Machine learning is the fastest-growing area, and Python is very popular for building a machine learning model. In fact, diving into hands-on projects is one of the best ways to learn new skills, understand things, and build a portfolio. If you require a few milligrammes, you grind as much as you can afford, and if you can afford a couple of pennies, you spend the coins on a precise scale, which you use in the morning to dig out as much as you can that day, and then you repeat this process. The basics aren’t any different; here are some beginner-friendly machine-learning projects you can start with Python:
Iris Flower Classification
Design an ML model that can classify iris flowers by grouping the flowers into various species that differ in characteristics like petal length and width. One of the most famous datasets for beginners in machine learning is the Iris dataset. With only a few features, this project allows you to understand how to work with classification algorithms, and you can use algorithms like logistic regression or k-nearest neighbours (KNN) for this task.
Key Skills:
- Classification algorithms
- Data pre-processing
- Visualisation using libraries like Matplotlib or Seaborn
Tools:
- Scikit-learn
- Pandas
- Seaborn
House Price Prediction
This would be to create a regression model that can be used to predict house prices with other salient features that would be measured, such as square footage, number of bedrooms, etc. A regression algorithm which is used to predict continuous values like the prices of houses is what we have here. There is also the need to work with large data, feature engineering, and understand which factors play the most important role in the final prediction.
Key Skills:
- Linear regression
- Handling missing data
- Feature scaling
Tools:
- Scikit-learn
- Pandas
- NumPy
Handwritten Digit Recognition (MNIST Dataset)
How to recognise the handwritten digits in the MNIST database.
And it’s a great introductory project if you want to understand image data. In this, we’re going to build a machine or deep learning model that can recognise manually written digits from 0 to 9. You can begin with some simple K nearest neighbours or neural networks using tensorflow or kera.
Key Skills:
- Image classification
- Neural networks
- Model evaluation (accuracy, precision, recall)
Tools:
- TensorFlow/Keras
- Scikit-learn
- Matplotlib
Titanic Survival Prediction
It predicts which one of the passengers on the Titanic disaster willingly survived.
The tasks are classified, and the Titanic dataset is quite popular as a data analysis + machine learning teaching project. You are free to test various preprocessing techniques and algorithms to build a more improved model.
Key Skills:
- Data cleaning and pre-processing
- Logistic regression, decision trees
- Handling categorical variables
Tools:
- Scikit-learn
- Pandas
- Seaborn
Stock Price Prediction
Using historical data, the model will predict future stock prices.
In this project, we are actually dealing with the time series data or forecasting the stock prices. It is a hard project for beginners, but at the same time, you will get very hands-on experience with Regression techniques and Time series forecasting. Best of all, you can use a slightly more advanced model, such as Long Short-Term Memory (LSTM) networks, if you have time.
Key Skills:
- Time-series analysis
- Regression models
- Data visualisation
Tools:
- Scikit-learn
- Pandas
- Matplotlib
Customer Segmentation Using K-Means Clustering
Group cluster customers by behaviour (e.g., past purchase history, demographics). In this project, it introduces you to artificially so-called unsupervised learning algorithms like clustering as an example. The segmentation of data and finding a pattern that you couldn’t possibly figure out is an amazing tool which means clustering.
Key Skills:
- K-Means clustering
- Dimensionality reduction techniques
- Data visualisation
Tools:
- Scikit-learn
- Pandas
- Matplotlib
Conclusion
These projects are a great source for beginners to increase their experience in Python if you’re looking to increase your experience in it. You will gain valuable experience with some of the strongest Python libraries for machine learning, Scikit-learn, TensorFlow and Pandas by completing classification, regression, clustering, and NLP tasks. If you’re preparing for the world of data science, just starting, or even just trying to improve your coding skills, these projects will really help you solidify your knowledge. The London School of Emerging Technology(LSET) knows hands-on projects are important when it comes to Python with Machine Learning, so at LSET, their course on ML with Python gives much more importance to the practical aspect of ML, and students need to work on their project provided for hands-on experience. Students get a higher advantage in the job market when they come with experience and projects in hand. This Halloween, students will get a 10% discount on the existing 20% discount on Upfront payment on all LSET Courses. The offer will be valid till 31st of October.