markdown K-Nearest-Neighbor-KNN Python
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<h1><center>K-Nearest Neighbors</center></h1>
In this Lab you will load a customer dataset, fit the data, and use K-Nearest Neighbors to predict a data point. But what is **K-Nearest Neighbors**?
**K-Nearest Neighbors** is an algorithm for supervised learning. Where the data is 'trained' with data points corresponding to their classification. Once a point is to be predicted, it takes into account the 'K' nearest points to it to determine it's classification.
### Here's an visualization of the K-Nearest Neighbors algorithm.
<img src="https://ibm.box.com/shared/static/mgkn92xck0z05v7yjq8pqziukxvc2461.png">
In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points) we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A.
In this sense, it is important to consider the value of k. But hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the 'K' Nearest Neighbors (points) when it predicts the classification of the test point.
<h1>Table of contents</h1>
<div class="alert alert-block alert-info" style="margin-top: 20px">
<ol>
<li><a href="#about_dataset">About the dataset</a></li>
<li><a href="#visualization_analysis">Data Visualization and Analysis</a></li>
<li><a href="#classification">Classification</a></li>
</ol>
</div>
<br>
<hr>
Lets load required libraries
```python
import itertools
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter
import pandas as pd
import numpy as np
import matplotlib.ticker as ticker
from sklearn import preprocessing
%matplotlib inline
```
<div id="about_dataset">
<h2>About the dataset</h2>
</div>
Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset, with predefined labels, we need to build a model to be used to predict class of a new or unknown case.
The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns.
The target field, called __custcat__, has four possible values that correspond to the four customer groups, as follows:
1- Basic Service
2- E-Service
3- Plus Service
4- Total Service
Our objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.
Lets download the dataset. To download the data, we will use !wget to download it from IBM Object Storage.
```python
!wget -O teleCust1000t.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/teleCust1000t.csv
```
--2019-01-12 08:15:38-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/teleCust1000t.csv
Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193
Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 37048 (36K) [text/csv]
Saving to: ‘teleCust1000t.csv’
teleCust1000t.csv 100%[=====================>] 36.18K --.-KB/s in 0.02s
2019-01-12 08:15:38 (2.22 MB/s) - ‘teleCust1000t.csv’ saved [37048/37048]
__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)
### Load Data From CSV File
```python
df = pd.read_csv('teleCust1000t.csv')
df.head()
```
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>region</th>
<th>tenure</th>
<th>age</th>
<th>marital</th>
<th>address</th>
<th>income</th>
<th>ed</th>
<th>employ</th>
<th>retire</th>
<th>gender</th>
<th>reside</th>
<th>custcat</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>2</td>
<td>13</td>
<td>44</td>
<td>1</td>
<td>9</td>
<td>64.0</td>
<td>4</td>
<td>5</td>
<td>0.0</td>
<td>0</td>
<td>2</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>3</td>
<td>11</td>
<td>33</td>
<td>1</td>
<td>7</td>
<td>136.0</td>
<td>5</td>
<td>5</td>
<td>0.0</td>
<td>0</td>
<td>6</td>
<td>4</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>68</td>
<td>52</td>
<td>1</td>
<td>24</td>
<td>116.0</td>
<td>1</td>
<td>29</td>
<td>0.0</td>
<td>1</td>
<td>2</td>
<td>3</td>
</tr>
<tr>
<th>3</th>
<td>2</td>
<td>33</td>
<td>33</td>
<td>0</td>
<td>12</td>
<td>33.0</td>
<td>2</td>
<td>0</td>
<td>0.0</td>
<td>1</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>4</th>
<td>2</td>
<td>23</td>
<td>30</td>
<td>1</td>
<td>9</td>
<td>30.0</td>
<td>1</td>
<td>2</td>
<td>0.0</td>
<td>0</td>
<td>4</td>
<td>3</td>
</tr>
</tbody>
</table>
</div>
<div id="visualization_analysis">
<h2>Data Visualization and Analysis</h2>
</div>
#### Let’s see how many of each class is in our data set
```python
df['custcat'].value_counts()
```
3 281
1 266
4 236
2 217
Name: custcat, dtype: int64
#### 281 Plus Service, 266 Basic-service, 236 Total Service, and 217 E-Service customers
You can easily explore your data using visualization techniques:
```python
df.hist(column='income', bins=10)
```
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f3903017c50>]],
dtype=object)
### Feature set
Lets define feature sets, X:
```python
df.columns
```
Index(['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',
'employ', 'retire', 'gender', 'reside', 'custcat'],
dtype='object')
To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array:
```python
X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values #.astype(float)
X[0:5]
```
array([[ 2., 13., 44., 1., 9., 64., 4., 5., 0., 0., 2.],
[ 3., 11., 33., 1., 7., 136., 5., 5., 0., 0., 6.],
[ 3., 68., 52., 1., 24., 116., 1., 29., 0., 1., 2.],
[ 2., 33., 33., 0., 12., 33., 2., 0., 0., 1., 1.],
[ 2., 23., 30., 1., 9., 30., 1., 2., 0., 0., 4.]])
What are our labels?
