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🔎Data Understanding, Visualization , Preparation & Cleaning - Clustering algorithms (unsupervised learning) - Classification algorithms (supervised learning) - Sequential Pattern Mining


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Data mining project.

Final project for the Data Mining Course A.Y. 2020/2021 @ University of Pisa The project consists in data analysis based on the use of data mining tools.

Learning Goals

  • Fundamental concepts of data knowledge and discovery.
  • Data understanding
  • Data preparation
  • Classification & Regression
  • Pattern Mining and Association Rules
  • Outlier Detection
  • Time Series Analysis
  • Sequential Pattern Mining
  • Ethical Issues

Further info:

Final grade: 30/30

Project Description

Task 1 data understanding and preparation.

Explore the dataset with analytical tools and describe data semantics, assessing data quality, the distribution of the variables and correlations. Improve the quality of your data and prepare it by extracting new interesting features to describe the customer profile and his purchasing behavior. Defines additional indicators for the construction of a customer profile that can lead to an interesting analysis of customer segmentation.

Explore the new set of features for a statistical analysis (distributions, outliers, visualizations, correlations).

  • Data semantics
  • Distribution of the variables and statistics
  • Assessing data quality (duplictates, missing values, outliers)
  • Variables transformations & generation
  • Pairwise correlations and eventual elimination of redundant variables

Task 2: Clustering analysis

Based on the customer’s profile, explore the dataset using various clustering techniques. Carefully describe your decisions for each algorithm and which are the advantages provided by the different approaches.


High-correlated features elimination and Normalization

Clustering Analysis by K-means

  • Identification of the best value of k
  • Characterization of the obtained clusters by using both analysis of the k centroids and comparison of the distribution of variables within the clusters and that in the whole dataset
  • Evaluation of the clustering results

Analysis by density-based clustering

  • Parametr tuning
  • Characterization and interpretation of the obtained clusters

Analysis by hierarchical clustering

  • Compare different clustering results got by using different version of the algorithm
  • Find the optimal cut
  • Show and discuss different dendrograms using different algorithms


  • Final evaluation of the best clustering approach and comparison of the clustering obtained

Optional task for clustering:

Explore the opportunity to use alternative clustering techniques in the library:

Task 3: Predictive Analysis

Consider the problem of predicting for each customer a label that defines if (s)he is a high-spending customer, medium-spending customer or low-spending customer.

  • Define a customer profile that enables the above customer classification, reasoning also on the suitability of the customer profile, defined for the clustering analysis.
  • Compute the label for any customer. Note that, the class to be predicted must be nominal.
  • Perform the predictive analysis comparing the performance of different models discussing the results and discussing the possible preprocessing that they applied to the data for managing possible problems identified that can make the prediction hard. The evaluation should be performed on both training and test set.

Task 4: Sequential Pattern Mining

Consider the problem of mining frequent sequential patterns. To address the task:

  • Model the customer as a sequence of baskets
  • Apply the sequential pattern mining algorithm
  • Discuss the resulting patterns

Optional Task:

Eextend the algorithm and analysis considering one or more temporal constraints.

Contributors 2

  • Jupyter Notebook 100.0%

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Data Mining

Learn Data Mining from basics in this free online training. This free Data Mining course is taught hands-on by experts. Learn about Data Description, Data Manipulation, Skewness & a lot more. Best for Beginners. Start now!

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What you learn in Data Mining ?

About this free certificate course.

This Data Mining course will introduce you to prominent Data Mining concepts. The course begins by introducing you to data description concepts. You will understand the basics of data, data manipulation, and skewness using histograms in the first half of the course. You will then learn to visualize outliers using boxplots, correlation using scatter plots, and understand what machine learning is. You will also understand regression analysis, multiple linear regression, and logistic regression, with demonstrated examples in the latter part of this course. There is an assessment to evaluate your knowledge at the end of the course. Complete the course for free and avail your certificate. You can also study the attached materials for reference.   

After this free, self-paced, intermediate-level guide to Data Mining, you can enroll in the Data Science course and embark on your career with the professional Post Graduate certificate. Learn various concepts in depth with millions of aspirants across the globe!

  • Course Outline

You will learn mathematics concepts for data mining tasks such as statistics, its types, population, parameter, sample, mean, median, mode, normal distribution, interquartile range of IQR, and its upper and lower limits. This section comprehends a demonstration of the outlier concept at the end of the course for your better understanding.

You shall understand data and learn to infer insights from the datasets using the diabetes dataset in this section.

This section explains how to work with or manipulate the data with different methods in a given set to extract a particular range of values. You will also understand how a dataset not showing accurate data can be recognized and be replaced with the median since it does not get affected by outliers.

You shall understand the outlier concept in-depth in this section. You will learn to detect and impute outliers and understand their working later in this section. You will also learn to infer/express data using the histogram. 

You will learn to express missing data and express data in box plots for simple representation and also understand outlier analysis concepts in this section. 

You will learn to represent correlation with different methods and scatter plots or heat maps using automobile dataset to perform exploratory data analysis in this section. 

This module begins by defining machine learning. It then discusses how a machine understands the tasks with examples and explains supervised and unsupervised learning concepts in machine learning. 

This section shall define regression, brief different types of regression, and then explain what regression analysis is in machine learning. You will learn to work with regression analysis to understand the data better. 

This section shall explain simple linear regression. You will learn to import classes and packages and work with Google Colaboratory to understand linear regression better. 

 You will understand the concept of multiple data points, to begin with in this section and then learn to work with multiple linear regression with NumPy.

You will learn to work with the dataset by understanding a project on Salary Prediction. You will also learn to work with NumPy, Pandas, and Matplotlib library for the project. 

You will learn a supervised learning technique to classify the data based on the classifying points and logistic regression in this section. 

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Frequently Asked Questions

What are the prerequisites required to learn the Data Mining course?

Data Mining is an intermediate-level course. Before you begin with this course, you will have to do a little homework on data science if you do not have a thorough understanding of it.

How long does it take to complete this free Data Mining certificate course?

 This free certificate course is 2.5-hours long. You can learn Data Mining concepts and work at your convenience to understand the subject since the course is self-paced. 

Will I have lifetime access to this free course?

Yes, once you enroll in this Data Mining course, you will have lifetime access to this Great Learning Academy's free course. You can log in and learn at your leisure.

What are my next learning options after I complete Data Mining?

Once you complete this free course, you can follow up with the data mining process and data mining applications, or you can also opt for a Master's in Data Science that will aid in advancing your career growth in this leading field

Is it worth learning Data Mining?

Yes, it is beneficial to learn Data Mining. Different techniques are used to understand data to work on projects and building models. These techniques contribute to analyzing data through identifying patterns and relationships to solve business problems. 

What are Data Mining tools used for?

Data mining tools help data miners discover patterns, trends, and groupings within a huge dataset and transform the data into more structured information. Popular data mining tools include MonkeyLearn, RapidMiner, Oracle Data Mining, and IBM SPSS Modeler. 

Why is Data Mining so popular?

Data mining is a simple technique that opens data science, artificial intelligence, and machine learning professionals to business opportunities since it can be leveraged for predictive and descriptive abilities. The predictive and descriptive capabilities of data mining can predict the future trend and also heighten profits. 

Will I get a certificate after completing this free Data Mining course?

Yes, you will get a certificate of completion for the Data Mining course after completing all the modules and cracking the assessment/quiz. All the assessment tests your knowledge and badges your skills.

What knowledge and skills will I gain upon completing this course?

You will understand data manipulation, skewness, visualization, machine learning, and regression analysis concepts. You will learn to work with different representation techniques for visualization purposes and be able to work on projects efficiently after you have completed this data mining course.

How much does this Data Mining course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning data mining techniques for data science, big data, artificial intelligence, and machine learning concepts can get started with this course. You can also refer to the attached materials for additional knowledge. 

Is there any limit on how many times I can take this free course?

Once you enroll in the Data Mining course, you have lifetime access to it. So, you can log in anytime and learn it for free online at your convenience. 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no stricture to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject. 

Why choose Great Learning Academy for this Data Mining course?

Great Learning is a global educational technology platform committed to developing skilled professionals. Great Learning Academy is a Great Learning project that provides free online courses to assist people in succeeding in their careers. Great Learning Academy's free online courses have helped over 4 million students from 140 countries. It's a one-stop destination for all of a student's needs.

This course is free and self-paced, but it also includes solved problems, demonstrated codes, sample projects, and presented examples to help you comprehend the numerous areas that fall under the subject and awards you a certificate to showcase your skills. The course is conducted by topic experts and is carefully tailored to cater to both beginners and professionals.

Who is eligible to take this course?

Anybody interested in learning the Data Mining concepts and techniques for Data Science and AIML can take up the course. So, enroll in our Data Mining course today and learn it for free online.

What are the steps to enroll in this Data Mining course?

Enrolling in Great Learning Academy's Data Mining course is a simple and straightforward approach. You will have to sign-up with your E-Mail ID, enter your user details, and then you can start learning at your own pace. 

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Data mining course.

Data Mining, alternatively known as Knowledge Discovery in Database(KDD), is considered the most beneficial technique that helps researchers, entrepreneurs, and individuals to extract valuable insights from the collected dataset. It includes processes like Data cleaning, Data cleaning, integration, selection, transformation, mining, pattern evaluation, and knowledge presentation. This technique, where we extract the information to identify the trends, patterns, and helpful information that allows businesses to make data-driven decisions through Data Mining, is proven to be advantageous for their growth.

Data Mining can also be considered as a type of investigation where we try to find the hidden patterns and information from various categories of data collected. These data are stored in particular areas like data warehouses. Its efficient analysis and data mining algorithms help in decision-making and other helpful information, resulting in cost-cutting and generating revenue. Data Mining has advanced techniques to find the patterns and trends from the storage of large amounts of data. It utilizes complex mathematical algorithms to evaluate large data for future trend predictions. Many Businesses use Data Mining techniques to extract specific data from vast data storage.

Data Mining is similar to Data Science. The Data Mining process includes different types of services like text mining, video mining, web mining, social media mining, and pictorial data mining. Softwares are used to achieve Data Mining more efficiently. There are many high-end software through which you can locate data that is hard to find manually. Through Data Mining, you can predict your Business goals, identify data, prepare data, model and evaluate data, and present the data. You can perform Data Mining on various types of data like Relational Database. It is a collection of multiple data sets organized by tables, columns, and records. You can extract the required data and access the information you are looking for from the databases through these databases. 

When you search the required data from the databases, tables convey and share the information that facilitates the data searching, reporting, and organization. Data Warehouse is also a type of data that helps in Data Mining. A data warehouse in a business is a technology that collects all the relevant data and provides valuable insights. Marketing and financial domains offer a lot of data from multiple places that can be stored in Data Warehouses. These extracted data are analyzed and are utilized for decision-making for businesses. Data Warehouses are designed for data analysis purposes. Many organizations are making use of Data Warehouses for data storage and analysis.

There are many advantages of Data Mining. Its techniques enable businesses to get knowledge-based information, and it also helps them make lucrative changes in the operation and production processes. Data Mining is more cost-efficient than other statistical applications. It also allows businesses to make crucial decisions regarding their growth. It helps them to uncover various patterns, trends, and behaviors. Data Mining can be done and implemented on the new system or the existing ones. Data Mining is quicker than other methods and helps you analyze enormous data sets in a shorter period. Data Mining is considered an excellent technique to analyze and manipulate data.

You can find many Data Mining applications in health care, education, fraud detection , CRM, manufacturing engineering, financial banking, lie detection, market basket analysis, and more. Data Mining is very useful for businesses with intense consumer demands such as retail, financial, communication, marketing agencies, sales, etc. It also helps the companies predict what products the customers need and their preferences. Data has the power to determine future events and to uncover them Data Mining plays an important role. It helps many organizations realize the method to develop and promote their data in favor of customers, which in return brings them revenue.

Data Mining also faces some challenges during its implementation. These challenges can be related to the techniques, data, methods, performance, etc. Data Mining is effective if you tackle all these challenges that arise during its execution appropriately. It must be correct and complete to get useful insights from the collected data. Sometimes these incomplete and noisy data create havoc while Data Mining as you will be dealing with a large amount of data. These problems may also occur because of human mistakes or the data measuring instruments. Sometimes these large data can be inaccurate and unreliable. It is difficult to collect data from customers who are unwilling to provide their information, making data incomplete.

To enter Data Mining in-depth, you must first get a brief introduction to Data Mining. You should go through various Data Mining examples to understand its mechanism. It will also help you face the challenges that come during its execution. These challenges may be due to incomplete and noisy data, data distribution, complex data, performance, data privacy and security, data visualization, and more. Data Mining involves refined data analysis tools, and it helps you find previously unknown patterns and relations in the vast data sets. The tools get help from statistical models, mathematical algorithms, and Machine Learning techniques like neural networks or decision trees to analyze these data. Hence, Data Mining involves analysis and prediction.

With the help of advanced statistics, mathematics, and Machine Learning techniques, Data Mining has become more effective and efficient. It involves Machine Learning, database management, and statistics, and professionals aim to understand these techniques and how to process them in their favor. Many have made Data Mining their career, and it is also in demand as we live in a data-driven world. From the recent Data Mining projects , developers have come across various Data Mining techniques like Classification, Clustering, Regression, Outer, Sequential Patterns, Prediction, and Association Rules. All these Data Mining techniques make Data Mining more efficient and effective, and it helps professionals improve Data Mining performances. 

To learn all the types and techniques of Data Mining, enroll in a free Data Mining course offered by Great Learning. Register in this course and get in-depth knowledge of Data Mining and its mechanism. Complete the course to get free Data Mining certificate and grab more job opportunities.

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University of Illinois at Urbana-Champaign

Data Mining Project

This course is part of Data Mining Specialization

Taught in English

Some content may not be translated

Jiawei Han

Instructors: Jiawei Han +2 more


Financial aid available

7,093 already enrolled

Coursera Plus

(44 reviews)

Skills you'll gain

  • Data Clustering Algorithms
  • Data Analysis
  • Natural Language Processing
  • Data Mining

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There are 7 modules in this course

Note: You should complete all the other courses in this Specialization before beginning this course.

This six-week long Project course of the Data Mining Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization, including Pattern Discovery, Clustering, Text Retrieval, Text Mining, and Visualization, to solve interesting real-world data mining challenges. Specifically, you will work on a restaurant review data set from Yelp and use all the knowledge and skills you’ve learned from the previous courses to mine this data set to discover interesting and useful knowledge. The design of the Project emphasizes: 1) simulating the workflow of a data miner in a real job setting; 2) integrating different mining techniques covered in multiple individual courses; 3) experimenting with different ways to solve a problem to deepen your understanding of techniques; and 4) allowing you to propose and explore your own ideas creatively. The goal of the Project is to analyze and mine a large Yelp review data set to discover useful knowledge to help people make decisions in dining. The project will include the following outputs: 1. Opinion visualization: explore and visualize the review content to understand what people have said in those reviews. 2. Cuisine map construction: mine the data set to understand the landscape of different types of cuisines and their similarities. 3. Discovery of popular dishes for a cuisine: mine the data set to discover the common/popular dishes of a particular cuisine. 4. Recommendation of restaurants to help people decide where to dine: mine the data set to rank restaurants for a specific dish and predict the hygiene condition of a restaurant. From the perspective of users, a cuisine map can help them understand what cuisines are there and see the big picture of all kinds of cuisines and their relations. Once they decide what cuisine to try, they would be interested in knowing what the popular dishes of that cuisine are and decide what dishes to have. Finally, they will need to choose a restaurant. Thus, recommending restaurants based on a particular dish would be useful. Moreover, predicting the hygiene condition of a restaurant would also be helpful. By working on these tasks, you will gain experience with a typical workflow in data mining that includes data preprocessing, data exploration, data analysis, improvement of analysis methods, and presentation of results. You will have an opportunity to combine multiple algorithms from different courses to complete a relatively complicated mining task and experiment with different ways to solve a problem to understand the best way to solve it. We will suggest specific approaches, but you are highly encouraged to explore your own ideas since open exploration is, by design, a goal of the Project. You are required to submit a brief report for each of the tasks for peer grading. A final consolidated report is also required, which will be peer-graded.


In this module, you will become familiar with the course, your instructor, your classmates, and our learning environment.

What's included

1 video 6 readings 1 discussion prompt 1 plugin

1 video • Total 13 minutes

  • Welcome to the Data Mining Project! • 13 minutes • Preview module

6 readings • Total 60 minutes

  • Orientation Overview • 10 minutes
  • Syllabus • 10 minutes
  • About the Discussion Forums • 10 minutes
  • Updating Your Profile • 10 minutes
  • MeTA Installation and Overview • 10 minutes
  • Data Set and Toolkit Acquisition • 10 minutes

1 discussion prompt • Total 60 minutes

  • Getting to Know Your Classmates • 60 minutes

1 plugin • Total 15 minutes

  • Welcome! Please tell us about yourself. • 15 minutes

Task 1 - Exploration of a Data Set

Task 2 - cuisine clustering and map construction, task 3 - dish recognition, task 4 & 5 - popular dishes and restaurant recommendation, final report.

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The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.

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14 Data Mining Projects With Source Code

Introduction, what is data mining, data mining projects for beginners, 1. housing price predictions, 2. smart health disease prediction using naive bayes, 3. online fake logo detection system, 4. color detection,  5. product and price comparing tool , data mining projects for intermediate, 6. handwritten digit recognition, 7. anime recommendation system, 8. mushroom classification project, 9. evaluating and analyzing global terrorism data , data mining projects for advanced, 10. image caption generator project, 11. movie recommendation system, 12. breast cancer detection, 13. solar power generation forecaster, 14. prediction of adult income based on census data, why are data mining projects so important, additional resources.

In today’s digital era, data has become the most important tool. All the computing processes right from the inception of collecting, tidying, analyzing, and finally interpreting it according to the business strategies is done on data. Every second, billions of data is generated to understand customers’ necessity for new offers, analysis of market risks and much more. With technological advancement, businesses and firms tend to follow data mining programs to develop all the future schemes.

The process of extracting the most useful information from lots of data to quickly identify all the present trends and patterns for businesses and huge firms to understand customers and make out important decisions is called Data Mining. In simple terminology, data mining is a way to recognize hidden patterns from the extracted information of the data required for the business with the help of data wrangling techniques to categorize important data stored in proper data warehouses with the help of data mining algorithms to generate maximum revenue for a business. Data mining, also known as knowledge discovery of data (KDD), uses highly complex mathematical algorithms for segregating data to evaluate the probability of the future decisions for the company’s business.

If you are planning to build your career in data mining, regardless of the fact that you are a student or a professional data analyst, it is always beneficial to have some outstanding data mining project ideas on hand. Not only building projects on data mining will help in building a strong portfolio, but also it will enhance skills.  

Confused about your next job?

Undeniably, data mining is an amazing career option and for that, following are outstanding data mining project ideas for beginners, intermediate and advanced students along with source code for additional help.

Let’s look at some data mining project examples for beginners.

In this data mining project, a housing dataset is used which includes all the prices of the different houses. In this project, the dataset for prediction of price is added along with location, size of the house, and additional information required for it. Depending on the level of sophistication, you can follow a predictive model with simple techniques such as regressions or machine learning libraries. The application of this project is in the real estate companies. This project utilizes algorithms and techniques for price predictions of the houses based on different housing datasets. Either you can carry out linear regression with a data analytics tool such as Tableau or Excel, or you can choose a machine learning library along with programming language “R” or Python.

Source Code: Housing Price Predictions  

Nowadays, medical care is something that anyone might need immediately, but unavailable due to various reasons. The smart health disease prediction is an end user support system that allows users to get guidance immediately with the help of an online intelligent health system. The system holds complete information about symptoms and the diseases associated with it. The system analyzes diseases associated with the symptoms for the patient and advises them for X-ray, blood test or CT scan as requested by the system. Users can also directly get in touch with the specialist doctors for any ailment and share your reports. It is not just one time, rather a proper login detail is shared for future use. 

Source Code –  Smart Health Disease Prediction

Each year, thousands of brands lose a huge portion of the sales due to unauthorized knock off brands and their counterfeits. These counterfeit products are made up of inferior quality and hence damage the credibility of the brand. Moreover, consumers feel cheated with their hard-earned money while shelling it out for just a mere counterfeit. Online fake logo detection system will distinguish between original product and forgeries for the consumers. Along with helping users to fight against the forged products, it also helps brands to combat piracy.

There are around 16 million colors according to different RGB color values, but a human mind can only remember quite a few. It is common that after seeing the color, you are still not able to name the color. In this data mining project, you are going to build an amazing app which is going to help in recognizing color from any image. All you need is a labeled data of available colors and then the program runs to evaluate which color resembles most with the selected color value and helps in detecting colors easily. You can use the Python programming language in which Codebrainz Color Names dataset will be used for the project.

Source Code: Color Detection  

With the increase in popularity of e-commerce portals, shopping websites are magnifying to a great extent to enable online shoppers to purchase anything with just one click and get it delivered at your doorstep. To purchase an item, people tend to spend quite a lot of time in searching a product and comparing it with other websites by themselves. In this project, you can compare product and price of a product to buy cheap and best deal available. Also, it will track consumer demand and inform when the commodity price is lowest and notify consumers proactively. 

Source Code: Price Comparing tool

Let’s look at some data mining project examples for intermediates.

One of the best data mining projects is the Handwritten Digit recognition project among the data scientists and all the machine learning enthusiasts. In this project, machine learning algorithms are used to distinguish and classify images of the digits written by hand. With the help of computer vision AI model, machine learning techniques and Convolutional Neural Networks, this project can be created which will have a nice graphical user interface to write or draw on the canvas and for the output a model is good to predict the digit. Python and R, both are good languages for this project. Python’s Scikit-learn model using algorithms such as K-Nearest Neighbors and a Support Vector Classifier will be apt for the project.

Source Code:  Handwritten Digit recognition  

Looking out for  data mining projects with source code?  The Anime Recommendation system is one of the best projects as it includes a data set containing information regarding user preference from 73,516 users on 12,294 anime. Every user in the database will be able to add anime to the list and share ratings compiling a data set with those ratings. Anime recommendation system project helps in creating a system that produces efficient data based on the user viewing history and sharing rating.

Source Code:  Anime Recommendation System  

In this data mining project, details of the samples related to the 23 species of gilled mushrooms from the Lepiota and Agaricus Family of Mushrooms available in the Audubon Society Field Guide to North American Mushrooms (1981). Each mushroom variety is categorized as edible, poisonous, unknown edibility or not recommended. So, in this project you will be able to distinguish mushrooms from the respective group although there is no rule “leaflets three, let it be” to define if it is edible or not.

Source Code:   Mushroom Classification

Terrorism has mushroomed due to its deep roots at certain locations of the world. With increase in its activities, it is important to stop its spread or analyze the global terrorism data to identify the terrorist activities. Internet plays a major role in spreading terrorism by way of videos and speeches among youth to join the terrorist organizations. This project will help in detecting, evaluating, and analyzing global terrorism data and flag them for human review. Data mining helps in scanning and mining from all the unorganized and unstructured pages or data available that promotes terrorism and flag them. 

Source Code:  Evaluating and Analyzing Global Terrorism Data 

Let’s look at some data mining project examples for advanced learners.

In this interesting data mining project, image is an easy and memorable task for human beings, but for computers just a bunch of numbers for each pixel of color value. In this project, the most difficult task for the computer is to understand the image and then generate the description of it. If you are planning to go with Python programming language, Keras framework would be perfect with Flickr 8K data set.

Source Code – Image Caption Generator

Top-Notch companies such as Amazon or Netflix use this system to recommend their customers with the movies in their database. To design this movie recommendation project, you can choose any one approach out of two. First option is a content-based filter in which the system finds some similarity around different projects in terms of features or attributes that could be actor, genre or director of the movie. Another option is collaborative filtering that compares tastes of two accounts and suggests based on the user ratings. This system helps companies to engage their customers to the respective platforms. You can use MovieLens dataset if opting to go with the R programming language.

Source Code:  Movie Recommendation System  

Data mining projects hold a special place in medical contributions. In this project, breast cancer is detected using the Python programming language. In this IDC_regular dataset helps in detecting actual presence of the commonest form of breast cancer i.e., Invasive Ductal Carcinoma. In this form of cancer, it targets milk ducts invading the fibrous or fatty breast tissue outside the duct. If you want to build this project using Python language, you should use Keras library for classification and IDC_regular dataset.

Source Code:  Breast Cancer Detection  

With the help of extracted data from two solar power plants over a period of 34- days, two pairs of files are available. Each pair includes one power generation dataset, and another is sensor reading dataset. In the power generation dataset, each inverter extracts information which has several lines of solar panels connected to it. An array of sensors optimally located at the plant collects the sensor data. In this project, you will be able to get answers of the amount of power generated in a month, any faulty performing equipment in the plant or panel cleaning/ maintenance update.

In this project, the dataset is evaluated based on a transparent open box (TOB) network for data mining and predictions. It provides accurate information from the hourly data record from power generation dataset and sensor reading dataset.

The following project is the classification project to predict the income level of an individual that exceeds 50K based on the census data available at the repository. The dataset that is used in the projects are variables such as age, type of work, working hours, sex and many more. It helps in understanding the standard of living of the city, benefit of setting up the business or bank loan eligibility. Also, it helps in understanding the real estate preferences by average income of the people residing in the area. In this project, you will also be able to figure out the type of tourist places that people from other countries would like to travel.  

Source Code:  Adult Census Income Level Prediction

In this data-centric world, data mining projects hold great importance in everyday life. It provides us a reliable source of resolving tough problems and different issues in this challenging world. Some of the benefits are: –

  • With the help of new and legacy systems, data mining helps in making well-informed decisions.
  • It offers cost-effective solutions compared to other applications designed with other technologies.
  • It helps data scientists to deal with huge amounts of data and scrutinize the essential data out of it.
  • It makes businesses make profitable production and operational adjustments according to the demand.

To cut the long story short, data mining is the process of analyzing huge chunks of data to discover business intelligence which helps in solving problems, seizing new opportunities, and mitigating long term risks. The process of discovering useful patterns and relationships in large volumes of data helps in understanding a problem deeply and tactics to deal with it diligently. It is widely used in research, medical, business and security to turn large data into useful information. Get started from the above list of projects from beginner to advanced and sharpen your skills. These data mining projects with source code will help in learning new abilities.

