Top 8 Data Mining Projects & Topics in Python [For Freshers]

Do you want to test your data mining skills? You’ve come to the right place then because this article will show you the top data mining projects in Python. Pick any one of the following that matches your interests and requirements. 

We have discussed every project in detail so you can understand each one easily and start working on it right away. 

Top Data Mining Project Ideas in Python

1. TourSense for Tourism

The TourSense project is among the best data mining project ideas in Python for advanced students looking for a challenge. TourSense is a framework for preference analytics and tourist identification by using city-scale transport data. It focuses on overcoming the limitations of the conventional data sources used for tourism-related data mining such as social media and surveys. 

In this project, you’ll have to design a tourist preference analytics model, so it’s vital to be familiar with the basics of machine learning for this project. Your solution should have a functional and interactive user interface to simplify usage for a client.

Your solution should be able to go through real datasets and identify tourists among them. The combination of the tourist identification system and the preference analytics model will help the user in making better-informed decisions about their potential clients and understanding the tourism trends in their areas.

A tool like this would be perfect for travel agencies, hotels, resorts, and many other enterprises operating in the travel and hospitality sector. If you’re interested in using your Python skills in those industries, then you should try your hand with this project. 

2. Intelligent Transport System

In this project, you’d be creating a multi-purpose traffic system that simplifies traffic management. It is an excellent project for anyone looking to use their technical skills in the public sector. 

Your traffic model would have to ensure that the transport system remains efficient and safe for its passengers. For your intelligent transport system, you can take the past three years of data from a reputed bus service company. After you have taken the data, you should apply uni-variate multi-linear regression to forecast passengers for your system.

Now you can compute the minimum number of buses necessary for your intelligent transport system. Once you’re done with these steps, you will need to validate the results with statistical implementations such as mean absolute deviation (MAD) or mean absolute percentage error (MAPE). 

As a beginner, you can concentrate on simply mining the data and creating the optimized system that manages the transport (such as the required number of buses). If you want to make the project more challenging, you can add the functionality of allocation adequate resources, and reducing traffic congestion by checking the timing and statistics of commute. 

This project will help you test multiple sections of your data science knowledge and understand how they are interlinked. 

3. Graph-Based Multi-View Clustering

You will design a graph-based multi-view clustering model that weighs data graph matrices for all views and generates a combined matrix, giving you the final clusters.

Graph-based multi-view clustering (GMC) is significantly better than the conventional clustering solutions because the latter need you to produce a final cluster separately. The conventional clustering methods don’t give much attention to every view’s weight, which is a very influential factor for generating the final matrix. On top of that, they all operate on fixed graph similarity matrices for all views. 

Creating and implementing a properly functioning GMC-based solution is a challenge in itself. However, if you want to take it up a notch, you can partition the data points into the required clustered without using a tuning parameter. Similarly, you can optimize the objective function with an iterative optimization algorithm. 

Working on this project will make you familiar with clustering algorithms and their implementation, which are among the most popular classification solutions in data science. 

4. Consumption Pattern Prediction 

Of late, there’s been a massive upsurge in consumer and business data. From online shopping to ordering food, there are many areas now where people generate tons of data daily. Companies use predictive models to suggest new products or services to their users. This allows them to enhance their user experience while ensuring that the customer gets personalized suggestions that have the highest chance of generating sales. 

While a conventional recommendation system can rely on simple data such as the user’s entered interests but for a fully-functional and effective recommendation system you’d need data on the user’s past behaviour (past purchases, likes, etc.). 

To tackle this issue, you will create a mixture model that has both novel and repeated events. It focuses on giving accurate consumption predictions according to the user’s preferences in terms of exploitation and exploration. This is one of the most peculiar data mining project ideas in Python because you’ll have to perform experimental analysis by using real-world datasets. 

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Depending on your experience and expertise, you can pick the right number of data sources. 

This project will give you experience in mining data from multiple sources. You’ll also learn about recommendation systems, which is a prominent topic in machine learning and data science. 

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5. Social Influence Modeling

This project requires you to be familiar with deep learning as you’ll be conducting sequential modelling of user interests. First, you’ll need to perform a preliminary analysis of two datasets (Epinions and Yelp). After that, you’ll discover the statistically sequential actions of their users and their social circles including social influence on decision-making and temporal autocorrelation. 

Finally, you’ll be using the SA-LSTM (Social-Aware Long Short-Term Memory) deep learning model which can predict the points of interest and the kind of items a specific user will visit or buy the next time. 

