Top 10 Machine Learning Classification Projects
Python is the most trending language today. It is witnessing incredible growth and popularity year by year. Its popularity is mainly because of its simple programming syntax, code readability, large and fast-growing user community. The kind of libraries and framework python provides makes work more productive and easier. With the rise of Data Science i.e. Machine Learning and Artificial Intelligence, it has come into the focus of attention.
In the article, we will be going to see 10 Machine Learning classification projects. But before proceeding further let’s understand what Machine Learning and Classification is?
The machine is the idea that allows the machine to learn from the examples and experience without being explicitly programmed. Machine Learning uses a computer program to identify patterns in the data. It automates things that researchers have been doing for years. It provides statistical tools to explore the data. Basically, we can say Machine Learning is the process of teaching the computer system how to make accurate predictions when data is fed to it. It is all about creating and implementing an algorithm that let machine receive data and use this data to
- Make predictions
- Analyze Patterns
- Give recommendations on its own
- Classifying objects
In Machine Learning we have three different approaches that are Supervised, Unsupervised, Reinforcement learning. We will not get dipper into these topics and now try to understand about Classification.
Classification is a supervised machine learning algorithm. It is the technique of categorizing given data into classes. In classification, the output is a categorical variable where a class label is predicted based on the input data. A class is selected from a finite set of predefined classes. The classes are also called as targets, labels, or categories. There are various classification algorithms available such as Logistic Regression, Naive Bayes Classifier, K-Nearest Neighbor, etc.
The classification has various applications. To get a practical insight into it you need to work in real-time. So, now let us move forward towards the classification projects which will help to gain real-world experience.
- Iris Classification
The Iris Flower Dataset is the machine learning project which is one of the best datasets for classification. The goal of this project is to classify the flowers into among the three species – virginica, setosa, or Versicolor based on length and width of petals and sepals. This project is often referred to as the “Hello World” of machine learning. The dataset is small and easy to handle.
- MNIST Dataset
The MNIST dataset which is an acronym that stands for the Modified National Institute of Standards and Technology dataset. It is a dataset of handwritten digits which contains more than 60,000 training images and 10,000 testing images. In this, the task is to classify the image of a handwritten digit from 0 to 9. This dataset is ideal for anyone starting image classification. This dataset is often considered as ‘hello world’ of object recognition for machine learning and deep learning.
- Credit Card Fraud Detection
This dataset contains data about the transactions made by credit cards by European cardholders. In this project, you have to recognize the fraudulent credit card transactions. You have to Classify the transactions into valid and fraud cases so that the customers of credit card companies are not charged for items that they did not purchase. It is a great problem to learn about classification.
- Email Spam Classification
Email Spam Detection is perhaps one of the most popular document classification tasks for beginners. This dataset has two columns. The first one is labeled which is either ham or spam which is a fancy way of saying whether the email is spam or not. The next columns contain email text based on which we will be classifying our emails. So, the task in this dataset will be to classify the emails into a spam or not spam.
- Customer Churn Prediction
Customer Churn refers to the process of identifying all the possible the customer or clients who will terminate their relations with the company. It is a very important factor for any organization as it used to estimate the growth of the organization but also for predicting trends of future customers. The task in this project is to classify the customer on whether they will stay with the company or terminate their interrelation.
- Diabetes Prediction using PIMA Diabetes Dataset
This dataset contains information on all female patients and their medical features. The task of this dataset is to predict whether or not a patient has diabetes based on the medical parameter given. You have to classify the patient as ‘1’ when the patient has diabetes and ‘0’ if the patient doesn’t have diabetes.
- Breast Cancer Detection
This dataset contains the information of features of breast cancer patient cells and normal person cells. Features are taken from a digitized image of fine needle aspirate of a breast mass which specify the cell nuclei present in the image. The task in this dataset is to build the ML model to classify malignant and benign tumors.
- Fake News Detection
Nowadays as is social media dominating our lives it has become very important to distinguish between fake news from real news. With the growth of social media platforms, the spread of fake news is also very rapid. The task of this project is to use a text classification approach to create an ML model that will differentiate between real and fake news.
- Human Activity Recognition
This dataset contains the fitness activity of various peoples captured from their smartphone with inertial sensors. The goal of this dataset is to build a classification ML model that can accurately identify human fitness activities.
- Mall Customer Dataset
This dataset contains information about the people visiting the mall. Based on the features present in the data you have to divide the customers into different groups on their behaviors.
Here is the list of machine learning classification project ideas. Only by doing practice and working with ML tools, algorithms you will get real-world exposure to ML. To do practice you can these datasets and many other datasets on websites like Kaggle, UCI machine learning repository. I hope this article will help you to get the basic idea of the implementation of Machine Learning in our day to day life.