The year is 2022. We are all aware of the industry’s constant expansion. Python’s growth in the industry from 2018 to 2021 was over 40%, and it is predicted that this growth will increase to 20% in the upcoming years. In the last few years, the number of Python developers has climbed by 30%, especially in the Machine Learning field.
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In this article, we will cover all the Machine Learning Projects for Final Year with the link to their datasets. This article covers Machine Learning Project Ideas for beginners, intermediate, and advanced Python developers. You only need to bookmark this page on Machine Learning Project Ideas for Final Year, and you’ll never run out of worthwhile project ideas to work on.
It is usually beneficial to have a practical understanding of any technology you are developing. You may obtain all the practical skills you need to advance in your career and increase your employability in the market with these Machine Learning Project Ideas. Therefore, there is never a better moment to learn Python, and there is never a better way to learn Python than by doing ML projects.
Beginners Level Machine Learning Project Ideas for Final Year
1. Titanic Survival Prediction
Description:
Building this project will be enjoyable because you’ll be guessing whether or not someone would have survived if they were on the Titanic ship. You will use the Titanic dataset, which contains actual data on the passengers and crew who perished on board the Titanic, for this beginner’s project.
Dataset: Titanic Survival Prediction
Source Code: Titanic Survival Prediction (P1)
Source Code: Titanic Survival Prediction (P2)
2. Stock Price Prediction
Description:
Discovering the future worth of business stock and other financial assets traded on an exchange is made possible with the aid of stock price prediction utilizing machine learning. Gaining significant profits is the whole point of making stock price predictions. Although please note that it is impossible to predict the exact price of the stock. Other variables, including biological and psychological ones, as well as rational and irrational conduct, are included in the prediction. These forces work together to create a volatile and dynamic market for shares. Because of this, it is quite challenging to create precise stock price predictions.
Here, we’ll create a project that uses the Python programming language and the linear regression model to estimate stock values. The stock prices of a publicly traded company can be used as historical data. To predict the future stock price of this firm, we will use a number of machine learning methods, starting with straightforward ones like linear regression.
Dataset: Stock Price Prediction
Source Code: Stock Price Prediction
3. EmojiLive – Live Emoji Creator
Description:
Moving ahead in our series of Machine Learning Project ideas, the next beginner-friendly project is Live Emoji Creator. This machine learning project’s goal is to categorize human facial expressions and translate them into emojis. Basically in this project, you have to build a model that identifies the user’s face and on the basis of his/her expression, an emoji should be displayed. Apart from this, if the user changes his/her facial expression then the emoji should be capable of doing the same.
To identify face emotions, you will construct a convolution neural network. This identification of the user’s face will be done using a web camera. Once the face is identified then, you’ll associate those feelings with matching emojis or avatars.
Download Dataset: Live Emoji Creator
Source Code: Live Emoji Creator
4. Fake News Detection
Description:
There are a lot of unresolved problems with fake news detection that need to be studied. For instance, understanding the crucial components involved in the dissemination of news is a crucial first step in reducing the propagation of fake news. To determine the primary factors contributing to the propagation of false information, graph theory and machine learning approaches can be used.
This project’s main goal is to spot textual patterns that distinguish genuine news from false pieces. We’ll make use of the Kaggle dataset and feed it into the models. In order to detect “fake news,” or inaccurate news items that originate from unreliable sources, we encourage you to use a variety of machine learning and natural language processing packages, including NLTK, re (Regular Expression), and Scikit Learn.
Dataset: Fake News Detection
Source Code: Fake News Detection
5. Breast Cancer Detection
Description:
Over the past decade, machine learning techniques have been widely used in intelligent health systems, particularly for breast cancer diagnosis and prognosis. So with the help of Machine Learning, we can build a model to classify the type of cancer, so it will be easy for doctors to provide treatment at the right time. Early diagnosis of breast cancer can dramatically improve prognosis and chances of survival, as it can promote the timely clinical treatment of patients. This is a Classification problem and the main aim is to build the is model which classifies Malignant and Benign types of Cancer.
This project’s goal is to evaluate recent research that has been done to categorize these tumors. Medical images are classified into benign and malignant using machine learning methods like Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Random Forest (RF).
Dataset: Breast Cancer Detection
Source Code: Breast Cancer Detection
Intermediate Level Machine Learning Project Ideas for Final Year
1. Recommendation System
Description:
Systems that make recommendations to users based on a variety of parameters are known as recommender systems. The recommender system handles the abundance of information by filtering the most crucial information based on the information provided by a user and other criteria that take into account the user’s choice and interest. There are many different kinds of recommendation systems, including some based on content, popularity, and movies.
You may test and develop the recommendation models for your ML project using libraries like recommenderlab. Your machine learning project will also be complemented by packages like ggplot, reshape2, and data.table. For building recommendation engines, it is recommended to use datasets like Google Local, Amazon product reviews, MovieLens, Goodreads, NES, and Librarything.
Dataset: Movie Recommendation System
Dataset: Amazon Product Reviews
Source Code: Recommendation System
2. Handwritten Digit Recognition
Description:
The model’s capacity to identify human-written digits is known as handwritten digit categorization. Since everyone has a different writing style, the size and alignment of the handwritten digits may vary. Therefore, it is difficult for machines to read handwritten digits. The answer to this issue is handwritten digit classification, which uses a picture of a digit to identify the digit that is contained in the image.
