This repo contains the submission of DSND first project (Tested on Ubuntu 16.04 LTS and Python 3.x)
The code provided is to help identifying what else could affect the price of a house other than the common attributes (like number of rooms, footage area of the house, ..etc.) with more less recognized yet effective attributes using the powerful DATA SCIENCE.
You should have at least Python 3.x and some mathematical, statistical and visualization libraries in order to run this code in your local machine. Here I used:
- pip (for installing the required libraries)
- numpy
- pandas
- seaborn
- matplotlib
Let's start first with installing the required libraries using pip
python -m pip install --user numpy seaborn matplotlib pandas
If all the libraries are installed correctly open terminal (ctrl + alt + t) or any Python editor of your choice
in case you used terminal type python3 then type:
import numpy
import seaborn
import matplotlib.pyplot
import pandasNow you are ready to run the code on your local machine (if needed)...
- First, clone the repo using:
git clone https://github.com/AbdelrahmanAEmam/DSND-Project1.git - Second, go to the repo location
cd path/to/repo - Finally, open submission.ipynb (for best practice use jupyter notebook)
Using Data Science it's proven that (age of the houe, the distance to nearest MRT, number of convenience stores) and many other features have a big effect on house pricing.
Check out this Blog on Medium here