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30 Data Mining Projects [with source code]

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Introduction

Data mining has become an increasingly important field in recent years as the amount of available data has exploded. With the rise of big data, businesses and organizations have found themselves with a wealth of information that they can use to gain insights into their operations, customers, and markets. Data mining projects are a key way to harness the power of this data and turn it into actionable insights.

In this article at OpenGenus, we will explore some of the most interesting and innovative data mining project ideas that have been undertaken in recent years. These projects demonstrate the power of data mining to uncover insights and drive real-world outcomes. From predicting disease outbreaks to identifying fraudulent behavior, data mining has the potential to transform the way we do business and solve some of the world's most pressing problems.

These projects are a strong addition to the portfolio of Machine Learning Engineer.

List of Data Mining projects:

  1. Fraud detection in credit card transactions
  2. Predicting customer churn in telecommunications
  3. Predicting stock prices using financial news articles
  4. Predicting customer lifetime value in retail
  5. Banking credit defaulter identification
  6. Personalized product recommendations in e-commerce
  7. Detecting fictitious insurance claims
  8. Social media post sentiment analysis
  9. Traffic prediction using sensor data
  10. Predicting customer preferences in hospitality
  11. Predicting diabetes risk using patient data
  12. Estimating customer lifetime value
  13. Email classification
  14. Movie prediction
  15. Customer segmentation in retail
  16. Predicting house prices
  17. Healthcare fraud detection
  18. Recommending movies to users
  19. Predicting student performance
  20. Finding creditworthy borrowers
  21. Forecasting flight delays
  22. Healthcare insurance claim fraud detection
  23. Recommending products to users based on their browsing history
  24. Predicting customer churn in subscription services
  25. Identification of potentially fraudulent transactions in banking
  26. Predicting employee attrition
  27. Recommending products to users
  28. Detecting cyberattacks
  29. Forecasting weather patterns
  30. Identifying fake news

Let's see each one of them one by one :

Fraud detection in credit card transactions

The objective of fraud detection in credit card transactions is to separate out fraudulent from legitimate transactions. By examining transaction patterns and metadata, as well as supervised learning algorithms like logistic regression or random forests, this can be accomplished.

  • Project title: Fraud detection in credit card transactions
  • Dataset used: European credit card holders consisting of rows of transactions made by credit cards. The total number of transactions captured were 500,000 and the number of features captured were 320.
  • Difficulty level: 4
  • Concepts involved: Data Cleaning, Memory Reduction, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/mathiasjess/Credit_Card_Fraud.git

fraud

Predicting customer churn in telecommunications

The goal of telecom customer churn forecasting is to identify which customers are most likely to leave a telecom company and why. Data on usage patterns, demographics, and customer support interactions can be used to achieve this, along with machine learning tools like decision trees and neural networks.

  • Project title: Predicting customer churn in telecommunications
  • Dataset used: List of people leaving a organization
  • Difficulty level: 3
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, Decision tree
  • Source code: https://github.com/Nikitasinha17/Telco-Customer-Churn-Prediction-.git

Predicting stock prices using financial news articles

Using financial news articles to forecast stock prices: The objective is to create a model that can assess news articles and forecast their effects on stock prices. This can be done by applying time series forecasting techniques like ARIMA or LSTM and using natural language processing (NLP) techniques to extract pertinent information from news articles.

  • Project title: Predicting stock prices using financial news articles
  • Dataset used: contain the twitter feed from companies
  • Difficulty level: 4
  • Concepts involved: Data Cleaning, Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, Decision tree, Sentiment Analyzer
  • Source code: https://github.com/TapasSenapati/StockPrediction.git

Predicting customer lifetime value in retail

Estimating the anticipated revenue that a customer will generate over the course of their relationship with a retail company is the goal of customer lifetime value prediction in retail. RFM (recency, frequency, monetary) analysis, demographic data, and historical transaction data can all be used for this.

  • Project title: Predicting customer lifetime value in retail
  • Dataset used: contain data of customers from different companies
  • Difficulty level: 4
  • Concepts involved: Data Cleaning, Down Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, Decision tree, Sentiment Analyzer, Noise removing
  • Source code: https://github.com/mukulsinghal001/customer-lifetime-prediction-using-python.git

Banking credit defaulter identification

The objective is to identify which clients are likely to default on their loans. This can be achieved by applying machine learning techniques like logistic regression or decision trees, as well as data on previous loan applications and repayment histories, as well as socioeconomic and demographic factors.

  • Project title: Banking credit defaulter identification
  • Dataset used: data of credit card clients
  • Difficulty level: 5
  • Concepts involved: Data Cleaning, Down Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, Decision tree, Sentiment Analyzer, Noise removing
  • Source code: https://github.com/MaxineTan/DataMiningProject.git

Personalized product recommendations in e-commerce

The goal of personalized product recommendations in e-commerce is to give customers recommendations based on their browsing and purchasing patterns. By examining product descriptions and reviews, collaborative filtering algorithms and NLP techniques can accomplish this.

