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Machine Learning Using scikit-learn Fresco Play Handson Solution HackerRank

Machine Learning Using scikit-learn Fresco Play Hands on Preprocessing, Nearest Neighbors Technique, Decision Tree, Ensemble method, SVM, Clustering.
D Shwari
Machine Learning Using scikit-learn Fresco Play Handson Solution HackerRank - www.pdfcup.com

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Machine Learning Using scikit-learn Fresco Play Hands on Solution Hacker Rank:-

LAB 1: Welcome to Machine Learning Using Scikit-Learn | 1 | Preprocessing

Solution 1: Preprocessing.


#Write your code here
from sklearn import datasets
from sklearn import preprocessing
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import Imputer
import numpy as np

# Task 1:
# Load Popular iris data set from sklearn.datasets module and assign  it to variable 'iris'.
iris = datasets.load_iris()

# Perform Normalization on iris.data with l2 norm and save the transformed data in variable iris_normalized.
iris_normalized = preprocessing.Normalizer(norm= 'l2').fit(iris.data)
iris_normalized = iris_normalized.transform(iris.data)

# Print the mean of every column using the below command.
# print(iris_normalized.mean(axis=0))
print(iris_normalized.mean(axis=0))


# Task 2:
# Convert the categorical integer list iris.target into three binary attribute representation and store the result in variable 'iris_target_onehot'.
enc = preprocessing.OneHotEncoder()
iris_target_onehot = enc.fit_transform(iris.target.reshape(-1, 1))

# Execute the following print statement  print(iris_target_onehot.toarray()[[0, 50, 100]])
print(iris_target_onehot.toarray()[[0, 50, 100]])


# Task 3:
# Set first 50 row values of iris.data to Null values. Use numpy.nan.
iris.data[0:50, :]= np.nan

# Perform Imputation on 'iris.data' and save the transformed data in variable 'itis_imputed'
inputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
inputer = inputer.fit(iris.data)
iris_imputed = inputer.transform(iris.data)
"""Alternative way, use this only if above statement will not work:
imputer = SimpleImputer(missing_values='NaN', strategy='mean')
imputer = imputer.fit(iris.data)
iris_imputed = imputer.transform(iris.data)"""

# Print the mean of every column using the below command. 
# print(iris_imputed.mean(axis=0))
print(iris_imputed.mean(axis=0))

LAB 2: Welcome to Machine Learning Using Scikit-Learn | 2 | Nearest Neighbors

Solution 1: Hands-On KNN.


#Write your code here
from sklearn import datasets
from sklearn import model_selection
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import numpy as np

# Task 1:
# Import popular iris data set from the sklearn.datasets module and  assign it to variable iris.
iris = datasets.load_iris()

# Split iris.data into two sets names X_train, X_test. 
# Also, split iris.target into two sets  Y_train, Y_test. Set the random_state to 30.
X_train, X_test, Y_train, Y_test = train_test_split(iris.data, iris.target,
stratify=iris.target, random_state=30)

# Print the shape of X_trian dataset.
# Print the shape of X_test dataset.
print(X_train.shape)
print(X_test.shape)

# Task 2
# Fit K nearest neighbors model on X_train data and Y_train labels, with default parameters.
# Name the model as knn_clf
knn_classifier = KNeighborsClassifier()  
knn_clf = knn_classifier.fit(X_train, Y_train) 

# Evaluate the model accuracy on training data set and print it's score.
# Evaluate the model accuracy on testing data set and print it's score.
print(knn_clf.score(X_train,Y_train))
print(knn_clf.score(X_test,Y_test))


# Task 3:
# Fit multiple K nearest neighbors models on X_train data and Y_train labels with
#  n_neighbors parameters value changing from 3 to 10.
starting_index = 3
ending_index = 10
neighbors = np.arange(starting_index, ending_index)
train_accuracy =np.empty(len(neighbors))
test_accuracy = np.empty(len(neighbors))

# Evaluate each model accuracy on testing data set. Hint: Use for loop.
for i,k in enumerate(neighbors):
    #Setup a knn classifier with k neighbors
    knn = KNeighborsClassifier(n_neighbors=k)
    
    #Fit the model
    knn.fit(X_train, Y_train)
    
    #Compute accuracy on the training set
    train_accuracy[i] = knn.score(X_train, Y_train)
    
    #Compute accuracy on the test set
    test_accuracy[i] = knn.score(X_test, Y_test) 

index_of_max_accuracy = list(np.where(test_accuracy== max(test_accuracy)))

# Print the n_neighbors value of the model with highest  accuracy.
f = index_of_max_accuracy[0][0]+starting_index
print(f)

LAB 3: Welcome to Machine Learning Using Scikit-Learn | 3 | Decision Trees.

Solution 1: Decision Tree - Hands-On.


#Write your code here
import numpy as np
from sklearn import datasets
from sklearn import model_selection
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split

# Task 1:
# Import numpy and set random seed to 100.
np.random.seed(100)

# Load popular Boston dataset from sklearn.datasets module and assign it to variable boston.
boston = datasets.load_boston()

# Split boston.data into two sets names  X_train X_test. Also, split boston.target into two sets Y_train and Y_test.
# Set random_state to 30.
X_train, X_test, Y_train, Y_test = train_test_split(boston.data,
boston.target, random_state=30)

# Print the shape of X_train dataset.
# Print the shape of X_test dataset.
print(X_train.shape)
print(X_test.shape)


# Task 2:
# Build a Decision tree Regressor model from X_train set  and Y_train labels, with default parameters.
# Name the model as dt_reg.
dt_reg = DecisionTreeRegressor()
dt_reg = dt_reg.fit(X_train, Y_train)

# Evaluate the model accuracy on training data set and print it's score.
print( dt_reg.score(X_train, Y_train))

# Evaluate the model accuracy on testing data set and print it's score.
print( dt_reg.score(X_test, Y_test))

# predict the housing proce for first two samples of X_test set and print them. (Hint: Use predict( function.)
print( dt_reg.predict(X_test[:2] ))


# Task 3:
# Fit multiple Decision tree regressors on X_train data and Y_train labels with max_depth parameters values changing from 2 to 5.
starting_index = 2
ending_index = 5

# Evaluate each model accuracy on testing data set. Hint: Make use of for loop.
test_accuracy = []
for i in range(starting_index , ending_index):
    knnn = DecisionTreeRegressor(max_depth = i)    
    #Fit the model
    knn= knnn.fit(X_train, Y_train)
     
    #Compute accuracy on the test set
    test_accuracy.append(knn.score(X_test, Y_test)) 


# Print the max_depth value of the model with highest accuracy.
print(test_accuracy.index(max(test_accuracy))+ starting_index)

LAB 4: Welcome to Machine Learning Using Scikit-Learn | 4 | Ensemble Methods.

Solution 1: Hands-On - Ensemble.


#Write your code here
import numpy as np
from sklearn import datasets
from sklearn import model_selection
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor

# Task 1:
# Import numpy and set random seed to 100
np.random.seed(100)

# Load popular Boston Dataset from sklearn.datasets module and assign it to variable boston.
boston = datasets.load_boston()

# Split boston.data into two sets names X_train and X_test. 
# Also, split boston.target into two sets Y_train and  Y_test.
# Set random_state to 30.
X_train, X_test, Y_train, Y_test = train_test_split(boston.data,
boston.target,   random_state=30 )

# Print the shape of X_train dataset.
print(X_train.shape)

# Print the shape of X_test dataset.
print(X_test.shape)

# Task 2:
# Build a Random Forest Regressor model from X_train set and Y_train labels, with default parameters.
# Name the model as rf_reg
rfr = RandomForestRegressor()
rf_reg = rfr.fit(X_train, Y_train) 

# Evaluate the model accuracy on training data set and print it's score.
print(rf_reg.score(X_train, Y_train))

# Evaluate the model accuracy on testig data set and print it's score.
print(rf_reg.score(X_test, Y_test))

# Predict the housing price for first two samples of X_test set and print them.
print( rf_reg.predict(X_test[:2] ))

# Task 3:
# Build multiple Random forest Regressor on X_train set and  Y_train labels with max_depth parameters
# value changing from 3 to 5 and also setting n_estimators to  one of 50,100,200 values.
starting_index = 3
ending_index = 5

c_estimators = [50,100,200]
test_accuracy = []

# Evaluate each model accuracy on testing data set. Hint: Use for Loop.
for i in range(starting_index , ending_index+1):
    rfr = RandomForestRegressor(n_estimators =c_estimators[1] , max_depth=i)    
    #Fit the model
    rfr_fit= rfr.fit(X_train, Y_train)
    test_accuracy.append(rfr_fit.score(X_test, Y_test)) 

# Print the parameter value in the form of tuple (a,b).
# 'a' refers to max_depth value and 'b' refers to n_estimators.
index = test_accuracy.index(max(test_accuracy))+ starting_index
print((index,c_estimators[1]))

LAB 5: Welcome to Machine Learning Using Scikit-Learn | 5 | SVM.

Solution 1: Hands-On - SVM.


#Write your code here
import numpy as np
from sklearn import datasets
from sklearn import model_selection
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
import sklearn.preprocessing as preprocessing

# Task 1: 
# Load popular digits dataset from sklearn.datasets module  and assign it to variable digits.
digits = datasets.load_digits()

# Split digits.data into two sets names X_train and X_test. 
# Also, split boston.target into two sets Y_train and  Y_test.
# Set random_state to 30.
X_train, X_test, Y_train, Y_test = train_test_split(digits.data,
digits.target, stratify= digits.target,  random_state=30 )

# Print the shape of X_train dataset.
print(X_train.shape)

# Print the shape of X_test dataset.
print(X_test.shape)


# Task 2:
# Build an SVm classifier from X_train set and Y_train labels,  with  default parameters. 
# Name the model as svm_clf
svm_classifier = SVC()
svm_clf = svm_classifier.fit(X_train, Y_train) 

