<jupyter_start><jupyter_text>This data set containes boston housing data.<jupyter_code>from sklearn import datasets
boston = datasets.load_boston()
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
boston.feature_names
med_home_val = np.median(boston['target'])
avg_home_val = np.mean(boston['target'])
print('Median Home Value: ', med_home_val)
print('Mean Home Value: ', avg_home_val)<jupyter_output>Median Home Value:  21.2
Mean Home Value:  22.5328063241<jupyter_text>The relationship between per-capita crime rate and the pupil-teacher ratio.<jupyter_code>df = pd.DataFrame(boston.data)
df_filtered = df.T.query('3')
plt.scatter(boston.data.T[10], boston.data.T[0])
plt.title("Crime Rate vs. Pupil Teacher Ratio")
plt.ylabel("Per-Capita Crime Rate")
plt.xlabel("Pupil-to-Teacher Ratio")
plt.show()<jupyter_output><empty_output><jupyter_text>It seems like the per capita crime rate is greater in areas with greater student to teacher ratios.  Apparently having a ratio of 20 is the sweet spot and crime rates skyrocket... ?  The relationship between the proportion of black citizens and the distance to employment centers<jupyter_code>plt.scatter(boston.data.T[11], boston.data.T[7])
plt.title("Black citizens vs. Distance to Employment Centers")
plt.ylabel("Black Citizens")
plt.xlabel("Distance to Employment Centers")
plt.show()<jupyter_output><empty_output><jupyter_text>There seems to be a clear relationship between areas with a greater number of black citizens and an increasing distance to employment centers. In areas with a high concentration of black citizens the distances to data centers is farther.  The relationship between median value of owner-occuped homes and nitric oxide concentration along with median home value and the proportion of non-retail business<jupyter_code>plt.scatter(boston['target'], boston.data.T[4], c="green")
plt.scatter( boston.data.T[2], boston.data.T[4], c="red")
plt.title("Nitric Oxide Concentration vs. Median Home val / prop of on retail buisiness")
plt.xlabel("Median Home val / prop of non retail buisiness")
plt.ylabel("Nitric Oxide Concentration")
plt.show()<jupyter_output><empty_output><jupyter_text>There seems to be a relationship in the data between areas with higher value homes and less NOX levels.    Relationship between average number of rooms per dwelling and median home price<jupyter_code>plt.scatter(boston['target'], boston.data.T[5], c="green")
plt.title("Avg num rooms per dwelling vs. median home price")
plt.xlabel("Median Home Price")
plt.ylabel("Avg Num of rooms per Dwelling")
plt.show()<jupyter_output><empty_output>
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