Logistic-Regression-using-Python

In ML Algorithms we have :

1. Supervised Learning
2. Unsupervised Learning

In Supervised Learning we have :

1. Regression

2. Classification

   Firstly, we discuss about different types of Regressions.

   1. Linear Regression
      1. Simple Linear Regression
      2. Multi-linear Regression
   2. Logistic Regression
   3. Lasso Regression
   4. Ridge Regression

In this repository we discuss about Logistic Regression

Logistic Regression:

      It works on same concept of Linear Regression but it is applicable when input X is continuous and the output Y to be predicted is descrete such as (yes,No),(Male,Female).
      Here X is independent variable and Y is dependent variable.
      Since output to be taken is descrete, we consider output in either 0 or 1. For example if Y is descrete (Yes,No) then it will taken as (1,0)
      Since we have taken input X as continuous, the output Y will also be in terms of continuous varaible to make it descrete we will pass it through a sigmoid function.
                                  sigmoid=(e^y)/(1+e^y)
      Sigmoid function will bring all the values in between [0,1], which are nothing but probabilities. Now from these probability values we will fix a threshold value like if P>=0.5 then '1', if P<0.5 then '0'. So that all the values of Y interms of 0 and 1

Performance Metrics:

  Performance Metrics are those which help us in deciding whether model is good or not.
  1. Accuracy: 
                Accuracy = (TP+TN)/(TP+FN+FP+TN)
          It tells us about from total observations how many are predicted correctly.
  2. Recall/TPR:
                  Recall= TP/(TP+FN)
          From all positive elements ,how many are actually predicted as positive.
  
  3. Specitivity/TNR:
                   Specitivity = TN/(TN+FP)
           From all negative elements how many are actually predicted as negative
  4. Precision:
                    Precision=TP/(TP+FP)
           From all predicted positives, how many are actual positives
  5. F1-score:
                    F1-score = 2*Recall*precision/(Recall+Precision)
           It is used to say from all observations how many are predicted incorrectly

Data Used :

       claimants, bank - full

Programming:

      Python

The Codes regarding this Logistic Regression model with different business problems Claimants ,Bank-full with their datasets are present in this Repository in detail