ML Project – Flight Booking Cancellation Prediction using Decision Tree

Program 1

Flight Booking Dataset

# Flight Booking Cancellation Prediction Using Decision Tree
# To build a machine learning model that predicts whether a booked flight will be canceled or not,
# based on various attributes like source, destination, airline, travel class, halt type, and flight duration.

# Flight Booking Canceltion
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load Data Set
df_fl=pd.read_csv("D://scikit_data/flight/flight_booking_data.csv")

df_fl.head()

df_fl.shape

df_fl.info()

# Label Encoding
le=LabelEncoder()
df_fl['Source_new']=le.fit_transform(df_fl['Source'])
df_fl['Destination_new']=le.fit_transform(df_fl['Destination'])
df_fl['Airline_new']=le.fit_transform(df_fl['Airline'])
df_fl['TravelClass_new']=le.fit_transform(df_fl['TravelClass'])
df_fl['Halt_new']=le.fit_transform(df_fl['Halt'])

df_fl.head()

df_fl=df_fl.drop(['Source','Destination','Airline','TravelClass','Halt'],axis='columns')

df_fl

# Independed(input) and Depended(output) variables

df_input=df_fl.drop('Cancellation',axis='columns')

df_input

# Depended Variables
df_output=df_fl.drop(['Source_new','Destination_new','Airline_new','TravelClass_new','Halt_new','Duration'],axis='columns')out

df_output

# Create Model
model=DecisionTreeClassifier()

model.fit(df_input,df_output)

model

model.score(df_input,df_output)

df_fl.head()

model.predict([[2,3,1,1,2.0,0]])

df_fl

src=int(input("Enter Source(Bangalore-0, Chennai-1,Delhi-2,Kolkata-3,Mumbai-4 ) :"))
dest=int(input("Enter Destination(Bangalore-0, Chennai-1,Delhi-2,Kolkata-3,Mumbai-4) :"))
al=int(input("Enter Airline:(Vistara-0,Air India-1,GoAir-2,IndiGo-3,SpiceJet-4) :"))
tc=int(input("Enter TravelClass(Business-0, Economy-1) :"))
dur=float(input("Enter Duration  :"))
hlt=float(input("Enter Halt (Stop -0 , Non-stop 1)  :"))
result=model.predict([[src,dest,al,tc,dur,hlt]])
if(result==1):
    print("********Flight Cancelled*******")
else:
    print(".......Flight Not Cancel Enjoy your trip.....")