A simple hands-on practice on Scikit-learn. In this work I have tried to showcase the housing prices in California, datasets are available on GitHub

import pandas as pd 
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
housing = pd.read_csv('housing.csv')
housing.head()

housing.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
longitude             20640 non-null float64
latitude              20640 non-null float64
housing_median_age    20640 non-null float64
total_rooms           20640 non-null float64
total_bedrooms        20433 non-null float64
population            20640 non-null float64
households            20640 non-null float64
median_income         20640 non-null float64
median_house_value    20640 non-null float64
ocean_proximity       20640 non-null object
dtypes: float64(9), object(1)
memory usage: 1.5+ MB

• There is 20,640 instance in the dataset, which means that it is fairly small by Machine learning standards. Notice the total_bedrooms attribute has only 20,433 non-null values, meaning that 207 districts are missing this feature. We need to take care of this later.
• ocean_proximity column was repetitive, which means it is probably a categorical attribute, we will see how many districts belong to each category by using the value_counts() method.

Big Data Jobs

housing['ocean_proximity'].value_counts()

<1H OCEAN     9136
INLAND        6551
NEAR OCEAN    2658
NEAR BAY      2290
ISLAND           5
Name: ocean_proximity, dtype: int64
housing.describe()

From the histogram, we can see that slightly over 800 districts have a median_house_value equal to about $100,000.

housing.hist(bins=50, figsize=(20,15))
plt.show()

• Since the median income is a very important attribute to predict median housing prices. If we look at the median income histogram more closely most of the median income values are clustered around 2 to 5 (i.e., $20,000 — $50,000), but some median goes far beyond 6 ($60,000).
• It is important to have a sufficient number of instances in our dataset for each stratum, or else the estimation of the dataset may be biased. This means that we should not have too many strata.

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