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Here we will use the standard scalar class

The following code imports required libraries:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

14- Importing the Dataset

Execute the following command to import the dataset.

banknote_data = pd.read_csv(rD:\Datasets\banknote_data.csv)

The script above reads the dataset and stores it in banknote_data dataframe.

15- Analyzing the Data

The following script returns the data dimensions:

banknote_data.shape

The above script returns (1372, 5) which means that our dataset contains 1372 records and five attributes.

Execute the following script to eyeball the data:

banknote_data.head()

The output looks like this:

16- Data Preprocessing

To following script divides the data into feature and label set.

features= banknote_data.iloc[:,0:4].values

labels= banknote_data.iloc[:,4].values

Finally let’s divide the data into 80 % training and 20% test sets:

from sklearn.model_selection import train_test_split

train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.2, random_state = 0)

17- Scaling the Data

If you are using KNN algorithm , it is always a good practice to scale your data. Remember we discussed scaling in the 3rd chapter. Here we will use the standard scalar class.

from sklearn.preprocessing import StandardScaler

feature_scaler = StandardScaler()

train_features = feature_scaler.fit_transform(train_features)

test_features = feature_scaler.transform(test_features)

18- Training the Algorithm and making Predictions

To implement KNN Algorithm with Scikit learn we need to use the KNeighborsClassifier class of the sklear.neighbors library. The value of K is specified as value for the n_neighbors parameter as shown in the following script. We use a value of 3 for K. Execute the following script to train the model on train_features and train_labels

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