Data science is a branch of study that combines subject-matter knowledge, programming abilities, and competence in math and statistics to draw forth important insights from data. Data Science Projects are data-driven insights that are utilized to inform decisions in order to accomplish a certain business objective. A data analytics project may be one of the requirements for receiving your degree. It could be challenging to select the top data science projects for your senior year. If you don’t have a lot of free time, many of them have a steep learning curve and may not be the ideal choice. With this article, our team has decided to help you with some best data science final year project ideas and some basic information about those.
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Top Data Science Final Year Project Ideas 2025
1. Breast Cancer Detection
Over the past ten years, data science techniques have been used more and more in intelligent health systems, particularly for the detection and prognosis of breast cancer. Breast cancer is one of the most prevalent cancers in the world. With the use of machine learning and data science, we can build a model to categorize the type of cancer in order to make it simple for clinicians to provide therapy when it is necessary. Early diagnosis of breast cancer can greatly enhance prognosis and survival chances since it can stimulate fast therapeutic treatment of patients.
2. Visual Caption Generator
One of the most intriguing data science projects is this one. Humans find it simple to convey what is in a picture, but for computers, an image is nothing more than a collection of numeric values that correspond to each pixel’s colour value. In order to develop the image caption generator for this data science project, convolutional neural networks (CNN) and recurrent neural networks (LSTM) can be implemented.
3. Predicting Bitcoin Price
This project’s primary goal is to forecast the price of bitcoin using machine learning algorithms. One of the models is based on long short-term memory (LSTM) recurrent neural networks, while the other two are based on gradient boosting decision trees. In every instance, we construct investment portfolios based on the forecasts and contrast their effectiveness in terms of return on investment.
4. Diabetes Prediction
the concept of using Python’s pandas and machine learning to visualize data. utilizing data from several people’s medical histories (prime Indians dataset from UCI repository). This data collection includes details about the user’s age, sex, and diabetes-related symptoms. Create a testing and training set and forecast the likelihood that patients will get diabetes in the next five years. Data is categorized and shown using various graphs. It can be found by creating a precise prediction model that can automatically distinguish between different accidental scenarios. The cluster will be helpful in developing safety measures and accident prevention strategies.
5. Feedback Classification with Random Forest
This project is about to develop a framework that will enable us to identify false profiles using ML algorithms, improving the security of people’s social lives. Support Vector Machine (SVM) is an elegant and reliable technique for binary classification in a big dataset. Despite the decision boundary’s nonlinearity, SVM can distinguish between fake and real profiles with a respectable level of accuracy (>90%).
6. Age and Gender Detection
This is quite challenging and can be considered as an intermediate level final year project for you. In this Python project, deep learning will be used to accurately identify a person’s gender and age using just one image of their face. We will employ the models taught by Tal Hassner and Gil Levi. The expected age and gender can be “Male” or “Female” and can fall into one of the following ranges: 0-2, 4-6, 8-12, 15-20, and so on.
It can be difficult to estimate an individual’s age from a single shot because of factors including cosmetics, lighting, impediments, and facial expressions. As a result, we approach this as a classification problem rather than a regression issue.
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7. Movie Recommendation System
A recommendation system presents suggestions to users after conducting a screening procedure based on surfing history and user preferences. Personal data from the user is used as input. The information was collected from input that was displayed as surfing information. This information reflects both the reviews that have been left and the previous usage patterns for the product.
8. Fake News Detection
Everyone in this technologically advanced society is aware of what fake news actually is. Online fake news dissemination has grown very popular. You must have all observed how false information was being disseminated online by unreliable sources. Such information forces you to confront problems, but it also carries the risk of inciting violence and a great deal of terror in some situations. By using Python and building a model with Passive Aggressive Classifier and Tfid Vectorizer to separate the genuine news from the bogus, you can establish a data science project to stop its spread.
9. Online traffic (Time series data) Prediction
Today, predicting online traffic is a major problem since it can impact how well-known websites operate. Time-series forecasting is now a well-liked topic as a result of project-making. Making predictions about future time series values is one of the hardest problems in the field. Forecasting network traffic and displaying it in a dashboard that changes in real time would be the most efficient way to convey the information. Tracking and analyzing real-time data would be made easier by creating a dashboard. We’ve decided to use sequence modeling for time series forecasting.
Pro Tip: Always look out for real world problems and work around to solve it. As it was said in The Data Science Handbook, “Real-world data is often messy, incomplete, or imbalanced, and solving these problems will make you a much better data scientist than just solving textbook problems.”
10. Election Result Prediction (Sentiment Analysis)
Using the Sentimental Analysis method, you may teach a computer to recognise emotions in text. Texts can take many different forms, including simple reviews, social statements, tweets, and text messages. A substantial amount of high-value and varied social data has been gathered on digital platforms. It is possible to computationally process and analyze this vast amount of social data to discover people’s preferences and affinities with any topic.
11. Prediction of Credit Card Fraudulent Activities
In the modern era, a system is required that can monitor the patterns of all credit card transactions and stop them if any patterns are suspicious. Thanks to a number of machine learning techniques, it is now possible to categorize transactions into normal and abnormal categories. Historical data and an algorithm that can more closely match our data are the only requirements.
It is crucial to have an understanding of ideas like decision trees, gradient-boosting classifiers, logistic regression, and artificial neural networks (ANN). Tools like NumPy, Pandas, Matplotlib, Seaborn, XGBClassifier, and frameworks like Scikit-Learn can be used to create this project.
12. Speech Emotion Detector
Speech is one of the most basic kinds of communication, and it may convey a wide range of feelings, including calmness, anger, excitement, and enthusiasm, to name a few. By comprehending the emotions underlying the speech, it is possible to reorganize our activities, services, and even products to offer more personalized service to certain people.
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This project aims to identify and extract emotions from a variety of sound files that have human speech in them. Similar work can be done with Python’s Librosa, SoundFile, NumPy, Scikit-learn, and PyAudio packages. The dataset might be drawn from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), which offers more than 7300 files for downloading.
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