babitapant Goto Github PK

airlines_prediction

Fare price prediction of Airlines

car_selling_pred

cat_dog

fraud-transaction

imbalance-data-set

how to balance imbalance data set

ineuron

Ineuron Assignment

ineuron-assignments

2.Problem Statement 1. Write a function to compute 5/0 and use try/except to catch the exceptions. 2. Implement a Python program to generate all sentences where subject is in ["Americans", "Indians"] and verb is in ["Play", "watch"] and the object is in ["Baseball","cricket"]. Hint: Subject,Verb and Object should be declared in the program as shown below. subjects=["Americans ","Indians"] verbs=["play","watch"] objects=["Baseball","Cricket"] Output should come as below: Americans play Baseball. Americans play Cricket. Americans watch Baseball. Americans watch Cricket. Indians play Baseball. Indians play Cricket. Indians watch Baseball. Indians watch Cricket.

machine-learning

This is my LR code for GCP deployment

numpy_assignment

Problem statement

pandas-assignment

Doing Assignment

xyz-corporation-lending-data-project

A short primer of underwriting in the credit industry - In general, whenever an individual/corporation applies for a loan from a bank (or any loan issuer), their credit history undergoes a rigorous check to ensure that whether they are capable enough to pay off the loan (in this industry it is referred to as credit-worthiness). The issuers have a set of model/s and rule/s in place which take information regarding their current financial standing, previous credit history and some other variables as input and output a metric which gives a measure of the risk that the issuer will potentially take on issuing the loan. The measure is generally in the form of a probability and is the risk that the person will default on their loan (called the probability of default) in the future. Based on the amount of risk that the issuer is willing to take (plus some other factors) they decide on a cutoff of that score and use it to take a decision regarding whether to pass the loan or not. This is a way of managing credit risk. The whole process collectively is referred to as underwriting. Overview of the problem In this project you will have to put yourself in the shoes of a loan issuer and manage credit risk by using the past data and deciding whom to give the loan to in the future. The text files contain complete loan data for all loans issued by XYZ Corp. through 2007-2015. The data contains the indicator of default, payment information, credit history, etc. The data should be divided into train (June 2007 - May 2015) and out-of-time test (June 2015 - Dec 2015) data. You will have use the training data to build models/analytical solution and finally apply it to test data to measure the performance and robustness of the models.

yolo_v5