Analytics

List of latest Data mining projects with python

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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What is data mining?

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.

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How Data Mining is Done?

The procedure of data mining can be understood by the following steps:

  1. Identifying the project Objective: The very first step in data mining is to understand the objective of the business and its goals. Accordingly, a road map has to be developed to include a timeline, work assignment and actions.
  2. Data gathering and data understanding: In this stage data is gathered and explored. The gathered data is studied to understand and segregate the data which are relevant to the business problem. Data transformation like adding, removing and finding data quality of problems occurs in this stage.
  3. Data Modelling and Evaluation: In this phase mathematical models are used to identify data patterns using advanced data tools. The results obtained by data modelling are evaluated and balanced with respect to the project goals to decide whether they should be deployed in the business.
  4. Deployment: In this stage, the findings from data mining is used for day-to-day business operations. The data insights and information that can be deployed is obtained through data in this stage.

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How python is used in data analysis?

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:

  1. Computing and identifying data forms: Python is used to understand the form a data takes and identifies different data types from a large set of data. Its libraries like Pandas and Numpy are used to perform this task quickly using parallel processing.
  2. Extracting important data from the web: its libraries such as Scrapy and BeautifulSoup are used to extract important data from the internet.
  3. Graphical visualization of data: The data are best understood when they are represented by graphs, pie-charts, etc. Python libraries like Seaborn and Matplotlib are used for the same.
  4. Machine Learning: Python’s machine learning library Scikit-Learn is used to apply complex machine learning techniques to analyse the data and determine the pattern. 

Note: The above functions are used when the data is in text format.

  1. Image processing: if the data is in the image format then also it can be processed using dedicated image processing open-source library of python, OpenCV.

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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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Check out the following list for more data mining projects:

  • Data mining for sales prediction in tourism
  • Detecting fraud apps using sentiment analysis
  • Personality prediction system using CV analysis 
  • Higher education access prediction software
  • Movie success prediction using data mining
  • Cleaning data with forbidden itemsets
  • Efficient Mining of Frequent Patterns on Uncertain Graphs
  • Model-Based Synthetic Sampling for Imbalanced
  • Modelling the Parameter Interactions in Ranking SVM with Low-Rank Approximation
  • Student Information Chatbot Project
  • Content Summary Generation Using NLP

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List of latest Data mining projects with python
Skyfi Labs Last Updated: 2020-07-05





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