Predict Hotel Cancellation

StayWithUS is a leading online portal that enables consumers to book individually owned residences for short-term vacation rental. Recently they have noticed that many customers are canceling their reservations at the last minute. This reduces their occupancy rate and is leading to decreased revenue.
Perform analysis on the given data to learn how some behaviors affect reservations. Build a model that will have the ability to predict which reservations might get canceled.
Explain how different features affect the decision.
Files
train.csv - data used for training along with target variable test.csv - data on which predictions are to be made sample_submission.csv - sample format of submission


Schera
File
ALL
1
Feature	Description
id	Unique identifier for each booking.
lead_time	Time between booking date and reservation date (in days)
arrival_week	Week number of the arrival date.
duration	Booking duration (in Days)
prev_cancel	Number of previous bookings that were cancelled by the customer prior to the current booking.
booking_changes	Number of
changes, amendments made to the booking between booking date and reservation/ cancellation date.
waiting_period	Waiting period for booking confirmation (in Days)
per_Day_price	Per night booking price (in US $)
parking	Number of car parking spaces required by the customer.
special_request	Number of special requests made by the customer.
segment	Market segment designation. segment	
Market segment designation. In categories, "TA" means "Travel Agents" and "TO" means "Tour Operators".	
deposit	
Whether the customer made a deposit to guarantee the booking.	
cust_type	
Type of booking, assuming one of four categories.	
is_ cancelled	
Value indicating if the booking was cancelled (1) or not (0).	
Problem
Perform an analysis of the given data and learn how different features are related to and affect the likelihood that a reservation will be canceled. With the given data, build a machine learning model that can be used to predict
'is_cancelled'.
For each record in the test set (test.csv), predict
'is_cancelled'. Submit a CSV file with a header row and one row per test entry. The file (submissions.csv) should have exactly 2 columns:
* ﻿﻿id
* ﻿﻿is_cancelled
Evaluation Metric:
The metric used for evaluating the performance of the classification model is 'Accuracy'.
Accuracy = number of correct predictions/total number of
predictions
Deliverables
* ﻿﻿Well commented Jupyter notebook
* ﻿﻿'submissions.csv'
The notebook should contain the solution, visualizations, and a discussion of the thought process, including the top features that go into the model. If required, please generate new features. Make appropriate plots, annotate the notebook with markdowns, and explain the necessary inferences. A person should be able to read the Notebook and understand the steps taken and the reasoning behind them. The solution will be graded on the basis of the usage of effective visualizations to convey the analysis and the modeling process:



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I am done with the following :
Libraries
import pandas as pd
pd. set_option("display-max_columns". 101)
Data Description	


Data Wrangling & Visualization
[3]: # The dataset is already loaded below
data = pd. read_csv("'train.csv")
data.head()


#Explore_columns 
data.columns

#Description 
data. describe()



Help me with the below things :

Visualization, Modeling, Machine Learning
Build a classification model and to determine whether a customer will cancel a booking. Please explain the findings effectively to technical and non-technical audiences using comments and visualizations, if appropriate.
* ﻿﻿Build an optimized model that effectively solves the business problem.
* ﻿﻿The model's performance will be evaluated on the basis of accuracy.
* ﻿﻿Read the test.csv file and prepare features for testing.
#Loading Test data
test_data_pd.read_csv('test.csv')
test_data.head ()