Data Preparation and Feature Engineering
We are skilled at turning raw data into features usable by machine learning. Our process guarantees your data is clean, formatted, structured, and tuned for building high performing models.
Optimized Data Preparation & Feature Engineering
Preparing data and engineering contributing features is one of the most important steps to building successful machine-learnt models. The quality and structure of data matters greatly when it comes to successful outcomes in model accuracy and performance. Our service offers to turn raw, unstructured data into a format that is structured and organized while using the right features to support the correct model performance.
In this service, we will assist you in cleaning, preprocessing, and structuring your data according to your specifications and constraints. This may involve
missing value handling, normalization, scaling, and encoding categorical variables. We will help in determining the features that will contribute most to your specific case, and maybe even engineer new ones.
Feature engineering and feature contribution are critical to reveal the hidden patterns present in the data available for modelling, in the case of modelling with machine learning. If you can leverage additional features to contribute to the building of the models, this will likely enhance its predictive power, which is always good.
At Statswork, our data experts will implement the advanced techniques and industry leading best practices to assure that your data is fully prepared and optimized for use in machine learning applications. We work closely with you and your team and understand your business goals as we tailor our approach. You may come with data that requires successful cleaning prior to being derived model ready.
We present the main components of our Data Preparation and Feature Engineering service. These components are designed to facilitate cleaning, structuring, and preparing the data to ensure the best possible input to create models that are most effective.
Data Cleaning and Preprocessing
We clean and preprocess your data to manage the missing data, outliers, and inconsistencies to create high quality data to work with in your models.
Feature Selection and Extraction
We select and extract the features out of your raw data that are the most important to your models to improving their overall performance and effectiveness by removing irrelevant or redundant features.
Data Transformation and Normalization
We apply transformations, such as normalization and scaling, to the data to achieve consistency across variable scales as one of the improvements to model performance.
Encoding Categorical Data
We encode categorical data with approaches, such as one-hot encoding or label encoding, to allow machine learning models to efficiently process non-numeric data.
Feature Engineering for Model Improvement
We derive and create a new feature using domain information, or an algorithmic technique, to improve a model’s ability to identify patterns to make accurate predictions
Industries
Data collection allows sectors to train computer vision models, improve automation, improve diagnostics, ensure safety, and spur innovation via AI applications.
In five stages, your data will be cleaned, prepped, and machine learning ready.
Stage 1: Data collection & Review – Review and collect your raw data.
Stage 2: Data Cleaning – Handle your missing values and/or error outliers.
Stage 3: Feature Engineering – Engineer valuable feature to help improve model accuracy.
Stage 4: Data transformation & Encoding – Standardize and encode the data.
Stage 5: Final Data Set – Deliver the final data set for model training.
Inputs & Outputs
Input – Raw data and model requirements.
Output – Cleaned, prepped, and engineered dataset for model training.
Begin dealing with the shopping behaviour data to convert more shoppers to buyers and personalize the buyer journey today!