  • Project title: Personalized product recommendations in e-commerce
  • Dataset used: data of credit card clients
  • Difficulty level: 2
  • Concepts involved: Pre-processing, data clean up, noise remove
  • Source code: https://github.com/alanramponi/recommEngine.git

Detecting fictitious insurance claims

The objective is to spot fictitious or suspicious insurance claims. This can be accomplished by examining patterns in historical fraud cases and claims data, as well as by using supervised learning algorithms.

  • Project title: Detecting fictitious insurance claims
  • Dataset used: data of insurance claiming clients
  • Difficulty level: 2
  • Concepts involved: Pre-processing, data clean up, noise remove, analyzing data
  • Source code: https://github.com/rakiiibul/auto_insurance_fraud.git

Social media post sentiment analysis

The goal is to examine posts on social media and categorize them according to sentiment (positive, negative, or neutral). NLP methods like sentiment analysis and machine learning algorithms like SVM or Naive Bayes can be used for this.

  • Project title: Social media post sentiment analysis
  • Dataset used: data of social media comments-Twitter
  • Difficulty level: 4
  • Concepts involved: Preprocessing and Cleaning, data clean up, noise remove, analyzing data, Story Generation and Visualization from Tweets
  • Source code: https://github.com/sharmaroshan/Twitter-Sentiment-Analysis.git

Traffic prediction using sensor data

The objective of traffic prediction using sensor data is to foresee traffic patterns and levels of congestion on roads and highways. Using sensor data from GPS devices and traffic cameras, as well as machine learning techniques like time series forecasting or clustering, this can be accomplished.

Predicting customer preferences in hospitality

The goal of customer preference forecasting in the hospitality industry is to identify the features and services that guests are most likely to seek out in a hotel or resort. Demographic information, historical reservation and review information, and machine learning methods like clustering or decision trees can all be used for this.

Predicting diabetes risk using patient data

The objective is to identify patients who are at risk of developing diabetes in the future. Diabetes risk prediction using patient data. Using patient information like BMI, blood sugar levels, and family history, as well as machine learning techniques like logistic regression or decision trees, this can be accomplished.

diabetes

Estimating customer lifetime value

The objective is to forecast the anticipated revenue that a client will produce over the course of their relationship with an insurance provider. RFM analysis, demographic data, and historical claim data can all be used for this.

  • Project title: Estimating customer lifetime value
  • Dataset used: Customer lifetime evaluation data
  • Difficulty level: 5
  • Concepts involved: Data Cleaning, Down Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, Decision tree, Sentiment Analyzer, Noise removing
  • Source code: https://github.com/sanjay-rendu/data_mining_project.git

Email classification

The objective is to categorize emails as spam or not. NLP methods like text classification and machine learning algorithms like SVM or Naive Bayes can be used for this.

  • Project title: Email classification
  • Dataset used: all received email data
  • Difficulty level: 3
  • Concepts involved: Data Cleaning, Down Sampling, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/iamdooboy/Data-Mining.git

spam-filter

Movie prediction

Predicting which movies are likely to become hit and which are to be flop using ratings. Utilizing data on usage trends, demographics, and people interactions, as well as machine learning techniques like decision trees or neural networks, this can be accomplished.

  • Project title: Movie prediction
  • Dataset used: Other movies data(ratings and box office)
  • Difficulty level: 3
  • Concepts involved: Data Cleaning, Down Sampling, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/iaperez/DataMiningProject-Movie.git

Customer segmentation in retail

Predicting which movies are likely to become hit and which are to be flop using ratings. Utilizing data on usage trends, demographics, and people interactions, as well as machine learning techniques like decision trees or neural networks, this can be accomplished.

Predicting house prices

The objective is to create a model that can forecast a home's selling price based on attributes like size, location, and amenities. Regression methods like linear regression and decision trees can be used to accomplish this.

Healthcare fraud detection

The objective is to spot potentially fraudulent healthcare claims. This can be accomplished by examining patterns in historical fraud cases and claims data, as well as by using supervised learning algorithms.

  • Project title: Healthcare fraud detection
  • Dataset used: User's history of browsing, review history
  • Difficulty level: 4
  • Concepts involved: Preprocessing, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, decision tree, anomaly detection
  • Source code: https://github.com/Rainie-Hu/Fraud-Detection.git

Recommending movies to users

Providing users with personalized movie recommendations based on their viewing preferences and ratings is the goal of this feature. By examining movie descriptions and reviews, collaborative filtering algorithms and NLP techniques can accomplish this.

  • Project title: Recommending movies to users
  • Dataset used: User's history of browsing, review history
  • Difficulty level: 2
  • Concepts involved: Preprocessing, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/spChalk/Movie-Recommendation-System.git

Predicting student performance

The objective is to forecast a student's academic performance using their demographic information and prior grades. Machine learning methods like decision trees and regression can be used for this.

Finding creditworthy borrowers

The objective is to identify the loan applicants who have the highest likelihood of repaying their loans. This can be accomplished by examining historical loan application and repayment data as well as supervised learning algorithms like logistic regression or random forests.

Forecasting flight delays

Based on past experience and outside variables like weather, the aim is to forecast the likelihood that a flight will be delayed. Machine learning methods like decision trees or neural networks can be used to accomplish this.

  • Project title: Forecasting flight delays
  • Dataset used: Flight data (Arrival & departure)
  • Difficulty level: 2
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/Fukeng/Flight-delay-forecast.git

Healthcare insurance claim fraud detection

The goal is to spot erroneous or suspicious healthcare insurance claims. This can be accomplished by examining patterns in historical fraud cases and claims data, as well as by using supervised learning algorithms.

  • Project title: Healthcare insurance claim fraud detection
  • Dataset used: Healthcare insurance data of customers
  • Difficulty level: 3
  • Concepts involved: Preprocessing, analyzing data, noise detection, removing duplicates, data cleaning
  • Source code: https://github.com/rakiiibul/auto_insurance_fraud.git

Recommending products to users based on their browsing history

Users will receive personalized product recommendations based on their browsing history and preferences. Recommending products to users based on their browsing history. By examining product descriptions and reviews, collaborative filtering algorithms and NLP techniques can accomplish this.

  • Project title: Recommending products to users based on their browsing history
  • Dataset used: browser history data of customers
  • Difficulty level: 2
  • Concepts involved: Preprocessing, analyzing data, removing duplicates
  • Source code: https://github.com/zhtea/chrome_mining.git

Predicting customer churn in subscription services

Identifying subscribers who are likely to churn (cancel their subscription) is the goal of customer churn prediction in subscription services. Using data on usage patterns, demographics, and customer support interactions, as well as machine learning methods like decision trees or neural networks, this can be accomplished.

  • Project title: Predicting customer churn in subscription services
  • Dataset used: Customer usage pattern data
  • Difficulty level: 4
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, decision tree, anomaly detection, filtering
  • Source code: https://github.com/jason-learn/Churn-Prediction-Challenge.git

Identification of potentially fraudulent transactions in banking

The objective is to locate transactions. This can be done by examining transaction patterns and metadata, as well as supervised learning algorithms.

  • Project title: Identification of potentially fraudulent transactions in banking
  • Dataset used: Bank transactions
  • Difficulty level: 5
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, decision tree, anomaly detection, NLP, filtering, examine patterns
  • Source code: https://github.com/jackyhuynh/Realtime_Fraud_Transaction_Detection.git

Predicting employee attrition

Based on their performance, tenure, and other factors, the goal is to identify the employees who are most likely to leave a company. Machine learning methods like logistic regression and decision trees can be used to accomplish this.

  • Project title: Predicting employee attrition
  • Dataset used: Employee data
  • Difficulty level: 3
  • Concepts involved: Data Cleaning, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/SharonLiXX/Data-mining.git

Recommending products to users

Users will receive personalized product recommendations based on their social media activity and preferences. Recommending products to users based on their social media activity. Collaborative filtering algorithms and NLP techniques for social media post analysis can be used to accomplish this.

  • Project title: Recommending products to users
  • Dataset used: List of social media activity of users
  • Difficulty level: 5
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, decision tree, anomaly detection, NLP, filtering
  • Source code: https://github.com/alanramponi/recommEngine.git

Detecting cyberattacks

By examining network activity and patterns, it is possible to identify cyberattacks in real time. Machine learning methods like clustering and anomaly detection can be used to accomplish this.

  • Project title: Detecting cyberattacks
  • Dataset used: List of network activities in certain time period
  • Difficulty level: 4
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection, decision tree, anomaly detection
  • Source code: https://github.com/scusec/Data-Mining-for-Cybersecurity.git

Forecasting weather patterns

Predicting weather patterns like temperature, precipitation, and wind speed is the objective. Regression and time series forecasting are two examples of machine learning techniques that can be used to accomplish this.

  • Project title: Forecasting weather patterns
  • Dataset used: Weather of different area
  • Difficulty level: 3
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/lawrensiya/Project-Tenki.git

weather

Identifying fake news

The aim is to detect fake news articles by analyzing their content and metadata. This can be achieved using NLP techniques such as sentiment analysis and machine learning algorithms such as SVM or Naive Bayes.

  • Project title: Identifying fake news
  • Dataset used: List of news
  • Difficulty level: 2
  • Concepts involved: Data Cleaning, Under Sampling, Dimensionality Reduction, Feature Selection, Outlier detection
  • Source code: https://github.com/pmacinec/fake-news-datasets.gitw

With this article at OpenGenus, you must have a strong idea of Data Mining project ideas.

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30 Data Mining Projects [with source code]
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