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Berlin-Airbnb-Dataset-Analysis

Data science analysis of the Berlin Airbnb dataset of 2018

Authors: Valerio Paolicelli, Matteo Antonio Senese

It concerns in:

  • Data pre-processing and dataset explanation
    • Dimensionality reduction: PCA
    • General data pre-processing
  • Classification algorithms:
    • SVM
    • Decision Tree
    • Random Forest
    • KNN
  • Regression algorithms:
    • Linear Regression
    • Decision Tree
    • Random Forest

The project is developed in python, with the support of specific libraries like sklearn, pandas, matplotlib and numpy. The analysis is collected in a sort of report, inside the jupyter-notebook, that contains HTML, python and Latex code.

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Data science analysis of the Berlin Airbnb dataset of 2018

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  • HTML 60.4%
  • Jupyter Notebook 39.6%