# Evaluate the model accuracy on testing data set and print it's score.
print( svm_clf.score(X_test, Y_test))


# Task 3:
# Perform StandardScaler of digits.data and store the transformed data in variable digits_standardized.
pss = preprocessing.StandardScaler()
pas = pss.fit(digits.data)
digits_standardized = pss.transform(digits.data)

# Again, Split digits_standardized into two sets names X_train and X_test. 
# Also, split digits.target into two sets Y_train and  Y_test.
# Set random_state to 30; and perform stratified sampling.
X_train, X_test, Y_train, Y_test = train_test_split(digits_standardized,
digits.target, stratify= digits.target,  random_state=30 )

# Build another SVM classifier from X_train set and Y_train labels, with default parameters.
# Name the model as svm_clf2.
svm_clf2 = svm_classifier.fit(X_train, Y_train) 

# Evaluate the model accuracy on testing data set and print it's score.
print(svm_clf2.score(X_test, Y_test))

LAB 6: Welcome to Machine Learning Using Scikit-Learn | 6 | Clustering.

Solution 1: Hands-On - Clustering.


#Write your code here
from sklearn import metrics
from sklearn import datasets
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering 
from sklearn.cluster import AffinityPropagation
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import homogeneity_score

# pls try yourself, if facing any error comment below.

About the author

D Shwari
I'm a professor at National University's Department of Computer Science. My main streams are data science and data analysis. Project management for many computer science-related sectors. Next working project on Al with deep Learning.....

13 comments

  1. Anonymous
    i will try on six code machine learning but i comes error please help me
    1. Anonymous
      Error is related to what? Could you elaborate once?
  2. Anonymous
    i will try on six code machine learning but i comes error please help me
  3. Anonymous
    can you please provide full code for "LAB 6: Welcome to Machine Learning Using Scikit-Learn | 6 | Clustering."
    1. Anonymous
      #Write your code here
      from sklearn import metrics
      from sklearn import datasets
      from sklearn.cluster import KMeans
      from sklearn.cluster import AgglomerativeClustering
      from sklearn.cluster import AffinityPropagation
      from sklearn.model_selection import train_test_split
      from sklearn.datasets import load_iris
      from sklearn.metrics import homogeneity_score

      # Task 1
      iris = load_iris()

      km_cls = KMeans(n_clusters=3, random_state=42)
      km_labels = km_cls.fit_predict(iris.data)

      km_h_score = homogeneity_score(iris.target, km_labels)

      print('{:.4f}'.format(km_h_score))

      # Task 2
      agg_cls = AgglomerativeClustering(n_clusters=3)
      agg_labels = agg_cls.fit_predict(iris.data)

      agg_h_score = homogeneity_score(iris.target, agg_labels)

      print('{:.4f}'.format(agg_h_score))

      # Task 3
      af_cls = AffinityPropagation()
      af_labels = af_cls.fit_predict(iris.data)

      af_h_score = homogeneity_score(iris.target, af_labels)

      print('{:.4f}'.format(af_h_score))
  4. Anonymous
    can you please provide full code for "LAB 6: Welcome to Machine Learning Using Scikit-Learn | 6 | Clustering."
    1. Anonymous
      Glad to help you, please share the runtime error details you are getting.
  5. Anonymous
    #Write your code here
    from sklearn import metrics
    from sklearn import datasets
    from sklearn.cluster import KMeans
    from sklearn.cluster import AgglomerativeClustering
    from sklearn.cluster import AffinityPropagation
    from sklearn.model_selection import train_test_split
    from sklearn.datasets import load_iris
    from sklearn.metrics import homogeneity_score

    # Task 1
    iris = load_iris()

    km_cls = KMeans(n_clusters=3, random_state=42)
    km_labels = km_cls.fit_predict(iris.data)

    km_h_score = homogeneity_score(iris.target, km_labels)

    print('{:.4f}'.format(km_h_score))

    # Task 2
    agg_cls = AgglomerativeClustering(n_clusters=3)
    agg_labels = agg_cls.fit_predict(iris.data)

    agg_h_score = homogeneity_score(iris.target, agg_labels)

    print('{:.4f}'.format(agg_h_score))

    # Task 3
    af_cls = AffinityPropagation()
    af_labels = af_cls.fit_predict(iris.data)

    af_h_score = homogeneity_score(iris.target, af_labels)

    print('{:.4f}'.format(af_h_score))
  6. Anonymous
    i need frescoplay hands on for optimization and hyperparameter tuning batch normalization
    1. Anonymous
      Hey, it is published for LATAM region right now, for APAC it will be available by next month.
    2. Anonymous
      i need batch normalization hands on in hyperparameter tuning I tried by myself 6 test cases it is getting passed and failing in 7th one.
      kindly help me in this.
    3. Anonymous
      Please share the Course ID once?
    4. Anonymous
      I need solution for course ID:63161,ID:57962,ID:56963