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
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Get the project at http://nevonprojects.com/smart-health-prediction-using-data-mining/ A smart system that suggests a persons disease and suggestions to cure based on his symptoms, also has online doctor to consult for further treatment and cure.
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15 Hot Trending Data Mining Research Topics 2018 1. Medical Data Mining 2. Education Data Mining 3. Data Mining with Cloud Computing 4. Efficiency of Data Mining Algorithms 5. Signal Processing 6. Social Media Analytics 7. Data Mining in Medical Science 8. Government Domain 9. Financial Data Analysis 10. Financial Accounting Fraud Detection 11. Customer Analysis 12. Financial Growth Analysis using Data Mining 13. Data Mining and IOT 14. Data Mining for Counter-Terrorism Key Research Application Fields: • Crisp-DM • Oracle Data mining • Web Mining • Open NN • Data Warehousing • Text Mining WHY YOU NEED TO OUTSOURCE TO PhD Assistance: a) Unlimited revisions b) 24/7 Admin Support c) Plagiarism Generate d) Best Possible Turnaround time e) Access to High qualified technical coordinators and expertise f) Support: Skype, Live Chat, Phone, Email Contact us: India: +91 8754446690 UK: +44-1143520021 Email: [email protected] Visit Webpage: https://goo.gl/HwJgqQ Visit Website: http://www.phdassistance.com
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Using public social media data from twitter and Facebook, actions and announcements of terrorists – in this case ISIS – can be monitored and even be predicted. With his project #DataShield Wassim shares his idea of having a tool to identify oncoming threats and attacks in order to protect people and to induce preventive actions. Wassim Zoghlami is a Tunisian Computer Engineering Senior focussing on Business Intelligence and ERP with a passion for data science, software life cycle and UX. Wassim is also an award winning serial entrepreneur working on startups in healthcare and prevention solutions in both Tunisia and The United States. During the past years Wassim has been working on different projects and campaigns about using data driven technology to help people working to uphold human rights and to promote civic engagement and culture across Tunisia and the MENA region. He is also the co-founder of the Tunisian Center for Civic Engagement, a strong advocate for open access to research, open data and open educational resources and one of the Global Shapers in Tunis. At TEDxMünster Wassim will talk about public social media data mining for counter-terrorism and his project idea DataShield. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
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The best way to learn data science and showcase your skills is by doing some actual projects – we learn best by doing. So, how do we choose a project to work on? Where do we start? One way to approach it is to first look at some career websites and find a few jobs in data science that you aspire to have in the future. Write down the skills, qualifications, day-to-day expectations, and overall job description from the jobs that interest you. This will give you the project “requirements” that you can work with to formulate a project. http://storybydata.com/data-science-learn-by-doing-global-super-store-project/
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Get this project at http://nevonprojects.com/data-mining-for-automated-personality-classification-2/ Here we use data mining algorithm to mine a training data set for automated human personality classification.
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( Data Science Training - https://www.edureka.co/data-science ) Watch sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science. Data Science is one of the most-sought after professions today. Universities across the world are offering courses in this discipline which stands testimony to this emerging profession. There are a very few professionals with the required skill and the demand for data scientists is racing ahead. The tutorial wil give a brief understanding about Data Science. The topics covered in the video: 1.Problem Statement 2.Variable Desriptions 3.Data EXploration 4.Data Cleaning and Preparation 5.Reading from Other Sources 6.Titanic Data Sets 7.Decision Trees and Random Forests 8.Build a Decision Tree 9.Build a Random Forest 10.Linear Regression 11.Logistic Regression 12.Machine Learning 13.Data Mining 14.Machine Learning and Data Mining Resources 15.Solving a Data Science Problem using R, Hadoop, Mahout Related Posts: http://www.edureka.co/blog/who-can-take-up-a-data-science-tutorial/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/enroll-for-a-data-science-course/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/types-of-data-scientists/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project http://www.edureka.co/blog/core-data-scientist-skills/?utm_source=youtube&utm_medium=referral&utm_campaign=develop-datascience-project Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. ‘Develop a Data Science Project’ have been widely covered in our course ‘Data Science’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
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Used Technology: Apache Hadoop , Apache Flume , javaFx & SceneBuilder 1. set up single node hadoop cluster on VmWare Workstation which has ubuntu OS. 2. set up Apache flume with hadoop cluster 3. install ecllipse EE ide 4. install Javafx & Scene Builder plug in to ecllipse 5. include all hadoop jar file to a new project to run hadoop pgm 6. include all flume jar file to that project to stream data from different sourses. 7. create a new javafx project and make windows with css & img file 8. write Apache Flume configuration file to download data from twitter source 9. write java code for downloading data from facebook using Facebook 4j API 10.write Hadoop Map-Reduce program to analyse tweets for their polarity(+ve , -ve ,neutral) and rate. 11. display that result in the form of Pie Chart, Scattered Chart, tag cloud and individual comments/tweets rating.
