Here is my code and attached the error attached the error at the end, please advice to fix it$............$ import pandas as pd

# Step $1$: Data Preparation

airports$\_$df $=$ pd$.$read$\_$csv $(\text{'}/$Users$/$sureshvelusamy$/$Documents$/$Karthika$/$PY scripts$/$Airport$\_$Codes.csv$\text{'})$

tickets$\_$df $=$ pd$.$read$\_$csv $(\text{'}/$Users$/$sureshvelusamy$/$Documents$/$Karthika$/$PY scripts$/$Tickets$.$csv$\text{'})$

flights$\_$df $=$ pd$.$read$\_$csv $(\text{'}/$Users$/$sureshvelusamy$/$Documents$/$Karthika$/$PY scripts$/$Flights$.$csv$\text{'})$

# Perform necessary data cleaning and filtering steps

tickets$\_$df$[\text{'}$ITIN$\_$FARE'$]=$ tickets$\_$df$[\text{'}$ITIN$\_$FARE'$].$str$.$replace$(\text{'}$$$\text{'},\text{'}\text{'}).$astype$(\text{'}$float$64\text{'})$

tickets$\_$df$[\text{'}$ITIN$\_$FARE'$]=$ tickets$\_$df$[\text{'}$ITIN$\_$FARE'$].$astype$(\text{'}$Int$64\text{'})$

# Step $2$: Identify the $10$ busiest round trip routes

filtered$\_$flights$\_$df $=$ flights$\_$df$[$flights$\_$df$[\text{'}$CANCELLED$\text{'}]==0]$

Roundtrip$\_$Tickets$=$ tickets$\_$df$[$tickets$\_$df$[\text{'}$ROUNDTRIP$\text{'}]==1]$

MLAirport$=[\text{'}$medium$\_$airport', 'large$\_$airport'$]$

round$\_$trip$\_$routes $=$ filtered$\_$flights$\_$df$.$groupby$([\text{'}$ORIGIN$\text{'},$ 'DESTINATION'$]).$size$().$reset$\_$index$($name$=$'Count'$)$

top$\_10\_$busiest$\_$routes $=$ round$\_$trip$\_$routes.nlargest$(10,$ 'Count'$)$

# Step $3$: Calculate the profitability of round trip routes

revenue$\_$per$\_$route $=$ Roundtrip$\_$Tickets.groupby$([\text{'}$ORIGIN$\text{'},$ 'DESTINATION'$])[\text{'}$ITIN$\_$FARE'$].$sum$().$reset$\_$index$($name$=$'TotalRevenue'$).$nlargest$(10,$ 'TotalRevenue'$)$

revenue$\_$per$\_$route$[\text{'}$ORIGIN$\text{'}]=$ pd$.$to$\_$numeric$($revenue$\_$per$\_$route$[\text{'}$ORIGIN$\text{'}],$ errors$=$'coerce'$)$

revenue$\_$per$\_$route$[\text{'}$DESTINATION$\text{'}]=$ pd$.$to$\_$numeric$($revenue$\_$per$\_$route$[\text{'}$DESTINATION$\text{'}],$ errors$=$'coerce'$)$

revenue$\_$per$\_$route$[\text{'}$TotalRevenue$\text{'}]=$ pd$.$to$\_$numeric$($revenue$\_$per$\_$route$[\text{'}$TotalRevenue$\text{'}],$ errors$=$'coerce'$)$

print$($revenue$\_$per$\_$route.dtypes$)$

# Calculate the cost components for each route

cost$\_$per$\_$mile $=8$

cost$\_$per$\_$mile$\_$other $=1\mathrm{.}18$

airport$\_$cost$\_$medium $=5000$

airport$\_$cost$\_$large $=10000$

delay$\_$cost$\_$per$\_$min $=75$

def calculate$\_$route$\_$cost$($route$)$:

origin, dest $=$ route

distance $=$ flights$\_$df$[($flights$\_$df$[\text{'}$ORIGIN$\text{'}]==$ origin$)$ & $($flights$\_$df$[\text{'}$DESTINATION$\text{'}]==$ dest$)][\text{'}$DISTANCE$\text{'}].$mean$()$

num$\_$round$\_$trips $=$ round$\_$trip$\_$routes$[($round$\_$trip$\_$routes$[\text{'}$ORIGIN$\text{'}]==$ origin$)$ & $($round$\_$trip$\_$routes$[\text{'}$DESTINATION$\text{'}]==$ dest$)][\text{'}$Count$\text{'}].$values$[0]$

