- Linear Regression - To make predictions for sales forecast, price optimization, marketing optimization, financial risk assessment.
- Logistic Regression - To predict customer churn, to predict response versus advertisement spending, predict lifetime value of customer, and to monitor how business decisions affect predicted churn rates.
- Naive Bayes - Build spam detector, analyze customer sentiments, or automatically categorize products, customers or competitors.
- K-means clustering - Useful for cost modeling and customer segmentation
- Hierarchical clustering - Model business processes, or to segment customers based on survey responses, hierarchical clustering will probably come in handy.
- K-nearest neighbor classification - Type of instance based learning. use it for text document classification, financial distress prediction modeling, and competitor analysis and classification.
- Principal component analysis - Dimensionality reduction method that you can use for detecting fraud, for speech recognition, and for spam detection.
Say we have a JSON String like below - { "billingCountry":"US" "orderItems":[ { "itemId":1, "product":"D1" }, { "itemId":2, "product":"D2" } ] } And, our aim is to get output parsed like below - itemId product 1 D1 2 D2 First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),...
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