How do you create a data mining project?

To create a data mining project, follow these steps

  • Understand business and project’s objective
  • Understand the problem deeply and collect data from proper sources.
  • Cluster the essential data to resolve the business problem.
  • Prepare the model using algorithms to ascertain data patterns.
  • Evaluate the data according to the business goal or to find a remedy for the problem.
  • Last, deploy the solution and get the results to make decisions.

What are the 3 types of data mining?

The 3 types of data mining are

  • Hypothesis testing
  • Directed data mining
  • Undirected data mining

What tools are used in data mining?

Top tools used in data mining are

  • Rapid Miner
  • Oracle Data Mining
  • IBM SPSS Modeler

  What are different tasks associated with data mining?

The following activities are performed for data mining.

  • Classification
  • Association Rule Discovery
  • Sequential Pattern Discovery
  • Deviation Detection

Data mining is a process of analyzing big data and creating business intelligence decisions. You can pick data mining projects to strengthen your skills and climb the success ladder. Whether you are a beginner, intermediate or advanced learner, this list will help you in proving your mettle.

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Data Mining: The Complete Guide

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Data analysts play a vital role in turning raw data into business insights. Top-level analysis sharpens this data, giving it life and importance for both decision makers and stakeholders. For this reason, data professionals seeking to expand their skills should learn about data mining and how to employ it in their work.

Data mining isn’t a new concept — businesses have used it for decades, in various forms, to uncover useful information in the ever-growing cloud of data businesses create. However, simply collecting more data doesn’t always produce sound decisions. In fact, too much data can paralyze decision-making, a challenge known as being “data rich, but information poor.” Data mining helps turn that challenge into possibility and, as a result, its importance only continues to grow.

In this article, we’ll provide a comprehensive overview of data mining, offering insight into a skill that can help advance your career in data science. Specifically, we will cover the following:

  • What is data mining and why is it important?
  • The 6 stages of the data mining process
  • The top data mining tools to master
  • The most commonly used data mining techniques
  • In-demand skills in data science

What is Data Mining and Why is it Important for Companies?

Data mining addresses the need to shape data into insight. It is the process of analyzing large amounts of data to discern trends, non-intuitive patterns, or even anomalies. Data miners apply a variety of tools and technologies to uncover these findings, and then use them to help businesses make better decisions and forecasts.

Companies derive benefit from data mining in many ways: anticipating demand for products, determining the best ways to incent customer purchases, assessing risk, protecting their business from fraud, and improving their marketing efforts.

Why Companies Are Eager to Use Data Mining

According to SAS, the term “data mining” emerged in the 1990s. The process is also known as “knowledge discovery of databases” and was performed manually before computer-processing power and other technologies made it faster and more efficient.

Every time someone swipes a credit card, clicks on a website, or scans a product in a checkout line, a point of data is created. Each of these data points remain dormant until they can be extracted, compiled, and compared to other points. Companies derive no benefit from data sitting inertly; they must interact with this data to harness the insights it contains, unlocking the value every global business finds that essential.

International Data Corporation (IDC) projects worldwide spending on business analytics and big data to reach $215.7 billion in 2021 and further forecasts spending to grow by 12.8% through 2025. In addition, MicroStrategy’s 2020 Global State of Analytics report found that 94 percent of business intelligence and analytics decision makers said data and analytics are important to growth — and more than half say they use data and analytics to drive process, cost efficiency, strategy, and change.

Line graph of projected worldwide spending on business analytics and big data

Data mining is central to this growth in data analytics, and many industries need employees versed in the process, including retail, finance and insurance, communications, healthcare, and many others. Some jobs in which data mining techniques could be important include data analyst, data scientist, software engineer, financial analyst, and business analyst.

Real-World Examples of Data Mining

Examples of data mining are everywhere. Retail companies rely heavily on data mining, especially those that offer reward cards and affinity memberships. Consumers who purchase a particular brand of shampoo, for instance, might receive coupons for other products that fit their personal shopping behavior or products that have similar consumer segments.

Those who shop or consume entertainment online have created a wealth of data to be mined. Surely you have received recommendations for movies to watch or shoes to buy based on your purchases, viewing habits, and web clicks. Your data, and that of billions of other consumers, is mined to generate these “recommended for you” pop-ups.

In addition, financial institutions use data mining to detect fraud, protecting them and their customers. And, healthcare providers are improving treatment methods based on data mining patterns distilled from patient studies and clinical trials.

Want to further your knowledge and gain in-demand skills in the field of data science in as little as 24 weeks? Learn how through Columbia Engineering Data Analytics Boot Camp .

The 6 Stages of the Data Mining Process

Data mining follows an industry-proven process known as CRISP-DM. The Cross-Industry Standard Process for Data Mining is a six-step approach that begins with defining a business objective and ends with deploying the completed data project.

Step 1: Business Understanding

Step 2: data understanding, step 3: data preparation.

Data mining projects begin with business understanding — with companies determining their objectives for a project. Which data does the company wish to study? What are the goals of that study? What problems does the project seek to solve, or what opportunity does it seek to pursue? This stage is essential to determine the right datasets to be analyzed. As a result, data analysts should have a clear understanding of their company’s mission, strategy, and objective needs.

With a stated objective, the data mining project moves to the next phase: defining the data. In this step, analysts gather data, describe it (the amount, whether it includes numbers and strings, how it’s coded, etc.), and verify its quality. Some key questions for this step: Are there any data gaps? Does the data contain errors? Are fields coded correctly? Is any data duplicated?

It’s important to note that not every data point a company stores will fit every project. Gathering the proper data will save time as well as ensure the quality and applicability of insights derived during the project.

Data preparation is often the most time-consuming step of a mining project. In fact, according to IBM, data preparation can consume 50-70% of a project’s time and effort. Data preparation involves selecting, cleaning, sorting, and formatting the data to be studied. In addition, data from multiple sources will need to be merged or adjusted, and new data may need to be constructed. Once the data has been thoroughly reviewed and prepared, it is ready to be studied.

Step 4: Modeling

Step 5: evaluation, step 6: deployment.

In the modeling stage, data analysts and scientists employ many types of modeling techniques (which we’ll explore later) to uncover insights. Perhaps they will run models to find patterns or anomalies. For example, they may run a predictive model to learn whether past data can determine a future outcome. Or, they may run association rule mining (via machine learning models) to discover non-intuitive patterns that provide valuable insights analysts didn’t even know were there. It’s important to realize that analysts often run multiple models on the same set of data, depending on the project’s goals and requirements.

In the evaluation stage, analysts assess whether results answer the business understanding questions properly, meet the project’s objectives, or uncover any unexpected patterns. They will also assess whether the correct models were used.

If the initial objective is unmet — or new questions arise — data analysts will return to the modeling phase. In addition the data may need to be adjusted as well. Once the data results answer the business understanding questions, the project reaches its final stage.

In the deployment stage, data analysts report their findings and recommend a plan to make those insights actionable. Perhaps the data mining project found that retail customers buy mayonnaise frequently when buying air freshener — a completely non-intuitive insight. With this information, the retailer can craft a marketing plan to take advantage of this insight from a promotional and floor plan perspective.

Which Data Mining Tools to Master

Now that you understand the CRISP-DM process, let’s cover some of the top data mining tools and technologies analysts use. Many tools are available, and those who work in data science and analytics likely are familiar with many of these.

Apache Spark

Ibm spss modeler.

Python consistently ranks among the world’s most-used and most-wanted programming languages , according to Stack Overflow. As an object-oriented language with an easy-to-learn syntax, Python has many uses. Developers create websites and games with Python, and AI programmers build training models with it. In addition, data scientists use Python frequently for data mining and analytics.

Python’s vast collection of mathematical and scientific libraries and modules help make the language a data mining powerhouse. Pandas, Numpy, and Matplotlib are just three of the libraries available that Python users employ in data mining projects. Python’s website lists a host of companies that rely on the language, including the HR platform Gusto. This business platform says Python’s databases “allow for quick and painless development of data mining tools.” If you’re interested, consider learning Python at a data analytics bootcamp .

R, like Python, is a popular language used in data analytics. R’s programming environment centers on “data manipulation, calculation, and graphical display” — all key elements of data mining.

Data analysts use R to perform several data mining techniques such as classification and clustering, as well as visualization of results. R, which is free and open-source, delivers more than 18,000 companion packages , including dozens that involve data mining.

Tableau is one of the world’s leading business intelligence platforms , according to Gartner, and companies use it widely to assess, analyze, and communicate data insights.

Tableau offers both free and paid versions of its platform, into which users can import data from simple spreadsheets or massive data warehouses. Tableau also gives users the ability to uncover data patterns or trends (a key pursuit of mining) and visualize their findings.

With Tableau, analysts aren’t required to learn how to use programming languages such as Python and R to perform a data mining project. Charles Schwab, Honeywell, Red Hat, and Whole Foods are among the many companies that use Tableau . And Tableau Public , the platform’s free online version, enables anyone to create data visualizations.

