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20 Interesting Data Mining Projects in 2024 (for Students)

  • Feb 07, 2024
  • 9 Minutes Read
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  • By Apurva Sharma
20 Interesting Data Mining Projects in 2024 (for Students)

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 we present 20 interesting data mining project ideas for students that they can make for their final year as well. So let’s get Started!

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 that is relevant to the company’s business. This is passed through various data-wrangling techniques.

We categorize them into useful data, which is collected and stored in particular areas such as data warehouses, efficient analysis, and data mining algorithms, which help their decision-making and other data requirements which benefits them in cost-cutting and generating revenue.

It is not an easy subject to understand in university when there is always so much more work to be done. You can get expert data mining help online now for instant doubt-solving.

According to Glassdoor, the average salary of a Data Mining Engineer in the US is around $120,000. But what is the best way to practice way? By making some amazing data mining projects.

20 Data Mining Project Ideas for Students

While there are many beginner-level data science projects available, we select some of the best project ideas for students that they can build to either showcase it on their resume or make it for their final year submission:

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 spreading like wildfire.

In the Fake news detection project for data mining, you will learn how to classify news into Real or Fake in this project. It is one of the new ideas for data mining projects which is quite popular among students.

You will use PassiveAggressiveClassifier to perform the above function. 

fake new detection for data mining projects


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 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. 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.

diabetes prediction data mining project idea


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 previous data.

The data can be =the location and size of the house and facilities near the house. This data mining project is an evergreen topic in the USA. Find the dataset here.

5) Credit Card Fraud Detection

With the increase in online transactions, credit card fraud has 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 the Parkinson's disease detection project for data mining, 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. An enthusiast in this field can easily get involved and excited by such topics.

This data set contains information on user preference data from 73,516 users on 12,294 anime. Each user can add anime to their list and give a rating and this data set is a compilation of those ratings. The aim 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.

mushroom classification project idea for data mining


9) Solar Power Generation Data

This data has been extracted from two solar power plants in India over 34 days. 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.

The sensor data is extracted from a plant level; a single array of sensors is optimally located at the plant. These are concerns at the solar power plant:

  1. Can we predict the power generation for the next couple of days?
  2. Can we identify the importance of panel cleaning/maintenance?
  3. 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 from 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. 

This data mining project is quite difficult than others but it will surely add a lot of credibility to your knowledge of the subject. Find the dataset: here.

11) Fraud Detection in Monetary Transactions

Detecting fraudulent transactions is a very significant use case in today’s scenario of digitized monetary transactions. To address this problem, Synthetic Data is generated using PaySim Simulator and it is made available at Kaggle.

The data contains transaction details like transaction type, amount of transaction, customer initiating the transaction, old and new balance in Origin i.e., before and after transaction respectively, and same as in Destination Account along with the target label, is fraud.

o, based on the transaction details, a Classification Model can be developed that can detect fraudulent transactions.

12) Adult Census Income Prediction

The US Census Data is made available at the UCI Machine Learning Repository. The Dataset contains variables like age, work class, hours per week, sex, etc. including other variables that can foretell whether the annual income of an individual is greater than 50K dollars or not.

This is a Classification Problem for which a Machine Learning model can be trained to predict the Income Level of an individual.

13) Titanic Survival Prediction

To get started with Data Mining, this is the go-to project. A Titanic Dataset is created by Kaggle and a competition for the same is being hosted in this link. The data contains explanatory variables like Passenger details like Class, Gender, Age, Fare, etc.

These variables are responsible for predicting whether a passenger will survive the Titanic Disaster or not with Survived (0/1) as the target variable. So, the Project Expectation is to build a Classification ML Model that predicts the probable survival of the passenger in Titanic.

14) Air BNB Market Analysis

Analyzing the Air BNB market is pretty important for the company to figure out where the demand is and how to advertise to people. Using data mining algorithms, they can take a look at where customers are coming from, where properties are located, and how much they cost.

15) NBA Shooting Analysis

If you're just starting in data analysis, looking at NBA shooting stats is a great way to practice. The stats include information about where players shoot from, where they're most likely to score, and how the defender affects the shot.

By using data mining algorithms, you can analyze all of this data to help coaches and players improve their games. Students will love to make this data mining project because everyone likes NBA.

16) Movie Recommendation System

If you watch movies regularly, you must have also spent hours just finding a movie to watch. To save you time, this project is gonna help you a lot. The Movie Recommendation System aims to suggest movies to us based on our preferences, viewing history, ratings, and similarities with other users.

We can structure this project in different ways:

  • Collaborative Filtering: Utilizes user-item interactions to recommend items. It can be implemented using techniques like User-based or Item-based collaborative filtering.
  • Content-Based Filtering: Recommends items similar to those you have liked before based on content attributes like genre, actors, director, etc.
  • Hybrid Approaches: Combines collaborative and content-based filtering for more accurate recommendations.

First, use a dataset containing user ratings, movie metadata, and user interactions. Second, preprocess the data by handling missing values, normalizing ratings, or encoding categorical variables. Then, build recommendation models (such as matrix factorization, and k-nearest neighbors) using libraries like Surprise, Scikit-learn, or custom implementations.

Finally, evaluate the models using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or precision/recall.

17) Customer Segmentation

Customer Segmentation is also one of the projects based on data mining. It involves grouping customers based on similar characteristics, behaviors, or preferences to tailor marketing strategies or services.

Let’s take a brief look at the approach we have to use:

  • RFM Analysis: It segments customers based on the recency, frequency, and monetary value of their purchases.
  • Clustering Algorithms: Utilizes techniques like k-means clustering or hierarchical clustering to group customers based on features such as demographics, purchase history, or preferences.
  • RFM and Demographic Fusion: Combines RFM analysis with demographic data for more refined segmentation.

It is also an amazing idea for Data Science projects that students can make.

18) Predicting Loan Defaulters

All the banks and organizations that lend money need to first assess the risk of loan default based on customer’s past data. To automate this task and save time, we can build a model to assess the risk of loan default based on applicant data and historical loan performance.

It is a simple model, and we can create in such simple steps:

  • Collect and preprocess historical loan data including applicant details, loan amount, repayment status, etc.
  • Split the dataset into training and testing sets.
  • Train classification models on historical data and evaluate their performance using metrics like accuracy, precision, recall, or ROC-AUC.
  • Use the trained model to predict the likelihood of default for new loan applications.

19) Web Click Prediction

Web Click Prediction involves using data mining techniques to predict or forecast user behavior on websites, particularly predicting what links or content a user is likely to click on. 

First collect the data on user behavior such as clickstreams, timestamps, referral sources, etc. Now, preprocess the data by cleaning it and extracting relevant features from the data that could be used for prediction (e.g., user demographics, browsing history, time of day, device used).

Employ the machine learning algorithms (such as decision trees, logistic regression, and neural networks) to build predictive models, and train the models using historical click data and relevant features.

20) Social Network Analysis

Everyone is very active on social media nowadays, and their behavior on these websites tells a lot about their preferences. We can utilize these data to identify communities, influencers, or patterns.

Social Network Analysis involves analyzing the relationships and connections among individuals or entities in a network. This project requires the following things:

  • Graph Theory and Algorithms: Utilizes graph-based algorithms such as PageRank, community detection algorithms (like Louvain or Girvan-Newman), or centrality measures (like betweenness or closeness centrality).
  • Network Visualization: Visualizes the network structure to understand the relationships and patterns visually.
  • Influencer Identification: Identifies influential nodes or users in the network based on their connections and interactions.

Here, we will perform network analysis using libraries like NetworkX (in Python) or custom implementations in C++. After that, apply graph algorithms to detect communities, find influential nodes, or analyze network properties.

Applications of Data Mining

Here are some major applications:

  1. 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, predicting loan payments, and determining credit ratings.
  2. 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.
  3. Intrusion Detection: Network resources can face threats and actions of cybercriminals can intrude on their confidentiality. Therefore, the 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.
  4. 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.
  5. 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.

How do I Start a Data Mining Project?

The first thing you would need to do is define a problem statement. Your project is only as good as your problem statement. Once you have defined a problem statement, gather data to solve the problem statement.

The data needs to be properly cleaned and in the format that you require it to be. After you have the data, run the data mining algorithms and visualize the results. This can help you gain insights from the data and help in choosing appropriate models to train the data on.

Best Ideas for Final Year Projects

You can choose ideas like Social Network Analysis, Web Click Prediction, and Air BNB Market Analysis for your first data mining project. As we know most students are making it to final year submission. These are very complex and require a lot of data and algorithms. 

Not only will these projects expand your understanding but also your teachers or supervisors will also favor such topics that are more related to the current times.


Now you have the list of Data Mining projects for beginners. So what are you waiting for, select one and start working on it. It is a composite discipline that can represent a variety of methods or techniques used in different analytic methods.

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About The Author
Apurva Sharma
Hi, I am Apurva Sharma, a data science engineer, deep learning evangelist and DevOps enthusiast