logistic regression : highly sensitive model I am a newbie to data science and ML. I am working on a classification problem where the task is to predict loan status (granted/not granted).

I am running a logistic regression model on the data. The accuracy of my model is 82%. However, my model is more sensitive (sensitivity = 97%) and less specific(specificity = 53%). 

I want to increase the model's specificity. At this stage, after referring to a bunch of internet resources, I am confused about how to proceed.

Below is my observation :
In Testing data, 

a percentage of 1's in the class label is 73.17073170731707.

Testing data has more 1's than 0's in the class label. Is this the reason behind model being highly sensitive. 

I am attaching my data file and code file.  Please take a look at it.

Data sample :



Process : 
 Data --> missing value imputation -->distribution analysis-->log transformation for normal distribution ---> one hot encoding --> feature selection --> splitting data --> model selection and evaluation

Code snippets :



Here I have selected "3 best features": Credit History, Property Area 



How should I proceed? Any help (even if it's just a kick in the right direction) would be appreciated.
Actually, what is happening is natural. There is a trade-off between sensitivity and specificity. If you want to increase the specificity, you should increase the threshold of your decision function but note that it comes at a price and the price is reducing the sensitivity.