Top 10 Big Data Project Ideas with Source Code

In today’s modern world, Big Data has become one of the most important resources. A huge amount of data is generated through websites, mobile applications, social media, sensors, and many other sources every second. But using only data is not useful; you have to analyze, process, and visualize the data to make better decisions.

In this blog, we are going to discuss the Top 10 Big Data Project Ideas along with their working, key features, technologies used, and source code. So let’s get started.

Table of Contents:

Top 10 Big Data Project Ideas
Why Work on Big Data Projects?
Conclusion

Top 10 Big Data Project Ideas

You may already know about the basics of Big Data, but working on the following projects will help you understand it better and provide you with hands-on experience on real-world projects.
Given below are 10 Big Data Projects along with their working, key features, technologies used, and their source code:

For this project, you need to start by using APIs in order to gather posts, tweets, or comments from social media platforms like Twitter (X) or Reddit. After that, with NLP (Natural Language Processing), you can analyze the text to detect the emotion behind it, whether it is positive, negative, or neutral. Companies use this kind of project to know how people feel about their products, services, or events. It also assists in real-time tracking of trends and customer reactions.

Project Complexity: Intermediate – You have to collect large volumes of data generated by social media, clean it, and then use the NLP and Machine Learning models for sentiment classification.

Technologies Used: The technologies used in this project are Python, Hadoop, Spark, NLTK, and Twitter API.

Learning Outcomes:

From this project, you will learn how to collect and handle large amounts of data from social media using APIs.
You will also understand how NLP works in analyzing texts.
You will also gain experience in training and testing ML models for the classification of sentiments.

Features of the Project:

Shows real-time sentiment trends from social media platforms.
Displays results in charts and graphs.
It is able to filter the sentiments by topic, hashtag, or keywords.

Source Code: The source code for this project is given below:

https://github.com/gokseltokur/Social-Media-Sentiment-Analysis

2. Fraud Detection System for Transactions

In this project, you need to look at the past transaction records to find patterns of normal behavior, like the amount that is usually spent, where it is spent, and how often. After that, you have to compare new transactions to these patterns to detect anything unusual, like sudden transactions or purchases from unknown locations. This helps to identify possible fraud so that it can be prevented quickly.

Project Complexity: Advanced – You have to process big financial data and design complicated anomaly detection algorithms, as well as include real-time monitoring solutions to find suspicious activity.

Technologies Used: The technologies used for this project are Python, Spark, Kafka, and Spark MLlib

Learning Outcomes:

In this project, you need to work with large transaction datasets.
You will also learn how to build algorithms that could help identify abnormal patterns.
You will also gain experience in building real-time systems that will send alerts instantly.

Features of the project:

Sends instant alerts and detects unusual transactions in real time.
It uses advanced algorithms for reducing false alarms.
It can adapt and improve its accuracy in detecting as more data is processed.

Source Code: The source code for this project is given below: https://github.com/shivamsaraswat/credit-card-fraud-detection

3. Healthcare Data Analysis

In this project, you have to take large amounts of patient information, such as medical history, test results, and treatment records, and analyze it to find useful patterns. This can help predict which diseases might increase in the future, see how well hospitals are performing, and make better decisions about where to use resources like doctors, beds, and equipment. In short, it helps improve healthcare services and patient outcomes.

Project Complexity: Advanced – It works with sensitive healthcare information, complicated statistics, and predictive analytics, along with the development of visual dashboards to draw insights without compromising data privacy.

Technologies Used: The technologies used here are Hadoop, Hive, Tableau, and Python.

Learning Outcomes:

You will learn how to work with large and sensitive healthcare datasets safely.
You will know how to analyze the data to predict the trends and enhance services.
You will also gain experience in developing clear, informative visual reports that can help in decision-making.

Features of the project:

Tracks hospital performance with clear and detailed reports.
Predicts the future trends of diseases to help prevent them early.
Maximizes the utilisation of resources such as staff, beds, and equipment.

Source Code: The source code for this project is given below:

https://github.com/topics/healthcare-analysis

4. Real-Time Traffic Prediction System

In this project, you need to collect real-time location data with the help of GPS and traffic sensors that are placed on roads. After that, this data is analyzed to see where traffic is building up and how it is moving. Based on this, the system predicts possible traffic jams and suggests alternative routes. This helps to save time and avoid traffic jams.

