Rome_house_market_analysis

Analysis of the real estate market of Rome (Italy) using data scraped from the Immobiliare.it website

Project Overview

• Data cleaning

• Exploratory Data Analysis.

Code and Resourses used

Python Version: 3.8.2

Packages: Pandas, Numpy, Matplotlib, Seaborn, Wordcloud, NLTK

Data Cleaning

• Pull out information from string features and m
• Fill missing numerical values with feature median
• Convert Object data into numerical
• Create a binary column for missing data with Boolean values

EDA: Exploratory Data Analysis

The EDA made shows how data is distributed and relation between different features.

Used both wordcloud and nltk to represent and give a deeper insight on the description column.

Following few highlights from the graphs dispalyed: