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.