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Data Mining Assignment
Download a good dataset from Internet. It should have 100's of records and good number of parameters (deadline for downloading your dataset: March 30, 2017).
Mention the source of the dataset
Describe the Problem the dataset is about? Explain in a few paragraphs.
Select one of the data-mining task will you apply?
- Association rule mining
Upload your dataset and the above details on Google drive and ask me for recommendation.
When is approved, apply all step in CRISP-DM process in rapidMiner. Write a detailed report of all of your work. Submit that report to me.
This initial phase focuses on understanding the project objectives and requirements from a business perspective, and then converting this knowledge into a data mining problem definition, and a preliminary plan designed to achieve the objectives. A decision model, especially one built using the Decision Model and Notation standard can be used.
The data understanding phase starts with an initial data collection and proceeds with activities in order to get familiar with the data, to identify data quality problems, to discover first insights into the data, or to detect interesting subsets to form hypotheses for hidden information.
The data preparation phase covers all activities to construct the final dataset (data that will be fed into the modeling tool(s)) from the initial raw data. Data preparation tasks are likely to be performed multiple times, and not in any prescribed order. Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.
In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed.
At this stage in the project you have built a model (or models) that appears to have high quality, from a data analysis perspective. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model, to be certain it properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.
Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that is useful to the customer. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data scoring (e.g. segment allocation) or data mining process. In many cases it will be the customer, not the data analyst, who will carry out the deployment steps. Even if the analyst deploys the model it is important for the customer to understand up front the actions which will need to be carried out in order to actually make use of the created models.
Data Descriptions -
How can we tell the greatness of a movie before it is released in cinema?
This question puzzled me for a long time since there is no universal way to claim the goodness of movies.
Many people rely on critics to gauge the quality of a film, while others use their instincts. But it takes the time to obtain a reasonable amount of critics review after a movie is released. And human instinct sometimes is unreliable.
Given that thousands of movies were produced each year, is there a better way for us to tell the greatness of movie without relying on critics or our own instincts?
Will the number of human faces in movie poster correlate with the movie rating?
To answer this question, I scraped 5000+ movies from IMDB website using a Python library called "scrapy".
Attachment:- Assignment Files.rar