Data is the most powerful weapon in today’s world. With technological advancement in the field of data science and artificial intelligence, machines are now empowered to make decisions for a firm and benefit them. Here is where data mining comes into the picture. This technique helps businesses and firms to analyze valuable user data to their benefit. But where do you start practicing your skills? Here we present some amazing data mining project ideas. This article sheds light on the definition of data mining, 10 data mining projects for students, and the applications of data mining. So let’s get Kraken!
What is Data Mining?
The method of extracting useful information to identify patterns and trends in the form of useful data that allows businesses and huge firms to analyze and make decisions from huge sets of data is called Data Mining.
In layman’s terms, Data Mining is the process of recognizing hidden patterns in the information extracted from the user or data which is relevant to the company’s business, and passing it through various data wrangling techniques for categorization into useful data, which is collected and stored in particular areas such as data warehouses, efficient analysis, data mining algorithms, which helps them decision making and other data requirements which benefits them in cost-cutting and generating revenue.
Data mining uses complex mathematical algorithms to perform data segmentation and evaluation of the probability of future decisions for the business. Data Mining is additionally referred to as Knowledge Discovery of Data (KDD).
10 Data Mining Project Ideas
While there are many data science project ideas available online, here are some of the best data mining projects for students:
1) Fake news detection
With the advent of the technological revolution, it is easier for users to have access to the internet which increases the probability of fake news to spread like a wildfire. In this project, you will learn how to classify news into Real or Fake. Also in current times, this will be one of the best data mining projects for project submissions. You will use PassiveAggressiveClassifier to perform the above function.
2) Detecting Phishing website
In recent times, technological advancement created a way for the development of e-commerce sites and most of the users started shopping online for which they have to provide their sensitive information like bank details, username, password, etc. Fraudsters and cybercriminals use this opportunity and create fake sites that look similar to the original to collect sensitive user data. In this data mining project, you will develop an algorithm to detect phishing sites based on the characteristics like security and encryption criteria, URL, domain identity, etc.
3) Diabetes prediction
Diabetes is one of the most common and hazardous diseases on the planet. It requires a lot of care and proper medication to keep the disease in control. In this data mining project, this project teaches you to develop a classification system to detect whether the patient has diabetes or not. As part of this project, you will learn about the Decision tree, Naive Bayes, SVM calculations, etc. Find the dataset: here.
4) House price prediction
In this data mining project, you will utilize data science techniques like machine learning to predict the house price at a particular location. This project finds applications in real estate industries to predict house prices based on the previous data for example the location and size of the house and facilities near the house. Find the dataset: here.
5) Credit Card Fraud Detection
With the increase in online transactions, credit card frauds have also increased. Banks are trying to handle this issue using data mining techniques. In this data mining project, we use python to create a classification problem to detect credit card fraud by analyzing the previously available data. We have made this credit card fraud detection project using machine learning here.
6) Detecting Parkinson’s disease
Data mining techniques are widely utilized in the healthcare industry to provide quality treatment by analyzing the patient’s medical records. In this data mining project, you will learn to predict Parkinson’s disease using python. The project works with UCI ML Parkinson’s dataset. Find more information about the project dataset: here.
7) Anime recommendation system
This is one of the favorite data mining project ideas among students. This project data set contains information on user preference data from 73,516 users on 12,294 anime. Each user is able to add anime to their completed list and give it a rating and this data set is a compilation of those ratings. The aim of the project is to create an efficient anime recommendation system based only on user viewing history. Find the dataset: here.
8) Mushroom Classification
This dataset contains details of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). Each mushroom species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter category is combined with the poisonous one. The facts suggest that there is no simple rule to determine if the mushroom is edible; no rule like "leaflets three, let it be'' for Poisonous Oak and Ivy. Find more information about the data: here.
9) Solar Power Generation Data
This data has been extracted from two solar power plants in India over a 34 day period. It has two pairs of files - each pair has one power generation dataset and one sensor reading dataset. The power generation datasets are extracted from the inverter level - each inverter has multiple lines of solar panels attached to it. And the sensor data is extracted from a plant level - a single array of sensors optimally located at the plant.
These are concerns at the solar power plant -
- Can we predict the power generation for the next couple of days?
- Can we identify the importance of panel cleaning/maintenance?
- Can we identify faultily or suboptimally performing equipment?
The dataset: here.
10) Heart Disease Prediction
Heart disease is one of the most common diseases. It needs a lot of care by the doctor to get diagnosed. In this data mining project, you will learn to develop a system to detect whether the patient is suffering from heart disease or not. In this project, you will learn about the Decision tree, Naive Bayes, SVM calculations, etc. Find the dataset: here.
Applications of Data Mining
- Financial Analysis: The banking and finance industry relies on high-quality and processed, reliable data. In the finance industry user, data can be used for a variety of purposes, like portfolio management, predict loan payments, and determine credit ratings.
- Telecommunication Industry: With the advent of the internet the telecommunication industry is expanding and growing at a fast pace. Data mining can help important industry players to improve their service quality to compete with other businesses.
- Intrusion Detection: Network resources can face threats and actions of the cybercriminals can intrude on their confidentiality. Therefore, detection of intrusion has proved as a crucial data mining practice. It enables association and correlation analysis, aggregation techniques, visualization, and query tools, which can efficiently detect any anomalies or deviations from normal behavior.
- Retail Industry: The established retail business owner maintains sizable quantities of data points covering sales, purchasing history, delivery of goods, consumption, and customer service. Database management has improved with the arrival of e-commerce marketplaces and emerging new technologies.
- Spatial Data Mining: Geographic Information Systems and many other navigation applications utilize data mining techniques to create a secure system for vital information and understand its implications. This new emerging technology includes the extraction of geographical, environmental, and astronomical data, extracting images from outer space.
Data mining is a composite discipline that can represent a variety of methods or techniques used in different analytic methods that helps firms and organizations to make efficient business decisions and benefit them, they perform this by types of questions and using various levels of user input or rules to arrive at a decision. In this way, user data can be used intelligently for the benefit of the firm. I hope this article helps you to understand various data mining projects and their applications.
Happy Learning :)