The Be-All End-All Guide to Building Your Data Science Portfolio

Less than 10 years ago, machine learning simply involved theories. It has now altered the method we use to interface with devices. Technology is becoming more and more human-like with machine learning leading the way. We’ve put together a complete roster of data science designs that provide needed projects for your portfolio with machine learning algorithms. Read on to find out different projects that are taught in many high-level courses:

Disease Detection: You put the K-nearest neighbor to use with help from the algorithm to expose breast cancer indications with help of a vector machine.

Credit Card Scam Exposure: You will be performing exposures to credit card fraud in this one while focusing on anomaly discovery through the application of probability densities.

Stock Market Grouping Algorithm: You are to put a K-means clustering algorithm to use in recognizing similar companies through correlating stock market progressions in a specified time frame.

Perhaps the best data set in classification history is the Iris flowers dataset. The record of this machine learning classification is typically regarded as the proverbial “Hello World” of data science. This dataset has numeric properties that novices need to consider when loading and controlling information. The iris dataset is also and goes into the memory easily and doesn’t need a particular kind of scaling or transformation to get started.

The objective of this project in your data science portfolio is to arrange the flowers into a few species — setosa, versicolor and virginica depending on the width and length of the sepals and petals. Websites such as Facebook, Twitter, Reddit and YouTube produce enormous quantities of big data to be mined in different ways for understanding public attitudes, opinions and trends. Sentiment analyzers examine different opinions regarding “content pieces” using data science and estimates through the application of AI technology.

Twitter information is also thought to be classic practice for beginners of opinion analysis. With a Twitter dataset, an amazing mix of tweet contents like retweets, hashtags, location and more can be had to pave a path for shrewd analysis. Twitter information allows you to determine what others think about a subject different topics like movies or other popular subjects from around the world.

Social media datasets are going to give you an understanding of the difficulties connected to machine learning with social media, and in turn, figure out things such as classifiers with more acuity.