Do you plan to complete your data science course this year? If so, one of the criteria for receiving your degree can be a data analytics project. Picking the best Data Science Projects for Final Year might be difficult. Many of them have a high learning curve, which might not be the best option if you don’t have a lot of free time. Additionally, several of these projects need expensive subscription plans for data science tools. These factors are the basis for our project recommendations, some of which we have personally written articles on to help you with building projects.
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Here we will provide you with Data Science Projects with Source codes. We at CopyAssignment, believe that apart from tutorials, what’s more, important is projects. And so, in this series of articles on project ideas, today we will cover Data Science Projects for Final Year. But why make projects in Data Science? In this ever-changing world, every industry is looking for capable and skilled data scientists. It is essential to keep pace with the current industry requirement and prepare accordingly. So let’s get started and roll our eyes on Beginners to Advanced Level Data Science Projects.
Beginner Level Data Science Projects
1. Road Lane Lines Detection
Explanation
We utilize our eyes to choose where to go when we are driving. Our constant guide for where to direct the vehicle is the lines on the road that indicate the locations of the lanes. Naturally, automatically detecting lane lines with an algorithm is one of the first things we would like to achieve when creating a self-driving automobile.
Using Python and OpenCV, you will use this project to find lane lines in photos. Color selection, region of interest selection, grayscaling, Gaussian smoothing, Canny Edge Detection, and Hough Transform line detection are some of the tools we have. To detect the line segments in the image, average or extrapolate them, and then draw them onto the image for presentation is our objective.
- Source Code: Road Lane Lines Detection
- Dataset: Road Lane Lines Detection
2. Forest Fire Detection
Explanation
With the use of convolutional neural networks (CNN), this study aims to identify the beginning or presence of a forest fire in a picture. The theory behind this model is that it might be used to analyze (aerial) surveillance footage of a forest in order to spot a fire or the beginning of a fire. Given that fires don’t spread quickly, the model might be used in real-time to monitor low-framerate surveillance footage and inform users in the event of a fire.
- Source Code: Forest Fire Prediction
- Dataset: Forest Fire Prediction
3. Parkinson’s Disease Detection
Explanation
Parkinson’s disease is a neurological condition that impairs movement. There are five distinct stages of the condition, which are progressive. We have begun employing data science to enhance healthcare and services since early disease detection has a significant positive impact on prognosis. We will therefore discover how to use Python to identify Parkinson’s disease in this data science project proposal.
To identify and forecast Parkinson’s, we’ll employ geometric diagrams. Then we’ll apply our dataset of sketches acquired from both Parkinson’s and non-patients. After evaluating the dataset, we will quantify the input photos using the HOG image descriptor, and then we will learn how to train a Random Forest classifier using the features that were retrieved.
- Source Code: Parkinson’s Disease Detection
- Dataset: Parkinson’s Disease Detection
4. Breast Cancer Detection and Classification
Explanation
Data science methods have been increasingly applied in intelligent health systems over the past ten years, particularly for the detection and prognosis of breast cancer. One of the most frequent malignancies in the world is breast cancer. In order to make it simple for doctors to administer treatment at the appropriate moment, we can construct a model to classify the type of cancer with the aid of machine learning and data science. Because it can encourage prompt clinical treatment of patients, early diagnosis of breast cancer can significantly improve prognosis and chances of survival.
The major objective of this classification challenge is to create a model that distinguishes between malignant and benign kinds of cancer.
- Source Code: Breast Cancer Detection
- Dataset: Breast Cancer Detection
5. Potato Leaf Disease Detection
Explanation
Our very last project in Beginners Level Data Science Projects for Final Year is Potato Leaf Disease Detection. In the area of image categorization, the most recent convolutional neural networks (CNNs) have produced excellent results. In this project, deep convolutional networks are used to develop a simple method of detecting potato leaf images in order to recognize plant diseases. Innovative training methods and the methodology employed make it simple and quick to implement the system in real-world settings. With the ability to differentiate between plant leaves and their surroundings, the developed model can detect various plant illnesses from healthy leaves.
- Source Code: Potato Leaf Disease Detection
- Dataset: Potato Leaf Disease Detection
Intermediate Level Data Science Projects
1. Age and Gender Detection
Explanation
Now moving on to our Data Science Projects for Final Year, our first project in the Intermediate Level section is Age and Gender Detection. In this Python project, we’ll use deep learning to precisely determine a person’s gender and age from just one snapshot of their face. The Tal Hassner and Gil Levi-trained models will be used. The anticipated gender and age can be either “Male” or “Female,” and they can fall into one of the following ranges: 0–2, 4–6, 8–12, 15–20, and so on.
Due to elements like cosmetics, lighting, obstacles, and facial expressions, it is quite challenging to determine an exact age from a single image. Therefore, rather than treating this as a regression problem, we turn it into a classification challenge.
- Source Code: Age and Gender Detection
- Dataset: Age and Gender Detection
2. Drowsiness Detection
Explanation
Driving when fatigued is one of the leading causes of accidents on the road today. Long-distance drivers often fall asleep behind the wheel, which is to be expected. In this post, we’ll show you how to create a drowsiness monitoring system that will let you know the moment the driver nods off. Using vision-based approaches like eyes detection, yawning, and nodding, drowsiness can be detected. Some people have the ability to fall asleep without yawning or nodding.
We suggest using Keras, OpenCV, and TensorFlow for the construction of this project.
- Source Code: Drowsiness Detection
- Dataset: Drowsiness Detection
3. Exploratory Data Analysis
Explanation
Our third project in Data Science Projects for Final Year is EDA. The Exploratory Data Analysis stage of the data analysis process is crucial since it aids in the interpretation of your data and frequently entails displaying it for enhanced exploration. You have a variety of visualization choices, including histograms, scatterplots, and heat maps. EDA can also reveal anomalies and unexpected results in your data. You are ready to begin once you have found the patterns and obtained the required insights from your data.
Python makes it simple to complete a project of this size, and the packages available include pandas, NumPy, seaborn, and matplotlib.
- Source Code: Exploratory Data Analysis
- Dataset: Exploratory Data Analysis
4. Movie Recommendation System
Explanation
Through a screening process based on browsing history and user preferences, a recommendation system offers ideas to the users. The user’s personal information is used as input. The data is derived from input that is presented as surfing data. This data reflects both the ratings that have been given and how the product has been used in the past.
We will utilize the four packages “recommenderlab,” “ggplot2,” “data.table,” and “reshape2” in our data science project.
- Source Code: Movie Recommendation System
- Dataset: Movie Recommendation System
5. Web Traffic Times Series Forecasting
Explanation
Forecasting online traffic is a big issue today because it can affect how well-known websites function. Project-making has made time-series forecasting a popular subject. One of the most challenging issues in the field is making predictions about future time series values. The most effective way to communicate the information would be to forecast the network traffic and show it in a dashboard that updates in real time. Making a dashboard would facilitate tracking and studying live data. Time series forecasting will be our chosen application for sequence modeling.
- Source Code: Web Traffic Forecasting
- Dataset: Web Traffic Forecasting
Advanced Level Data Science Projects
1. Traffic Signs Recognition
Explanation
Identification of Traffic Signs system developed using OpenCV – TSR (traffic sign recognition) enables a car to recognize roadside traffic signs like “speed limit,” “children,” or “turn ahead”. Several vehicle vendors are developing the technology. It uses image processing methods to find traffic signs. The three categories of detection techniques are color-based, shape-based, and learning-based.
- Source Code: Traffic Signs Recognition
- Dataset: Traffic Signs Recognition
2. Image Caption Generator
Explanation
This data science endeavor is intriguing. For people, it is simple to describe what is in a picture, but for computers, an image is nothing more than a collection of numbers that indicate the color value of each pixel. Therefore, it is difficult for computers to comprehend what is in the image, and it is even harder to produce a description in a language like English. In order to create the image caption generator for this project, we combined a convolutional neural network (CNN) with a recurrent neural network (LSTM).
- Source Code: Image Caption Generator
- Dataset: Image Caption Generator
3. Speech Emotion Recognition
Explanation
Now moving on to our last third project idea in Data Science Projects for the Final Year series is Speech Emotion Recognition. One of the simplest forms of communication is speech, which carries a variety of emotions like tranquillity, wrath, joy, and enthusiasm, to mention a few. It is feasible to rearrange our activities, services, and even products to provide more individualized service to certain persons by understanding the emotions underlying the speech.
The goal of this project is to recognize and extract emotions from various sound files that contain human speech. Python’s Librosa, SoundFile, NumPy, Scikit-learn, and PyAaudio packages can be used to create something similar. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), which has more than 7300 files available for download, might be used for the dataset.
- Source Code: Speech Emotion Recognition
- Dataset: Speech Emotion Recognition
4. Credit Card Fraud Detection
Explanation
In the present era, a system is needed that can track the patterns of all credit card transactions, and if any patterns are irregular, the transaction should be halted. The ability to divide transactions into regular and abnormal categories is now possible thanks to a variety of machine learning algorithms. The only prerequisites are historical data and an algorithm that can more closely match our data.
Knowledge of concepts such as decision trees, gradient-boosting classifiers, logistic regression, and artificial neural networks is essential (ANN). This project can be made using tools like NumPy, Pandas, Matplotlib, Seaborn, XGBClassifier, and frameworks like Scikit-Learn.
- Source Code: Credit Card Fraud Detection
- Dataset: Credit Card Fraud Detection
5. Flight Delay Prediction Model
Explanation
Due to the accompanying financial losses that the aviation industry is experiencing, flight delays have become a very important topic for air travel around the world. The causes of these delays are many and diverse, ranging from air traffic congestion to weather conditions, mechanical issues, challenges with passenger boarding, and simply the carriers’ inability to meet demand given their capacity.
To perform the predictive analysis, which includes a variety of statistical methods from supervised machine learning and data mining, which examines recent and historical data to create predictions or simply assess the upcoming delays, using Python 3.
- Source Code: Flight Delay Prediction Model
- Dataset: Flight Delay Prediction Model
Reference Links
- Best Data Science Course: Coursera – IBM Data Science Professional Certificate
- The best source to get Datasets: Kaggle
- Important Libraries for Data Science: Top 10 Libraries
Conclusion
All in all, we hope this article on Data Science Projects for Final Year served you as you expected. We have again mentioned all the project ideas along with their source code and the best datasets you must use while developing these projects. We believe Data Science is the field wherein there are no limits to building projects but here the projects we have mentioned are the ones that one must start their journey in Data Science. We hope our project ideas will boost your preparation and CV.
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