Views: 6161 ANKUR KUMAR
Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Learn It Up! Summer’s Hottest Learning Sale Is Here! Pick Any Sun-sational Course & Get Other Absolutely FREE! Link: http://bit.ly/summer-bogo-2019 Want to learn Machine learning in detail? Then try our course Machine Learning For Absolute Beginners at just $10. New Machine Learning Project Course for Beginners - http://bit.ly/2V8edMT You can even check FREE course on Predict Board Game Reviews with Machine Learning on http://bit.ly/2Wm2uKW Kickstarter Campaign on AI and ML E-Degree is Launched. Back this Campaign and Explore all the Courses with over 58 Hours of Learning. Link- http://bit.ly/aimledegree Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
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Financial data analysis is as much a broad area as Finance. You can use it for managing/mitigating different types of financial risk, taking decisions on investment, managing portfolio, valuing assets etc. Below are a few beginner level projects you can try working on. 1- Build a Credit Scorecard Model - Credit scorecards are basically used to assess credit worthiness of customers. Use German Loan data-set (publicly available credit data) to build credit scorecard for customers. The data set has historical data on default status of 1000 customers and the different factors that are possibly correlated with the customer’s chances of defaulting such as salary age, marital status etc. and attributes of the loan contract such as term, APR rate etc. Build a classification model (using techniques like Logistic Regression, LDA, Decision Tree, Random Forest, Boosting, Bagging) to classify good and bad customers (default and non default customers) and use the model to score new customers in future and lend to customers that have a minimum score. Credit scorecards are heavily used in the industry for taking decisions on grating credit, monitoring portfolio, calculating expected loss etc. 2- Build a Stock Price Forecasting Model - These models are used to predict price of a stock or an index for a given time period in future. You can download stock price of any of the publicly listed companies such as Apple, Microsoft, Facebook, Google from Yahoo finance. Such data is known as uni-variate time series data. You can use ARIMA (AR, MA, ARMA, ARIMA) class of models or use Exponential Smoothing models. 3- Portfolio Optimization Problem - Assume you are working as an adviser to a high net worth individual who wants to diversify his 1 million cash in 20 different stocks. How would you advise him? you can find 20 least correlated stocks (that mitigates the risk) using correlation matrix and use optimization algorithms (OR algos) to find out how you would distribute 1million among these 20 different stocks. 4- Segmentation modelling - Financial services are increasingly becoming tailored made. Doing so helps banks in targeting customers in a in a more efficient way. How do banks do so? They use segmentation modelling to cater differently to different segments of customers. You need historical data on customer attributes & data on financial product/services to build a segmentation model. Techniques such as Decision Trees, Clustering are used to build segmentation models. 5- Revenue Forecasting - Revenue forecasting can be done using statistical analysis as well (apart from the conventional accounting practices that companies follow). You can take data for factors affecting revenue of a company or a group of companies for a set of periods of equal interval (monthly, Quarterly, Half year, annual) to build a regression model. make sure you correct for problem of auto-correlation as the data has time series component and the errors are likely to be correlated (that violates assumptions of regression analysis) 6- Pricing Financial Products : You can build models to price financial products such as mortgages, auto loans, credit card transactions etc. (pricing in this case would be charging right interest rate to account for the risk involved, earn profit from the contract and yet be competitive in the market). You can also build models to price forward, future, options, swaps (relatively more complicated though) 7- Prepayment models - Prepayment is a problem in loan contracts for banks. Use loan data to predict customers could potentially prepay. You can build another model in parallel to this to know if a customer prepays, when is he likely to prepay in the life time of the loan (time to prepay). You may also build a model to know how much loss the company would incur if a section of the portfolio of customer prepay in future. 8 - Fraud Model - These models are being used to know if a particular transaction is a fraudulent transaction. Historical data having details of fraud and non-fraud transactions can be used to build a classification model that would predict chances of fraud happening in a transaction. Since we normally have high volume of data, one can try not just relatively simpler models like Logistic Regression or Decision trees but also should try more sophisticated ensemble models. ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Views: 5116 Analytics University
