa dataset iris dari repository UCI yang pada kesempatan ini dirubah tipe datanya dari CSV ke Text. Hal ini dilakukan agar dataset dapat digunakan untuk menguji algoritma machine learning yang akan saya demokan pada video tutorial berikutnya.
Views: 698 Umar Ghoni
Tubes Data Mining - Shervano Naodias
 
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Tubes Data Mining : Nama: Shervano Naodias Datasets: https://archive.ics.uci.edu/ml/datasets/Lenses Blog Tutorial: https://shgracias.wordpress.com/ Tools yang digunakan: - Weka - Notepad++ - Microsoft Excel - Photosop CS 6 - Camtasia
Views: 231 Shervano Naodias
UCI Machine Learning Repository -  Machine Learning Tutorials In Hindi #5
 
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This video is a part of the following Machine Learning Playlist - https://www.youtube.com/playlist?list=PL47S5PRS_XOej8y-tst51IY9J6tcOmrKg
Tutorial Klasifikasi Dataset UCI Ecoli pada WEKA
 
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Klasifikasi Menggunakan Metode Decision Tree, Naive Bayes dan Bayesian Network
Views: 784 Eka Putri
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 207139 APMonitor.com
Data Mining Algoritma Forecasting Online News Popularity form UCI Repository
 
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Algoritma Forecasting (atau peramalan) Data Mining. . . . Anggota kelompok : Dede Misbahul Munir : 16.12.0012 Bayu Navanto Sadiq : 16.12.0062 Abdul Aziz : 16.12.0180 Irvan Ulul Azmi : 16.12.0125 Muhammad Yafie Yulianto : 16.12.0070 . . . . . . . . . . . . . . . *Music Background : - mamamoo : Piano Man & Starry Night. - Gary : Entertaint. - Various Artist.
Views: 33 Bayu Navanto Sadiq
create a neural network for wine data
 
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► ► Subscribe To My New Artificial Intelligence Newsletter! https://goo.gl/qz1xeZ Learn how to create a neural network to classify wine in 15 lines of Python with Keras. Code: https://github.com/jg-fisher/wineNeuralNetwork Dataset: https://archive.ics.uci.edu/ml/datasets/wine Keras: https://keras.io/ -- Highly recommended for theoretical and applied ML -- Deep Learning: https://amzn.to/2LomU4y Hands on Machine Learning: https://amzn.to/2JSxhIv Hope you guys enjoyed this video! Be sure to leave any comments or questions below, subscribe and thumbs up (:
Views: 4008 John G. Fisher
First time Weka Use : How to create & load data set in Weka : Weka Tutorial # 2
 
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This video will show you how to create and load dataset in weka tool. weather data set excel file https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/weather.xls
Views: 38679 HowTo
Getting started in scikit-learn with the famous iris dataset
 
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Now that we've set up Python for machine learning, let's get started by loading an example dataset into scikit-learn! We'll explore the famous "iris" dataset, learn some important machine learning terminology, and discuss the four key requirements for working with data in scikit-learn. Download the notebook: https://github.com/justmarkham/scikit-learn-videos Iris dataset: http://archive.ics.uci.edu/ml/datasets/Iris scikit-learn dataset loading utilities: http://scikit-learn.org/stable/datasets/ Fast Numerical Computing with NumPy (slides): https://speakerdeck.com/jakevdp/losing-your-loops-fast-numerical-computing-with-numpy-pycon-2015 Fast Numerical Computing with NumPy (video): https://www.youtube.com/watch?v=EEUXKG97YRw Introduction to NumPy (PDF): http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf WANT TO GET BETTER AT MACHINE LEARNING? HERE ARE YOUR NEXT STEPS: 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) JOIN "Data School Insiders" to access bonus content: https://www.patreon.com/dataschool 4) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 5) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 150760 Data School
How to Import  CSV Dataset in a Python Development Environment (Anaconda|Spider) | Machine Learning
 
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While creating a machine learning model, very basic step is to import a dataset, which is being done using python Dataset downloaded from www.kaggle.com
Views: 24358 4am Code
How to import UCI Machine Learning Dataset into Azure Machine Learning
 
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How to import UCI Machine Learning Dataset into Azure ML
Views: 116 Algotics Academy
Testing and Training of Data Set Using Weka
 
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how to train and test data in weka data mining using csv file
Views: 14553 Tutorial Spot
Data Analysis Using R - Session 1 - Bank Marketing
 
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Data Analysis By using Bank Marketing data
Views: 8436 Naveen Balawat
Machine Learning 2 - Introduction to ML
 
