(Solved): This machine learning with python, I just want an explanation of the heatmap. Please someone who can ...

This machine learning with python, I just want an explanation of the heatmap. Please someone who can do this quickly, prefer with in an hour. Time is short. Will thumps up.

 

]: ?seaborn. axisgrid.FacetGrid at \( 0 \times 7 f 4 f b 27664 d 0\rangle \) ]: sns.displot(tips['total_bill'], kde = True) \( \langle \) seaborn.axisgrid. Facetgrid at \( \theta \times 7 f 4 f a f e 63 a d \theta\rangle \) [ ]: sns.displot(tips['total_bill'], kde = True, bins= 30) ?seaborn.axisgrid. FacetGrid at \( 0 \times 7 f 4 f a e 142 a 50\rangle \) [ ] : sns.pairplot(tips) 7. ?seaborn.axisgrid.Pairgrid at \( \theta \times 7 f 4 \) fadadf \( 2 d \theta\rangle \) \( \langle \) matplotlib.axes._subplots. AxesSubplot at \( \theta \times 7 f 4 f a d 28 f a 90\rangle \) In [ ]: sns.countplot \( \left(x=\right. \) sex \( ^{\prime} \), data=tips \( ) \) Dut [ ]: ?matplotlib.axes._subplots.Axessubplot at \( \theta \times 7 f 4 \mathrm{fad} 626 \mathrm{~d} g 0\rangle \) In [ ]: sns.boxplot ( \( x= \) day' \( ^{\prime}, y= \) 'total_bill', hue='smoker', data=tips, palette ='coolwarm' \( ) \) \( \langle \) matplotlib.axes._subplots. Axessubplot at \( \theta \times 7 f 4 f a d 29 d e 50\rangle \) In [ ]: sns. heatmap(tips. \( \operatorname{corr}(), \mathrm{cmap}= \) 'coolwarm', annot=True) \( ^{\text {' }} \), and Out [ ]: ?matplotlib.axes._subplots. Axessubplot at 0x7f4fab39b910> Out [ ]: ?matplotlib.axes._subplots.AxesSubplot at \( \theta \times 7 \mathrm{f} 4 \mathrm{fab} \theta \mathrm{fbdd} \theta\rangle \) #inclass assignment for \( 9 / 8 / 2022 \) tips.pivot_table(values='total_bill', index='size', columns= sex") #inclass assignments for \( 9 / 8 / 2022 \) understand the output of heatmap pivflights = flights. pivot_table(value)