airport$\_$cost $=($airports$\_$df$[$airports$\_$df$[\text{'}$IATA$\_$CODE'$]==$ origin$][\text{'}$TYPE$\text{'}].$values$[0]==$ 'medium$\_$airport'$)*$ airport$\_$cost$\_$medium $+($airports$\_$df$[$airports$\_$df$[\text{'}$IATA$\_$CODE'$]==$ dest$][\text{'}$TYPE$\text{'}].$values$[0]==$ 'medium$\_$airport'$)*$ airport$\_$cost$\_$medium $+($airports$\_$df$[$airports$\_$df$[\text{'}$IATA$\_$CODE'$]==$ origin$][\text{'}$TYPE$\text{'}].$values$[0]==$ 'large$\_$airport'$)*$ airport$\_$cost$\_$large $+($airports$\_$df$[$airports$\_$df$[\text{'}$IATA$\_$CODE'$]==$ dest$][\text{'}$TYPE$\text{'}].$values$[0]==$ 'large$\_$airport'$)*$ airport$\_$cost$\_$large

operational$\_$cost $=$ distance $*$ cost$\_$per$\_$mile $+$ distance $*$ cost$\_$per$\_$mile$\_$other $+$ airport$\_$cost

delay$\_$cost $=($filtered$\_$flights$\_$df$[($filtered$\_$flights$\_$df$[\text{'}$ORIGIN$\text{'}]==$ origin$)$ & $($filtered$\_$flights$\_$df$[\text{'}$DESTINATION$\text{'}]==$ dest$)][\text{'}$ARR$\_$DELAY'$].$sum$()+$ filtered$\_$flights$\_$df$[($filtered$\_$flights$\_$df$[\text{'}$ORIGIN$\text{'}]==$ dest$)$ & $($filtered$\_$flights$\_$df$[\text{'}$DESTINATION$\text{'}]==$ origin$)][\text{'}$DEP$\_$DELAY'$].$sum$())*$ delay$\_$cost$\_$per$\_$min

total$\_$cost $=$ operational$\_$cost $+$ delay$\_$cost

return total$\_$cost $*$ num$\_$round$\_$trips

cost$\_$per$\_$route $=$ revenue$\_$per$\_$route.apply$($lambda x: calculate$\_$route$\_$cost$(($x$[\text{'}$ORIGIN$\text{'}],$ x$[\text{'}$DESTINATION$\text{'}])),$ axis$=1)$

profit$\_$per$\_$route $=$ revenue$\_$per$\_$route$[\text{'}$TotalRevenue$\text{'}]-$ cost$\_$per$\_$route

top$\_10\_$profitable$\_$routes $=$ pd$.$DataFrame$(\{$

'Origin': top$\_10\_$busiest$\_$routes$[\text{'}$ORIGIN$\text{'}],$

'Dest': top$\_10\_$busiest$\_$routes$[\text{'}$DESTINATION$\text{'}],$

'TotalRevenue': revenue$\_$per$\_$route$[\text{'}$TotalRevenue$\text{'}],$

'TotalCost': cost$\_$per$\_$route,

'Profit': profit$\_$per$\_$route

$\}).$nlargest$(10,$ 'Profit'$)$

# Step $4$: Recommend $5$ round trip routes to invest in

recommended$\_$routes $=$ top$\_10\_$profitable$\_$routes.nlargest$(5,$ 'Profit'$)[[\text{'}$Origin$\text{'},$ 'Dest'$]]$

# Step $5$: Calculate the breakeven point for upfront airplane costs

upfront$\_$airplane$\_$cost $=90000000$ # $$90$ million

breakeven$\_$flights $=[]$

for route in recommended$\_$routes.itertuples$($index$=$False$)$:

origin, dest $=$ route

revenue $=$ revenue$\_$per$\_$route$[($revenue$\_$per$\_$route$[\text{'}$ORIGIN$\text{'}]==$ origin$)$ & $($revenue$\_$per$\_$route$[\text{'}$DESTINATION$\text{'}]==$ dest$)][\text{'}$TotalRevenue$\text{'}].$values$[0]$

cost $=$ cost$\_$per$\_$route$[($revenue$\_$per$\_$route$[\text{'}$ORIGIN$\text{'}]==$ origin$)$ & $($revenue$\_$per$\_$route$[\text{'}$DESTINATION$\text{'}]==$ dest$)]$

breakeven$\_$point $=$ upfront$\_$airplane$\_$cost $/($revenue $-$ cost$)$

breakeven$\_$flights.append$($breakeven$\_$point$)$

# Step $6$: Recommend Key Performance Indicators $($KPIs$)$

recommended$\_$kpis $=[\text{'}$Load Factor', $\text{'}$On$-$time Performance', 'Customer Satisfaction'$]$

# Print the results

print$("10$ Busiest Round Trip Routes:"$)$

print$($top$\_10\_$busiest$\_$routes$)$

print$("$

$10$ Most Profitable Round Trip Routes:"$)$

print$($top$\_10\_$profitable$\_$routes$)$

print$("$

Recommended $5$ Round Trip Routes:"$)..................$Error $..............................$ IndexError Traceback $($most recent call last$)$ Cell In$[174],$ line $85--->85$ cost$\_$per$\_$route $=$ revenue$\_$per$\_$route.apply$($lambda x: calculate$\_$route$\_$cost$(($x$[\text{'}$ORIGIN$\text{'}],$ x