Skip to main content



Business Value from Machine Learning Methods


  1. Linear Regression - To make predictions for sales forecast, price optimization, marketing optimization, financial risk assessment.
  2. 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.
  3. Naive Bayes - Build spam detector, analyze customer sentiments, or automatically categorize products, customers or competitors.
  4. K-means clustering - Useful for cost modeling and customer segmentation
  5. Hierarchical clustering - Model business processes, or to segment customers based on survey responses, hierarchical clustering will probably come in handy.
  6. K-nearest neighbor classification - Type of instance based learning. use it for text document classification, financial distress prediction modeling, and competitor analysis and classification.
  7. Principal component analysis - Dimensionality reduction method that you can use for detecting fraud, for speech recognition, and for spam detection.  

Comments

Post a Comment

Popular posts

Python [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: Missing Authority Key Identifier

  Error requests.exceptions.SSLError: HTTPSConnectionPool  Max retries exceeded with url:  (Caused by SSLError(SSLCertVerificationError(1, '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: Missing Authority Key Identifier (_ssl.c:1028)'))). Analysis & Solution Recently, we updated from Python 3.11 to 3.13, which resulted in error above. We did verify AKI = SKI in chain of certificates. Also, imported chain of certificates into certifi. Nothing worked for us. Seemingly, it is a bug with Python 3.13. So, we downgraded to Python 3.12 and it started working. Other problems and solution -  '[SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1006)'))) solution  pip install pip-system-certs [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: certificate has expired  (_ssl.c:1006) solution  1# openssl s_client -showcerts -connect  signin.aws.amazon.com:443  </dev/...
Post a Comment
Read more




Spring MongoDB Rest API not returning response in 90 seconds which is leading to client timeout

  We have Spring Boot  Rest API deployed in Kubernetes cluster which integrates with MongoDB to fetch the data.  MongoDB is fed with data by a real time Spark & NiFi job.  Our clients complained that for a request what they send they don't have response within 90 seconds. Consider it like an OMS ( Order ManagEment System).  On further analysis, we found that Spark & NiFi processing is happenning within 10 seconds after consuming response data from Kafka. Thus, initally out thought was that it due to delay from upstream to produce data in to Kafka.  Thankfully, our data had create / request  timestamp, and when response was received, and when response was inserted into MongoDB. Subtracting response insert time from request time seemed to be well within 90 seconds. But, still client did timeout on not seeing a response within 90 seconds. This led to confusion on our side.  But, then we realized it was due to Read Preference . We updated this...
Post a Comment
Read more




MongoDB Regex Query taking more time in Production but same query perform well in UAT

   We came across a situation where-in, MongoDB Query was taking more time in Production like 10 seconds and 4.2 seconds but same query performed well in UAT taking under 400 ms. The very first thought that was evident to us that it is because of amount of data which differed in UAT and Production. Then we ran following to see the execution plan -   db.collection.aggregate(<queries>).explain() This gave us Winning and Rejected Plans. Under which, we analyzed that although it was using 'IXSCAN.' But, it was incorrect index- as we had one compound index built on time field and other fields, and there was other index just on time field for TTL purposes. Winning plan picked TTL index rather than compound index. Thus, we dropped TTL index and built TTL index on a different time field.  That got our query performance time from 10 seconds to 726 ms. Also, for other query the performance came down from 8 seconds to 4.3 seconds. Then, we ran following -  ...
Post a Comment
Read more




What is Leadership

 
Post a Comment
Read more




Spark MongoDB Connector Not leading to correct count or data while reading

  We are using Scala 2.11 , Spark 2.4 and Spark MongoDB Connector 2.4.4 Use Case 1 - We wanted to read a Shareded Mongo Collection and copy its data to another Mongo Collection. We noticed that after Spark Job successful completion. Output MongoDB did not had many records. Use Case 2 -  We read a MongoDB collection and doing count on dataframe lead to different count on each execution. Analysis,  We realized that MongoDB Spark Connector is missing data on bulk read as a dataframe. We tried various partitioner, listed on page -  https://www.mongodb.com/docs/spark-connector/v2.4/configuration/  But, none of them worked for us. Finally, we tried  MongoShardedPartitioner  this lead to constant count on each execution. But, it was greater than the actual count of records on the collection. This seems to be limitation with MongoDB Spark Connector. But,  MongoShardedPartitioner  seemed closest possible solution to this kind of situation. But, it per...
Post a Comment
Read more