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In this lesson, we start with a quick prediction using a classifier that predicts if someone make more or less than $50K annually. This classifier uses a dataset from UCI known as Adult Census. We start with an introduction to scikit-learn then we go through the three main types of predictive algorithms: classification, regression and clustering. Then we discuss some public sources Data Sets. Then we explain how we build the predictive model that we showed int he beginning of the video. It is built using a kNN (Known Nearest Neighbors) Classifier. We discuss also how to do a simple grid search for fine tune your algorithm parameters. Links used in the video: Amazon AWS: http://goo.gl/RIeSjK/ Roshan Project: http://goo.gl/oFmMc1/
Views: 7622 Roshan
Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50
 
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Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU C5.0 Decision Tree - Classification Decision trees are very powerful classifiers, which utilize a tree structure to model the relationships among the features and the potential outcomes. An all-purpose classifier which has a highly automatic learning process; it can handle numeric or nominal features. C5.0 uses entropy, a concept analogous to the information theory that quantifies the randomness, or disorder, within a set of class values. C50_model{anglebrace}- C5.0(train_Predictors, train_Target) C50_predict{anglebrace}- predict(C50_model, test_data) Get the data from Balance Scale Data Set. Attribute Information: Class Name: 3 (L, B, R) Left-Weight: 5 (1, 2, 3, 4, 5) Left-Distance: 5 (1, 2, 3, 4, 5) Right-Weight: 5 (1, 2, 3, 4, 5) Right-Distance: 5 (1, 2, 3, 4, 5) http://archive.ics.uci.edu/ml/datasets/Balance+Scale Citation Policy: If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Here is a BiBTeX citation as well: @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree,
Views: 1612 BharatiDWConsultancy
Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50
 
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Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU K – Nearest Neighbors (K-NN) Get the data from Balance Scale Data Set. Attribute Information: Class Name: 3 (L, B, R) Left-Weight: 5 (1, 2, 3, 4, 5) Left-Distance: 5 (1, 2, 3, 4, 5) Right-Weight: 5 (1, 2, 3, 4, 5) Right-Distance: 5 (1, 2, 3, 4, 5) http://archive.ics.uci.edu/ml/datasets/Balance+Scale Citation Policy: If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Here is a BiBTeX citation as well: @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN
DATA MINING METODE KLASIFIKASI - PART 1
 
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Pada tutorial ini akan membahas bagaimana mengimplementasikan dataset pada WEKA. Adapun tahapan dalam tutorial ini adalah: 1. Mencari dataset : - http://www.kdnuggets.com/datasets/ind... - http://www.rdatamining.com/resources/... - https://archive.ics.uci.edu/ml/datase... -http://www.inf.ed.ac.uk/teaching/cour... 2. Melakukan filtering (preprocessing) 3. Mengimplementasikan metode klasifikasi (algoritma j48/c4.5) 4. Testing dengan menggunakan data yang belum memiliki nilai target/label aplikasi WEKA yang open source bisa di download di link ini https://sourceforge.net/projects/weka/
Views: 1283 LAGUNA BLADE
Analysis of Breast Cancer Wisconsin Data Set
 
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Made by : Shreya Chawla Saloni Chauhan Monika Yadav Vrinda Goel
Views: 267 Vrinda Goel
Blood Donation Behaviour Prediction using Azure Machine Learning (Azure ML)
 
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Dataset Download Link = https://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center
Views: 188 Algotics Academy
Tutorial Classification Dataset UCI Ecoli on Weka with Bayesian Network Method
 
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Tugas Besar Data Mining Semester 3 Politeknik Negeri Batam Jurusan : Teknik Informatika Dosen : Ibu Hilda Herasmus Nama anggota : - Agung Suaini Hidayat - Algi Pratama Putra - Muhamad Rava Rizky Ramadhan
Breast Cancer Diagnosis with Artificial Neural Network
 
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Building, training, exporting and embedding an artificial neural network for use in a custom application for diagnosing cancer in breast tissue samples. Using patient data samples from UCI Machine Learning Repository for research. The resulting application and AI builder are available for download. Send email to [email protected] to request. Or visit tinmansystems.com/aibuilder
Views: 71613 TinMan Systems
Naive Bayes Classifier in R
 
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Implementation of Naive Bayes Classifier in R using dataset mushroom from the UCI repository. You may wanna add pakages e1071 and rminer in R because they were not present in R x64 3.3.1 by default. Music - Daft Punk - Instant Crush ft. Julian Casblancas
Online News Popularity Demo - Data Mining Project Fall 2015 OU
 