Aspiring data miners can learn to use Tableau for business intelligence at Columbia Engineering Data Analytics Boot Camp .

SAS, an analytics software company, offers multiple platforms for data mining that users with limited statistical or programming skills can employ. The SAS Enterprise Miner platform’s process flow addresses each step of the CRISP-DM process and is scalable from single users to large enterprises.

SAS also sells products for AI and machine learning, data management, cloud computing, and more. Users can access a range of training resources, even including some live classes.

Apache Hadoop is an open-source framework for storing and processing significant amounts of data. Those who work with big data understand the challenges of working with the scale, and types, of data generated. The Hadoop framework makes storing, accessing, and analyzing data faster and easier. Many corporations, such as Facebook, Chevron, eBay, and LinkedIn consider Hadoop integral to their data strategies.

Apache Spark , part of the Hadoop ecosystem, was developed to update Hadoop’s MapReduce function for processing data. According to InfoWorld, Spark has become a big player in the world of big data and machine learning.

Spark’s primary benefit is its speed — the platform can run Hadoop workloads much faster than in the conventional framework. Spark also includes libraries for working with Structured Query Language (SQL) in databases and machine learning, among others. More than 100 companies and organizations use Spark for their big data projects .

RapidMiner is a platform that automates many data analytics tasks. The RapidMiner Studio offers an API with various user-friendly features: a visual interface with drag-and-drop capabilities, a modeling library of more than 1,500 algorithms and functions, and templates for assessing customer churn, performing predictive analyses, and detecting fraud.

As with similar platforms, users can connect most data sources, including in-house databases, to RapidMiner and query data without writing complex SQL code. RapidMiner also provides tools for preparing and visualizing data,one of the most time-consuming components of data mining projects.

IBM’s SPSS Modeler is a visual data science and machine learning framework designed to help data scientists work more quickly. It employs more than 40 algorithms for data analysis, can be used with multiple data sources (including Hadoop and cloud-based environments), and integrates with Apache Spark.

The SPSS Modeler also integrates with programming languages such as Python and R, and has a large statistics library, as well as an extensive collection of videos and learning tutorials.

To advance your career, consider applying to Columbia Engineering Data Analytics Boot Camp to learn the latest technical skills in data science.

What Are the Most Commonly Used Data Mining Techniques?

Data scientists employ different ways to store and query data, as well as a variety of models to analyze it. The techniques and terminology are plentiful, and aspiring data analysts must be familiar with them.

Machine Learning

Data mining and machine learning share some characteristics in that both fall under the data science umbrella; however, they are important differences.

While data mining is the process of extracting information from data, machine learning is the process of teaching computers the process of data analysis. Specifically, data scientists develop algorithms to teach computers to perform many of the data mining processes that companies require; increasing both efficiency and the volume of analysis that can be completed.

Machine learning often is used as a component of data mining. Many companies use machine learning to perform multi-attribute segmentation analysis on their customer base. Streaming services, for example, can use machine learning to sift through users’ viewing habits and recommend new genres or programs they might like. The better the algorithm, the more accurate and detailed those recommendations can be.

Machine learning is one of the advanced topics of Columbia Engineering Data Analytics Boot Camp , which covers the technical and practical skills needed to pursue a career in data analytics.

Data Visualization

The best data mining projects can produce the sharpest and most useful insights. But if they remain static numbers on a page, they’re worthless to decision-makers.

Data visualization allows analysts to share their discoveries through charts, graphs, scatterplots, heat maps, spiral graphics, flow charts, and more. These visualizations can be static or interactive and, most importantly, they can effectively convey critical insights needed to make key business decisions.

In addition, several of the tools listed above offer visualization platforms, which means team members who cannot code can still create data visualizations; however, many data scientists learn HTML/CSS or JavaScript to boost their visualization skills.

Data visualization is a major part of Columbia Engineering Data Analytics Boot Camp — in fact, the fourth module is devoted to it. Learners go in-depth in visualization, which is key to making insights actionable.

Statistical Techniques

Data mining applies various statistical methods to analyze large data sets, and data mining platforms (such as those discussed above) can make data mining easier. However, learning data mining statistical techniques provides analysts with greater understanding of the work they do and how to do it more effectively.

Some statistical techniques include regression, classification, resampling (utilizing multiple samples from the same data set), and support-vector machines (an algorithmic subset of classification).

Statistical modeling and forecasting are key elements of the introductory module to Columbia Engineering Data Analytics Boot Camp .


Data analysts apply the association rule to find relationships in non-intuitive data patterns and understand what business value is associated with those patterns, if any.

Transaction analysis is a common form of association. Retailers scan an aggregation of many customers’ shopping trips, looking across many transactions to find patterns. While the analysis will highlight patterns you might expect to find (e.g., peanut butter and jelly, mayonnaise and bread), association also uncovers patterns that indicate non-intuitive relationships such as coffee creamer and air freshner. A deeper dive is then conducted on these identified associative patterns, and they are either validated and passed on as insights (e.g., the coffee creamer/air freshener pattern occurs due to seasonal items such as gingerbread creamer and balsam pine air freshener) or discarded as anomalies (e.g., coincidentally coinciding promotional schedules putting two items frequently on sale at the same time).


The classification technique looks at the attributes associated with a dataset where a certain outcome was common (e.g., customers who received and redeemed a certain discount). It then looks for those common attributes across a broader dataset to determine which data points are likely to mirror that outcome (e.g., which customers will be likely to redeem a certain discount if it is given to them). Classification models can help businesses budget more effectively, make better business decisions, and more accurately estimate return on investment (ROI).

Decision trees , a subset of machine learning, are algorithms used when running classification or regression models in data mining. The algorithm can ask simple yes or no questions of data points to classify them into groups and lead to helpful insights. For example, a decision tree may be used by financial institutions to pinpoint successful loan eligibility based on relevant categorical data like income threshold, account tenure, percentage of credit utilized, and credit score.

With clustering, data miners identify and create groups in a dataset based on similar characteristics. The process divides the data into subsets, or clusters, for analysis. Doing so provides for more informed decision-making based on targeted collections of data.

Analysts use several types of clustering techniques . They employ the partitioning method, for instance, to divide data into clusters to be analyzed separately. The K-Means algorithm is a popular method of partitional clustering. This algorithm works by first allowing the user to select a number of K-clusters to be used as centroids (or central points) or iterations through which the algorithm will run. Then, objects closest to these points are grouped to form “K number of clusters,” and with each iteration, the centroid distance for each cluster shifts and is updated accordingly. This process is repeated until there are no more changes in the centroid distance for each cluster — or until each iteration is fulfilled. A fun way to use the K-Means algorithm in partition clustering is to look for underutilized/undiscovered players when choosing a fantasy football team. The algorithm can use a superstar player’s stats as the centroids, and then run through iterations identifying clusters of attributes or players (by attribute).

Conversely, in the hierarchical method, individual data points are viewed as a single cluster, which then can be grouped based on their similarities. A dendrogram is one practical example of the hierarchical method; it is a tree-like network structure consisting of interconnected data points, or nodes, used to show taxonomic relationships. Dendrograms are a common visualization technique for displaying hierarchical clusters. In our fantasy football example, a dendrogram might be used to visualize the process by which we selected or passed on player choices, based on our evaluation and desired attributes.

Data Cleaning and Preparation

According to Forbes, one of the m ajor problems in data analytics is bad data. That’s why data cleaning and preparation are so important.

This process focuses on acquiring the right data and making sure it’s accurate and consistent. Errors, formatting differences, and unexpected null sets can inhibit the mining process.

Stages of data cleaning include verifying the data is properly formatted, deleting unnecessary or irrelevant data, removing duplicate sets, and correcting simple issues such as input errors. Even the best algorithm won’t work with incomplete or corrupted data.

Data Warehousing

Businesses that produce products need accessible, secure, organized locations in which to store them for distribution. The same applies to their data.

Businesses that create a significant amount of data must collect and store it properly to analyze it properly. Data warehousing is a three-stage process commonly known as ETL, which stands for extract, transform and load. Data is extracted from its source to a staging area, where it is transformed (or cleaned) and validated. Then it is loaded into the data warehouse.

Proper warehousing is vital for businesses that generate a large volume of data, particularly regarding customers. By properly storing all this data, businesses can mine it for patterns and trends more easily.

Outlier Detection

Most data mining techniques look for patterns in data. Outlier detection seeks to find instances that stand out as unique.

This process looks for data that conflicts with the rest of a set. This can include errors (perhaps some data was input incorrectly) or data that provides unique business insights. Analysts can test for numeric outliers, perform a DBScan (which identifies noise points), or isolate anomalies in a large dataset (an isolation forest).

Outlier detection can help businesses understand unique purchases (a run on bathing suits in winter, for instance), detect fraudulent transactions, and improve the logistical flow of the production process.

