Machine Learning Solutions
Data Science
Data is the new electricity and organizations need to harness the power of data to create and capture value. How can you unlock the potential of the data that resides in your organization? What value can you derive from the organizational data? What data sources can you supplement to create exponential value? How good is the quality of your data?
Amizen Labs can help you unlock the potential of your data using data discovery, data exploration, and data science.
Machine Learning Solution
Machine learning models can help you gain significant competitive edge. Machine learning models implemented in Marketing, Operations, Finance, Human Resources, Executive Decision systems, Sales, Customer Service, Procurement, Legal, and Communications can create the flywheel effect for your organization.
Amizen Labs can help you plan and implement machine learning solutions for your organization. Our proven methodology can guide you through each step:
Supervised Machine Learning
What are supervised machine learning models?
Supervised machine learning models use human labeled outcome / decision along with related data elements to learn the relationship and patterns that can predict the outcome.
When should you consider using supervised machine learning model?
The supervised machine learning models are best suited when you have data and labels (outcomes) available, and are looking for an algorithm that can predict the outcome on new data. Supervised machine learning can significantly boost your organizational capabilities in making informed and optimized decisions.
How does supervised machine learning model work?
Key steps for supervised machine learning model include:
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Data Preparation: This is an important step for ensuring good quality and unbiased data is utilized for supervised machine learning model. The data preparation requires selection of features, checking for multicollinearity, data transformation, selection of balanced unbiased data elements and outcome / label data, and splitting the data for training and validation.
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Model Development: Selection, application, and optimization of the supervised machine learning model using training data while ensure there is no overfitting.
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Model Validation: Application of the supervised machine learning model that has been developed using training data on the validation dataset that the model has never seen. The model performance is then evaluated with positive, negative, false positive, and false negative prediction.
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Model Deployment: Operationalize and deploy the supervised learning model in production on-premise, in-cloud, or edge.
What are some of the algorithms and use cases?
Depending on the signature of data and use case, one or more of the following supervised machine learning algorithms can be used:
Linear Regression
Logistic Regression
Decision Tree
Random Forest
Support Vector
Machine
AdaBoost
Quadratic Discriminatory Analysis (QDA)
Support
Vector Machine
Neural Network
Linear Regression
Easily interpretable. Model uses independent input variables to predict the dependent output variable.
Sample Use Case for Linear Regression
Predict sales based on independent variables such as competition price, marketing channel spend, etc.
Predict quality of wine using temperature and humidity.
Unsupervised Machine Learning
What are unsupervised machine learning models?
Unsupervised machine learning models do not require outcome / label / decision but rather they automatically explore the data and extract key patterns. For example, feeding customer data to unsupervised model can reveal customer segments / clusters based on their buying pattern or demographics thus enabling segmented engagement and incentives.
When should you consider using unsupervised machine learning model?
Unsupervised machine learning models can be used in scenarios where you want the model to find out patterns / classification / segmentation / clustering for you. For example, customer segmentation, online visitor behavior segmentation, prospect funnel segmentation, customer loyalty segmentation, etc.
How does unsupervised machine learning model work?
Key steps in unsupervised machine learning model:
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Data Preparation: This is an important step for ensuring good quality and unbiased data is utilized for unsupervised machine learning model.
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Unsupervised Model: Utilize unsupervised model to derive structure from data. The model identifies patterns / clusters / segments.
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Interpret, analyze, and apply findings: Interpret and analyze patterns / clusters / segments resulting from unsupervised models and applications to improve business outcomes.
What are some of the algorithms and use cases?
These unsupervised machine learning models can be utilized to explore patterns, classification, segmentation, and clustering.
K-Means Clustering
Hierarchial Clustering
AdaBoost
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