A data warehouse offers the benefits of fact-based decision making, and these days nearly everyone agrees on their value. But data warehouse project have an alarmingly high failure rate. In this video we explain why and offer a way you can succeed where others have failed. You can find our article on data lakes here: http://www.leapfrogbi.com/2016/04/19/what-you-should-know-before-you-go-sailing-on-the-data-lake/ You can find our video series on dimensional modeling here: https://www.youtube.com/playlist?list=PLrbIyvYCdg0iAUQoxG5vI_yKqzZ2AcgGe Follow LeapFrogBI on: - Facebook https://www.facebook.com/LeapFrogBI-385646044834444 - Twitter http://www.twitter.com/leapfrogbi - LinkedIn https://www.linkedin.com/company-beta/2524834/
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Alexey Zinoviev presented this paper on the JavaDayKiev'15 conference Slides: http://www.slideshare.net/zaleslaw/javadaykiev15-java-in-production-for-data-mining-research-projects This paper covers next topics: Data Mining, Machine Learning, Hadoop, Spark, MLlib
Views: 329 Alexey Zinoviev
This Video shows the demonstration of “Online Auction Using Shill Bidding Prevention”. Human cheating has been a barrier to establishing trust among e-commerce users, throughout the last two decades. In particular, in online auctions, since all the transactions occur among anonymous users, trust is difficult to establish and maintain. At present, shill bidding is the most severe and persistent form of cheating in online auctions, but still there are only a few or no established techniques for shill defense at run-time. Evaluating the strengths and weaknesses of existing approaches to combating shill bidding. Proposed system prevents shilling activities by monitoring bidding behavior and user history. IP tracking techniques are proposed to detect shilling. The system also takes necessary actions against shill activities at run-time. The experimental results demonstrate that, by prevention, detection and response mechanisms, the proposed auction system keeps the auction users secured from shill bidding and therefore establishes trust among online auction users. Sellers may find lower prices where their items are sold at auctions which potential bidders are avoiding. To prevent this shill bidding process, we developed such a system that makes the bidding process neat and clean. To get this project, visit http://nevonprojects.com/online-auction-using-shill-bidding-prevention/ We provide Product Delivery and Customer Support Worldwide, so enter your country details on the website for the pricing details. CHECK OUT COLLECTION OF SOME OF OUR OTHER “Web - Based Project” 1) College Admission Predictor https://youtu.be/A8A6CCd1kzY 2) Exam Cell Automation System https://youtu.be/cfbuEkNyjYk 3) Web Content Trust Rating Prediction Using Evidence Theory https://youtu.be/92jgFhEWnf0 4) Customer Behavior Prediction Using Web Usage Mining https://youtu.be/YXS3r71wFAY 5) Web Mining For Suspicious Keyword Prominence https://youtu.be/CrWD8YzVafk To subscribe this channel click the link https://www.youtube.com/channel/UCisTN-GbgzzLRXftgnCJGKg?sub_confirmation=1 “Nevon Express” is our other channel, watch it at https://www.youtube.com/channel/UCJbZbcQI5PNDvP4TSZUkHRQ
Views: 13002 Nevon Projects
Final Year project ideas for BSc IT, CS, BCA, BCom (CA), MCom (CA), MCA, MSc (CS), BE and ME students Which topic to Select for Project? Which language / platform to use for the Project? Where to Host Website? Follow me on https://www.facebook.com/ganesh.gagi Final Year Project Topics Link https://drive.google.com/file/d/1R9ogU1Wd7yD0iKv6jMrQhRszXmgdX-Ct/view #TechFeverTablet #TipsForProject #FinalYearProjectIdeas #ProjectIdeas #LatestTechnology #IoT #Android #Flutter #Embedded
Views: 9356 techfever tablet
An overview of my Capstone Project for the Coursera Data Mining Specialization offered by the University of Illinois at Urbana-Champaign, by Phil Ferriere (2016) Contact: https://www.linkedin.com/in/philferriere For a longer, detailed review [42 slides, 25mn], please check out https://youtu.be/Fx30sj78Ef4