Prediction is a fundamental pursuit of data mining. Businesses use predictive modeling to answer the question, “What’s going to happen next?”

Predictive models find patterns in data, then use those patterns to create forecasts. The forecasts can include consumer spending habits, inventory needs for a supplier, sites people might visit based on their internet usage, or a baseball team’s projected strikeout rate against an upcoming pitcher.

Several types of predictive models are available. Forecast modeling seeks to answer a specific question. For example, how many SUVs should a car dealer have on the lot next month? Time-series modeling analyzes data based on its input date — such as product sales over a particular year that may assist in year-over-year sales forecasting.

Regression is used in data mining to analyze relationships between variables as part of the predictive modeling process. It can be used to project sales, profits, required product volume, weather data, and even patient recovery rates for medical providers. Analysts primarily employ two regression models. Linear regression estimates the relationship between two variables. For instance, a social researcher might study the relationship between a person’s home location and overall happiness, employing regression analysis to determine if there is a linear relationship between those two variables. Linear regression could also be used to predict housing prices in a real estate market where homes are generally increasing in size and structure. In this case, one variable (changes in home size and structure) is analyzed in relation to another (subsequent shifts in price).

Multiple regression, on the other hand, explains the relationship among multiple variables or data points. For example, when analyzing medical data points like blood pressure or cholesterol levels, analysts may use multiple regression models to explore related variables like height, age, and time spent on aerobic exercise in a given week.

In a regression model, decision trees can also be used to diagram results determining the probability of a specific outcome within two results. Consider this example: A company has a set of data that identifies customers as male or female and by their age. With a decision tree algorithm, it can ask a series of questions (“Is the customer a female?” and “Is the customer younger than 35?”) and group the results accordingly. This is a common tool in marketing strategy to target potential customers based on demographics.

Sequential Patterns

Sequential pattern mining looks for events that frequently occur in data. The process is similar to the association rule in that it seeks to find relationships, but these form an ordered pattern.

One example is shopping patterns. Retailers often place products near each other because customers often shop in sequences ((think of breakfast foods such as cereal, oatmeal, and granola bars in the same aisle). Another example is targeting internet advertising based on a browser’s click sequence. By using sequential pattern mining, businesses can make forecasts based on the results.

Tracking Patterns

The process of pattern tracking is fundamental to data mining. Essentially, analysts monitor trends and patterns in data associated with the progression of time, allowing them to forecast potential time-sensitive outcomes.

This is important for businesses to understand how, when, and how often their products are being purchased. For example, a sports equipment manufacturer tracks the seasonal sales of baseball gear, soccer balls, or snowboards and can choose times for restocking or marketing programs. In addition, a local retailer in a vacation destination might track buying patterns before a holiday weekend to determine how much sunscreen and bottled water to stock.

In-Demand Skills To Enhance Your Data Analytics Experience

According to the U.S. Bureau of Labor Statistics, the computer and information research science industry — which encapsulates data analytics — is expected to grow by 22 percent by 2030. Data mining is one skill that can boost your employability within this field. Here are a few other in-demand skills — all of which are part of Columbia Engineering Data Analytics Boot Camp :

  • Microsoft Excel: It’s far more than a spreadsheet. Analysts can perform VBA scripting, statistical modeling, and forecasting using Excel, which is still a powerhouse in the data world.
  • Python tools: Libraries such as NumPy, Pandas, Matplotlib, and Beautiful Soup contribute significantly to Python’s importance in data science.
  • Working with databases: Consider learning to program in SQL, NoSQL, and MySQL; as well as how to work with frameworks such as MongoDB to become proficient with databases.
  • Visualization techniques: Decision-makers appreciate data that is not only actionable but also accessible and visually compelling. Make your data come alive by learning how to visualize it with HTML/CSS, JavaScript, and other solutions.

An image that highlights the projected employment growth of computer and research scientists through 2030.

Companies demand more than data — they need skilled professionals who understand how to turn data into business success. You can build a fascinating career and help shape the future by becoming proficient in data mining and other analytic techniques.

If you’re ready to expand your career, consider enrolling in Columbia Engineering Data Analytics Boot Camp . In this rigorous 24-week course, you will learn the technical and practical foundational skills needed to begin or further a career in data science.

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15 Data Mining Projects Ideas with Source Code for Beginners

Explore some easy data mining projects ideas with source code in python for beginners to strengthen your skills and build a portfolio to get you hired.

15 Data Mining Projects Ideas with Source Code for Beginners

In this blog, you will find a list of interesting data mining projects that beginners and professionals can use. Please don’t think twice about scrolling down if you are looking for data mining projects ideas with source code.

data mining projects ideas

Table of Contents

  • Easy Data Mining Projects

Data Mining Projects for Students/ Beginners

Data mining projects using weka.

  • Data Mining Projects with Source Code

Data Mining Projects Github

Faqs on data mining projects, 15 top data mining projects ideas.

Data Mining involves understanding the given dataset thoroughly and concluding insightful inferences from it. Often, beginners in Data Science directly jump to learning how to apply machine learning algorithms to a dataset. They often miss the crucial step of performing basic statistical analysis on the dataset to understand it better. This basic analysis helps in realising important features of the dataset and saves time by assisting in selecting machine learning algorithms that one should use.


Design a Network Crawler by Mining Github Social Profiles

Downloadable solution code | Explanatory videos | Tech Support

This blog has a list of Data Mining project ideas to help our readers learn the significance of analysing a dataset before applying machine learning methods. All the project ideas in this blog have been divided into the following five categories for your convenience.

Simple Data Mining Projects on Kaggle

Data Mining Projects for Students /Beginners

Data Mining Python Projects with Source Code

ProjectPro Free Projects on Big Data and Data Science

Suppose you have no idea about data mining projects, what is it, why should one study them, and how it works, then these data mining project ideas for beginners might be a great start for you. Below you will find simple projects on data mining that are perfect for a newbie in data mining.

Data Mining Project on Walmart Dataset 

Data Mining Project on Walmart Dataset 

Dataset: In this Data Mining project, you will use the Walmart dataset, which has historical data of sales, markdown data, and macro-economic feature values for the Walmart stores. The dataset has three files, namely features_data, sales_data, and stores_data.

Project Idea: By merging using unique key values, you can take a look at the statistics of the dataset using Pandas dataframes and Matplotlib library of Python Programming language. The dataset has non-numerical values and a few random negative values for certain features. So, by working on this dataset, you can learn how to handle such kinds of values. You can try performing univariate and bivariate analyses for feature variables to draw insightful conclusions from the data. Data Mining Project with Source Code in Python and Guided Videos - Machine Learning Project-Walmart Store Sales Forecasting .

New Projects

Data Mining Project on Credit Card Fraud Detection Dataset

Many people are interested in using a credit card for the benefits it usually provides. Still, when the thought of fraudulent transactions through the card crosses their minds, they immediately drop the idea of owning it. Credit card issuing companies thus have to ensure that the fraudulent transactions are kept as low in number as possible.

Data Mining Project on Credit Card Fraud Detection Dataset

Dataset: For this project, you can use the Credit Card Fraud Detection Dataset on Kaggle to build one of the most interesting data mining mini-projects. The dataset has as many as 31 columns for you to explore. 

Project Idea:   You can learn how to apply the Nearmiss technique and SMOTE method for undersampling and oversampling data respectively. You can scale different variables to draw better conclusions from the data and also learn how to treat outliers in a dataset.

Complete Solution: Credit Card Fraud Detection Data Science Project

Data Mining Project on Wine Quality Dataset

If you are looking for data mining projects using R or data mining projects with source code in R, then this project is a must try.

Data Mining Project on Wine Quality Dataset

Dataset: For this project, you can use the R programming language. The dataset for this project is multivariable and is readily available on the UCI Machine Learning Repository. It contains information about red and white wine. You can work with a dataset of each type of wine separately or work with both datasets. 

Project Idea: The dataset has chemical features like pH, acidity content, sugar content, citric acid content, etc., for different samples of wine. Using R, you can plot different kinds of graphs like box plots and univariate plots. You can also learn how to perform correlation analysis and bivariate analysis by working with this dataset.

Complete Solution: Wine Quality Prediction in R using Kaggle Wine Dataset 

Recommended Reading:

  • Data Science Programming: Python vs R
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If you have a fair idea of simple data mining projects and want to become a pro at data mining, you should start with this section. This section has a list of data mining projects for beginners.

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Data Mining Project on Sentiment Analysis

For eCommerce websites like Amazon, Flipkart, eBay, Alibaba, the customers’ feedback on all the products is crucial. They motivate a more significant number of customers by convincing them that the products are worth the price.

Data Mining Project on Sentiment Analysis

Dataset: For this project, you can download the Drug Review Dataset from UCI Machine Learning Repository. The dataset has many columns, including patients’ ID, name of the drug, the disease a specific patient is suffering from, review for the drug, etc. 

