Data Mining is the process that enables any organization to find hidden meaning and underlying structure in seemingly unstructured data. In essence, data mining allows companies to churn huge volumes of unstructured data and turn it into useful information. Usually, data science with python is employed in conjunction with traditional data mining techniques to amplify and ramp up the entire process.
Importance of Data Mining:
In the current paradigm, each passing second generates Terabytes of data. Data mining becomes an indispensable tool for any organization. The main reason why data is of such importance is that it allows the company to understand things like customer mindsets, popular trends, which offer to run, analyze the market. Moreover, a good data scientist would be able to make accurate predictions from historical data which would help shape the future of any corporation. So, data mining is not only important for any 21st Century organization, but also to the professionals either looking to transition into data science or are working toward building a career in data science.
Top Data Mining Projects for Beginners:
It is an indisputable fact that projects are by far the best way to learn and master any tech stack. Not only would projects allow you to apply your learning but they also do a great job of mimicking real-world scenarios albeit on a small scale. If you are looking to pick up on data mining skills or are looking to sharpen your data mining, the projects listed below would have something for you.
1. Prediction of Housing Prices:
This project is a staple in the data mining community mainly because of its scalability and the learning it provides. As the name suggests, in this project, you will have to predict the prices of houses. If you are a beginner, then it is recommended that you find a well-documented and enriched data set and start your project from there. Whereas, if you are on the experienced side of the spectrum, then it is recommended that you scrape the data and create the dataset yourself. Post the data collection stage, beginners will be applying basic linear regression, basic feature scaling, and basic hyperparameter tuning. Whereas, the experienced ones should apply more advanced concepts such as boosting, model chaining and grid search for the best hyperparameters. So, not only will this project serve as the cornerstone for the ones looking to get their hands dirty with data but it will also help the experienced ones to build upon this project and take it to the next level.
2. Fraud Detection:
It is another staple data mining project. In this project, you would be predicting whether the transactions done by the credit card are fraudulent or not. Fraud transactions are a real threat to society, and with the power of data, you would be able to create a classifier which upon seeing the details of the transaction would flag it as fraudulent or legitimate. As stated, you would be building a classifier; hence, for the beginners, they will be learning about classification through Logistic Regression (yes, Logistic Regression is not a regression rather it is mainly used as a classifier). You would learn the crucial details about False positives, False negatives, True Positives, True Negatives, Precision, Recall, Precision vs Recall tradeoff, just to name a few. Don’t worry if you already know all about the aforementioned things because you would be expected to take this project to the next level by automating and deploying your model. Also, you would be using better classifiers than the simple logistic regression.
3. Fake News Detection:
Since the internet is so commonplace, it is very easy for anyone to fake news. Fake news has a tendency of spreading like wildfire, and thus it becomes near impossible to contain the spread of misinformation. This project would cater to that demographic. You would be using a classifier and would harness the power of NLP or Natural Language Processing. This is a tricky project, and thus beginners would have to struggle to complete this project; however, it will provide them with the knowledge of Natural Language Processing which is crucial in the data mining community.
4. Data Cleaning with Forbidden Itemsets (FBIs):
The terabytes of data generated daily contain many errors. This dirty data contaminates the information pool and must be cleaned urgently. A popular repairing method utilizes FBIs to clean data that may be corrupted with illegal values, failed logic, lack of constraints, and other issues. FBIs allow errors to be detected by discovering any unlikely co-occurrences in the datasets. This mechanism is well established as a method to minimize error and corruption in data.
5. Personality Classification of Users:
Many sectors use models based on the personality of the users to recommend options that may be more suited for them. From career guidance to targeted advertising, categorized personality traits allow users to have a tailor-made experience in the virtual realm and help companies to perform more targeted campaigns. Data Mining techniques can be used for personality classification with the help of previously collected information. The existing information can be used as a base to establish signs and tendencies that correspond to personality traits. These can later be extracted and compared to existing patterns of behavior obtained from mining the usage data of the individual, helping to automate the classification process.
6. Movie Recommendation System:
If you have ever been to websites like Amazon or Netflix, you would have noticed one striking similarity between the two. Both of them have recommendations based on the stuff which you have already watched or purchased. If you have ever wondered how they do that well, the answer lies in this project. You will be building a recommendation system. In this system, you would have to recommend movies based on what types of movies the users have already liked or have seen. You can also make this project more comprehensive by slapping it with a full-stack application and serving it to users with the help of cloud computing. Beware, newbies, you will be struggling a lot with this project, but in the end, you will acquire the skills to create a powerful recommendation system.
Conclusion
The age of information has seen the advent of Data based strategies across all sectors. However, before all the analysis and conclusion, it is important to collect organized, structured, and reliable data to base them on. Data Mining is the foundation upon which Data Science rests and the tools of the trade must be kept sharp at all times. Practicing these projects will allow you to grasp the fundamentals and advance your career in the field.