Added a new generalized approach to logistic regression.
Created a readme file for better understanding.
Used functions to split the code. The user/learner can directly call the model() function to train the model.
To make it more interactive and easy for debugging, added print() statements about the cost for every 1000 iterations.
In the end, a graph has been plotted against the cost and the number of iterations.
Added the function description after each function which is useful when the input/output parameters are unknown. The user can use the editor's features (like shift + tab in jupyter notebook or hover in VSCode to get the function definition).
This approach is generalized in a way that the model will work for n features.
referring to #57
model()function to train the model.print()statements about the cost for every 1000 iterations.shift + tabin jupyter notebook or hover in VSCode to get the function definition).This approach is generalized in a way that the model will work for n features.