{{ message }}

Instantly share code, notes, and snippets.

🎯
Focusing

# Samar Pratap Singh Samar-080301

🎯
Focusing
Created May 30, 2020 — forked from mGalarnyk/CalculateTotalCarLoanInterest.py
View CalculateTotalCarLoanInterest.py
 import numpy as np import pandas as pd term = 60 P = 34689.96 def calc_interest(P,emi,interest_rate = 0.0702): interest_paid = np.floor(((interest_rate/12)*P)*100)/100 principal_paid = np.round(emi-interest_paid, 2) new_balance = np.round(P - principal_paid,2) return(emi, interest_paid, principal_paid, new_balance)
Created May 30, 2020 — forked from mGalarnyk/machineLearningWeek1Quiz2.md
Machine Learning (Stanford) Coursera (Week 1, Quiz 2) for the github repo: https://github.com/mGalarnyk/datasciencecoursera/tree/master/Stanford_Machine_Learning
View machineLearningWeek1Quiz2.md

# Machine Learning Week 1 Quiz 2 (Linear Regression with One Variable) Stanford Coursera

Github repo for the Course: Stanford Machine Learning (Coursera)

## Question 1

Consider the problem of predicting how well a student does in her second year of college/university, given how well she did in her first year.

Specifically, let x be equal to the number of "A" grades (including A-. A and A+ grades) that a student receives in their first year of college (freshmen year). We would like to predict the value of y, which we define as the number of "A" grades they get in their second year (sophomore year).