If you’re interested in studying deep learning then this is certainly among the best data mining projects in Python for you. It will make you familiar with the basics of deep learning and how a deep learning model functions. You’ll also learn how you can use a deep learning model in real-life applications. 

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6. Automated Personality Classification

Have you tried personality tests? If you find them enjoyable, then you would certainly love working on this project. 

In this data mining project, you’d create a personality prediction system. Such a system has many applications in career guidance and counselling as it helps predict a candidate’s temperament and compatibility with different roles. 

This is a particularly interesting project for students interested in management and human resources. You’ll be creating a personality classification solution that separates the participants into different personality-types according to the past patterns of classification and the input data provided by the participants. 

Note that it’s an advanced-level project and you should be familiar with multiple data science concepts for working on it. Your personality classification system should store the personality-related data in a dedicated database, collect every user’s associated characteristics, extract the required features from a participant’s input, study them, and link the user behaviour and personality-related present in the database. The output would be a prediction of the participant’s personality type. 

7. Sentiment Analysis and Opinion Mining

Sentiment analysis is a collection of processes and techniques that help organizations retrieve information about how their customers perceive their products or services. It helps organizations understand the reaction of their customers to a particular product or service. Due to the advent of social media, the importance of sentiment analysis has risen considerably in the last few years. 

In this project, you’ll create a simple sentiment analysis tool that performs data mining for collecting content on a brand (social media posts, tweets, blog articles, etc.). After that, your system would have to check the content and compare it with a pre-selected collection of positive and negative words and phrases.

Some positive phrases or words may include “good customer service”, “excellent”, “nice”, etc. The same goes for negative words and phrases. After conducting the comparison, the solution would give the verdict on how the customers perceive a particular product or service. 

8. Practical PEKs Scheme 

This is a project for cyber-security enthusiasts. Here, you’ll be creating a Public Encryption with Keyword Search (PEKS) solution. It helps in preventing email leaks and as a result, any leak of sensitive information and communication. The solution would allow users to go through a large encrypted email database quickly and help them perform boolean and multi-keyword searches. Keep in mind that the solution would ensure that no additional information of a user is leaked while performing these functions. 

In a public-key encryption system, the system has two keys, a private one and a public one. The recipient of the message keeps the private key while the public key remains available to everyone. 


Working on data mining projects in Python can teach you a lot about data science and its implementations. Data mining is an essential aspect of data science and if you want to pursue a career in data science, you must be adept at this skill. These data mining project ideas in Python would certainly help you ace the nitty-gritty of data mining.

However, if you want a more individualized learning experience, we recommend taking a data science course. It would teach you all the necessary skills for becoming a data science professional including data mining. You’ll learn under the guidance of industry experts, who’d answer your questions, resolve your doubts, and guide you throughout the course. 

Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.

What are top 5 data mining techniques?

The business problems addressed by these data mining techniques are diverse, and the findings from them are often diverse as well. Once you know the type of problem you are solving, the type of data mining technique you will use will be obvious.
Classification Analysis - This type of analysis is used to help the business identify key data, and metadata. Classification of data in different classes is an important function of this tool.
Association Rule Learning - It is an association rule learning methodology that will help you find interesting relations (dependency modelling) in large databases.
Anomaly or Outlier Detection - When encountering data elements in a set of data that do not fit an expected pattern or expected behaviour, it is referred to as an anomaly or outlier detection.
Clustering Analysis - The method of uncovering groups and clusters in the data is known as clustering analysis. Clustering analysis seeks to maximise the degree of association between 2 objects that belong to the same group and minimise the association between objects that belong to different groups.
Regression Analysis - The method of identifying and analysing the relationship between variables is called regression analysis. In order to learn the relationship between the dependent variable and independent variables, try varying one of the independent variables.

How do I start a data mining project?

You will follow these steps every time you launch a data mining project:
Once you've identified the source of your raw data, find an appropriate database, or even Excel or text files, and choose one to use for your modelling.
The data source view defines a subset of the entire data in the data source to be used for analysis.
Explain how you'd design a mining structure to support simulation.
Choose a mining algorithm and specify how the algorithm will handle the data, and add the model to the mining structure.
Include the training data in the model, or filter the training data to include just the desired data.
Try out different models, test them, and rebuild them.
After the project is finished, you can deploy it so that it can be browsed or queried by users, or used programmatically by software that makes predictions and analyses.

What are the major types of Data Mining tools?

1. Query and reporting tools.
2. Intelligent agents.
3. Multi-dimensional analysis tool.
4. Statistical tool.

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