K Nearest Neighbors Project

Welcome to the KNN Project! This will be a simple project very similar to the lecture, except you'll be given another data set. Go ahead and just follow the directions below.

Import Libraries

Import pandas,seaborn, and the usual libraries.



In [1]:

Get the Data

Read the 'KNN_Project_Data csv file into a dataframe



In [2]:

Check the head of the dataframe.



In [23]:









    Out[23]:






  
    
      
      XVPM
      GWYH
      TRAT
      TLLZ
      IGGA
      HYKR
      EDFS
      GUUB
      MGJM
      JHZC
      TARGET CLASS
    
  
  
    
      0
      1636.670614
      817.988525
      2565.995189
      358.347163
      550.417491
      1618.870897
      2147.641254
      330.727893
      1494.878631
      845.136088
      0
    
    
      1
      1013.402760
      577.587332
      2644.141273
      280.428203
      1161.873391
      2084.107872
      853.404981
      447.157619
      1193.032521
      861.081809
      1
    
    
      2
      1300.035501
      820.518697
      2025.854469
      525.562292
      922.206261
      2552.355407
      818.676686
      845.491492
      1968.367513
      1647.186291
      1
    
    
      3
      1059.347542
      1066.866418
      612.000041
      480.827789
      419.467495
      685.666983
      852.867810
      341.664784
      1154.391368
      1450.935357
      0
    
    
      4
      1018.340526
      1313.679056
      950.622661
      724.742174
      843.065903
      1370.554164
      905.469453
      658.118202
      539.459350
      1899.850792
      0

EDA

Since this data is artificial, we'll just do a large pairplot with seaborn.

Use seaborn on the dataframe to create a pairplot with the hue indicated by the TARGET CLASS column.



In [4]:









    Out[4]:





<seaborn.axisgrid.PairGrid at 0x1197505f8>

Standardize the Variables

Time to standardize the variables.

Import StandardScaler from Scikit learn.



In [5]:

Create a StandardScaler() object called scaler.



In [6]:

Fit scaler to the features.



In [7]:









    Out[7]:





StandardScaler(copy=True, with_mean=True, with_std=True)

Use the .transform() method to transform the features to a scaled version.



In [8]:

Convert the scaled features to a dataframe and check the head of this dataframe to make sure the scaling worked.



In [9]:

Train Test Split

Use train_test_split to split your data into a training set and a testing set.



In [10]:



In [11]:

Using KNN

Import KNeighborsClassifier from scikit learn.



In [12]:

Create a KNN model instance with n_neighbors=1



In [13]:

Fit this KNN model to the training data.



In [14]:









    Out[14]:





KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform')

Predictions and Evaluations

Let's evaluate our KNN model!

Use the predict method to predict values using your KNN model and X_test.



In [24]:

Create a confusion matrix and classification report.



In [16]:



In [17]:



In [18]:









    



             precision    recall  f1-score   support

          0       0.77      0.74      0.75       152
          1       0.74      0.77      0.75       148

avg / total       0.75      0.75      0.75       300

Choosing a K Value

Let's go ahead and use the elbow method to pick a good K Value!

Create a for loop that trains various KNN models with different k values, then keep track of the error_rate for each of these models with a list. Refer to the lecture if you are confused on this step.



In [25]:

Now create the following plot using the information from your for loop.



In [20]:









    Out[20]:





<matplotlib.text.Text at 0x11cbdb710>

Retrain with new K Value

Retrain your model with the best K value (up to you to decide what you want) and re-do the classification report and the confusion matrix.



In [21]:









    



WITH K=30


[[127  25]
 [ 23 125]]


             precision    recall  f1-score   support

          0       0.85      0.84      0.84       152
          1       0.83      0.84      0.84       148

avg / total       0.84      0.84      0.84       300

	XVPM	GWYH	TRAT	TLLZ	IGGA	HYKR	EDFS	GUUB	MGJM	JHZC
0	1.568522	-0.443435	1.619808	-0.958255	-1.128481	0.138336	0.980493	-0.932794	1.008313	-1.069627
1	-0.112376	-1.056574	1.741918	-1.504220	0.640009	1.081552	-1.182663	-0.461864	0.258321	-1.041546
2	0.660647	-0.436981	0.775793	0.213394	-0.053171	2.030872	-1.240707	1.149298	2.184784	0.342811
3	0.011533	0.191324	-1.433473	-0.100053	-1.507223	-1.753632	-1.183561	-0.888557	0.162310	-0.002793
4	-0.099059	0.820815	-0.904346	1.609015	-0.282065	-0.365099	-1.095644	0.391419	-1.365603	0.787762

	XVPM	GWYH	TRAT	TLLZ	IGGA	HYKR	EDFS	GUUB	MGJM	JHZC	TARGET CLASS
0	1636.670614	817.988525	2565.995189	358.347163	550.417491	1618.870897	2147.641254	330.727893	1494.878631	845.136088	0
1	1013.402760	577.587332	2644.141273	280.428203	1161.873391	2084.107872	853.404981	447.157619	1193.032521	861.081809	1
2	1300.035501	820.518697	2025.854469	525.562292	922.206261	2552.355407	818.676686	845.491492	1968.367513	1647.186291	1
3	1059.347542	1066.866418	612.000041	480.827789	419.467495	685.666983	852.867810	341.664784	1154.391368	1450.935357	0
4	1018.340526	1313.679056	950.622661	724.742174	843.065903	1370.554164	905.469453	658.118202	539.459350	1899.850792	0