We are witnessing an era of the data revolution. Every organization across the world are trying to make use of data to improve their business. As a result, the demand for skilled Data scientists is skyrocketing. We know that Machine Learning is an important part of Data Science and the Best way to learn it is, of course, practicing it. Any professional taking a Machine learning course should be doing their own projects. Practicing your lessons will help you get familiar with the common ML libraries. Here are a few projects you could try along with your machine learning training.
1. Iris Flowers Classification ML Project
It is the "Hello world" of Machine Learning. This project involves classifying the flowers into 3 different species according to the size of their petals. You can use the Iris Flowersdataset which consists of the numeric attributes of each flower. This data set is considered to be the best available in this classification genre. You will have to use Supervised Machine Learning algorithms to load and handle this data. Also, you can work on this small data without any special transformation or scaling.
2. BigMart Sales Prediction ML Project
The product of this project is a regression model that can predict the sales in 10 BigMart outlets spread across 10 different cities. You can use the BigMart Dataset which consists of the sales data of 1559 products from 10 different outlets. Using Unsupervised Machine Learning algorithms, you can predict the sales of each 1559 product in each outlet.
3. Analysis of Social Media Sentiment Using Twitter Dataset
There are huge amounts of data created by our social media platforms on a regular basis. By mining these data we can understand a lot about the trends, public opinions, and sentiments going on the world. Among them, the data created by Twitter is fund to be best suited for beginners. Using the Twitter data set which consists of around 3 MB data, you can find out what is world talking about the various topics such as movies, elections or sports. This project will help you develop skills in social media mining and classifiers.
4. Recommender system with Movielens Dataset
The modern customers are looking for more customized content everywhere. The applications like Netflix and Hulu are using recommender systems to find content matching each of their customers. This project is about making such a recommender system. You can use the Movielens Dataset which contains around 1.000,200 movie ratings of 3,900 movies made by 6,040 users. You can start building this recommender system with a World-cloud visualization of the movie titles.
5. Stock Prices Predictor
If you would like to work in the finance domain, this project is an excellent choice for you. The aim of this project is to build a predictor system which can learn about the performance of a company and forecast its stock price. You will have to deal with a large variety of data such as prices, volatility indices, fundamental indices and many more. The dataset required for this project can be found at Quandl.
These projects will introduce you to some challenges and their solutions in machine learning. Your machine learning certification will be complete only with such a hands-on experience with ML.