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Ever wondered how exactly does Netflix suggests the things you would like to watch? Or how Amazon suggests or shows up an advertisement for the things you want to buy? They pretty much seem to read our mind. But how do they do that? The answer is data mining. They deploy data mining techniques to build their recommendation systems. Not just this, the ability of data mining techniques to predict outcomes and discover meaningful information from large data sets finds its application for various purposes such as search engine algorithms, business forecasting, healthcare bioinformatics, etc. The wide application of data mining makes it an essential skill to learn, especially for CSE students. We will discuss more data mining and data mining projects with python here.
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Data mining is the process of discovering significant patterns, relations and trends from large sets of data to forecast the outcomes. Data mining uses mathematical algorithms to segregate the data and predict subsequent outcomes. It is regarded as an area under the field of Data Science which is used to predict future outcomes. Data mining uses statics and advanced mathematical algorithms to establish meaningful relationships between data. It is used to develop Machine Learningframeworks that are used in Artificial Intelligence.
Data mining automatically discovers patterns. It uses building models to discover patterns automatically, most of these models are applied to new data a.k.a Scoring. A model is built up using algorithms on a data set. Data mining can give accurate predictions and generate rules. For instance, it can help in predicting the credibility of debtors using demographics and personal information. It also helps in selecting an interest rate depending upon the score of the individual. It helps in grouping the data, like grouping people based on the demographics and age group to be vulnerable to certain diseases. It thus generates information that can be acted on like a person found vulnerable to certain disease can take precautions to prevent it and patients can be prioritized using the available data. The example can be best understood during the situation of COVID-19, data mining is used here to discover patterns in the symptoms and grouping the vulnerable patients according to their ages and medical history. It is further used to predict how much people can be affected by it and action is taken on this information by prioritizing patients and optimise the medical resources.
The procedure of data mining can be understood by the following steps:
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Python is best suited for data analysis owing to its readability, easy and faster executable codes, large and effective libraries, wide scalability, large support,visualization and graphics, open-source and its ability to support both structured and object-oriented programming.
Now, let’s understand how it is used in data analysis:
Note: The above functions are used when the data is in text format.
We will be able to truly appreciate data mining if we perform some data mining projects and these projects will turn out to be more effective if we do pythondata mining projects or data mining projects with python. Here is the list of some data mining projects:
Fun fact: Netflix once offered a one-million-dollar prize for an algorithm that would increase the accuracy of its recommendation system.
1. Smart Health Disease Prediction Using Naive Bayes: This data mining project aims to provide immediate health guidance through an intelligent online health care system. The data of symptoms and their related diseases are fed in the system. It enables users to enter their symptoms and employs a Naïve Bayes algorithm which will predict the disease. If the person is normal, then the system smartly suggests a personalised balanced diet chart for the person. It suggests various X-ray, CITI scans and related tests. The user can upload these reports later and consult the doctors who can log in to this system.
2. Protecting user data in profile matching social networks: In this data mining project you will create Profile matching social networks such as matchmaking sites, the user puts in lots of personal information such as income, address and their required preferences. This information is required to be secured. Homomorphic encryption and multiple servers to match profiles, keeping the personal information of the users secured.
3. GERF: Group Event Recommendation Framework: It is an efficient way to suggest social events such as trips, concerts, exhibitions, etc to a group of users. This data mining model uses learning -to -rank algorithm to identify group preferences and this data mining project can add additional factors effectively.
4. Mining behavioural sequence Constraints for classifications: Sequence classification deals with finding differential patterns and predicting concise sequential pattern for data. This can be achieved by a simple mathematical tool but to ensure accuracy and wider scalability sequence classification technique with behavioural constraint templet is used. The interesting Behavioural Constraint Miner (iBCM) serves the purpose as it provides various patterns of sequence like simple occurrence, looping, position-based behaviour and it also notifies about the absence of a negative trait.
5. PKEs over encrypted emails in the cloud server: The security of emails is important, given its wide usage across organisations. Public encryption with keyword research technology provides security protection with operability functions.
6. Smart Transportation System: Creation of bus schedule, keeping in mind transport service’s efficiency, transport safety, traffic congestion, identifying passengers and optimisation of resources. This can be achieved by applying regressions and other techniques to have a smart transportation system.
7. PrivRank for social media: Social media websites gather information about users to provide personalised recommendations and thus it is important to protect the data of users.
8. Sentimental analysis and Opinion mining for mobile networks: This analysis helps people to get a concise and accurate review of their posts. Social media influencers or marketing companies regularly put posts on social media. Reading all the comments and analysing them manually is a tedious task. That’s where sentiment analysis and opinion mining system comes into the picture, it can give the status the post and can also provide the graphs of the comments.
9. Mining the K-most frequent negative patterns through learning: Using Negative sequence pattern of behavioural informatics, we can extract more information. For example, data associated with not undergoing medical treatment can reveal more information than the data on taking the treatment. Topk-NSP+ algorithm helps in further exploring this field of bioinformatics.
10. Predictive analysis for digital Agricultural: Weather forecasting is a complicated process but it is crucial. Combinations of empirical and dynamical Artificial Neural techniques provide methods to solve non-linear problems which are difficult to solve using traditional techniques.
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