https://github.com/blurred-machine/hackbout_albatross

This repository has all the files for our project at Hackbout.
https://github.com/blurred-machine/hackbout_albatross

Last synced: 10 months ago
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This repository has all the files for our project at Hackbout.

• Host: GitHub
• URL: https://github.com/blurred-machine/hackbout_albatross
• Owner: blurred-machine
• Created: 2020-03-04T07:21:42.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-03-05T06:10:02.000Z (over 6 years ago)
• Last Synced: 2025-02-26T12:15:29.689Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 11.5 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Hackbout_Albatross

This repository is a part of [**Hackbout Hackathon**](https://www.hackbout.tech)

```

    void HackBout(){

        while (36Hrs){

            If (Alive){

                Print ("I ♥‿♥ Hacking at NMIT.");

            }

            else{

                drink_redbull (get_wings);

                continue;

            }

        }

    }

```

In this project we have worked on implementing the **Dynamic Pricing Strategies** into the E-commerce domain. In this project we have worked on **Airbnb open dataset** provided for the **New York city**.

### How to run the project?

 - To run the project on local host the first requirement is a Python3.x environment.

 - Secondly any of the IDE that support the python environment is required. (Jupyter Notebook used here).

 - Clone the repository from the link [https://github.com/paras009/Hackbout_Albatross.git](https://github.com/paras009/Hackbout_Albatross.git)

 - Run all the cells in .ipynb file by keeping the file structure as provided.

 - The final result of the process will result in dynamic pricing of hotel room bookings.

### Steps for Implementation:

 - The data source is taken from the open data published by **Airbnb** at their official website.

 - For the data extracted, **wrangling** is done to make the data clean and understandable to machine.

 - **Exploratory Data Analysis(EDA)** is performed using different graphical analysis to understand the insights from the data.

 - **Feature Engineering** is implemented on various features of the dataset to get the importance of each feature present in the data.

 - Now the clean data is used to implement **Machine Learning training** to the various regression models and the final ouptut is the **dynamic pricing predictions** for all the hotels. To get the model accuracy, **R2-score** for each model is calculated.

 - Total of 5 Regression models have been implemented, out of which **ElasticNet Regression Model** showed the least Loss score and out-performed the other models.

### Final Result: