an example of deep learning with python code , keras

https://www.guru99.com/keras-tutorial.html

read in data to R, and check if any missing values in the data

code to read in the data into R:

data1<-read.csv("data1.csv", stringAsFactors=FALSE)
view(data1)

a line of code to check if any missing values in the data:
length(which(!complete.cases(data1))

will give the value 0, if there is no missing values in the data

twitter tweets sentiment analysis

twitter tweets sentiment analysis using naive bayes classifier

https://towardsdatascience.com/creating-the-twitter-sentiment-analysis-program-in-python-with-naive-bayes-classification-672e5589a7ed

very good article on text mining using r and corpus

Text Mining - Exploration of a Text Corpus with R

https://rstudio-pubs-static.s3.amazonaws.com/163802_0f005a14bcfb4c4b8ee17ac8a8e6c3e9.html

interesting vlog for python

https://www.youtube.com/user/joejamesusa/videos

he is awesome

pandas and its difference from numpy and scipy

https://www.youtube.com/watch?v=e60ItwlZTKM

scipy

https://www.youtube.com/watch?v=MtdLd2lrvag


numpy

https://www.youtube.com/watch?v=8Mpc9ukltVA

predictive modeling and the accuracy

https://en.wikipedia.org/wiki/Predictive_modelling

Possible fundamental limitations of predictive models based on data fitting[edit]

1) History cannot always accurately predict the future. Using relations derived from historical data to predict the future implicitly assumes there are certain lasting conditions or constants in a complex system. This almost always leads to some imprecision when the system involves people.

2) The issue of unknown unknowns. In all data collection, the collector first defines the set of variables for which data is collected. However, no matter how extensive the collector considers his/her selection of the variables, there is always the possibility of new variables that have not been considered or even defined, yet are critical to the outcome.

3) Adversarial defeat of an algorithm. After an algorithm becomes an accepted standard of measurement, it can be taken advantage of by people who understand the algorithm and have the incentive to fool or manipulate the outcome. This is what happened to the CDO rating described above. The CDO dealers actively fulfilled the rating agencies' input to reach an AAA or super-AAA on the CDO they were issuing, by cleverly manipulating variables that were "unknown" to the rating agencies' "sophisticated" models.

https://indatalabs.com/blog/predictive-models-performance-evaluation-important

building classifier using naive bayes algorithm

building classifier using naive bayes algorithm

https://www.machinelearningplus.com/predictive-modeling/how-naive-bayes-algorithm-works-with-example-and-full-code/

A comprehensive python tutorial

https://www.youtube.com/watch?v=_uQrJ0TkZlc

Django

overleaf is a good website for latex

overleaf is a good website for online latex editing

overleaf.com