Top Machine Learning Projects With Python Code
Machine learning is gaining tremendous demand in the software industry, and this is not only from the recruiter side. In the upcoming placement sessions, many freshers have started targeting ML or the Data Science industry for three primary reasons: 1. Higher package 2. Smart work 3. Future stability.
However, the central issue in getting the resume shortlisted by ML companies is that they seek projects in Machine Learning and Data Science. Some companies use ML algorithms to shortlist resumes with ML projects automatically. So, in this article, we will list some popular projects that can be mentioned in the resumes to increase the chances of getting shortlisted for ML interviews. All these projects are in Python; hence, you are advised to revise the fundamentals of Python. Also, ML projects need a proper pipeline for smooth completion; therefore, one can look at the guide to building an ML projects blog.
We will categorize these projects into two major categories: 1. Supervised learning projects and 2. Unsupervised learning projects. Further, these big categories can be divided into smaller sub-categories based on the nature of input data. One can learn this categorization separately inside the blog of Classification of Machine Learning models on five different bases.
Supervised Learning Projects
Supervised Learning is a type of Machine Learning where machines learn to map a mathematical function between 'known' input and 'known' output variables. It can be further classified into two major sub-categories based on the nature of the input data: 1. Classification Projects 2. Regression Projects.
‣ Classification Projects
Classification is a type of Supervised Learning where the 'known' output is categorical or qualitative data. For example, classifying emails into Spam and non-spam categories. One can learn about all types of data in the Data Pre-processing of Structured Data in Machine Learning blog.
• Projects with Structured Data
Structured datasets contain a well-defined structure and are generally stored in Excel or CSV formats. These datasets require data Pre-processing of structured data before feeding it into the machine learning model. Classification projects with structured data are:
1. Cancer Classification using Machine Learning
Cancer classification is one of the classical projects and a part of every curriculum on Machine Learning. This project uses SVM (Support Vector Machines) to classify the cells as malign and benign based on the fluid's properties. One shall include this course to gain attention from companies working with medical diagnoses.
Here is the complete code for the cancer classification project.
2. Uber Surge Price Calculator
Uber is not a taxi provider company; it's a Machine Learning company. It uses ML technology to upscale the business in almost all its verticles. This project lists ML usage by Uber and demonstrates one of the use cases of calculating the surge price multiplication factor. Cab service companies decide the fare between the origin and destination locations using this surge multiplication factor based on the cab demand. This project uses the Random Forest algorithm for this demonstration.
Here is the complete code for the Uber surge multiplier prediction project.
3. PUBG Cheater Detection Using ML
PUBG, also known as BGMI, is a top-rated online mobile game among the youth. As the game is online, there are high chances of encountering cheaters/hackers, which can adversely affect growth. Hence, companies use advanced ML techniques to detect the presence of cheaters in the battleground and suspend their accounts. This project uses the Random Forest algorithm to detect the cheaters in the PUBG game.
Here is the complete code for the PUBG cheater detection project.
• Projects with Unstructured Data
Unstructured datasets do not contain any predefined structure in them. Some famous examples of these datasets are Audio signals, text documents, and Images. Textual datasets require text data pre-processing and then word-vector encoding to make machines understand this data. Classification projects with unstructured data are:
‣ Projects having text data involvement are:
1. Email Spam, non-spam filtering using Machine Learning
Mailing companies like Gmail, Outlook, and Yahoo are heavily investing in their technology to provide security to their users. One possible method is segregating spam emails automatically to avoid phishing attacks. This project demonstrates the capability of Machine learning in the cyber-security domain, where the ML model classifies emails into Spam and non-spam categories based on internal textual content. It uses the KNN classifier for this task.
Here is the complete code for the email spam classification project.
2. Twitter Sentiment Analysis
Sentiment analysis is a machine learning technique companies use to understand the sentiment of their customers. Customers write online reviews, and companies classify these reviews as positive, negative, and neutral. This project is trendy in Machine Learning and is considered one of the best projects to work with text data. The referred blog uses the Naive Bayes algorithm to predict the sentiment of Twitter users via tweets.