Project Idea: As you must have observed on popular eCommerce websites, the reviews are not always informative. So, the first thing you can do is analyse the dataset and separate the relevant and informative reviews from the non-relevant ones. A simple approach for this would be to pick lengthy reviews. To better understand the customers’ sentiments, you can use Python to evaluate metrics like Noun score, Review polarity, Review subjectivity, etc.

Complete Solution: Ecommerce product reviews - Pairwise ranking and sentiment analysis 

Data Mining Project on Financial Dataset

Covid-19 has affected a large number of lives that humankind could not even estimate. During this pandemic, the world witnessed the global market going through abrupt and unexpected highs and lows.

Dataset: As a fun idea, an Indian user on Kaggle came up with a fun idea of collecting data for data mining projects. He prepared a google form and circulated it among individuals to collect information about their financial investments . So, the dataset has an individuals’ gender and age along with the details about their deposits in different investment options (gold bonds, PPF, Fixed deposits, etc.)

Project Idea: With the help of the Kaggle user’s dataset to analyse the preferences of Indians in investing their money. You can also do a gender-based analysis to understand which gender is likely to pick specific investment options. As the dataset also contains the age of the individuals, you can use it to know the bias of younger and older people for investing their money.   

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Data Mining Project on a Customers Dataset

For a company, analysing its customers’ preferences is very important. Most companies have now started mining customers data to understand their customers’ choices and behaviour better. This approach helps them recommend appropriate products to their customers and inventory management of their warehouses.

Data Mining Project on a Customers Dataset

Dataset: For this project, you can work with the Foodmart Store Dataset. This dataset has information on the customers of Foodmart, a convenience store chain in the US. They have provided different files for different feature values, such as products data, sales statistics, etc. 

Project Idea: You can merge the different dataset files and start the data mining process by cleaning it a bit. After the basic steps, you can perform univariate and bivariate analyses on the dataset. You can use the dataset to evaluate associate rules for customers purchases. Using this dataset, you can explore the differences between Apriori and Fpgrowth algorithms. Additionally, you can implement other data science techniques used for Market Basket Analysis.

Complete Solution by ProjectPro: Market basket analysis using apriori and fpgrowth algorithm

Recommended Reading: 7 Types of Classification Algorithms in Machine Learning

Weka stands for Waikato Environment for Knowledge Analysis. It is a tool developed by the University of Waikato to make mining data from various datasets an easy task. If you want to experience how to use Weka, check out the data mining sample projects below.

Data Mining Project on Boston House Pricing Dataset

Boston House Pricing Dataset is one of the most popular datasets among beginners in Data Mining and Machine Learning . You can easily download the dataset from the UCI Machine Learning Repository.

Data Mining Project on Boston House Pricing Dataset

Dataset: The dataset has details of 506 houses. The details are contained in 14 columns that describe various characteristics of the houses.

Project Idea: After importing the Weka dataset, you can easily visualise all the features using the “Visualise all” buttons. Notice the distribution of each variable in the resulting graph and conclude it. You can view the relationship between variables by clicking on the Visualize tab and playing with the point size to see all the plots. You can use Weka to perform feature selection and effortlessly create normalise and standardised versions of the dataset. You can also implement data analysis methods on this dataset to explore it in depth.

Get FREE Access to Machine Learning Example Codes for Data Cleaning, Data Munging, and Data Visualization

Data Mining Project on Students Performance Dataset

It will not be difficult for most of us to appreciate that a class in any school never has students of the same kind. Each student has an individual personality that defines their behaviour and interests. Not all of them are good at academics. It is thus an exciting task to work on the dataset of a class and analyse student performances.

Data Mining Project on Students Performance Dataset

Dataset: There is a Student Performance dataset available on Kaggle that you can use for this data mining project. It contains information about the socio-economic background of students and their grades in various subjects.

Project: You can use the dataset to analyse the significance of socio-economic factors in affecting a student’s performance. You can do a gender-based analysis as well for understanding how gender relates to the student’s grades.

When browsing the internet for data mining projects for final year students, most students look for easy implementation examples and have their source code readily available. The code allows them to understand the difficulty level and customise their projects. If you are a final year student looking for such projects, look at the list of projects below.

Data Mining Project on Cafe Dataset

You can find another interesting application of data mining projects in the datasets of food cafes. Deciding the items and their prices on a menu card is not an easy task for cafe owners. They have to constantly analyse their customers’ choices to set the optimum prices of their food items on the menu.

Dataset: The dataset for this project can be downloaded from here . It has three files that contain information about the cafe’s sales, transactions, and time labels for each transaction.

Project Idea: Using the dataset mentioned above, you can verify a few fundamental economic trends in the dataset as a first step. These trends will include analysing price trends and sales of all the items, sales on special holidays and weekends, and more such trends. You can draw more insights by visualising the dataset through the seaborn library of the Python Programming Language. Another metric that you must evaluate for this project is the Price Elasticity of all cafe items.

Source Code: Machine Learning project for Retail Price Optimization

Explore Categories

Data Mining Project on Amazon Review Dataset

Amazon Reviews are a boon for customers and Amazon itself as it can analyse the data to draw relevant inferences.

Data Mining Project on Amazon Review Dataset

Dataset: The dataset you can work on for this project will be the Amazon Reviews/Rating dataset which has about 2 million reviews for different products. 

Project Idea: Hands-on practice on this data mining project will help you understand the significance of cosine similarity and centred cosine similarity. And, after normalising the ratings, you can create a user-item matrix to identify similar customers.

Source Code: Build a Collaborative Filtering Recommender System in Python

Data Mining Project on San Francisco Salaries Dataset

When there are severe disparities in the distribution of wealth among the rich and the poor of a country, it is termed economic inequality. There could be many reasons behind it, like income inequality, social differences, etc. One can work on a salary dataset to understand the situation better.

Project Idea: For this project, you can use the San Francisco Salaries Dataset to understand the income inequality in San Francisco city. In addition, you can also analyse the factors responsible for the promotions of certain employees. It would be easy to use the R programing language for this project and visualise the datasets through ggplot, scatter plots, box plots, and whisker plots. To look at the distribution of the salaries, you can also try plotting the density plots.

If you are looking for data mining projects using R, you must add this project to your list of cool data mining projects.

Source Code: Explore San Francisco City Employee Salary Data

Data Mining Project on MNIST Dataset

Modified National Institute of Standards and Technology (MNIST) released a widely used dataset by beginners in Deep Learning. That is because most new algorithms are tested on it for analysing their performance and efficiency.

Data Mining Project on MNIST Dataset

Dataset: The MNIST dataset has about 10K grayscale images of handwritten digits (0 to 9), with each image having the size of 28 x 28 px. You can easily access the dataset in Python through its TensorFlow library.

Project Idea: Python has exciting libraries like Seaborn and Matplotlib’s Pyplot for visualising any kind of dataset. Using these libraries, you can analyse different types of handwriting styles of people for the same number. As a bonus, you can try designing a CNN model using Keras and Tensorflow to predict the digit for a given image.

Source Code: Digit Recognizer Data Science Project using MNIST Dataset

Data Mining Project on Fake News Dataset

With the internet becoming easily accessible to the world, information is now available to us at the touch of a button. We no more need to spend hours looking for books to know the answers as they are just a google search away. While this is a boon for most of us, it occasionally becomes a bane as we come across web pages with irrelevant and misleading information.

Data Mining Project on Fake News Dataset

Dataset: You can use the Fake News dataset available on Kaggle for this project. It has a collection of fake and real news articles. The information provided to you will be in columns that contain

unique id for each article

Title of the article

Author of the article

The text contained in the article

A tag that denotes whether the article is fake or relevant.

Explore More  Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

Project Idea: The Fake news dataset can be explored to understand the characteristics of fake news articles. You can plot different graphs in Python to analyse the important keywords specific to fake news texts. Also, you can identify authors who are usually behind this. If you have a thing for NLP , you can try a few methods to inspect the dataset better.

Complete Solution: Fake News Classification Project with Source Code and Guided Videos in Python

  • 15 NLP Projects Ideas for Beginners With Source Code for 2021
  • 15+ Machine Learning Projects for Resume with Source Code

GitHub is the go-to website if you are particularly interested in straightforward data mining projects with source code. These projects are easy to understand, and GitHub users write beginner-friendly codes for the newbies in Data Mining projects. Below we have listed data mining application projects that are pretty popular and easy to implement.

Data Mining Project on Mushroom Classification

Many people avoid eating mushrooms as they don’t have an excellent idea of which mushrooms are poisonous and edible. It thus becomes essential to understand different types of mushrooms so that everyone can enjoy the taste of mushrooms without any worries.

Data Mining Project on Mushroom Classification

Dataset: Kaggle has a dataset on Mushrooms that contains interesting information about different types of mushrooms. The dataset mostly has physical features of the mushrooms like cap colour, cap shape, gill colour, gill shape, etc. Each mushroom has been labelled as ‘e’ (edible) or ‘p’ (poisonous).

