10 Data Science Breakthroughs That Solve Business Problems

Let’s be honest, being a data scientist is hard. A data scientist needs to be capable in computer programming and have deep knowledge in statistics and machine learning, the latter being an active research field. This means that the tools used by data scientists are ever-changing and evolving. Keeping up to date with every data science breakthrough and making the most out of the tools requires time and expertise. 

Furthermore, data scientists have to adjust existing tools to their own needs all while often needing to develop new ones from scratch. 

With our data science breakthroughs, we’re doing this hard work for you. That leaves you the most important task: being curious about your enterprise and how you can improve your business. In the following points we will review some of the innovative capabilities of our platform.

1. Automated merging of numerous data sources

Companies typically store different information on their customers on different tables. For example, one table holds static information such as gender, address, age, etc., while the other has information regarding actions their customers took in the past. 

Extracting information from various tables where the amount of data on specific customers can vary demands experience in data handling libraries (e.g., pandas). It’s also very, very time-consuming. With our platform, joining tables is as easy as clicking a button. 

2. Missing data imputations by autoencoders

Many datasets contain missing values. Some algorithms tolerate missing values, and the fact that a value is missing might prove informative. However, imputing missing values is often necessary and sometimes valuable on its own. 

One way of dealing with missing data is to drop samples that contain a missing value. Unfortunately, this has the disadvantageous consequence of losing data. Another straightforward method is using the mean (or the most frequent value, in case of categorical features). But doing so discards possible interaction between the features and is suboptimal. 

More sophisticated data science technologies make use of neural networks and, specifically, autoencoders to model the underlying patterns of the data. They then use this model to infer the missing values. At Pecan we developed our own autoencoder-based machinery for this purpose.

3. Data leakage prevention

In the process of preparing the data for a learning task, you don’t want to let the algorithm receive access to information that in reality, it will not have. A breach in this respect leads to what is called data leakage. This means, at the very least, that the results in deployment are worse than in testing. 

At Pecan, we associate every observation with a time period. This approach ensures that no data from the future leaks into the model, thus reducing the chances for leakage.

4. Encoding and embeddings

Dealing with categorical features is not trivial for machine learning since algorithms cannot handle non-numerical data. Consequently, categories should be replaced, somehow, by numbers. A common approach is the one-hot encoding. However, this does not work well when the number of categories is large. On the other hand, replacing categorical values arbitrarily with numbers makes the algorithm think that there is ordering among the categories. 

For example, if the entries USA and France are replaced with 1 and 2, respectively, then the algorithm is forced to handle France as ‘2 times USA.’ How to handle this problem optimally is still an open problem. 

But the trend is to embed the categorical values in some high-dimensional space so that their relative location is meaningful for the problem at hand. This process usually involves training a neural network. Our platform does this automatically. 

5. Automated hyperparameter optimization

Most of the more powerful machine learning models come with a set of parameters that are fixed prior to the learning process and determine how the model trains on the data. In some cases, the default hyperparameters perform well (e.g., LightGBM) and in others, there is just no default (e.g., neural networks). The search for better hyperparameters consists of assigning specific values and seeing whether the performance increases. This process is very time consuming since the evaluation of every assignment requires model training and certain intuition is needed regarding which assignments are more promising. 

Automatically finding the best hyperparameters with the least number of assignments is the purpose of automated hyperparameter optimization. From the variety of algorithms that perform this task, we chose those that use a Bayesian approach. This means that based on previous evaluations a probabilistic model of the loss function is built that guides the next assignment.

6. Leveraging all the (really) big data, till the last byte

When the amount of data gets really big, standard machinery fails. Our data science innovations are based on Spark, which lets you use all your data, making sure that the learning models are exposed even to the tiniest of nuances.

7. Model ensembling for superior performance

Every machine learning algorithm has its disadvantages. Often the best performance is achieved by harnessing multiple algorithms together. Our platform trains several different algorithms and then combines their predictions to obtain superior performance. 

8. Feature importance at the individual level

After learning has occurred, it is very informative for the entrepreneur to understand what in the data has the most effect on the behavior of her clients. This is called feature importance and gives a global point of view on the task. You can obtain deeper understanding by analyzing the model on the sample level, thus enabling you to take actions targeted at individuals. Our data science technologies provide both.

9. Clustering, targeting specific groups

This is a type of unsupervised learning aimed at finding meaningful intrinsic structures within the data by grouping similar points together. The division of the data into similar segments enables decision-makers to target specific groups of individuals with campaigns better suited for them. This is another capability of our platform.

10. Preventing feature drift automatically

A fundamental assumption in machine learning is that the date that will be encountered in deployment comes from the same distribution as the data used for learning. Deviations from this assumption usually result in a degradation of the performance of the model. 

Our platform automatically tracks the performance of the models and schedules a new training session whenever necessary.

We believe that with these data science breakthroughs, we’ve solved some of the most burdensome challenges facing data scientists. The road is clear now for building accurate models in a much shorter time frame.

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