Here is the complete code for the Twitter Sentiment Analysis project.
‣ Projects having Image data involvement are:
1. Optical Character Recognition
OCR (Optical Character Recognition) is one of the most innovative uses of Machine Learning in the real world. Many companies, including tech giants Microsoft and Google, are utilizing the benefits of recognizing characters from text documents. This project demonstrates how a simple linear model of Logistic Regression can recognize handwritten characters after learning from the famous MNIST dataset.
Here is the complete code for the Handwritten Digit Recognition project.
‣ Regression Projects
Regression is a type of supervised Learning with 'known' output present in the form of continuous, numerical, or quantitative data. The datasets are primarily structured; hence, we need structural data pre-processing techniques. Regression projects with structured data are:
• Projects with Structured Dataset
1. Life Expectancy Prediction
This is one of the classical Machine Learning projects and is very popular among the freshers attempting to learn Machine Learning or Data Science. Expected life is directly linked with the development index of any area. Hence, organizations like WHO use ML techniques to predict the Life Expectancy of people living in any part of the world. This project uses the Linear Regression algorithm to fit the input features (GDP, mortality, population, income, etc.) to the output of expected life in years.
Here is the complete code for the Life expectancy prediction project.
2. Drug Discovery Using Machine Learning
Machine learning helps medical science discover drugs quickly. Earlier drug discovery took significant time, and the disease persisted for longer. ML played a vital role even in the COVID-19 vaccine's quick discovery. This project demonstrates the capability of ML to assist and tackle newly emerging and spreading diseases. It uses the XG-Boost algorithm to predict the effect of various compounds on a particular target protein and then uses this effect to discover the drug.
Here is the complete code for the drug discovery project.
Unsupervised Learning Projects
Unsupervised Learning is a type of Machine Learning where machines try to fit a function on 'known' input and 'unknown' output. Here, machines internally generate the pseudo output and then fit a function on input and output pairs. This area of projects is the true future for the upcoming machine learning era. The reason is that supervised learning algorithms are highly dependent upon data labeling, which is time-consuming and expensive.
We can divide the unsupervised learning projects into two major categories: 1. Clustering 2. Dimension Reduction.
‣ Clustering Projects
Clustering is an unsupervised technique where a machine groups similar data samples together, and this group is called a cluster. Popular algorithms for this technique are k-means and Hierarchical clustering.
1. Personality Prediction using Machine Learning
There are five main types of human personalities: openness, neuroticism, agreeableness, extroversion, and conscientiousness. This project groups persons into these five personalities based on the traits shown on their social media platforms. It uses the most famous k-means clustering algorithm in machine learning.
Here is the complete code for the personality prediction project.
2. Music Recommendation System Using Machine Learning
Recommendation systems are the most used ML technique in today's world. Amazon and Netflix are the two biggest and most successful tech companies because of their strong recommendation system. This project demonstrates how ML can be used to recommend music to users based on their precious music listening history. It uses the k-means algorithm to develop this recommendation system.
Here is the complete code for the Music Recommendation System project.
‣ Dimension Reduction Projects
Dimensionality reduction is one of the prime applications of unsupervised Learning. Here, machines try to represent the same amount of information with fewer features. For example, converting ten into three features and retaining most information. Popular algorithms for this are PCA and t-SNE.
Image Compression using Principal Component Analysis
Image data consumes higher bandwidth, and hence, there is a need to reduce the size of the images for transmission. Machine Learning can help with that as well. This project uses PCA (Principal Component Analysis), a dimension reduction technique that compresses the image by 80% with the minimum loss in information.
Here is the complete code for image compression using PCA.
Conclusion
In this article, we have listed some popular Machine Learning projects and provided links to their complete codes. We suggest first going through the step-wise implementation mentioned in the blogs, self-implementing them, and using the codes for assistance.
Enjoy Learning, Enjoy Algorithms!
