Consumer analytics blogs

1. Top 50 blogs on Consumer Analytics
   some of the blogs no longer exist
   https://www.ngdata.com/best-customer-analytics-blogs/
2. How to Use Customer Behavior Data to Drive Revenue (Like Amazon, Netflix & Google)
   https://www.pointillist.com/blog/customer-behavior-data/
3. Using R for Customer Analytics
   https://ds4ci.files.wordpress.com/2013/09/ciwr_2introandpracticals.pdf
4. Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn
   https://www.business-science.io/business/2017/11/28/customer_churn_analysis_keras.html
5. Marketing Analytics and Data Science
   https://www.r-bloggers.com/marketing-analytics-and-data-science/
6. Using R to predict if a customer will buy
   https://www.masterdataanalysis.com/r/using-r-predict-customer-will-buy/
7. Customer Segmentation using python
   http://blog.yhat.com/posts/customer-segmentation-using-python.html
8. Using R for customer segmentation
   https://ds4ci.files.wordpress.com/2013/09/user08_jimp_custseg_revnov08.pdf
9. Using r to analyze your customer data warehouse
   https://www.bedrockdata.com/blog/using-r-to-analyze-your-customer-data-warehouse

two data science blogs seems pretty good for data science

Win-Vector blog seems pretty good for data science

http://www.win-vector.com/blog/

Data Science Dojo blog

https://blog.datasciencedojo.com/

Consumer analytics articles

https://www.ngdata.com/best-customer-analytics-blogs/

https://ds4ci.files.wordpress.com/2013/09/ciwr_2introandpracticals.pdf

https://www.business-science.io/business/2017/11/28/customer_churn_analysis_keras.html

https://www.r-bloggers.com/marketing-analytics-and-data-science/

https://www.masterdataanalysis.com/r/using-r-predict-customer-will-buy/

http://blog.yhat.com/posts/customer-segmentation-using-python.html

https://ds4ci.files.wordpress.com/2013/09/user08_jimp_custseg_revnov08.pdf

https://www.bedrockdata.com/blog/using-r-to-analyze-your-customer-data-warehouse

https://legacy.gitbook.com/book/josepcurtodiaz/customer-analytics-with-r/details

Two articles about classifier

Metrics to evaluate machine learning algorithm

https://machinelearningmastery.com/metrics-evaluate-machine-learning-algorithms-python/


How to handle imbalanced data in classification

https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/

One trick on big data analytics

I once worked on big data projects. I analyzed 5,000,000,000 rows of data each day. I used hadoop/hive. To analyze the data with some scripts took a long time. Sometimes when there were some errors with the scripts, the program would break, and I needed to start over. And it cost time. So sometimes it took relatively long time to get projects done.

So, when you have the problem, start with small samples of the data. Then the programs run faster. you will get the jobs done sooner. time saving.

Feature engineering for machine learning

https://perso.limsi.fr/annlor/enseignement/ensiie/Feature_Engineering_for_Machine_Learning.pdf

feature engineering is an important topic in predictive modeling.

Naive Bayes Classifier , a good post

http://ataspinar.com/2016/02/15/sentiment-analysis-with-the-naive-bayes-classifier/