We all know that the success of Data Analytics depends largely on the quality and format of data. This fact is equally true when it comes to Machine Learning. Machine Learning models require data in particular formats. Transforming the data – or what we call ‘features’ in the ML lingo – is called data pre-processing. We at Aceso Analytics engineer and prepare raw data to train your ML model as effectively as possible. Ofcourse, our go-to tool for this is Tensorflow, powered by Keras.
Data Preprocessing takes 70% of the analytics work. Let us handle the preprocessing stage so that you can focus on what’s important.
When it comes to Machine Learning, Data Processing is more comprehensive than what you see in standard data analysis for other purposes.
Broadly speaking, our Data Preprocessing includes:
Any work that has data in it begins with the assessment of data. Machine Learning is no different. Data Engineers at Aceson Analytics conduct comprehensive data quality analyses to
1) Fix missing data issue
2) Correct inconsistent data
3) Remove duplicate or redundant data and more.
The next part of our pre-processing data service is Feature selection. When we create a Machine Learning model, we have to keep the problem or the requirement in mind. Not all data is needed for the ML model to address the problem. Hence, the logical step is to exclude those features (attributes) that are not needed.
Next in our data pre-processing process comes Feature Adjustments, where we improve the efficiency of Machine Learning data with the aim of making the ML model as unbiased as possible. Techniques like normalised scaling, standardised scaling, solving issues with outliers etc., are included in this process. The basic idea is to make sure that the model does not tilt towards any specific portion of the data.
Although Machine Learning works mostly with big data, when you dig deeper, you have to aggregate the data (features) to make the model training more manageable. For example, instead of feeding the ML model that does sentiment analysis with the comments from all the social networks, we can pick only one social network and feed the model with the comments that appear there. Another way to reduce the sheer volume of data is feature sampling, where we select a subset of the actual data. But we have to make sure that the sample data remains similar to the actual data.
Aceso Analytics takes care of Feature Transformation, where a categorical feature (where the attributes are taken from a specific set of attributes) is transformed to an integer-based value – or vice versa. This is done because some ML models are efficient only when the values are numerical, and some are efficient only when there are categorical features.
Data pre-processing for Machine Learning is a work of art. More precisely, when it comes to Feature Engineering, automation can’t help you. Data pre-processing depends on how creative you are, how observant you are and how much domain knowledge you possess. Your Machine Learning endeavour will get an added boost when you get to work with Aceso Analytics.
Before processing data, you have to be extremely clear about the problem that your ML model aims to solve. This is where Aceso Analytics comes in. Our ML engineers give shape to the problem at hand. Humans can understand the problem even if it appears vague. But Machine Learning needs a clear definition of the problem. The clearer the problem (or requirement), the more chance there is of the model to be successful.
As we said, Machine Learning is an art. Your data might not have the features that your Machine Learning model needs. We at Aceso Analytics picture the problem in a creative way to understand exactly what features will be helpful in the case at hand. For example, suppose you have a dataset that contains the prices of apartments in various locations. The dataset contains the number of bedrooms each apartment has. You can create a new feature – price per bedroom – by dividing the total price of an apartment by the number of bedrooms it has.
Creating new features is the most exciting and crucial part of training an ML model. The quality of your predictive model depends largely on how you deconstruct the available data to come up with new data. Contact Aceso Analytics to get a creative edge for your Machine Learning endeavours.
Processing unstructured data for Machine Learning is a different beast altogether. Machines don’t understand images, audio, or even log files, for that matter. The problem is – the majority of data sources today generate unstructured data. So if you want your ML model to excel, you need to have a method to successfully parse unstructured data for the model. Contact Aceso Analytics to find structure in your unstructured data and empower your ML model!