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Demonstration of a project in CS 5593 Data Mining in Fall 2015 at the University of Oklahoma for the Classification of Online News Popularity based on the "Online News Popularity Data Set" in the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity). The project was developed by Maxime Brisse, Aitor Algorta and Sven Erik Jeroschewski.
Views: 704 Sven
Data Mining: SAS Enterprise Miner Tutorial (UCI Mashable)
 
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A tutorial on using SAS Enterprise Miner on UCI Mashable Article Dataset
Views: 741 Jagpreet Sethi
Download the Bank Marketing Data Set
 
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How to download the Bank Marketing Data Set from the the UCI Machine Learning Repository using the Unix command, curl.
Views: 4077 Clinton Brownley
Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50
 
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Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU C5.0 Decision Tree - Classification C50_model{anglebrace}- C5.0(train_Predictors, train_Target) C50_predict{anglebrace}- predict(C50_model, test_data) Get the data from Balance Scale Data Set. http://archive.ics.uci.edu/ml/datasets/Balance+Scale Citation Policy: If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Here is a BiBTeX citation as well: @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Intro - DIY- 25 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Use Case - DIY- 26 -of-50 Data Science & Machine Learning - C5.0 Decision Tree Exercise - DIY- 27 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN, C5.0 Decision Tree,
CloudVista Demo: visualizing the extended KDD Cup 1999 Dataset
 
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CloudVista: visualizing the clustering structure in the "extended" KDD Cup 1999 training data, which has about 40 million records, 41 dimensions, 13.5GB in total. The original dataset has about 4 million records. The additional records in the extended version are generated by randomly picking one original record and adding a random noise to each dimension. The extended version is used to show how larger datasets can be possibly visualized in the CloudVista system. Frame rate: 4 frames per second The original dataset can be found at http://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data
Views: 881 gtkeke
RapidMiner Stats (Part 2): Simple Data Exploration
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video demonstrates how to use RapidMiner "Statistics" tab to explore attributes of a loaded data set. It briefly explains different attribute types, such as numeric, polynomial and binomial, and then shows how to create 2D and 3D scatter plots of numeric attributes. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 1961 ironfrown
RapidMiner Stats (Part 3): Working with Attributes
 
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This video is part of the Segment on Statistical Data Analysis in a series on RapidMiner Studio. The video demonstrates how to manipulate attributes to select them, to create new and modify existing attributes, and how to discretize values of a continuous (real) attribute into a nominal (categorical) attribute. A simple pie chart is then used to visualize the resulting data. The data for this lesson includes demographic information and academic achievements of students taking Mathematics in two Portuguese schools. The data for the video can be obtained from: * http://visanalytics.org/youtube-rsrc/rm-data/student-mat.csv * http://visanalytics.org/youtube-rsrc/rm-data/student-names.txt The original source of the data can be found at the UCI Machine Learning Repository: * http://archive.ics.uci.edu/ml/datasets/Student+Performance Videos in data analytics and data visualization by Jacob Cybulski, visanalytics.org. Also see the following publication describing the project which resulted in the collection and analysis of this data set: P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7.
Views: 1418 ironfrown
Glass Identification Classification Problem using Machine Learning Models
 
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Introduction to Data Science(IDS) Project, Dept. Of Computer Science and Engineering, The LNM Institute of Information Technology Team members: Sailok Chinta- 16ucs056 Leela Surya Teja- 16ucc051 Amit Sharma - 16ucs035 Manan Jethanadani - 16ucs099 Link of dataset:https://archive.ics.uci.edu/ml/datasets/glass+identification
Introduction to Clustering with R-Studio on IoT vibration accelerometer sensor data
 
10:00
https://archive.ics.uci.edu/ml/datasets/Dataset+for+ADL+Recognition+with+Wrist-worn+Accelerometer
Views: 1096 Romeo Kienzler
Problem Statement (HD)
 
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In this video, I provide a statement of the problem and introduce the UCI Machine Learning Repository dataset (https://archive.ics.uci.edu/ml/datasets/Wine+Quality). I discuss features and the target for this classification data science problem. Note: You may have to adjust the visual on the gear in the right lower corner of the video by clicking on the gear and adjusting it HD (1080) resolution.
Views: 29 Adam Morris
Hướng dẫn download từ UCI data
 
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Dành cho lớp KTHH05
Views: 2491 Minh Nguyễn
สอนทำ Weka ด้วย Data Set ของ UCI
 
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นี่เป็นการสอนแค่พื้นฐานนะครับ ไม่ได้เจาะรูอะไรกับตัวโปรแกรม ผมก็พึ่งเริ่มต้นเหมือนกัน -Software http://www.cs.waikato.ac.nz/ml/weka/downloading.html -DataSet https://archive.ics.uci.edu/ml/datasets.html
Views: 971 Peanuts Natdanai