Hackbout_Albatross

This repository is a part of Hackbout Hackathon

    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
• 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: