5 Beginner-Friendly Python Machine Learning Projects
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In the previous article, I talked about how to become a machine learning engineer, and in the last step, I briefly talked about building machine learning projects, along with the types of machine learning projects you should be participating in. In today’s article, I will be talking about 5 beginner-friendly projects you can complete in a week!
It’s important to know that when building machine learning projects, it’s very important that you analyze the datasets. This allows you to check for inconsistencies and irregularities in the datasets(which they will be).
You also learn how to feature engineer your datasets, which basically means you use your existing knowledge of the field to edit your dataset, for example, you’re analyzing a medical dataset and you notice that the BMI of an individual is 0. Now we all know that for living human beings, BMI can never be 0.
Now that we have that out of the way, let’s jump right into the list:
Predicting the probability of Diabetes: Diabetes is a chronic medical condition associated with elevated blood sugar levels in the body. Diabetes often leads to cardiovascular disease, stroke, kidney, damage, and long-term damage to the extremities(that is, limbs and eyes).
One of the barriers to early detection and diagnosis of diabetes is that the early stages of diabetes are often non-symptomatic. People who are on the path to diabetes(also known as prediabetes)often do not know that they have diabetes until it is too late. The value of Artificial Intelligence in healthcare is not in replacing physicians and other healthcare workers, but rather to augment their activities. AI has the potential to support healthcare workers throughout a patient’s journey and to assist healthcare workers in discovering insights into a patient’s wellbeing using data.
The diabetes mellitus dataset provided for this project here, comes from the Pima Indians diabetes dataset, provided by the National Institute of Diabetes and Digestive and Kidney Diseases. In this project, you will learn to explore data by plotting different graphs such as histograms, learn how to split data into training, testing, and validation datasets, learn to build a multilayer perceptron model, and perform result analysis on your results.
Predicting Taxi Fares in New York: Yellow cabs in NYC are perhaps one of the most recognizable icons in the city. Tens of thousands of commuters in NYC rely on taxis as a mode of transportation around the city. In recent years, the taxi industry in the city has been put under increasing pressure from ride-hailing apps such as Uber, Lyft, etc. In August 2018, the Limousine commission of the city launched an app that allows people to book a yellow taxi from their phones. In this project, you will learn how to create an algorithm to provide fair pricing upfront, which is no simple feat.
The algorithm should consider various environmental variables such as the time of the day, traffic conditions, pick-up and drop-off locations in order to make an accurate fair prediction. The dataset that you should be used for this project is the NYC taxi fares dataset provided by Kaggle, which I will link here. The dataset provides an interesting opportunity to use big datasets in machine learning projects, as well as to visualize geolocation data.
Cats_vs_Dogs Image Classification: Computer Vision is an engineering field where the goal is to create programs that can extract meaning from images. Computer vision researchers worked on signal processing, object recognition, etc. In Image classification, the input to the problem is an image and the required output is simply a prediction of the class that the image belongs to. In this project, you will learn the basic architecture of Convolutional Neural Networks(CNN). The dataset is provided by Microsoft and I will link it right here.
Predicting if the movie reviews are bad or good: In building this project, you will learn about LSTM(Long Short Term Memory) and RNN(Recurrent Neural Network) and how can they be used in sequential problems, such as Natural Language Processing (NLP). You will develop and train an LSTM network to predict the sentiment of movie reviews on IMDB and I will link the dataset right here.
Removing Noise from Images: An autoencoder is a type of neural network used to learn efficient data codings in an unsupervised manner. Autoencoders learn a Latent Representation of the input. This representation is usually a compressed representation of the original input. All autoencoders have an Encoder and a Decoder. The role of the encoder is to encode the input to a learned, compressed representation, and the role of the decoder is to reconstruct the original input using the compressed representation. We can use autoencoders for data compression(such as converting images into jpg) and you can also use autoencoders for removing noise from images. For this project, consider using the mnist handwritten digits dataset provided by TensorFlow which I will link right here.
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