a) Explore the numerical predictors and response (FARE) by creating a correlation table and examining some scatterplots between FARE and those predictors. What seems to be the best single predictor of FARE? [18]: df.corr( ) [18]: COUPON NEW HI S_INCOME E INCOME s POP E POP DISTANCE PAX FARE COUPON 1.0D00D0 0.020273 - -0.347252 3046409 -0.107763 0.094970 0.746005 -0.136974 CA96537 NEW Q070723 1.020030 0.034147 Q113377 -Q010672 0038583 CROPS C.310495 0.521730 HI -Q.347252 0.054147 1.050090 -.027302 Q002393 -0.172495 -0.062456 -0.312174 -0.160961 0.025195 5_INCOME -Q08:403 0,036597 -0,017387 -0138954 0517187 -0272780 0.0/8153 0.138197 0.209135 E_INCOME 0.046099 0.113377 0.032390 -0.136064 1.0DOC05 -0.144059 0.450410 C.176531 0.259961 0.326092 5 POP -0107753 -0.01672 -0.17247 0.517187 -144059 1.010100 -0280143 C.01 8437 0.264511 0.145097 E POP 0.094970 0.050569 -0.062456 -0.272290 0.450410 -0290143 1.006000 C.115640 0.314690 0.205043 DISTANCE 0.746853 0.080956 -0.312374 0.098153 0174531 0018437 0115640 1.800804 -C.102487 40.570316 PAX -0.316974 0.010495 -0.160961 0.136197 0259961 (1204611 0.314690 -C.102492 FARE 0423537 0.091730 0.025195 0.209135 0324032 0.1-45097 0285043 0.57006 -C.090705 1.200900 Examagenda!\\!!\\[ce-packages\\seaborn\\madog id. prizess: Goerwarning: The "adze" parameter has been renamed to "height" ; ples update your code. From the correlation matrix "Distance " has the highest correlated value with the fare of 0.670016 . And from the scatter plots we can find the positive correlation between Distance vs Fare . So, Distance seems