#import pandas and load hand written digits dataset 
import pandas as pd
from sklearn.datasets import load_digits 
digits=load_digits()

#Get the features in digits 
dir(digits)

Output:

['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_name s']

#Display first five digits images 
%matplotlib inline
import matplotlib.pyplot as plt 
plt.gray()
for i in range(5):
    plt.matshow(digits.images[i])

<Figure size 432x288 with 0 Axes>

#get the data of digits 
digits.data[:5]

Output:

array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,
       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,
       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,
        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,
       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.],       
       [ 0.,  0.,  0., 12., 13.,  5.,  0.,  0.,  0.,  0.,  0., 11., 16.,         
       9.,  0.,  0.,  0.,  0.,  3., 15., 16.,  6.,  0.,  0.,  0.,  7.,
       15., 16., 16.,  2.,  0.,  0.,  0.,  0.,  1., 16., 16.,  3.,  0.,
        0.,  0.,  0.,  1., 16., 16.,  6.,  0.,  0.,  0.,  0.,  1., 16.,
       16.,  6.,  0.,  0.,  0.,  0.,  0., 11., 16., 10.,  0.,  0.],       
       [ 0.,  0.,  0.,  4., 15., 12.,  0.,  0.,  0.,  0.,  3., 16., 15.,
       14.,  0.,  0.,  0.,  0.,  8., 13.,  8., 16.,  0.,  0.,  0.,  0.,
        1.,  6., 15., 11.,  0.,  0.,  0.,  1.,  8., 13., 15.,  1.,  0.,
        0.,  0.,  9., 16., 16.,  5.,  0.,  0.,  0.,  0.,  3., 13., 16.,
       16., 11.,  5.,  0.,  0.,  0.,  0.,  3., 11., 16.,  9.,  0.],      
        [ 0.,  0.,  7., 15., 13.,  1.,  0.,  0.,  0.,  8., 13.,  6., 15.,
        4.,  0.,  0.,  0.,  2.,  1., 13., 13.,  0.,  0.,  0.,  0.,  0.,
        2., 15., 11.,  1.,  0.,  0.,  0.,  0.,  0.,  1., 12., 12.,  1.,
        0.,  0.,  0.,  0.,  0.,  1., 10.,  8.,  0.,  0.,  0.,  8.,  4.,
        5., 14.,  9.,  0.,  0.,  0.,  7., 13., 13.,  9.,  0.,  0.],       
        [ 0.,  0.,  0.,  1., 11.,  0.,  0.,  0.,  0.,  0.,  0.,  7.,  8.,
        0.,  0.,  0.,  0.,  0.,  1., 13.,  6.,  2.,  2.,  0.,  0.,  0.,
        7., 15.,  0.,  9.,  8.,  0.,  0.,  5., 16., 10.,  0., 16.,  6.,
        0.,  0.,  4., 15., 16., 13., 16.,  1.,  0.,  0.,  0.,  0.,  3.,
       15., 10.,  0.,  0.,  0.,  0.,  0.,  2., 16.,  4.,  0.,  0.]])

#Create DataFrame by taking digits.data output value
df=pd.DataFrame(digits.data)
df.head()

Output:

#Get the target value of digits
digits.target

Output:

array([0, 1, 2, ..., 8, 9, 8])

#Add new column named as target and assign the value of digits.target
df['target']=digits.target
df.head()

Output:

#Split dataset into training and testing data
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test=train_test_split(df.drop(['target'],axis='columns'),digits.data.target(,test_size=0.2)

#Create RandomForestClassifier model and trained the model
from sklearn.ensemble import RandomForestClassifier 
model=RandomForestClassifier()
model.fit(X_train,y_train)

Output:

RandomForestClassifier()

#Get the model accuracy
model.score(X_test,y_test)

Output:

0.9638888888888889

#Model predicted by taking X_test data
y_pred=model.predict(X_test)
y_pred

Output:

array([9, 3, 4, 7, 4, 0, 2, 4, 2, 7, 9, 9, 2, 3, 8, 8, 9, 1, 3, 3, 5, 3,       
    5, 7, 8, 1, 4, 6, 6, 3, 6, 7, 8, 2, 6, 1, 5, 8, 1, 4, 8, 6, 9, 2,       
    2, 5, 8, 4, 0, 4, 7, 5, 6, 9, 2, 1, 1, 0, 8, 4, 4, 4, 1, 0, 3, 1,       
    7, 8, 8, 0, 3, 9, 7, 2, 2, 9, 3, 7, 8, 8, 8, 7, 6, 4, 8, 2, 4, 1,       
    4, 7, 1, 8, 4, 6, 4, 5, 4, 1, 9, 3, 2, 1, 1, 4, 1, 0, 1, 7, 7, 9,      
    5, 2, 0, 2, 5, 7, 4, 5, 4, 6, 3, 6, 5, 4, 5, 1, 7, 8, 7, 3, 4, 3,       
    9, 8, 0, 6, 1, 8, 1, 7, 4, 2, 2, 7, 3, 1, 3, 5, 9, 8, 6, 2, 0, 0,       
    6, 7, 3, 4, 8, 3, 2, 4, 6, 4, 6, 1, 6, 5, 1, 6, 5, 7, 6, 6, 7, 1,       
    2, 5, 5, 0, 5, 6, 0, 9, 9, 6, 4, 6, 4, 8, 6, 0, 0, 1, 7, 0, 4, 3,       
    9, 3, 6, 0, 9, 0, 6, 6, 5, 0, 5, 5, 8, 9, 1, 3, 3, 2, 8, 3, 8, 5,       
    2, 7, 4, 9, 0, 6, 8, 4, 0, 4, 3, 1, 5, 8, 9, 7, 1, 7, 4, 3, 7, 6,       
    7, 1, 1, 9, 4, 0, 4, 7, 4, 7, 0, 8, 8, 1, 8, 4, 0, 1, 7, 6, 4, 6,       
    6, 0, 1, 2, 5, 3, 7, 3, 6, 2, 7, 5, 5, 9, 0, 2, 3, 7, 4, 7, 7, 7,       
    0, 2, 4, 7, 4, 6, 5, 7, 6, 1, 7, 1, 2, 0, 0, 5, 7, 9, 2, 3, 0, 5,       
    3, 1, 2, 9, 0, 0, 2, 6, 6, 5, 0, 4, 0, 1, 3, 8, 2, 7, 6, 5, 7, 5,       
    7, 9, 4, 5, 4, 4, 5, 6, 6, 1, 9, 0, 7, 1, 8, 4, 9, 0, 7, 7, 0, 5,       
    0, 8, 5, 6, 9, 8, 8, 2])

#Get the accuracy by confusion matrix
from sklearn.metrics import confusion_matrix 
cm=confusion_matrix(y_test,y_pred)
cm

Output:

array([[37,  0,  0,  0,  0,  0,  0,  0,  0,  0],
      [ 0, 36,  0,  0,  0,  0,  0,  0,  0,  0],
      [ 0,  0, 30,  0,  0,  0,  0,  0,  0,  0],
      [ 0,  0,  0, 30,  0,  0,  0,  1,  0,  0],
      [ 0,  0,  0,  0, 44,  0,  0,  3,  0,  0],
      [ 0,  0,  0,  0,  0, 32,  0,  0,  0,  1],
      [ 0,  0,  0,  0,  0,  0, 40,  0,  0,  0],
      [ 0,  0,  0,  0,  0,  0,  0, 39,  0,  0],
      [ 0,  1,  0,  0,  1,  1,  0,  1, 33,  0],
      [ 0,  0,  0,  0,  0,  2,  0,  1,  1, 26]], dtype=int64)

#Get the accuracy by invoking accuracy_score()
from sklearn.metrics import accuracy_score 
print(accuracy_score(y_test,y_pred))

0.9638888888888889

#Create the plot by taking cm value
%matplotlib inline
import matplotlib.pyplot as plt 
import seaborn as sns
plt.figure(figsize=(10,7)) 
sns.heatmap(cm,annot=True) 
plt.xlabel('Predicted') 
plt.ylabel('Truth')

Output:

Text(69.0, 0.5, 'Truth')