```python
y = df['custcat'].values
y[0:5]
```
array([1, 4, 3, 1, 3])
## Normalize Data
Data Standardization give data zero mean and unit variance, it is good practice, especially for algorithms such as KNN which is based on distance of cases:
```python
X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))
X[0:5]
```
array([[-0.02696767, -1.055125 , 0.18450456, 1.0100505 , -0.25303431,
-0.12650641, 1.0877526 , -0.5941226 , -0.22207644, -1.03459817,
-0.23065004],
[ 1.19883553, -1.14880563, -0.69181243, 1.0100505 , -0.4514148 ,
0.54644972, 1.9062271 , -0.5941226 , -0.22207644, -1.03459817,
2.55666158],
[ 1.19883553, 1.52109247, 0.82182601, 1.0100505 , 1.23481934,
0.35951747, -1.36767088, 1.78752803, -0.22207644, 0.96655883,
-0.23065004],
[-0.02696767, -0.11831864, -0.69181243, -0.9900495 , 0.04453642,
-0.41625141, -0.54919639, -1.09029981, -0.22207644, 0.96655883,
-0.92747794],
[-0.02696767, -0.58672182, -0.93080797, 1.0100505 , -0.25303431,
-0.44429125, -1.36767088, -0.89182893, -0.22207644, -1.03459817,
1.16300577]])
### Train Test Split
Out of Sample Accuracy is the percentage of correct predictions that the model makes on data that that the model has NOT been trained on. Doing a train and test on the same dataset will most likely have low out-of-sample accuracy, due to the likelihood of being over-fit.
It is important that our models have a high, out-of-sample accuracy, because the purpose of any model, of course, is to make correct predictions on unknown data. So how can we improve out-of-sample accuracy? One way is to use an evaluation approach called Train/Test Split.
Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.
This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the data. It is more realistic for real world problems.
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)
print ('Train set:', X_train.shape, y_train.shape)
print ('Test set:', X_test.shape, y_test.shape)
```
Train set: (800, 11) (800,)
Test set: (200, 11) (200,)
<div id="classification">
<h2>Classification</h2>
</div>
<h3>K nearest neighbor (KNN)</h3>
#### Import library
Classifier implementing the k-nearest neighbors vote.
```python
from sklearn.neighbors import KNeighborsClassifier
```
### Training
Lets start the algorithm with k=4 for now:
```python
k = 4
#Train Model and Predict
neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)
neigh
```
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=4, p=2,
weights='uniform')
### Predicting
we can use the model to predict the test set:
```python
yhat = neigh.predict(X_test)
yhat[0:5]
```
array([1, 1, 3, 2, 4])
### Accuracy evaluation
In multilabel classification, __accuracy classification score__ is a function that computes subset accuracy. This function is equal to the jaccard_similarity_score function. Essentially, it calculates how closely the actual labels and predicted labels are matched in the test set.
```python
from sklearn import metrics
print("Train set Accuracy: ", metrics.accuracy_score(y_train, neigh.predict(X_train)))
print("Test set Accuracy: ", metrics.accuracy_score(y_test, yhat))
```
Train set Accuracy: 0.5475
Test set Accuracy: 0.32
## Practice
Can you build the model again, but this time with k=6?
```python
# write your code here
k=6
neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)
neigh
```
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=6, p=2,
weights='uniform')
Double-click __here__ for the solution.
<!-- Your answer is below:
k = 6
neigh6 = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)
yhat6 = neigh6.predict(X_test)
print("Train set Accuracy: ", metrics.accuracy_score(y_train, neigh6.predict(X_train)))
print("Test set Accuracy: ", metrics.accuracy_score(y_test, yhat6))
-->
#### What about other K?
K in KNN, is the number of nearest neighbors to examine. It is supposed to be specified by the User. So, how can we choose right value for K?
The general solution is to reserve a part of your data for testing the accuracy of the model. Then chose k =1, use the training part for modeling, and calculate the accuracy of prediction using all samples in your test set. Repeat this process, increasing the k, and see which k is the best for your model.
We can calculate the accuracy of KNN for different Ks.
```python
Ks = 10
mean_acc = np.zeros((Ks-1))
std_acc = np.zeros((Ks-1))
ConfustionMx = [];
for n in range(1,Ks):
#Train Model and Predict
neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)
yhat=neigh.predict(X_test)
mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)
std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])
mean_acc
```
array([0.3 , 0.29 , 0.315, 0.32 , 0.315, 0.31 , 0.335, 0.325, 0.34 ])
#### Plot model accuracy for Different number of Neighbors
```python
plt.plot(range(1,Ks),mean_acc,'g')
plt.fill_between(range(1,Ks),mean_acc - 1*std_acc,mean_acc + 1 * std_acc, alpha=0.10)
plt.legend(('Accuracy ', '+/- 3xstd'))
plt.ylabel('Accuracy ')
plt.xlabel('Number of Nabors (K)')
plt.tight_layout()
plt.show()
```
```python
print( "The best accuracy was with", mean_acc.max(), "with k=", mean_acc.argmax()+1)
```
The best accuracy was with 0.34 with k= 9
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