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** Data Science Certification using R: https://www.edureka.co/data-science ** This Edureka "Data Science Projects" video explains how to solve a problem using data science and the basic approach of a Data Scientist to go about a problem. Below are the topics covered in this module: [01:30] Basic approach to solve a problem [02:21] Problem Statement 1 [02:56] Decision Trees and Random Forests [09:42] Problem Statement 2 [10:25] Logistic Regressions [21:44] Summary Check out our Data Science Tutorial blog series: http://bit.ly/data-science-blogs Check out our complete Youtube playlist here: http://bit.ly/data-science-playlist ------------------------------------- Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka #DataScienceProjects #DataScienceTutorial #MachineLearningProjects -------------------------------------- How it Works? 1. This is a 30-hour Instructor-led Online Course. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! ------------------------------------- About the Course Edureka's Data Science Training lets you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes using R. Data Science Training encompasses a conceptual understanding of Statistics, Time Series, Text Mining and an introduction to Deep Learning. Throughout this Data Science Course, you will implement real-life use-cases on Media, Healthcare, Social Media, Aviation and HR. ------------------------------------- Who should go for this course? The market for Data Analytics is growing across the world and this strong growth pattern translates into a great opportunity for all the IT Professionals. Our Data Science Training helps you to grab this opportunity and accelerate your career by applying the techniques on different types of Data. It is best suited for: Developers aspiring to be a 'Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Machine Learning (ML) Techniques Information Architects who want to gain expertise in Predictive Analytics 'R' professionals who wish to work Big Data Analysts wanting to understand Data Science methodologies ------------------------------------- Why learn Data Science? Data science is an evolutionary step in interdisciplinary fields like the business analysis that incorporate computer science, modelling, statistics and analytics. To take complete benefit of these opportunities, you need a structured training with an updated curriculum as per current industry requirements and best practices. Besides strong theoretical understanding, you need to work on various real-life projects using different tools from multiple disciplines to gather a data set, process and derive insights from the data set, extract meaningful data from the set, and interpret it for decision-making purposes. Additionally, you need the advice of an expert who is currently working in the industry tackling real-life data-related challenges. ------------------------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
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** Python Certification Training: https://www.edureka.co/python ** This Edureka video on Python Projects will help you establish a foothold on Python by helping you assess and obtain skills which are used to design, develop and analyze projects built in Python. 1. Introduction to Python 2. Installation and Working with Python 3. Python Projects- 3levels 4. Practical approach - Code Python Tutorial Playlist: https://goo.gl/WsBpKe Blog Series: http://bit.ly/2sqmP4s #pythonprojects #pythonprogramming #pythontutorial #PythonTraining #PythonEdureka #Edureka Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka ----------------------------------------------------------------------------------------------------------------------------------- How it Works? 1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide a deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in Python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real-life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in Python programming language to enhance your learning experience. After completing this Machine Learning Certification Training using Python, you should be able to: Gain insight into the 'Roles' played by a Machine Learning Engineer Automate data analysis using python Describe Machine Learning Work with real-time data Learn tools and techniques for predictive modeling Discuss Machine Learning algorithms and their implementation Validate Machine Learning algorithms Explain Time Series and it’s related concepts Gain expertise to handle business in future, living the present - - - - - - - - - - - - - - - - - - - Why learn Machine Learning with Python? Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
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In this data science project, you will work on credit card fraud dataset and learn how to identify a transaction as fraudulent or genuine. Find the solution to Credit Card Fraud Detection as a Classification Problem - https://goo.gl/4D4g4a Check out other interesting data science project ideas here - https://goo.gl/BvgtxE
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Monitoring Suspicious Discussions on Online Forums Using Data Mining
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iss video me maine bataya hai ki kiss tarah final yer project ka process work karta hai step by step par project tumhe hi dhundna hai tabhi toh tum kuch sikhonge and mujhe pata hai voh easy nahi hai par hum engineer hai ham easy kaam ke liye nahi bane so best of LUCK
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