Project Complexity: Advanced – You should handle a large amount of live data and implement predictive algorithms, and integrate mapping services to display correct traffic updates.

Technologies used: Some technologies that are used for this project are Spark Streaming, Kafka, Google Maps API, and Python.

Learning Outcomes:

In this project, you will learn how to process and analyze live streaming data.
You will also understand how you can use prediction models to predict the traffic pattern.
You will also get experience in integrating data analysis with mapping and navigation tools.

Features of the project:

It is used to predict congestion before it occurs.
It provides you with alternative routes in real time.
It shows live traffic updates on the map.

Source Code: The source code for this project is given below:

https://github.com/topics/traffic-prediction

5. Recommendation System for E-Commerce

In this project, you have to analyze what a user has bought before and the products that were viewed while browsing. After that, with the help of recommendation algorithms, you need to find similar related items that might be liked by the user. This helps to make shopping easier for users and helps to increase sales in businesses by showing the right type of products.

Project Complexity: Intermediate – This project involves understanding how the user data behaves, utilizing recommendations such as collaborative filtering or content-based filtering. It also provides the user with recommendations for products in real time.

Technologies used: The technologies used in this project are Hadoop, Spark MLlib, Python, and MySQL.

Learning Outcomes:

In this project, you will learn how to analyze the activity of the user and purchase data.
You will also learn how recommendation algorithms work to suggest products to the user.
You will also gain experience in creating systems that help you personalize your shopping experience.

Features of the project:

This project provides you with personalized product suggestions based on the search history of the user.
It also updates its recommendations in real time as the users browse.
It also supports both similar product suggestions and ideas related to the product.

Source code: The source code for this project is given below:

https://github.com/Vaibhav67979/Ecommerce-product-recommendation-system

6. Weather Forecasting Using Big Data

In this project, you have to study large collections of data related to past weather, like temperature, rainfall, and wind patterns, to identify trends and patterns. With the help of these insights, you can predict the weather in the future. This helps you to plan activities, agriculture, and extreme weather events.

Project Complexity: Intermediate – This project involves working with datasets related to weather, applying ML and statistical models, and generating accurate forecasts along with visual reports.

Technologies used: The technologies that are used for this project are Hadoop, Spark, Python, and OpenWeatherMap API.

Learning Outcomes:

In this project, you will learn how to work with large weather datasets and clean them.
You will also have a good understanding of how to use models to predict the future weather.
You will also get experience in creating specific visual observations of the weather trends.

Features of the project:

This project helps you to predict short-term and long-term weather conditions.
It also shows trends with the help of charts that are easy to read and understand.
It also helps you to make travel plans, farming, and be ready for emergencies.

Source Code: The source code for this project is given below:

https://github.com/andrea-gasparini/big-data-weather-forecasting

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7. Stock Market Data Analysis

In this project, you need to study the stock price data in the past along with the sentiment of the news articles, social media posts, or financial reports. You can identify trends using this information to determine whether the stock price will increase or decrease. This helps investors make better decisions in buying and selling stocks.

Project complexity: Advanced – In this project, you will have to combine big historical data on stocks, as well as sentiment analysis of news and social networking data. Then you have to apply predictive algorithms to forecast market trends accurately.

Technologies used: The technologies used for this project are Spark, Python, Pandas, and Yahoo Finance API.

Learning Outcomes:

In this project, you have to combine financial data with news and social media sentiments.
You will also have a good understanding of how prediction models work for forecasting stock prices.
You will also gain experience in building tools that will help you make better investment decisions.

Features of the project:

This project analyzes how you can forecast movements in terms of stock price trends and sentiments.
It helps to combine financial data with real-time new analysis.
It also assists investors in making informed decisions related to buying or selling.

Source Code: The source code for this project is given below:

https://github.com/topics/stock-market-analysis

8. Movie Recommendation Based on User Preferences

In this project, you need to look at the movies that the user has watched and how they rated them. After that, by using recommendation algorithms, you have to find other movies that the user might enjoy based on similar tastes or what other users with similar preferences liked. This makes it easier for users to discover new movies without wasting time searching.

