4 ML methods for prediction and personalization every data scientist should know
Companies are looking for more ML talent. Prove you have the machine learning knowledge to get a data science job in one of the best fields in the US. In this article, Yana Yelina explores four of the most common methods for ML algorithms.
Machine learning has long ceased to be futuristic hype and become ever more commonplace in the tech world. An array of companies are currently capitalizing on ML to quickly adapt to tectonic shifts in clients’ expectations and craft more personalized offerings.
Such a burning need for machine learning solutions leads to a high demand in adept data scientists. Not for nothing Glassdoor ranked this career #1 in their yearly list of 25 best jobs in the U.S.
However, to outsmart rivals and become an odds-on favorite for leading positions in high-profile companies, you should be well-versed in advanced ML-powered techs. In this article, we’ll walk you through four methods the knowledge of which will help you pull off the job offer.
1. Clustering algorithms
Falling under the family of unsupervised ML algorithms, clustering is used to analyze unlabeled data, segregate it into groups with similar traits, and assign into clusters. This is a subjective task, so you can use different algorithms to solve it.
Among the most popular ones is the k-means algorithm. It starts with estimating the centroids for clusters, the number (k) of which you define in advance. The second step consists in assigning data sets to the nearest centroid — based on the Euclidean distance. After that, the centroids for all clusters are recomputed.
The algorithm iterates between these two steps until a stopping criterion is fulfilled — in other words, until improvements are possible. It may happen, for example, when the maximum number of iterations is achieved or the sum of distances is minimized.
The applications of clustering are numerous across industries and business domains. This ML method can be used for document classification (based on tags, topics, etc.), customer segmentation (based on their purchasing history, app behavior, etc.) and recommendation engine development, social media analysis, anomaly detection, and more.
2. Regression analysis
In a nutshell, regression is a supervised ML method for defining relationships between a dependent (target) and independent (predictor) variable. Namely, this modelling technique can be used to:
- determine the strength of predictors over dependent variables: in practice, it might be the strength of relationship between sales and marketing spending, or age and income;
- forecast the outcomes when independent variables change: for example, predict additional sales income one will get by increasing the marketing budget;
- get point estimates, i.e. predict future trends, like the price of Facebook’s shares or bitcoin’s value in a year.
Regression comes in many different forms, with linear and logistic modelling techniques being the most popular ones.
A statistical type of analysis, linear regression analyzes various data points to define which variables are most significant predictors and to plot a trend line (disease epidemics, stock prices, etc.). Based on the number of independent variables, linear regression can be single or multiple.
This ML method is utilized to predict data value based on prior observations of data sets. Applying this method to customer service, it might be analysis of historical data on shopping behavior for tailoring more personalized offerings.
3. Association rules
Another ML method that every data scientist should learn to be in high demand is association rules. A popular technique for uncovering interesting relationships between different variables in huge data bases, association rules are actively harnessed to build recommendation engines, like those of Amazon or Netflix. Simply put, this method allows you to thoroughly analyze the items bought by different users (transactions) and define how they’re related one to another.
To understand the strength of associations among these transactions, the algorithm uses various metrics:
- Support helps to choose from billions of records only the most important and interesting itemsets for further analysis. You can even set a specific condition here, for example, analyze only itemsets that occur 40 times out of 12,000 transactions.
- Confidence tells us how likely a consequent is when the antecedent has occurred. Exemplifying it with products, — how likely it is for a user to buy a biography book of Agassi when they’ve already bought that of Sampras.
- Lift controls the consequent frequency to avoid a negative dependence or a substitution effect. A case in point: the rule may show a high confidence value for products that have a weak association. Lift takes into account the support of both antecedent and consequent to calculate the conditional probability and avoid such a fluke.
4. Markov chains
Last but not least, Markov chains are a common way to statistically model random processes. This method is used to describe a possible sequence of events (transitions) based solely on the process’ present state, independently from its full history.
Let’s assume our state space — a list of possible states — has two states: A and B. According to Markov chains, we get four potential transactions, with different probabilities of transitioning from one state to any other.
It stands to reason that the more current states you have, the more sequences of events are possible. To tally all transition probabilities, it’s handy to build a “transition matrix”.
Considering the fact that Markov chains make use of just real-time data without taking into account historical information, this method is not one-size-fits-all. An example of a good use case is PageRank, Google’s algorithm that determines the order of search results.
However, when building, for instance, an AI-driven recommendation engine, you’ll have to combine Markov chains with other ML methods, including the above-mentioned ones. To wit, Netflix uses a slew of ML approaches to provide users with hyper-personalized offerings.
Being in the loop about clustering, regression analysis, association rules, and Markov chains will certainly give you a leg-up when applying for a good data scientist job. Yet, it’s certainly not the full list of ML algorithms for prediction and personalization. Once you’re done with these ones, you can proceed to learning elastic nets, random forests, singular value decomposition, and others. But that’s a topic for another article.