Git:

Team: Sarita Bhateja, Kavya Guruprasad and Nandini Goswami

Technologies: Technologies: Spark, Python, Databricks, Tableau

Problem Statement

The project is to analyse the travel data which includes data such as destination city, source city, mode of commute, the hotel they will be staying at, type of audience such as youth, children, adults etc. This project will prove helpful for both the hospitality industry and customers.

Dataset Desciption

Attribute Information:

• City Pair : The origin-destination pair of travel
• From Location : The origin of travel
• To Location : The destination of travel
• Product Type : This attribute tells us about the mode of travel and the Combinations ->Here 1 represents Air,->2 represents car, ->3 represents Air+Car, ->4 represents Hotel, ->5 represents Air+Hotel, ->6 represents Hotel+Car, ->7 represents Air+Hotel+Car)
• Adults Traveling : Number of adults travelling
• Seniors Traveling : Number of seniors travelling
• Children Traveling : Number of children travelling
• Youth Traveling : Number of youth travelling
• Infant Traveling : Number of infants travelling
• Date of Travel : The date of travel
• Time of Travel : The time of travel
• Date of Return : The date of return
• Time of Return : The time of return
• Price of booking : The price of booking hotel
• Airline code : The code with which each airlines is denoted
• Airline : The airline name
• Hotel :The hotel name

System Design

We have implemented this using Apache Spark and Map reduce and used python as the language for writing the code. We have tried to find the top 20 destinations that people travel to, from and the most frequently visited city pairs where people prefer travelling. We have printed the top 20 places in descending order. We have found the top 10 cities that generate high airline revenues for travel. From this analysis airline companies can give better deals and make better profit.Top source-destination sites that people prefer travelling is also found. This can help airlines give better deals for in these routes. We also found The most often visited hotels by travellers, what combination of traveling do people prefer(Air+Hotel or Hotel+Car or Air+Hotel+Car), Top places from where people generally travel by car, Which airlines do people prefer the most. This kind of analysis is especially helpful to the travel industry to make wise decisions in mak-ing their costumer's stay a better one.