Visualizing Data

In [1]:

    

import warnings
warnings.filterwarnings('ignore')


    

In [2]:

    

%matplotlib inline
%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [3]:

    

# we need to make sure we have 0.20 for Plotting
# !conda update pandas -y


    

In [4]:

    

import pandas as pd
print(pd.__version__)


    

    




0.22.0

In [5]:

    

df = pd.read_csv('./insurance-customers-300.csv', sep=';')


    

In [6]:

    

y=df['group']


    

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df.drop('group', axis='columns', inplace=True)


    

In [8]:

    

X = df.as_matrix()


    

In [9]:

    

df.describe()


    

    
Out[9]:







  

    

      

      
max speed
      
age
      
thousand km per year
    
  
  

    

      
count
      
300.000000
      
300.000000
      
300.000000
    
    

      
mean
      
171.863333
      
44.006667
      
31.220000
    
    

      
std
      
18.807545
      
16.191784
      
15.411792
    
    

      
min
      
132.000000
      
18.000000
      
5.000000
    
    

      
25%
      
159.000000
      
33.000000
      
18.000000
    
    

      
50%
      
171.000000
      
42.000000
      
30.000000
    
    

      
75%
      
187.000000
      
52.000000
      
43.000000
    
    

      
max
      
211.000000
      
90.000000
      
99.000000
    
  

In [10]:

    

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

plt.clf()
plt.figure(figsize=(20, 20))

CMAP = ListedColormap(['#AA4444', '#006000', '#FFFF00'])

pd.plotting.scatter_matrix(df, c=y, cmap=CMAP, s=200, edgecolor='black', figsize=(20, 20), diagonal='kde')
plt.show()
# plt.savefig('scatter-matrix.png')


    

    






<matplotlib.figure.Figure at 0x1804cd40898>






    






<matplotlib.figure.Figure at 0x1804d446b00>






    

In [11]:

    

# ignore this, it is just technical code
# should come from a lib, consider it to appear magically 
# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html

import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap

cmap_bold = ListedColormap(['#AA4444', '#006000', '#AAAA00'])
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#FFFFDD'])
font_size=25

def meshGrid(x_data, y_data):
    h = 1  # step size in the mesh
    x_min, x_max = x_data.min() - 1, x_data.max() + 1
    y_min, y_max = y_data.min() - 1, y_data.max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    return (xx,yy)
    
def plotPrediction(clf, x_data, y_data, x_label, y_label, colors, title="", mesh=True):
    xx,yy = meshGrid(x_data, y_data)
    plt.figure(figsize=(20,10))

    if mesh:
        Z = clf.predict(np.c_[yy.ravel(), xx.ravel()])
        # Put the result into a color plot
        Z = Z.reshape(xx.shape)
        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
    
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.scatter(x_data, y_data, c=colors, cmap=cmap_bold, s=80, marker='o')
    plt.xlabel(x_label, fontsize=font_size)
    plt.ylabel(y_label, fontsize=font_size)
    plt.title(title, fontsize=font_size)


    

In [12]:

    

X_kmh_age = X[:, :2]
X_2_dim = X_kmh_age


    

In [13]:

    

plotPrediction(None, X_2_dim[:, 1], X_2_dim[:, 0], 
               'Age', 'Max Speed', y, mesh=False,
                title="All Data Max Speed vs Age")


    

In [11]:

    

corrmat = df.corr()


    

In [15]:

    

import seaborn as sns

sns.heatmap(corrmat, annot=True)
figure = plt.gcf()
figure.set_size_inches(10, 10)
plt.show()
# plt.savefig('corr.png')


    

In [16]:

    

def plot(col1, col2):
    # https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.jointplot.html#seaborn.jointplot
    sns.jointplot(df[col1],df[col2],dropna=True, kind="hex")
    figure = plt.gcf()
    figure.set_size_inches(10, 10)
    # for notebook
    plt.show()
#     plt.savefig('%s/%s_%s.png'%(IMG_DIR, col1, col2), dpi = DPI)


    

In [17]:

    

plot('max speed', 'age')


    

In [18]:

    

plot('max speed', 'thousand km per year')


    

In [19]:

    

plot('age', 'thousand km per year')


    

In [20]:

    

from sklearn import linear_model
from sklearn.metrics import mean_squared_error, r2_score


    

In [23]:

    

max_speed = df['max speed']
thousand_km_per_year = df['thousand km per year']
plt.scatter(max_speed, thousand_km_per_year,  color='black')
plt.xlabel('Max Speed')
plt.ylabel('Thousand Km per Year')


    

    
Out[23]:





Text(0,0.5,'Thousand Km per Year')






    

In [24]:

    

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(max_speed.reshape(-1, 1), thousand_km_per_year.reshape(-1, 1))


    

    
Out[24]:





LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [25]:

    

thousand_km_per_year_pred = regr.predict(max_speed.reshape(-1, 1))


    

In [27]:

    

mean_squared_error(thousand_km_per_year, thousand_km_per_year_pred)


    

    
Out[27]:





161.78972677283218

In [28]:

    

# Explained variance score: 1 is perfect prediction, pretty good score
r2_score(thousand_km_per_year, thousand_km_per_year_pred)


    

    
Out[28]:





0.31656894655030343

In [29]:

    

plt.scatter(max_speed, thousand_km_per_year,  color='black')
plt.xlabel('Max Speed')
plt.ylabel('Thousand Km per Year')
plt.plot(max_speed, thousand_km_per_year_pred, color='red', linewidth=3)


    

    
Out[29]:





[<matplotlib.lines.Line2D at 0x1805194cf60>]






    

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