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Time Series Data Mining Forecasting with Weka
 
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I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 20851 Web Educator
Forecast the Price of Gold with Excel and SQL Server - Data Mining Tutorial
 
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Learn about data mining with SQL Server 2012 Analysis Services and Excel 2013, using historical gold pricing data, to predict future prices. To follow this tutorial, you should have SSAS and the Data Mining Add-in for Excel.
Views: 6154 Edward Kench
SSAS: Forecast Video Tutorial (Data Mining Table Analysis Tool)
 
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In this tutorial we will learn how to use the Forecast Table Analysis Tool for Excel 2007. See the video transcript: http://msdn.microsoft.com/en-us/library/dd299423.aspx
Views: 18513 sqlserver
Using Data Mining in Forecasting Problems
 
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In this presentation, Analytics 2012 keynote speaker, Tim Rey from Dow Chemical Company, shares methodologies for using data mining to get the most value out of time series data.
Views: 8676 SAS Software
Advanced Data Mining with Weka (1.4: Looking at forecasts)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Looking at forecasts http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4206 WekaMOOC
Predicting Stock Prices - Learn Python for Data Science #4
 
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In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo 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/
Views: 461438 Siraj Raval
Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science
 
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In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 309786 Analytics University
Excel at Data Mining - Your First Predictions
 
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In this video, Billy Decker of StatSlice Systems shows you how to start data mining in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a set of predictions for new customers using a Logistic Regression models based upon old customers. For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at: https://dataminingaddins.codeplex.com/releases/view/87029 *This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server to which you have access rights.
Views: 20491 StatSlice Systems
Excel 2013 - Data Mining - Forecast What If Scenario
 
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Apresentação de como usar a ferramenta de mineração de dados do Excel 2013 para previsão de Forecast. Para mais acesse: http://excelb2b.com/
Views: 354 ExcelB2B
How to create a useful forecast model using Data Mining
 
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Watch our latest video and learn how to build a forecast model regarding the price of aluminium for six months.
Views: 21 EX METRIX
Data Mining Excel 2010 2013 Modelo Forecast
 
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Data Mining Excel 2010 2013 Modelo Forecast
Views: 146 AddKw
Forecasting Time Series Data in R | Facebook's Prophet Package 2017 & Tom Brady's Wikipedia data
 
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An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 16054 Bharatendra Rai
Different Data Mining Approaches for Forecasting Use of Bike Sharing System
 
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R Codes are available on below link: https://github.com/mayurkmane/ADM-Project-A12-Group Document related to this data mining study is available on below link: https://www.dropbox.com/s/r5qw4mofej23gbg/Group-A12%20ADM%20Project.pdf?dl=0 https://ie.linkedin.com/in/mayurkmane
Views: 104 Mayur Mane
Decision Tree 1: how it works
 
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Full lecture: http://bit.ly/D-Tree A Decision Tree recursively splits training data into subsets based on the value of a single attribute. Each split corresponds to a node in the. Splitting stops when every subset is pure (all elements belong to a single class) -- this can always be achieved, unless there are duplicate training examples with different classes.
Views: 429366 Victor Lavrenko
Getting Started with Orange 06: Making Predictions
 
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Making predictions with classification tree and logistic regression. Train data set: http://tinyurl.com/fruits-and-vegetables-train Test data set: http://tinyurl.com/test-fruits-and-vegetables License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 49406 Orange Data Mining
Time Series In R | Time Series Forecasting | Time Series Analysis | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future! Below are the topics we will cover in this live session: 1. Why Time Series Analysis? 2. What is Time Series Analysis? 3. When Not to use Time Series Analysis? 4. Components of Time Series Algorithm 5. Demo on Time Series
Views: 59468 edureka!
YouTube Data API - Data Mining #2
 
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Data mining YouTube using youtube.search.list and youtube.videos.list to forecast the senate races of 2014. And quantifying our probability using 2012 senate races data and stats from YouTube during the same period. Github/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/2.%20YouTube%20Data.ipynb
Views: 6025 Roshan
8/17/18 Using Analytic Solver Data Mining to Gain Insights from Your Data in Excel 1
 
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Live Webinar Recording: Do you want to learn and get results quickly from data mining and predictive analytics for your business? Have you found that "enterprise data mining" software involves far too much cost, risk and learning time? Do you want to apply traditional time series forecasting and regression, and also new data mining methods? Is the data you need found in SQL databases, data warehouses, public datasets, and Excel spreadsheets? Easily draw samples and build data mining models for all your data using Excel, PowerPivot and Analytic Solver Data Mining Use XLMiner's tools to uncover and visualize hidden relationships, clean and transform, and cluster your data Forecast future trends with a full range of traditional ARIMA, exponential smoothing, and regression methods Create classification and prediction models using the full spectrum of data mining methods -- from discriminant analysis and logistic regression to classification/regression trees, association rules, and neural networks
Views: 68 FrontlineSolvers
Movie Success Prediction Using Data Mining Project
 
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Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 16478 Nevon Projects
Excel - Time Series Forecasting - Part 1 of 3
 
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Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.
Views: 726462 Jalayer Academy
Tanagra Data Mining
 
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an "open source project" as every researcher can access to the source code, and add his own algorithms, as far as he agrees and conforms to the software distribution license.
Views: 13524 Emmanuel Felipe
Forecasting - Linear regression - Example 1 - Part 1
 
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In this video, you will learn how to find the demand forecast using linear regression.
Views: 55597 maxus knowledge
Introduction to Data Mining in SQL Server Analysis Services
 
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Data mining is one of the key hidden gems inside of Analysis Services but has traditionally had a steep learning curve. In this session, you'll learn how to create a data mining model to predict who is the best customer for you and learn how to use other algorithms to spend your marketing model wisely. You'll also see how to use Time Series analysis for budget and forecast prediction. Finally, you'll learn how to integrate data mining into your application through SSIS or custom coding.
Views: 6419 PASStv
Analytical Reporting Dashboard for Weather Data Mining Whitepaper- Part 1
 
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Analytical Reporting Dashboard for Weather Data Mining Whitepaper Part 1- Summarizing the Data for Ready Analysis Finding and using reliable weather forecasts is becoming a key competitive advantage in many industries. To add to the complexity, the sheer number of forecasting services and their variability in the accuracy of their forecasts by geography, forecast timeframe and weather parameter makes it hard for many companies to choose the correct forecast. Decision-makers need an easy-to-use, actionable summary of the massive weather forecast and observation data This paper • This paper is part 1 of a two part series on building and using an analytical framework for determining forecast accuracy. • It focuses on issues related to getting the data ready for analysis in an analysis reporting tool such as Tableau • How MineWeather solves these problems and makes it easy for the users to summarize the data.
Views: 114 Macrosoft Inc
Machine Learning for Time Series Data in Python | SciPy 2016 | Brett Naul
 
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The analysis of time series data is a fundamental part of many scientific disciplines, but there are few resources meant to help domain scientists to easily explore time course datasets: traditional statistical models of time series are often too rigid to explain complex time domain behavior, while popular machine learning packages deal almost exclusively with 'fixed-width' datasets containing a uniform number of features. Cesium is a time series analysis framework, consisting of a Python library as well as a web front-end interface, that allows researchers to apply modern machine learning techniques to time series data in a way that is simple, easily reproducible, and extensible.
Views: 36533 Enthought
How Do I: Setup and Configure the Excel Data-Mining Add-in?
 
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This video shows how configure to setup the Data Mining Client For Excel addin. This addin is part of the Microsoft SQL Server 2008 Data Mining Add-ins for Microsoft Office 2007 download available at: http://www.microsoft.com/DOWNLOADS/details.aspx?familyid=896A493A-2502-4795-94AE-E00632BA6DE7.
Views: 33225 DeveloperVideos
How to Use Time Series Data to Forecast
 
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This is Lecture series on Time Series Analysis Chapter of Statistics. In this part, you will learn use of time series data for forecasting. Watch all statistics videos at http://svtuition.com/watch/#ST
Views: 2023 Svtuition
SQL Server 2005 Data Mining Forecasting in Excel
 
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SQL Server 2005 Data Mining Forecasting in Excel
Views: 6880 GarryEdser
Gretl Tutorial 6: Modeling and Forecasting Time Series Data
 
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In this video we run a linear regression on a time series dataset with time trend and seasonality dummies. Then, we perform and evaluate the accuracy of an in-sample forecast, as well as perform an out-of-sample (i.e., into the future) forecast. TABLE OF CONTENTS: 00:00 Introduction 00:12 What we will do in this Video 00:40 Data 01:14 Glimpse Data in Excel 01:46 Load Data in Gretl 03:20 Plot Time Series 03:54 Create Additional Variables 04:38 Run Model with All Data 05:34 In-Sample Forecast 06:40 Evaluating Quality of In-Sample Forecast 10:37 Out-of-Sample Forecast
Views: 39659 dataminingincae
Rick’s Rant – Data Mining
 
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Rick's Rant for March 27th, 2018.
Views: 12882 MercerReport
Forecasting: Exponential Smoothing, MSE
 
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This video shows how to calculate exponential smoothing and the Mean Squared Error.
Views: 131039 Joshua Emmanuel
Data Mining using the Excel Data Mining Addin
 
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The Excel Data Mining Addin can be used to build predictive models such as Decisions Trees within Excel. The Excel Data Mining Addin sends data to SQL Server Analysis Services (SSAS) where the models are built. The completed model is then rendered within Excel. I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 72026 Steve Fox
Data mining tutorial for beginners FREE Training 01
 
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Published on Aug 2, 2014 1 intro data mining and scraping next tutorial here: http://youtu.be/gb4ufqFkT7A please comment below if you have any questions. Tq Category Education License Standard YouTube License
Views: 102321 Red Team Cyber Security
Linear Regression - Machine Learning Fun and Easy
 
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Linear Regression - Machine Learning Fun and Easy https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :) -------------------------------------------------- Support us on Patreon http://bit.ly/PatreonArduinoStartups --------------------------------------------------
Views: 82634 Augmented Startups
Import data from web to excel - Sports | Statistics | Weather forecast
 
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Import data into dynamic tables in Excel and stay up to date with latest information without browsing, Run time data or statistics analysis, maybe it be Sports information, weather forecast or statistics,connect link once and enjoy forever, illustrated with multiple examples.
Views: 16978 Excel to Excel
K-Means Clustering - Predicting Weather Geography
 
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Australian Weather Data: http://www.bom.gov.au/climate/dwo/
Views: 2780 Ritvik Kharkar
Forecasting with Neural Networks: Part A
 
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What is a neural network, neural network terminology, and setting up a network for time series forecasting This video supports the textbook Practical Time Series Forecasting. http://www.forecastingbook.com http://www.galitshmueli.com
Views: 11530 Galit Shmueli
Support Vector Machine (SVM) with R - Classification and Prediction Example
 
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Includes an example with, - brief definition of what is svm? - svm classification model - svm classification plot - interpretation - tuning or hyperparameter optimization - best model selection - confusion matrix - misclassification rate Machine Learning videos: https://goo.gl/WHHqWP svm is an important machine learning tool related to analyzing big data or working in data science field. R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 28881 Bharatendra Rai
A Deep Hybrid Model for Weather Forecasting
 
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Authors: Aditya Grover, Ashish Kapoor, Eric Horvitz Abstract: Weather forecasting is a canonical predictive challenge that has depended primarily on model-based methods. We explore new directions with forecasting weather as a data-intensive challenge that involves inferences across space and time. We study specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables. We show how the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. We also derive an efficient learning and inference procedure that allows for large scale optimization of the model parameters. We evaluate the methods with experiments on real-world meteorological data that highlight the promise of the approach. ACM DL: http://dl.acm.org/citation.cfm?id=2783275 DOI: http://dx.doi.org/10.1145/2783258.2783275
Analysis and Prediction of Spatiotemporal Traffic Congestion
 
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Traffic congestion impedes our mobility, pollutes the air, wastes fuel, and hampers economic growth. While physical bottlenecks, overpopulation, weather, and construction can all lead to congestion, a key contributor to traffic congestion is road accidents - events that disrupt the normal flow of traffic. Reducing the impact of traffic accidents has been one of the primary objectives for transportation policy makers. In this talk, we present a novel machine learning framework to forecast how travel-time delays - caused by accidents - occur and progress in the transportation network. This research is conducted by correlating 4 years of historical traffic sensor and accident data archived under ADMS project developed - by METRANS and IMSC centers of USC - for Los Angeles County Metropolitan Transportation Authority (Metro). Speakers: Ugur Demiyurek Associate Director, Integrated Media Systems Center USC Viterbi Dingxiong Deng Ph. D student, Computer Science Department University of Southern California Ugur Demiryurek is Associate Director of Research at IMSC, and has M.S. and Ph.D. degrees in Computer Science from USC. His research is focused on fundamental and applied data management with special interest in Geospatial Databases, Cloud Computing, and Machine Learning. He has been supported by grants from both government agencies (NSF, Caltrans, Metro) and industry partners (Microsoft Research, Oracle Labs, Intel, HP Labs). Demiryurek authored two book chapters and more than forty research articles since 2010 and holds three US patents. Prior to IMSC, Demiryurek worked for fortune 500 companies in database technology development and data scientist positions. He regularly serves on the program committee of various major database conferences including ACM SIGMOD, ACM SIGSPATIAL, IEEE ICDM, DASFAA, SSTD, and MDM, and is a member of IEEE and ACM.
Views: 1933 USC Price
How to Predict Stock Prices Easily - Intro to Deep Learning #7
 
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We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power. Coding challenge for this video: https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo Vishal's winning code: https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier Jie's runner up code: https://github.com/jiexunsee/Simple-Inception-Transfer-Learning More Learning Resources: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://deeplearning.net/tutorial/lstm.html https://deeplearning4j.org/lstm.html https://www.tensorflow.org/tutorials/recurrent http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/ https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/ Please subscribe! And like. And comment. That's what keeps me going. Join other Wizards in our Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 music in the intro is chambermaid swing by parov stelar 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/
Views: 408640 Siraj Raval
Predictive Modelling Techniques | Data Science With R Tutorial
 
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This lesson will teach you Predictive analytics and Predictive Modelling Techniques. Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE After completing this lesson you will be able to: 1. Understand regression analysis and types of regression models 2. Know and Build a simple linear regression model 3. Understand and develop a logical regression 4. Learn cluster analysis, types and methods to form clusters 5. Know more series and its components 6. Decompose seasonal time series 7. Understand different exponential smoothing methods 8. Know the advantages and disadvantages of exponential smoothing 9. Understand the concepts of white noise and correlogram 10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc 11. Understand all the analysis techniques with case studies Regression Analysis: • Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables. • It predicts the value of a dependent variable based on one or more independent variables • Coefficient explains the impact of changes in an independent variable on the dependent variable. • Widely used in prediction and forecasting Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Predictive-Analytics-0gf5iLTbiQM&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice. Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing. As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice. Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 198122 Simplilearn
How to Build a Forecasting Model in Excel - Tutorial | Corporate Finance Institute
 
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How to Build a Forecasting Model in Excel - Tutorial | Corporate Finance Institute Enroll in the Full course to earn your certificate and advance your career: http://courses.corporatefinanceinstitute.com/courses/fpa-rolling-12-month-cash-flow-forecast-course Master the art of building a rolling 12-month cash flow forecast model in our Financial Planning & Analysis (FP&A) course. In this course you will learn to build a cash flow model from scratch complete with assumptions, financials, supporting schedules and charts. -- FREE COURSES & CERTIFICATES -- Enroll in our FREE online courses and earn industry-recognized certificates to advance your career: ► Introduction to Corporate Finance: https://courses.corporatefinanceinstitute.com/courses/introduction-to-corporate-finance ► Excel Crash Course: https://courses.corporatefinanceinstitute.com/courses/free-excel-crash-course-for-finance ► Accounting Fundamentals: https://courses.corporatefinanceinstitute.com/courses/learn-accounting-fundamentals-corporate-finance ► Reading Financial Statements: https://courses.corporatefinanceinstitute.com/courses/learn-to-read-financial-statements-free-course ► Fixed Income Fundamentals: https://courses.corporatefinanceinstitute.com/courses/introduction-to-fixed-income -- ABOUT CORPORATE FINANCE INSTITUTE -- CFI is a leading global provider of online financial modeling and valuation courses for financial analysts. Our programs and certifications have been delivered to thousands of individuals at the top universities, investment banks, accounting firms and operating companies in the world. By taking our courses you can expect to learn industry-leading best practices from professional Wall Street trainers. Our courses are extremely practical with step-by-step instructions to help you become a first class financial analyst. Explore CFI courses: https://courses.corporatefinanceinstitute.com/collections -- JOIN US ON SOCIAL MEDIA -- LinkedIn: https://www.linkedin.com/company/corporate-finance-institute-cfi- Facebook: https://www.facebook.com/corporatefinanceinstitute.cfi Instagram: https://www.instagram.com/corporatefinanceinstitute Google+: https://plus.google.com/+Corporatefinanceinstitute-CFI YouTube: https://www.youtube.com/c/Corporatefinanceinstitute-CFI
FORECAST - EXCEL MINERIA DE DATOS
 
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DATA MINING EN EXCEL UTILIZANDO LA HERRAMIENTA FORECAST, EJEMPLO Y SU FUNCIONAMIENTO
Views: 18158 Lucita287
Big Data: Mining Football Statistics
 
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Data Mining Final Project for Big Data INSY 4970 at Auburn University
Views: 30763 wwl0002
Analytical Reporting Dashboard for Weather Data Mining- Part 2
 
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Mine-Weather reporting framework allows end users to compare different weather forecast services by parameter scores. This is a metric calculated using statistical principles by comparing forecasts and actual observational weather parameters (TEMPERATURE, WIND SPEED, RAINFALL RATE, ETC.)
Views: 55 Macrosoft Inc