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udemy-ML/12-K-Nearest-Neighbors/01-KNN-Exercise .ipynb

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Copyright by Pierian Data Inc. For more information, visit us at www.pieriandata.com

KNN Project Exercise

Due to the simplicity of KNN for Classification, let's focus on using a PipeLine and a GridSearchCV tool, since these skills can be generalized for any model.

The Sonar Data

Detecting a Rock or a Mine

Sonar (sound navigation ranging) is a technique that uses sound propagation (usually underwater, as in submarine navigation) to navigate, communicate with or detect objects on or under the surface of the water, such as other vessels.

The data set contains the response metrics for 60 separate sonar frequencies sent out against a known mine field (and known rocks). These frequencies are then labeled with the known object they were beaming the sound at (either a rock or a mine).

Our main goal is to create a machine learning model capable of detecting the difference between a rock or a mine based on the response of the 60 separate sonar frequencies.

Data Source: https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)

Complete the Tasks in bold

TASK: Run the cells below to load the data.

In [95]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
In [96]:
df = pd.read_csv('../DATA/sonar.all-data.csv')
In [97]:
df.head()
Out[97]:
Freq_1 Freq_2 Freq_3 Freq_4 Freq_5 Freq_6 Freq_7 Freq_8 Freq_9 Freq_10 ... Freq_52 Freq_53 Freq_54 Freq_55 Freq_56 Freq_57 Freq_58 Freq_59 Freq_60 Label
0 0.0200 0.0371 0.0428 0.0207 0.0954 0.0986 0.1539 0.1601 0.3109 0.2111 ... 0.0027 0.0065 0.0159 0.0072 0.0167 0.0180 0.0084 0.0090 0.0032 R
1 0.0453 0.0523 0.0843 0.0689 0.1183 0.2583 0.2156 0.3481 0.3337 0.2872 ... 0.0084 0.0089 0.0048 0.0094 0.0191 0.0140 0.0049 0.0052 0.0044 R
2 0.0262 0.0582 0.1099 0.1083 0.0974 0.2280 0.2431 0.3771 0.5598 0.6194 ... 0.0232 0.0166 0.0095 0.0180 0.0244 0.0316 0.0164 0.0095 0.0078 R
3 0.0100 0.0171 0.0623 0.0205 0.0205 0.0368 0.1098 0.1276 0.0598 0.1264 ... 0.0121 0.0036 0.0150 0.0085 0.0073 0.0050 0.0044 0.0040 0.0117 R
4 0.0762 0.0666 0.0481 0.0394 0.0590 0.0649 0.1209 0.2467 0.3564 0.4459 ... 0.0031 0.0054 0.0105 0.0110 0.0015 0.0072 0.0048 0.0107 0.0094 R

5 rows × 61 columns

Data Exploration

TASK: Create a heatmap of the correlation between the difference frequency responses.

In [ ]:
# CODE HERE
In [98]:
Out[98]:
<AxesSubplot:>

TASK: What are the top 5 correlated frequencies with the target\label?

Note: You many need to map the label to 0s and 1s.

Additional Note: We're looking for absolute* correlation values.*

In [99]:
#CODE HERE
In [101]:
Out[101]:
Freq_45    0.339406
Freq_10    0.341142
Freq_49    0.351312
Freq_12    0.392245
Freq_11    0.432855
Target     1.000000
Name: Target, dtype: float64

Train | Test Split

Our approach here will be one of using Cross Validation on 90% of the dataset, and then judging our results on a final test set of 10% to evaluate our model.

TASK: Split the data into features and labels, and then split into a training set and test set, with 90% for Cross-Validation training, and 10% for a final test set.

Note: The solution uses a random_state=42

In [102]:
# CODE HERE
In [105]:

TASK: Create a PipeLine that contains both a StandardScaler and a KNN model

In [106]:
# CODE HERE

TASK: Perform a grid-search with the pipeline to test various values of k and report back the best performing parameters.

In [ ]:
# CODE HERE
In [82]:
Out[82]:
GridSearchCV(cv=5,
             estimator=Pipeline(steps=[('scaler', StandardScaler()),
                                       ('knn', KNeighborsClassifier())]),
             param_grid={'knn__n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                                              12, 13, 14, 15, 16, 17, 18, 19,
                                              20, 21, 22, 23, 24, 25, 26, 27,
                                              28, 29]},
             scoring='accuracy')
In [83]:
Out[83]:
{'memory': None,
 'steps': [('scaler', StandardScaler()),
  ('knn', KNeighborsClassifier(n_neighbors=1))],
 'verbose': False,
 'scaler': StandardScaler(),
 'knn': KNeighborsClassifier(n_neighbors=1),
 'scaler__copy': True,
 'scaler__with_mean': True,
 'scaler__with_std': True,
 'knn__algorithm': 'auto',
 'knn__leaf_size': 30,
 'knn__metric': 'minkowski',
 'knn__metric_params': None,
 'knn__n_jobs': None,
 'knn__n_neighbors': 1,
 'knn__p': 2,
 'knn__weights': 'uniform'}

(HARD) TASK: Using the .cv_results_ dictionary, see if you can create a plot of the mean test scores per K value.

In [113]:
#CODE HERE
In [114]:
Out[114]:
array([0.84537696, 0.78065434, 0.77524893, 0.75917496, 0.75931721,
       0.74822191, 0.75945946, 0.71664296, 0.7113798 , 0.68421053,
       0.70042674, 0.68435277, 0.68449502, 0.67908962, 0.69530583,
       0.68990043, 0.7113798 , 0.70042674, 0.72204836, 0.67908962,
       0.70071124, 0.69530583, 0.69530583, 0.68463727, 0.68477952,
       0.67923186, 0.67411095, 0.65775249, 0.6685633 ])
In [115]:
Out[115]:
Text(0, 0.5, 'Accuracy')

Final Model Evaluation

TASK: Using the grid classifier object from the previous step, get a final performance classification report and confusion matrix.

In [117]:
#Code Here
In [121]:
Out[121]:
array([[12,  1],
       [ 1,  7]], dtype=int64)
In [122]:


              precision    recall  f1-score   support

           M       0.92      0.92      0.92        13
           R       0.88      0.88      0.88         8

    accuracy                           0.90        21
   macro avg       0.90      0.90      0.90        21
weighted avg       0.90      0.90      0.90        21

Great Job!

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