Machine Learning and Artificial Intelligence are evolving at a rapid pace every single day. Their impact is going to be so huge that it will completely alter the way technology functions. It is going to be very challenging for Data Scientists to keep up with the innovations, hence they need a thorough grip on the subject. As a beginner, you should expose yourself to maximum real-life cases wherein you can hone your Machine Learning skills. This is the best way to strengthen your skills.  So, how do you go about it? Real-life Machine Learning projects are the answer. 

Real-life business problems are complex and dynamic. Dealing with these problems is the most crucial part of Machine Learning. In this blog, we are enlisting 10 Machine Learning projects that will help you to ease your way into the field as pick essential skills. These projects are simple to understand, easy to relate and fun to do. 

(1) Transaction Prediction With GNY

GNY is a group that offers a unique Machine Learning Platform that is free to download. This platform also has an inbuilt Blockchain which protects the user data. It has a version that has a set of retail transactions and a collection of selected Machine Learning Codes. This is a breezy project for those who want to forecast future repeat buyers on the basis of purchase history. 

Dataset Download: MLWave

(2) Autokeras

Developed by DATA Lab at Texas A&M University, this is open-source software for Automated Machine Learning. Currently, AutoKeras is only compatible with Python 3.6. Keras has a built-in NIST dataset. You can also load the ImageClassifier from Auto Keras. The main aim of the Auto Keras is to provide easily accessible Deep learning Tools to professionals with minimal machine learning or data science background. Using this software, a programmer with beginner level Machine Learning expertise can apply algorithms to achieve superlative performance with fewer efforts. 

Highlights of the Project

  • High-level neural networks API written in Python.
  • Allows easy and fast prototyping along with its prominent features.
  • Compatible with: Python 2.7-3.6.
  • Supports convolution as well as recurrent networks, and also the combinations of these two networks.

(3) Iris Flower dataset

The Iris flower dataset offers a great introduction to data classification. It requires you to learn how to explore layers of data as well as how to load it. The data has four categories: Petal length, Petal width, Sepal width, and Sepal Length. The goal of this project is to classify the flowers into four species based on the properties of the flowers. This dataset allows you to use a supervised and unsupervised learning algorithm. This project uses the multiclass classification here, which means you must be able to accurately predict which class a data point belongs to. 

Highlights of the Project

  • You can download the data here UCI Machine Learning Repository
  • The Iris flower dataset is extremely small and there is no need of pre-processing.
  • To the task of this ML project is to classify the flowers into among the 3 species – Virginica, Setosa, or Versicolor.
  • You can get the source code from GitHub.

(4) Stock Market Predictions (Quandl)

The goal of this project is to predict the future price of the stocks using fundamental and technical indicators. To start off, you can take up a simple machine learning example where you can forecast the 6-month price movement on the basis of the fundamental indicators which you can find in the company’s quarterly or annual report. The Stock Price indicator project needs to handle data of various types and from various sources like indices, economic data, fundamental and technical indicators, etc. The stock market usually has shorter cycles which can help you to validate your predictions. 

Dataset download:

Highlights of the Project

  • You get to work on challenging data sets. The stock price data is granular, and there are a variety of elements like volatility indices, prices, fundamental indicators, and etc.
  • There is absolute flexibility. If you are just starting out, then you can restrict the scope of the project and only predict six-month price movements based on quarterly organization report. 

(5) Sports Score Predictor

Analytics is revolutionizing sports. The world of sports has a data deluge and it is being increasingly used by Analysts and sports companies. They do it to predict the outcome of the sports, designing new techniques, talent scouting and finalizing the game plan. Now you can also use this data and design creative machine learning projects. You could apply it to your college or office sports data and predict player performance, enhancing team management and forecasting scores. With a humongous amount of sports data available, you can sharpen your data visualization and data exploration skills. 

Data Download:

Read more: AI & MACHINE LEARNING IN SPORTS – CAN IT CHANGE THE GAME?

(6) Sentiment Analysis On Social Media

Today, anything and everything generates a lot of emotions and sentiments. The social media is full of reactions and comments to every possible event. Now, analyzing these sentiments is very crucial because it helps to understand how the customers are responding to a particular thing. How they are perceiving a situation, whether the campaign is working or not etc. Analyzing a sentiment will help you to design a model to approach a solution pragmatically. You can work on fun projects like analyzing sentiments after a movie release, election results, Union Budget, etc.

For data, you can access Reddit, Quora, Twitter, Facebook, LinkedIn, etc. All of them offer APIs for retrieving the data. However, Twitter is the most preferred one due to the homogeneity of the data format. It can also be easily integrated via Python.

Highlights of the Project

  • This is suitable for beginners in python.
  • You are work on social media post, short tweets, or reviews of the customer on the basis of system requirements.
  • For beginners, twitter data can be helpful because a tweet contains a hashtag, location and many such indicators which makes it easy for analysis.
  • This project also allows you to build a model to classify data as positive or negative.

(7) Recommender Systems (MovieLens)

One of the most widespread applications of machine learning is to recommend content, product/ services to the viewers or customers. You find it everywhere in your life. The Machine Learning algorithms study the consumption pattern and recommend similar things to the consumer. These are of two types: Content-Based and Interaction Based. Movielens provides a comprehensive dataset for movie ratings and you can experiment on a project with Movielens. You can study algorithms to predict which movie the users will prefer based on their ratings.

Data Download: Grouplens.org

Highlights of the Project

  • Movielens Dataset is a huge database that consists of 1,000,209 movie ratings of about 3,900 movies.
  • You can use both languages i.e. R and python, to develop the system.
  • This machine learning project is helpful for beginners.
  • You can build a movie recommender system, by creating a world-cloud visualization of the movie titles.  

(8) Healthcare Analysis

Remote patient monitoring, telemedicine, healthcare wearables, robotic surgery, etc. are all products of Machine Learning in the healthcare sphere. The healthcare and medical industry have a huge amount of data at their disposal. So, it will be a great learning experience for you to develop an interesting Machine Learning project based on healthcare.  The designed algorithm can create a Diagnostic care system as well as Preventive care applications

Data Download:

 WHO: Global Health Observatory (GHO) data

http://healthanalyticsindia.com/

(9) Boston Housing Price Prediction

The goal of this Machine Learning Project is to forecast the selling price of a new house by using ML principles on the house price data sets. The Boston House price data set is relatively small and has 500 odd observations.  This is a good project for beginners to get practical experience in the regression concept. The dataset has a total of 14 attributes which demographic specifications, non-retail business areas, crime rate, and various others.

Data Download

U.S Census Service

(10) Predicting Wine Quality Using Wine Quality Dataset

The main motive of this Machine Learning project is to develop a model to forecast the quality of wines based on various chemical properties. Wine quality certification is based on tests like the physiochemical, determination of density, alcohol quantity, volatile acidity, fixed acidity, pH and more. The dataset is huge and comprises of around 5000 observations. 

Data Download:

Wine Quality Prediction in R

Highlights of the Project

  • This project will teach you about data exploration.
  • You must know about the regression models to develop this project.
  • You will learn the ropes about data visualization.
  • You will also learn about R and fundamentals of statistics.

Bottom Line

These machine learning projects are tailor-made for beginners to help them sharpen their applied machine learning skills through interesting real-life use cases across domains such as Retail, Finance, Insurance, Manufacturing, and more. So, add these projects to your kitty and grab the opportunity to stand out as an expert Machine Learning professional. 

Read more: 8 Ultimate Myths About Machine Learning