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:

  1. Discovery & Strategy

  2. Machine Learning Proof of Concept

  3. Machine Learning Production Implementation

  4. Machine Learning Managed Services

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:

  1. 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.
     

  2. ​Model Development: Selection, application, and optimization of the supervised machine learning model using training data while ensure there is no overfitting.
     

  3. 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. 
     

  4. 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

Fresh Vegetables

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:
 

  1. Data Preparation: This is an important step for ensuring good quality and unbiased data is utilized for unsupervised machine learning model. 
     

  2. Unsupervised Model: Utilize unsupervised model to derive structure from data. The model identifies patterns / clusters / segments.
     

  3. 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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