Project Idea: For this project, we suggest you analyse both the edible and poisonous mushrooms separately. This approach will allow you to understand which factors are more prominent in deciding the nature of mushrooms. 

GitHub Repository: By Johanata Rodrigo: Mushroom's data mining

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Data Mining Project on Heart Disease Prediction

Healthcare is another domain where data mining techniques are widely used. If you are curious about data mining projects in healthcare, you should explore the heart disease dataset from the UCI Machine Learning Repository.

Dataset: The dataset contains 75 particulars of 303 people. These particulars include parameters related to an individual’s heart health like age, gender, serum cholesterol, blood sugar, etc.

Project Idea: For this project, you are advised to remove features that have missing values. So, you will be left with a dataset of 14 attributes. For this project, you can perform gender-based and age-based analysis to answer questions like -

What percentage of younger people are prone to be diagnosed with heart disease?

Are women more prone to heart diseases, or is it the other way?

Apart from this, you can study the parameters that play a vital role in determining the health condition of people’s hearts.

GitHub Repository: Heart-disease-prediction by Mansi Aggarwal

Data Mining Project on Netflix Dataset

Analyzing Netflix data provides insights into consumer preferences, which can be used to inform content creation and acquisition decisions. It can also help to optimize recommendations, improve user experience, and increase customer retention. Additionally, data analysis can reveal trends in viewer behavior and inform advertising strategies. 

Dataset: The "Netflix Dataset.csv" contains information on over 7,000 movies and TV shows available on Netflix as of 2019, including titles, directors, cast, ratings, duration, release year, and genre.

Project Idea: This project is an example of performing data mining techniques on a dataset of Netflix movies and TV shows using Python libraries and machine learning techniques. The project explores the data using descriptive statistics and visualizations and uses machine learning models to predict movie ratings. The project demonstrates the power of data mining and analysis in understanding trends and making predictions in the entertainment industry.

GitHub Repository: Netflix Data Analysis by  Kosaraju Sai Manas

Why you should work on Data Mining Projects?

Data Mining refers to the art of implementing statistical algorithms and mathematical techniques to understand the given dataset better. It also involves drawing interesting and relevant conclusions from different datasets. Businesses can then use these conclusions for decision making.

This blog introduced you to a few of the best data mining projects popular among the Data Science community. If you are looking forward to building a career in Data Science, data mining projects should be the first goal on your task list. That is because most Data Science and Machine Learning projects require you to first utilise basic data mining techniques before applying any machine learning algorithms to them.

Of course, as a beginner in Data Science, it is tough to have datasets for data mining projects and have their solution code to understand the data mining techniques. 

ProjectPro’s solved end-to-end projects in Data Science are designed and vetted by industry experts from JP Morgan, Uber, and Paypal to provide you projects on most recent tools and technologies. You can use these projects to realise your dream of making a career in Data Science. The exciting part of learning from ProjectPro is that you will be provided with a customised learning path based on your previous knowledge in Data Science. So, if you are a beginner or a professional, we have got you covered.

Access Data Science and Machine Learning Project Code Examples

What is Data Mining with examples?

Data Mining is the process of using mathematical and statistical tools over a dataset to draw relevant inferences from it.

Data Mining Examples

Data Mining methods can be applied to intelligent anti-fraud systems for analysing card transactions, credit ratings, and for inspecting purchasing patterns through customers shopping data.

What are the three types of data mining?

There are many types of data mining which include

Graphic Data Mining

Mining the Social media content

Textual Data Mining

Video and Audio Mining

What can data mining be used for?

Data Mining can be your first step whenever you are working on a data science project. Before using the dataset for your data science project, you must thoroughly use data mining methods to know your dataset. This step will help you clean up your data and understand which algorithm should be used to make predictions.

How do you present a data mining project?

You can use GitHub for presenting a data mining project. After implementing the projects in environments like IPython Notebook , you can upload your project in your personal GitHub repository and share it with the concerned people. Make sure you provide enough content in the read-me file to make it easy for the repository visitor to understand your Data Mining project.

How to describe Data Mining Projects in Resume?

When describing data mining projects on a resume, it's important to provide specific details such as the data sources used, the techniques and data mining algorithms applied, and the insights gained. Highlight the impact of the project on the organization and any resulting improvements. Quantify the results wherever possible.

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15 Interesting Data Mining Projects in 2023 (for Students)

  • Jan 16, 2023
  • 7 Minute Read
  • By Apurva Sharma

15 Interesting Data Mining Projects in 2023 (for Students)

Before diving into data mining projects, we need to understand their importance. 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. According to Glassdoor , the average salary of a Data Mining Engineer in the US is around $115,000. But what is the best way to practice way?

Here we present 15 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 which 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.

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 to spread like a wildfire. 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 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. 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. Here 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 is able to 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 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 is 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 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. In order 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

In order 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 out 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 definitely love to make this data mining project because everyone likes NBA.

Applications of Data Mining

Here are some major applications:

  • 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.
  • 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 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.
  • 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.

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 Fake News Detection, Heart Disease Prediction, and Air BNB Market Analysis for this your first data mining project. As we know that 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.

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.

Now you have the list of Data Mining projects for beginners. So what are you waiting for, select one and start working on it. Happy Learning :)

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  7. PDF GitHub

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  8. Data Mining PG Courses Online [2023]

    The University of Arizona offers MS in Information Science: Machine Learning. Cost to learn PG Programs on Data Mining. Here is the course list and fee details of the courses teaching Data Mining, PG Programs. Program Fee Details. Master of Data Science (Global) Program. USD 7800.

  9. PGPBA-BI DM Data Mining

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  10. Data Mining Project

    There are 4 modules in this course. Data Mining Project offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work. This course can be taken for academic credit as part of CU Boulder ...

  11. Free Data Mining Courses With Certificates For Beginners

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  12. Predicting Student Performance Using Data Mining and Learning ...

    The prediction of student academic performance has drawn considerable attention in education. However, although the learning outcomes are believed to improve learning and teaching, prognosticating the attainment of student outcomes remains underexplored. A decade of research work conducted between 2010 and November 2020 was surveyed to present a fundamental understanding of the intelligent ...

  13. Data Mining Project

    DM_Report_04.pdf. Update version. January 19, 2021 14:08. ... Data Mining Project. Final project for the Data Mining Course A.Y. 2020/2021 @ University of Pisa The project consists in data analysis based on the use of data mining tools. Learning Goals. Fundamental concepts of data knowledge and discovery. Data understanding;

  14. Free Data Mining Course with Certificate

    Great Learning Academy is a Great Learning project that provides free online courses to assist people in succeeding in their careers. Great Learning Academy's free online courses have helped over 4 million students from 140 countries. ... To learn all the types and techniques of Data Mining, enroll in a free Data Mining course offered by Great ...

  15. Data Mining Project

    The design of the Project emphasizes: 1) simulating the workflow of a data miner in a real job setting; 2) integrating different mining techniques covered in multiple individual courses; 3) experimenting with different ways to solve a problem to deepen your understanding of techniques; and 4) allowing you to propose and explore your own ideas ...

  16. Panning for gold by data-mining your project tracking data

    Applying statistical data mining techniques to project data is relatively uncommon and surprisingly so given the remarkable value that such techniques can reveal. These techniques can range from complex regression analysis to the simplest of grouping and graphical representations of data. By applying such techniques to project tracking data, we have a very good chance of realizing significant ...

  17. Data Mining Business Report Hansraj Yadav

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  18. What Is Data Mining? A Beginner's Guide (2022)

    Data mining is most commonly defined as the process of using computers and automation to search large sets of data for patterns and trends, turning those findings into business insights and predictions. Data mining goes beyond the search process, as it uses data to evaluate future probabilities and develop actionable analyses.

  19. 14 Data Mining Projects With Source Code

    10 minute read 14 Data Mining Projects With Source Code March 1, 2023 Table Of Contents show Introduction What is Data Mining? Data Mining Projects for Beginners 1. Housing Price Predictions 2. Smart Health Disease Prediction Using Naive Bayes 3. Online Fake Logo Detection System 4. Color Detection 5. Product and Price Comparing tool

  20. Data Mining: The Complete Guide for 2023

    Machine Learning. Data mining and machine learning share some characteristics in that both fall under the data science umbrella; however, they are important differences. While data mining is the process of extracting information from data, machine learning is the process of teaching computers the process of data analysis.

  21. 15 Data Mining Projects Ideas with Source Code for Beginners

    Last Updated: 16 Nov 2023 | BY Manika In this blog, you will find a list of interesting data mining projects that beginners and professionals can use. Please don't think twice about scrolling down if you are looking for data mining projects ideas with source code. Table of Contents 15 Top Data Mining Projects Ideas Easy Data Mining Projects

  22. 15 Interesting Data Mining Projects in 2023 (for Students)

    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

  23. Data Mining Project

    Test your machine learning skills by getting highest accuracy on the engineered image data set.