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Decision tree C4.5 C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. Authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date". advantages: •  Build models that could easily interpreted •  Easy to implement •  It can use categorical and continuous values •  Deals with noise disadvantages: •   Small variation in data can lead to different decision trees (especially when the variables are close to each other in value) •  Does not work very well on a small training set Naive Bayes In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on

a mining model is created by applying an algorithm to data, but it is more than an algorithm or a metadata container it is a set of data and patterns that can be applied to new data to generate predictions and make inferences about relationships.  using decision tree model to predict a customer buying behavior, to determine one of many few things such as, "if a certain amount of customer only browsed for the item or maybe had actually purchased the item", and many more. and using the following attributes to help us gain the prediction these attributes are: user_id, gender, age, marital_status, website_activity, browsed_electronics_12mo, bought_electronics_12mo, bought_digital_media_18mo, bought_digital_books, payment_method, e-reader_Adoption.  results: the picture above is the result of the frequent buyer from the decision tree this picture is the seldom buyer from the decision tree Tree Website_Activity = Frequent |

Rapid Miner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualization, model validation and optimization the reason why i choose rapidminer is because it is easy to use and it responded rapidly based on the instruction that is given, i will do the prediction by using three different models. those models are; decision tree, naive bayes & k-nearest neighbor. prediction using decision tree. Description A decision tree is a tree like collection of nodes intended to create a decision on values affiliation to a class. Each node represents a splitting

Big data Analysis implementation in daily activities, Using a Samsung's Health Tracker app to monitor my daily step's between June and July 2017 and compare it between the other app user .  it was all started when the app give constant notification about how much the time and distance I've covered when i was walking even on short period of time. and the app also provide me with a monthly report, this report also tells me about the total time, distance & steps that i gain in that month. and i was curious about this report and wanted to analyze more about the information since it is one of big data analysis implementation in daily activities so i picked up two months where at that month is where i spent my summer break on,  JUNE at this month it was the beginning of a holiday where i spent a lot of my time outside of my house to do certain activities. as it could be seen on the picture above i spent almost 4 hours on June in total and about 16.86 km in

                                            Big data implementation in the banking industries ·        Objective The financial industry is one of the most data driven of industries. At the end of 2012. It was estimated that financial and securities organisation were managing 3.8 petabytes of data per firm. Data sets have grown immensely in terms of size, type and complexity and are difficult to work on using traditional database management tools. Many large financial and banking institution are reaching the upper limits of their legacy system and are now seeking fresh analytics and framework solution ·        Problems Regulators are demanding greater transparency, customers want a more relevant and personalized experience, and CEOs are looking for sustainable growth opportunities. A common thread in all of these issues is data. Big data. What is big data? Forrester puts it succinctly in saying “big data encompasses techniques and technologies that make capturing value fr