Writing custom functions will be useful if the use case would be of same kind and you would like to use it in your workflow let’s consider any machine learning model related to binary -classification problem
In order to check the performance of the classification model we will extract confusion matrix and check its performance by seeing specific metrics like sensitivity, specificity and accuracy accordingly to business perspective
In order to extract sensitivity and specificity and accuracy standard approach would be generate confusion matrix and import sklearn.metrics and call respective functions
Let’s just say I want to check accuracy, sensitivity…
This is a scenario which i faced in my real life work and it was one of the most exciting work also for generating a simple html report i wrote something complex and i was asked to create a function a bit similar to print and to identify if a table is coming pass a dict into the function
Basics about list data structure in python
Indexing and slicing
Indexing is used to access elements at particular index position from your list where as slicing can be used to get elements between two particular index positions
We will discuss only about for loop for now
Google about usage of continue,pass,break statements and loops are powerful when you need iterate and check for multiple conditions.
Basic overview of some datatypes in python
The data types in python mostly used :
#Learn about type() command .
Basics of Numerical data type:
All the mathematical calculations can be done for numerical data types,most commonly used numeric data types are int,float mostly.A brief glimpse of numeric data type and operations you can do on it
Basic string operations you can do
Basic string operations are indexing and slicing and the concept of indexing and slicing works not only for strings but also for other data structures like lists etc…
I am a data science enthusiast and a non tech person who fell in love with Data Science domain and was clueless about how to start at the first later after doing some research found out some good info.
Basic skills needed :
So now got confused whether i should go with R or Python but after going through python syntax it really felt easy and user friendly when compared with R and python has huge library support when compared to R which makes lot of stuff easier.
For data science…