In this article, I will discuss some Python-based Machine Learning Projects for Undergraduate Students.
Although you can build Machine Learning projects using other tools, Python has its own advantage. Above all, Python offers lots of libraries that make applications of Machine Learning simple and straightforward. Moreover, python provides several packages for data visualization such as Matplotlib and Seaborn.
Beginners’ Level Python-Based Machine Learning Projects
The following projects will help you gain understanding of basic Machine Learning algorithms for prediction.
- House Price Prediction. Basically, the goal is to predict the price of a house on the basis of its location and other factors.
- Result Analysis and Prediction. Since a number of factors are responsible for the overall result of students. Here, the goal is to analysis the result to find out most important factors and to predict the result.
- Attendance Analysis. In order to find the presence of patters in attendance data we can use machine learning algorithms.
- Disease Prediction. Since it is important to predict the onset of a disease early, we can use machine learning algorithms for it.
- Russia Ukraine War Sentiment Analysis. Since tweets provide us a way to determine user sentiments, we need to analyze the tweets.
- Product Reviews Sentiment Analysis. Since users’ reviews has a great role in determining the chances of a product to be sold, we need to perform review sentiment analysis.
- Sales Prediction. When we want to predict the value of future sales of a product, we can use a basic algorithm like Linear Regression.
- Payment Fraud Prediction. In order to determine the possibility of a payment fraud we can use a binary classification technique.
- Weather Forecasting Using Time Series Analysis
Advanced Level Python-Based Machine Learning Projects
The following list shows the projects that require use of Machine Learning algorithms along with an understanding of Artificial Neural Networks (ANN) and its variants such as CNN (Convolution Neural Network), and LSTM (Long Short Term Memory).
- Image Segmentation. When we need to determine the presence of a certain object inside an image, first we need to perform image segmentation.
- User Emotion Analysis. In order to perform Emotion Analysis we can use Transfer Learning and a pre-trained model.
- Stock Market Prediction. Although we can use Linear Regression for this, the use of LSTM gives more accurate prediction.
- Image Classification. In order to classify an image as belonging to a specific category, we can use a Convolution Neural Network (CNN).
- Dot Net Framework
- Power Bi
- Scratch 3.0