Use this worksheet to complete your lab activity. Submit it to the applicable assignment submission folder when complete.

Deliverable:

A word document showing and explaining the results of the linear regression model

Using the Weather.csv dataset

Start by exploring the Weather.csv data using the describe$()$ function from the last module

You will need to import the Weather.csv dataset and the following libraries:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as seabornInstance

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.linear$\_$model import LinearRegression

from sklearn import metrics

$\%$matplotlib inline

Next plot data points on a $2$D graph to check for a relationship manually using the code below:

df$.$plot$($x$=$'MinTemp', y$=$'MaxTemp', style$=\text{'}$o$\text{'})$

plt$.$title$(\text{'}$MinTemp vs MaxTemp'$)$

plt$.$xlabel$(\text{'}$MinTemp$\text{'})$

plt$.$ylabel$(\text{'}$MaxTemp$\text{'})$

plt$.$show$()$

Plot the MaxTemp data using the code below:

plt$.$figure$($figsize$=(15,10))$

plt$.$tight$\_$layout$()$

seabornInstance.distplot$($df$[\text{'}$MaxTemp$\text{'}])$

Divide the data into attributes and labels by using the following:

X $=$ df$[\text{'}$MinTemp$\text{'}].$values.reshape$(-1,1)$

y $=$ df$[\text{'}$MaxTemp$\text{'}].$values.reshape$(-1,1)$

Split the data into training and testing sets using a $80\%$ training and $20\%$ testing split using the code below:

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}2,$ random$\_$state$=0)$

Train the algorithm using linear regression following the code below:

regressor $=$ LinearRegression$()$

regressor.fit$($X$\_$train, y$\_$train$)$ #training the algorithm

Print the intercepts and slope using the following:

#To retrieve the intercept:

print$($regressor$.$intercept$\_)$

#For retrieving the slope:

print$($regressor$.$coef$\_)$

Use the test data to see how accurately the model works using the following:

y$\_$pred $=$ regressor.predict$($X$\_$test$)$

Compare the actual output values to the predicted values using the following:

compare $=$ pd$.$DataFrame$(\{\text{'}$Actual$\text{'}$: y$\_$test.flatten$(),$ 'Predicted': y$\_$pred.flatten$()\})$

compare

Create a scatter plot with a line portraying the model to evaluate the model visually using the following:

plt$.$scatter$($X$\_$test, y$\_$test, color$=$'gray'$)$

plt$.$plot$($X$\_$test, y$\_$pred, color$=$'red', linewidth$=2)$

plt$.$show$()$