7 Machine Learning Projects For Beginners

The adoption of machine learning (ML) continues at a rapid pace, as it has proven itself a powerful tool for solving many problems. A good way to learn ML is by working on projects, especially those that are able to give you real, valuable experience.

In this article, we will discuss 7 simple machine learning projects, projects which will help you learn important ML skills, solidify them through experience, and ultimately improve your career prospects.

1. Titanic Survival Prediction

The Titanic dataset is great for beginners, as it has easy-to-understand data. The goal of a machnie learning project using the data would be to predict if a passenger survived the disaster or not. You will use features including age, gender, and class to help make your predictions.

More than anything, this project can help teach you how to prepare data, as well as clean data and deal with missing values. You will also learn how to split data into training and test sets. You can use algorithms like logistic regression or decision trees to build your models; logistic regression works well for predicting two outcomes, while decision trees predict based on questions associated with data splits. After training your model, you can check how well it works by using evaluation metrics like accuracy or precision.

This project helps you understand how to work with real data and evaluate your model. It is a natural starting point for those new to machine learning or those looking to start solidifying their skills.

2. Stock Price Prediction

Stock price prediction is another common ML project. In this project, you will predict future stock prices, using past stock data to make predictions. This is a time series problem because prices change over time.

You will learn how to analyze time series data, which is then used to help predict future trends using past data. You can use models like ARIMA or LSTM; ARIMA is a tried and true cornerstone of time series prediction, while LSTM is a recurrent neural network regularly used for time-related data modeling.

You will also create new features like lag values and moving averages, which will help make your model more robust. You can get stock data from Yahoo Finance, for example. You could then split your data, train your model, and check it using mean squared error, a common evaluation metric for this type of project.

3. Email Spam Classifier

This project involves making a spam email classifier, in which the resulting model will decide if an email is spam or not. This project introduces you to natural language processing (NLP). NLP is used to work with text data.

When building such a project, you will learn how to preprocess text, including techniques such as tokenization, stemming, and lemmatization. You will also turn text into numeric representation using a technique such as Term Frequency-Inverse Document Frequency (TF-IDF). TF-IDF helps turn text into numeric features that ML models can use.

To build your model, you can use algorithms like Naive Bayes or support vector machines (SVM). Naive Bayes works well for text classification, and SVM is especially good for high-dimensional data, which numeric representations of text data can be. You can use datasets like the Enron email dataset. After training your model, you can evaluate it using accuracy or other metrics like precision, recall, and F1 score, adding an additional set of metrics that you are familiar with to your arsenal.

4. Handwritten Digit Recognition

Handwritten digit recognition is a classic ML project which teaches you about computer vision. In this project, you will recognize handwritten digits from images. You will use the MNIST dataset, which has images of digits from 0 to 9.

To solve this problem, you will learn about deep learning and convolutional neural networks (CNNs). CNNs are great for processing image data. They use techniques like convolutional and pooling layers to extract features from images.

In the preparation phase, you will resize and normalize images. Then, you will train a CNN model to recognize the digits. After training, you can test the model on new images. This project helps you learn about image data and deep learning.

5. Movie Recommendation System

Recommendation systems are used by platforms like Netflix and Amazon. In this project, you will build a recommendation system which will suggest movies based on user preferences.

You will learn about two types of recommendation systems: collaborative filtering and content-based filtering. Collaborative filtering suggests movies based on what similar users like. Content-based filtering suggests movies based on what the user liked before.

In this project, try using collaborative filtering. You will use techniques like singular value decomposition (SVD), which can help make predictions easier. You can use the MovieLens dataset, which has movie ratings and information.

After building the system, you can evaluate it using metrics like root mean square error (RMSE) or precision-recall.

6. Customer Churn Prediction

Customer churn prediction helps businesses keep customers. In this project, you will predict which customers are likely to leave, using classification algorithms like logistic regression or random forests. Logistic regression is good for binary classification and are transparent in their predictions, while random forests are better for higher accuracy but sacrifice in their explanaibility.

You will work with imbalanced data. This happens when one group is much smaller than the other. You can fix this by oversampling or undersampling. You will also preprocess data by handling missing values and encoding categories.

After training your model, you will evaluate it using confusion matrices and F1 scores. You can use datasets like the Telco Customer Churn dataset, which has customer data points along with whether they left or stayed.

7. Face Detection

Face detection is an important task in computer vision, useful in security systems and social media apps. In this project, you will learn how to detect faces in images.

You will use object detection methods like Haar cascades. These are available in the OpenCV library, widely used for image processing. You will learn image processing techniques like filtering, edge detection, and face detection.

OpenCV has pre-trained classifiers for face detection. These classifiers make it easier to detect faces in images or videos. You can improve the system by adjusting the settings. This project helps you learn how to detect faces and objects in images.

Conclusion

As a beginner, these seven projects will teach you the basics of machine learning. Each project focuses on different ML skills, so you will end up learning about classification, regression, computer vision, as well as a variety of algorithms and evaluation metrics. By working on these projects, you will get hands-on experience, using real data and algorithms to solve problems.

Once you finish these projects, you can add them to your portfolio or resume, helping you stand out to employers. These projects are simple but effective for learning machine learning. They will help you build your skills and gain confidence in the field.

Best of luck in your projects.