Project complexity: Intermediate – This project involves analyzing the viewing patterns of the user and the ratings. After that, you have to apply recommendation algorithms like collaborative filtering and then deliver personalized recommendations.

Technologies used: The technologies used for this project are Python, Spark MLlib, Pandas, and Flask.

Learning Outcomes:

In this project, you will learn how to study the preferences of the user and the viewing history.
You will also understand the recommendation algorithms, like collaborative filtering.
You will also gain experience in creating systems that provide you with personalized movie suggestions.

Features of the Project:

This project provides you with personalized movie suggestions based on what the user likes.
It updates the recommendations as the user watches and then rates more movies.
It also uses various recommendation algorithms to suggest both similar and new genres to explore.

Source code: The source code for this project is given below:

https://github.com/topics/movie-recommendation-system

9. Customer Segmentation for Marketing

In this project, you have to take the purchase data of the customers and study their buying habits, like what products they should buy, how often, and how much they should spend. Then, you have to group customers with similar patterns in the form of clusters. This helps businesses to create dedicated marketing campaigns for each group, making promotions more effective and increasing sales.

Project Complexity: Intermediate – In this project, you have to analyze the purchase data of the customers, applying clustering algorithms like K-means, and then create visualized reports to support the marketing strategies.

Technologies used: The technologies used in this project are Spark, Python, K-Means, and Tableau

Learning Outcomes:

From this project, you will learn how you can analyze the purchase history and behavior of the customer.
You will also get a proper understanding of how the clustering algorithms group all the similar customers together.
You will also gain valuable experience in using data to create marketing strategies.

Features of this project:

This project will help you group customers based on their styling habits and preferences.
It helps to create clear visual reports for understanding customer segments better.
It also helps to plan targeted marketing campaigns for each group.

Source code: The source code for this project is given below:

https://github.com/AbhishekGit-hash/Data-Analytics-Customer-Segmentation

10. Energy Consumption Prediction

In this project, you need to study the data from smart meters that record the electricity usage in homes and businesses. By identifying the patterns about how and when electricity is used, you can predict its demand in the future. This helps the energy providers to manage resources better, reduce wastage, and ensure that you get a stable power supply during peak hours.

Project complexity: Advanced – In this project, you need to process a large amount of data from smart meters. Then you have to apply time-series forecasting models and build systems to optimize the distribution of energy in real time.

Technologies used: The technologies used in this project are Hadoop, Spark, Python, and TensorFlow.

Learning Outcomes:

From this project, you will learn how to work with large datasets related to time-based electricity usage.
You will also have a good understanding of how forecasting models predict demand for energy in the future.
You will also gain experience in building systems that help to optimize the distribution of energy.

Features of this project:

This project is used to predict the usage of electricity for the upcoming hours, days, or months.
It also helps to reduce wastage of energy by optimizing power distribution.
It also assists in sending alerts during peak usage times to manage demand better.

Source Code: The source code for this project is given below:

https://github.com/MohamadNach/Machine-Learning-to-Predict-Energy-Consumption

Why Work on Big Data Projects?

Now, let us talk about why big data projects are important:

Real-world exposure: By doing Big Data projects, you will learn how to deal with large datasets just like in MNCs.

Tool mastery: You will gain experience with various big data tools and frameworks.

Portfolio building: By doing big data projects, you can build a strong portfolio and resume, and impress your employers and clients.

Problem-solving skills: Big data projects will help you develop the skill of extracting useful information from raw datasets.

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Conclusion

Big data is changing how people solve problems and make better decisions every day. Working on these projects is a practical way to grow your skills and understand how data works in real life. Each of these big data projects lets you work with real datasets and tools that companies actually use. Whether it’s tracking social media opinions, spotting fraud, predicting traffic, or anything else from the list, you’ll sharpen your problem-solving skills and build something you can proudly show in your portfolio. Pick a project that interests you, learn step by step, and see how far big data can take you.

Explore our blog Hadoop Interview Questions and enroll in our Big Data Certification course.