In [9]:

    

from sklearn import datasets 
iris = datasets.load_iris()
X = iris.data
y = iris.target
X.shape


    

    
Out[9]:





(150, 4)

In [10]:

    

y.shape


    

    
Out[10]:





(150,)

In [11]:

    

# import pandas, a data processing and CSV file I/O library
import pandas as pd 

# import seaborn, a Python graphing library
import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
warnings.filterwarnings("ignore")
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", color_codes = True)

%matplotlib inline

# Load the Iris flower dataset in CSV format
iris2 = pd.read_csv("dataset/iris.csv")

# Quick check
iris2.head()


    

    
Out[11]:






  

    

      

      
sepal_length
      
sepal_width
      
petal_length
      
petal_width
      
species
    
  
  

    

      
0
      
5.1
      
3.5
      
1.4
      
0.2
      
setosa
    
    

      
1
      
4.9
      
3.0
      
1.4
      
0.2
      
setosa
    
    

      
2
      
4.7
      
3.2
      
1.3
      
0.2
      
setosa
    
    

      
3
      
4.6
      
3.1
      
1.5
      
0.2
      
setosa
    
    

      
4
      
5.0
      
3.6
      
1.4
      
0.2
      
setosa
    
  

In [12]:

    

# How many samples we have of each species 
iris2["species"].value_counts()


    

    
Out[12]:





setosa        50
versicolor    50
virginica     50
Name: species, dtype: int64

In [13]:

    

# The first way to plot things is using the .plot extension from Pandas dataframes
iris2.plot(kind="scatter", x =  "sepal_length", y = "sepal_width")


    

    
Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0xa5fb608c>






    

In [21]:

    

# use seaborn to plot a similar one 
# A seaborn jointplot shows bivariate scatterplots and univariate histogram in the same figure
sns.jointplot (x = "sepal_length", y = "sepal_width", data=iris2, size =5)


    

    
Out[21]:





<seaborn.axisgrid.JointGrid at 0xa5a8bf8c>






    

In [23]:

    

# Now use seaborn's FacetGrid to color the scatterplot by species 
sns.FacetGrid(iris2, hue= "species", size = 5).map(plt.scatter, "sepal_length", "sepal_width").add_legend()


    

    
Out[23]:





<seaborn.axisgrid.FacetGrid at 0xa5727dac>






    

In [24]:

    

# Use boxplot to visualize individual feature
sns.boxplot(x = "species", y = "petal_length", data=iris2)


    

    
Out[24]:





<matplotlib.axes._subplots.AxesSubplot at 0xa572f16c>






    

In [26]:

    

# We can extend this plot by adding a layer of individual points on top of it through Seaborn's triplot
# Use jitter=True so that all the points don't fall in single vertical lines above the species 
# saving the resulting axes as ax each time causes the resulting plots to be shown on top of the previous axes
ax = sns.boxplot(x="species", y = "petal_length", data = iris2)
ax = sns.stripplot(x="species", y="petal_length", data = iris2, jitter = True, edgecolor="gray")


    

In [27]:

    

# A violin plot combines the benefits of the previous two plots and simplifies them.
# Denser region of the data are fatter, and sparser thinner in a violin plot
sns.violinplot(x="species", y = "petal_length", data = iris2, size =5)


    

    
Out[27]:





<matplotlib.axes._subplots.AxesSubplot at 0xa57f132c>






    

In [28]:

    

# Another useful seaborn plot for looking at univariate relations is the kdeplot, 
# which creates and visualizes a kernel density estimate of underlying features
sns.FacetGrid(iris2, hue="species", size=5)\
    .map(sns.kdeplot, "petal_length")\
    .add_legend()


    

    
Out[28]:





<seaborn.axisgrid.FacetGrid at 0xa583102c>






    

In [30]:

    

# Another useful seaborn plot is the pairplot, which shows the bivariate relation between each pair of features
# From the pairplot, we'll see that the Iris-setosa is separated from the other two accross all feature combinations
sns.pairplot(iris2, hue="species", size =3)


    

    
Out[30]:





<seaborn.axisgrid.PairGrid at 0xa5538bec>






    

In [31]:

    

# The diagonal elements in a pairplot shows the histogram by default,
# but we can update these elements to show other things, such as kde
sns.pairplot(iris2, hue = "species", size = 3, diag_kind="kde")


    

    
Out[31]:





<seaborn.axisgrid.PairGrid at 0xa4bcfacc>






    

In [14]:

    

import numpy as np
from sklearn import cross_validation
X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.data, iris.target, test_size=0.2, random_state=0)


    

In [15]:

    

from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators = 10)
clf.fit(X_train, y_train)


    

    
Out[15]:





RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)

In [16]:

    

clf.predict(X_test)


    

    
Out[16]:





array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1, 0,
       0, 2, 0, 0, 1, 1, 0])

In [17]:

    

y_test


    

    
Out[17]:





array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1, 0,
       0, 2, 0, 0, 1, 1, 0])

In [18]:

    

## Cross validation score
clf.score(X_test, y_test)


    

    
Out[18]:





0.96666666666666667

In [ ]: