5 Data Mining Project Ideas to Build Your Knowledge

Data mining is defined as the practice of analyzing large databases in order to generate new information. Data mining techniques are used in various sectors, including retail, banking, medicine, television and radio. Data mining is an intersection of machine learning (ML) and statistics.

Professionals who work in the data mining field look through large volumes of data or datasets to identify patterns that can be used through statistical methods such as cluster analysis and anomaly detection.

How do you build data mining skills? Once you have learned some theory, a key part of your work should be projects that build upon the theory you know. To help you build your knowledge of data mining, we have written about five data mining projects you can build.

Why is Building Projects for Data Mining Important?

As a data miner, there are many different types of data you could be working with — text/web mining, different kinds of databases, such as those that are relational, spatial, and transactional. 

If you aspire to be a data miner, know that you will often work with a combination of these, so knowing how to blend the data together is a valuable skill. You may need to utilize metadata to help reduce errors that arise when merging different streams of data.

Also, there are many data mining techniques to learn. Searching for dependencies is a part of pattern exploration in data mining. Data miners can also focus on finding anomalies and explore what the anomalies mean. Having experience working on different data projects will allow you to learn which approaches work best for different kinds of solutions.

Working on data mining projects will also help you learn how to use tools which are utilized by industry professionals. Knowing how to use analytics softwares is important as it can reduce the difficulty of many data mining tasks. Get familiar with one, such as Looker and Spotfire, to add value to your professional career.

How to Approach Data Mining Projects

What should one practice in their data mining projects? We have made a list of some key data mining topics that you should cover in your projects. You do not need to cover all of the topics we discuss at once but trying to incorporate one or a few of them into your work is a good idea.

Here are a few data mining topics you should keep in mind while building your projects.

  • Sequential pattern mining. Identifying sequential patterns in transaction data.
  • Association rule mining. Identifying interesting associations between variables.
  • Cluster analysis. Identifying similarities in groups of data, and differences by extension.
  • Regression analysis. Analyzing the data to predict the likelihood of a particular variable given other variables.
  • Outlier analysis. Anomaly detection.
  • Classification. Classifying data and metadata.
  • Prediction. Using a combination of techniques to predict a future outcome.

Know that depending on which type of project you are working on, you may employ different database techniques. For example, spatial indices might be used when working with data where distance is relevant.

5 Project Ideas for Data Mining

There are several data mining tools you can employ according to what you are most familiar with. One of those is R, which has statistical, graphical, and analysis tools. Another tool is Oracle Data Mining (ODM), which generates insights and predictions.

Here are the the other widely used data mining tools:

  • Rapid miner
  • Oracle data mining
  • Kaggle
  • Python
  • Rattle
  • Teradata
  • R language
  • SAS data mining
  • BOARD
  • Solver

Using these tools above — depending on your existing skills and personal preferences — you will be able to build the five project ideas we list below, assuming you have some strong foundational data mining knowledge.

Idea #1: Event Recommendation

Amazon recommends new things for you to buy based on searches and past purchases using data mining. Therefore, you can also use data to predict events.

Today, people use apps to manage real life events. These apps allow users to accept or reject event invitations. You can model this by using datasets of past events and another with event information files such as these ones available on the Kaggle website

Using Python and its various libraries (Pandas, Scikit-learn, etc) can help you rank events by interest level for each user. Scikit-learn, for example, can be used for classification and clustering, while Pandas can be used for data manipulation and analysis.

Idea #2: House Price Prediction

In the real estate world, any advantage is welcome. Buying a house is a major milestone for many, but can also be a financial strain. Factors to consider are the average costs for an area, house size, and neighborhood features.

Apps that help predict the costs of buying homes exist, so there are new streams of data and insights you can incorporate. In any case, getting familiar with this type of project is useful and you can add your own approach.

Idea #3: Diabetes Diagnosis

With the cheap costs of ultra-processed, greasy, and unhealthy foods and surprisingly high costs of healthier options, diabetes is a problem plaguing many. Research linking ultra-processed food consumption to diabetes is still ongoing. 

Many studies have been conducted on the possible high risk factors for diabetes, and there are many datasets available in this subject. One such data set is the Pima Indians Diabetes Database

Factors to consider are age, weight fluctuations, and smoking habit. For this project you can use the Naive Bayes classifier to train and test data. A positive marker can be used for a diagnosis that was confirmed for a patient which exhibited factors which the model attributed to being related to diabetes.

Idea #4: Pokemon categorization

This Pokemon dataset is varied and large enough to apply some cluster analysis techniques. You can cluster Pokemon based on the most relevant/prominent similarities. 

A tool you can use is Carrot2, a clustering framework.

Idea #5:  Resume Ranking

When you do a LinkedIn or Google search for jobs, you can select certain areas of interest and sometimes even skill-level. But people seeking employment end up applying even to jobs they’re not really interested in because that is just what appeared in their search result. Imagine an app that can match people with jobs in order of relevancy and possible interest. 

There are not many free resume aggregating resources, but an option is to web-scrape a site with resumes to find such data, such as ZipRecruiter. From the resume, additional information can be gathered if a GitHub or Linkedin account is provided. 

All of this data can be merged in a database. Using Natural Language Processing (NLP) techniques and a ranking algorithm, candidates can be matched with the best role according to their skillset.

Conclusion

Data mining is about exploring the past to help the present and future. With big data being readily available, the usage possibilities across many different disciplines are endless.

It’s an exciting time to explore data mining projects. Such explorations can help businesses make better decisions, uncover insights about the universe, or even spot trends that affect our understanding of the human body. Or you can start small and mine crime data to make sure you move into an up and coming neighborhood.

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