Homework project 1 - nuaany-ai-group/meetups GitHub Wiki

For this homework we chose the California Housing Prices dataset from the StatLib repository2 This dataset was based on data from the 1990 California census.

The Big Picture

The first task you are asked to perform is to build a model of housing prices in California using the California census data. This data has metrics such as the population, median income, median housing price, and so on for each block group in California. Block groups are the smallest geographical unit for which the US Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). We will just call them “districts” for short.

Your model should learn from this data and be able to predict the median housing price in any district, given all the other metrics.

What Is It For

Your model’s output (a prediction of a district’s median housing price) will be fed to another Machine Learning system (see as below), along with many other signals.3 This downstream system will determine whether it is worth investing in a given area or not. Getting this right is critical, as it directly affects revenue.

Here is the Python function to fetch the data to start with:

import os
import tarfile
from six.moves import urllib

DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"
HOUSING_PATH = "datasets/housing"
HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + "/housing.tgz"

def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):
    if not os.path.isdir(housing_path):
        os.makedirs(housing_path)
    tgz_path = os.path.join(housing_path, "housing.tgz")
    urllib.request.urlretrieve(housing_url, tgz_path)
    housing_tgz = tarfile.open(tgz_path)
    housing_tgz.extractall(path=housing_path)
    housing_tgz.close()