Details:

dataset: https://github.com/ageron/handson-ml

a. Start with Example 1-1: Training and running a linear model using Scikit-Learn from Aurlien Gron textbook.

b. Show the code running under Python 3 along with correct output.

c. Modify the code have a second model using k neighbors regression with n neighbors value of 3.

d. Compare the results between the linear regression and k neighbors regression. Explain why they are different.

e. Measure the time used in the following stages: 1. Loading the data, 2. Training the model, and 3. Making the prediction. How does these measurements differ for the linear regression vs. k neighbors regression?

Please provide all source code, test cases, data files used, screen snapshots of your code being compiled, running and provide a report with your analysis and explanation of your results. You may combine all files into a single PDF file Word document file or place all files into a single ZIP archive.

Example 1-1. Training and running a linear model using Scikit-Learn import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd import sklearn # Load the data oecd bli pd.read csv("oecd bli_2015.csv", thousands-',') gdp per_capita pd.read_csv( "gdp_per_capita.csv", thousands-",',delimiter- 't', encoding-'latini', na values-"n/a") # Prepare the data country_statsprepare_country_stats(oecd bli, gdp per_capita) x = np.c-[country-stats("GDP per capita"]] y np.c[country stats["Life satisfaction"]] # Visualize the data country_stats.plot (kind-scatter, x-"GDP per capita", y'Life satisfaction") plt.show() # Select a linear model lin reg model- sklearn.linear_model.LinearRegression() # Train the model lin reg model.fit(X, y) # Make a prediction for Cyprus x-new- [[22587]] # Cyprus' GDP per capita print (lin-reg-model . predict (X-new)) # outputs [[ 5.96242338]] Example 1-1. Training and running a linear model using Scikit-Learn import matplotlib import matplotlib.pyplot as plt import numpy as np import pandas as pd import sklearn # Load the data oecd bli pd.read csv("oecd bli_2015.csv", thousands-',') gdp per_capita pd.read_csv( "gdp_per_capita.csv", thousands-",',delimiter- 't', encoding-'latini', na values-"n/a") # Prepare the data country_statsprepare_country_stats(oecd bli, gdp per_capita) x = np.c-[country-stats("GDP per capita"]] y np.c[country stats["Life satisfaction"]] # Visualize the data country_stats.plot (kind-scatter, x-"GDP per capita", y'Life satisfaction") plt.show() # Select a linear model lin reg model- sklearn.linear_model.LinearRegression() # Train the model lin reg model.fit(X, y) # Make a prediction for Cyprus x-new- [[22587]] # Cyprus' GDP per capita print (lin-reg-model . predict (X-new)) # outputs [[ 5.96242338]]