Data Analytics Project as part of DA course UE18CS312 ,5th semester.UE18CS312.The goal is to analyse and predict the price and other variables in the New york Airbnb data. Various visualisation techniques and other methods are used for exploratory data analysis.The data is searched for patterns.Correlation between the variables are calculated and models are developed so as to accurately predict the target variable. Also a recommendation system will be built to recommend Airbnb listings according to the user preference.
We were able to successfully analyse the dataset and gain several useful insights.We also developed several models specifically Lgbm classifier,linear regression, decision tree, ridge regression, lasso regression etc out of which decision tree seemed to be the most accurate.Then we also built a basic recommendation system which takes a keyword or a set of keywords from the user and returns the most similar airbnb listing based on name.
To reproduce the results: Clone the repository or just download the jupyter botebook file and dataset. Open in jupyter notebook and run all cells. You can also get the dataset from here:dataset link
If you can't view the jupyter notebook, please use this link:
https://nbviewer.jupyter.org/github/Ajitesh27/New-York-Airbnb-Data-Analytics-and-Prediction/blob/main/Final-Data%20Analytics%20Project%20DATA%20EXPLORERS.ipynb