Dataset: Handwritten Digit Recognition (MNIST)
Source Code: Handwritten Digit Recognition
3. Customer Segmentation
Description:
You have the data, but what kind of analysis can we perform with it? We can divide up our customer base into different groups according to how they shop. Remember that the data is quite large and that we cannot examine it with just our eyes. In order to do this, we will make use of computational power and machine learning methods. For this project, you can use Python’s K-Means algorithm to group customers into segments based on their behavior.
Dataset: Customer Segmentation
Source Code: Customer Segmentation
4. Hate Speech Detection
Description:
The model used for identifying and detecting hate speech on the internet is called “hate speech detection.” Many people post angry and insulting comments about other people on social media. So, in today’s online world, hate speech identification has become a crucial problem-solving tool. You can use Pandas, Numpy, Scikit-Learn, and NLTK.
Dataset: Hate Speech Detection
Source Code: Hate Speech Detection
5. Sales Forecasting Project
Description:
Moving on to our last project in this article on Machine Learning Projects for Final Year and Intermediate level is Sales Forecasting. The revenue generated by each of their outlets must be accurately forecasted for many retail businesses. These estimates make it possible to plan, optimize staffing, and ensure that there is enough inventory in each store. In this assignment, we create a predictive model to anticipate the revenue of each of the drugstore chain’s outlets using historical data from the company.
You must be familiar with various techniques for cleaning raw data in order to construct such ML projects. In-depth knowledge of regression analysis, particularly simple linear regression, is also required. For creating these kinds of applications, you must use libraries like Dora, Scrubadub, Pandas, NumPy, etc.
Dataset: Sales Forecasting
Source Code: Sales Forecasting
Advanced Level Machine Learning Project Ideas for Final Year
1. Sentiment Analyzer
Description:
We want you to create a straightforward sentiment analysis classifier for our project that will identify if user evaluations of movies were good, negative, or neutral. We will use the IMDb movie review dataset for this sentiment analysis in a Python project.
Projects involving sentiment analysis demand a thorough understanding of subjects like text analysis, NLP, and computational linguistics. Huggingface and TensorFlow, as well as supervised techniques like neural networks, Random Forest, decision trees, Support Vector Machines (SVM), and logistic regression, become useful machine learning frameworks. Object identification, image segmentation, and image registration are made easier by the use of libraries like OpenCV and SimpleITK.
Dataset: Sentiment Analyzer
Source Code: Sentiment Analyzer
2. Music Type Classification
Description:
The goal of this project is to create a machine learning project that can automatically identify various musical genres from audio data. These audio recordings will be categorized based on their low-level frequency and time domain characteristics. Because it has produced the best results for this issue in numerous studies, we will utilize the K-nearest neighbors approach. We have a dedicated article for K-Nearest Neighbour
We require a collection of audio tracks with a similar size and frequency range for this project. The GTZAN genre classification dataset, which was solely compiled for this work, is the most suggested dataset for the music genre classification project.
Dataset: Music Genre Classification
Source Code: Music Genre Classification
3. Sign Langauge Recognizer
Description:
Numerous technological developments and many studies have been made to benefit the deaf and dumb. Deep learning and computer vision are two tools that can be used to further the cause.
In this project, we develop a sign detector that can easily be expanded to recognize a huge variety of different signs and hand gestures, such as alphabets, and which can detect numbers from 1 to 10. This can be extended to constructing automatic editors, where a person can easily write by using just their hand gestures, which can be very helpful for the deaf and dumb in interacting with others as understanding sign language is not something that is common to all. We used the Python OpenCV and Keras libraries to create this project.
Dataset: Sign Language Recognizer
Source Code: Sign Language Recognizer
4. Credit Card Fraud Detection
Description:
A system that can monitor the patterns of all credit card transactions is required in the modern period, and if any patterns are abnormal, the transaction should be stopped. There are numerous machine learning techniques available now to categorize transactions into normal and abnormal categories. The sole requirements are historical data and an algorithm that can match our data more effectively.
It is necessary to be familiar with ideas like decision trees, gradient-boosting classifiers, logistic regression, and artificial neural networks (ANN). Libraries like NumPy, Pandas, Matplotlib, Seaborn, XGBClassifier, and frameworks like Scikit-Learn can be used to create this project.
Dataset: Credit Card Fraud
Source Code: Credit Card Fraud Detection
5. Human Activity Identification with Smartphones
Description:
Many modern mobile gadgets are built to recognize when we are performing a certain activity, like cycling or running, automatically. Machine learning is at work here. Machine learning engineers use a dataset with fitness activity records for a few people (the more, the better), which was gathered through mobile devices equipped with inertial sensors, to practice with this type of project. Then, students can create categorization models that can precisely forecast future actions. This may also aid in their comprehension of multi-classification puzzles.
Dataset: Human Activity Identification with Smartphones
Source Code: Human Activity Identification with Smartphones
Conclusion
We firmly believe that these Machine Learning Project Ideas will help you get a grip on ML. These projects are specially selected to make your resume look strong and industry ready. We encourage you to build these projects and if stuck anywhere then we have multiple articles on Machine Learning Projects.
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