Code Monkey home page Code Monkey logo

airbnb-nyc_kaggle's Introduction

Airbnb NYC - Kaggle Regression project

The intention of this mini project is to explore the data, engineer new features, do the preprocessing, perform feature selection, build the optimized model and score it on the test data under 8 hours. I will record my lessons learned and what I could have done better for future projects at the end of the notebook.

I will be implementing supervised machine learning on the NYC Airbnb data from Kaggle using ensemble models alongwith feature engineering and model selection. For this I will be using cleaned data acquired from Kaggle while walking through my analysis in an .ipynb file. Details about the dataset is provided in the notebook and below I will mention the machine learning techniques I will be using in this project.

New York City Airbnb Open data

Processes

Exploratory Data Analysis, Preprocessing, Feature Engineering, Feature Selection, Hyperparameter Optimization, Interpretation and feature importance, Ensembles

Tools and Model

ScikitLearn, pandas, Altair, SHAP, eli5, CatBoostRegressor, LGBMRegressor, Random Forest, Ridge, Lasso, Suppor Vector Machine (SVR), CountVectorizer, RandomizedSearchCV, RFECV, cross_validate, pipeline, column_transformer, SelectFromModel, OneHotEncoder, StandardScaler


To use this repo

Clone this Github repository, install the dependencies, and run the following commands at the command line/terminal from the root directory of the project:

conda env create --file env.yaml
conda activate Kaggle_projects

Setting up Kaggle API

To use the Kaggle API, sign up for a Kaggle account at https://www.kaggle.com. Then go to the 'Account' tab of your user profile (https://www.kaggle.com//account) and select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials. Place this file in the location ~/.kaggle/kaggle.json. I have already included the kaggle package in the repo environment, and running the below script should download the required files.

To download the data file

Run the following commands at the command line/terminal from the root directory of the project to download the data files in a /downloads folder:

python src/download_data.py --dataset=dgomonov/new-york-city-airbnb-open-data --file_path=downloads/

You can now run the notebook file.

To contribute to the repository:

  1. Fork the repository.
  2. Add the implementation of the algorithm with a clearly defined filename for the script or the notebook.
  3. Test the implementation thoroughly and make sure that it works with some dataset.
  4. Add a link with a short description about the file in the README.md.
  5. Create a pull request for review with a short description of your changes.
  6. Do not forget to add attribution for references and sources used in the implementation.

airbnb-nyc_kaggle's People

Contributors

artanzand avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.