1. Project Overview:
In this project, we will try to predict the possibility of a booking for a hotel based on different factors and also try to predict if they need special requests based on different features. The data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. From it, we can understand the customer’s’ behavior and it might help us make better decisions.
The process of our analysis will be by the following step: Define our Business question, understanding the Datasets, Data preparation and wrangling, analyze the data, model the data and conclusion.
2. Business Understanding:
My goal for this project is predicting which kind of customers need special request and predicting the possibility of a booking for a hotel by knowing different features. This will help the hotel booking company to make better decisions.
3. Data Understanding:
R library used: fun Modeling, tidyverse, Hmisc, DataExplorer, dplyr, caret, lattice, magrittr, ggplot2, scales, gridExtra, psych, plotly and many more.
The data set contains 119390 rows and 32 columns.
2. Bursting the Jargon bubbles — Deep Learning
3. How Can We Improve the Quality of Our Data?
4. Machine Learning using Logistic Regression in Python with Code
4.Data preparation / Wrangling:
We are replacing missing values in Children column from the corresponding Babies column. We are also replacing undefined as SC. Both means no meal package. Replacing Undefined with mode in the market segment column. Replacing Undefined with mode in the distribution channel column.
5. Analyzing the data:
•Categorical Data and Continuous Data analyzed. Uni variant, Bi variant and multi variant analysis performed.
- Analyzed to check the seasonal trend in the data set
MAJOR OBSERVATIONS FROM EDA
1.Number of bookings made were highest in the month of July and August and lowest in January.
2.Bookings were more for the City hotel than the Resort hotel.
3.41.7% of the total bookings were cancelled for City hotel and 21.7% for the Resort hotel.
4.Number of days that elapsed between the entering date of the booking and the arrival date is less for the people who cancelled.
5.As the hotels are in Portugal Europe, the bookings are mostly with European countries, Highest is Portugal with 48.59k bookings.
6.77% of the bookings are made with bed and breakfast.
7.Only 3% are repeated guests.