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HW1: Linear & Logistic Regression¶ In this homework, you will read the lecture notes first and then implement linear regression and logistic regression using this jupyter notebook. You will finish all blanks and run all the cells. Please export this to a pdf file that contains all results and turn in both the ipython file

In [1]: import pandas as pd import numpy as np import numpy as np import matplotlib.pyplot as plt 1.1¶ In [2]: data = pd.read_csv(‘./houses1.csv’) In [3]: data.describe() Out[3]: price (grands) bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition grade sqft_above sqft_basement age renovated_age sqft_living15 sqft_lot15 count 21613.000000 21613.000000 21613.000000 21613.000000 2.161300e+04 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000 21613.000000

COMP0142¶ Assignment 1B: Polynomial Regression & Regularization¶ Introduction¶ In this assignment you will implement Polynomial regression and Regularization using the library Scikit-learn. In Part A of the assignment we will focus on Polynomial regression with and without regularization. First, we will load the train and test dataset and we will fit a simple polynomial of

Coursework 2A Questions 1-3: Words¶ Last updated: 4th November 2019¶ To check for later updates, go to the coursework web page. Overview¶ The first part of the coursework is intended to develop basic skills of in extracting, testing, manipulating and presenting data. You will learn to work with a dictionary of English words to implement