clustering.php

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      <h2>Clustering</h2><br>
      <h4>Clustering comes under unsupervised machine learning. It is done only using the input data that is available, and is used to determine the hidden patterns, similarities and anomalies present in the data. Clustering problems have observations divided into subsets into different subsets, each of them containing similar information. These subsets are called clusters. With clustering the groups (or clusters) are based on the similarities of data instances to each other. No predefined output class is used in training and the clustering algorithm is supposed to learn the grouping. An example of use would be an online movie company recommending you a movie because other customers who had made similar movie choices as you in the past (i,e. the cluster you fall in) have favorably rated that movie.</h4>
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	  </br></br></br></br></br></br></br>
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      <h4><strong>IMPORTANCE:</strong> <p>Clustering is similar to classification, but the basis is different. In Clustering you don’t know what you are looking for, and you are trying to identify some segments or clusters in your data. When you use clustering algorithms on your dataset, unexpected things can suddenly pop up like structures, clusters and groupings you would have never thought of otherwise.</p></h4><br>
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		<h4> In the visualization you can see how the <strong>k-nearest neighbor (KNN)</strong> algorithm
		<strong>classifies</strong> new data points based on their <strong>k</strong> nearest neighbors, where the amount of neighbors k is specified using the slider above. The new point will be assigned the <strong>class</strong> that the majority of its k nearest neighbors hold.
		</h4>
		<h4>Click on the grey area to create new circles and see how their color changes based on their neighbors.</h4>
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		<h4>The initial circles are our "training" data set. The data set is divided into two classes: <strong>red and blue</strong>.
		  When we add a new circle, we want to find out which class (aka color) we need to assign it to, depending on its neighbors.
		  <br><br>
		  What's going on behind the scenes after you add a circle:
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		  <li>The algorithm goes through all other circles and calculates their distance from your new circle.</li>
		  <li>It then sorts them by distance from your circle in ascending order, meaning the circles with the smallest distances to the new circle come first.</li>
		  <li>It picks the first k entries from the result of step 2.</li>
		  <li>It looks at the k nearest neighbors' classes. If the majority of them are blue, our new point will be blue as well. If most of them are red, then the new point will be red.</li>
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		<h3>The weighted KNN:</h3></h4>
		<h4>If you check the <strong>"Weighted" checkbox</strong> above, the algorithm is justified, so that the <strong>inverse of the distance</strong> of the k neighbors is taken into account. This is important during "ties", meaning that you chose an even amount of neighbors k and half of them are class red while the other half are blue.
		  If you don't use the weighted version of KNN in this case, the neighbor with the closest distance will <strong>ALWAYS</strong> win, so that your circle will take on its color.
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		<h3><b>Import the libraries<b></h3><br>
		<h4>In this section, we will see how Python's Scikit-Learn library can be used to implement the KNN algorithm in less than 20 lines of code.</h4>
		<h4>We are going to use the famous iris data set for our KNN example. The dataset consists of four attributes: sepal-width, sepal-length, petal-width and petal-length. These are the attributes of specific types of iris plant. The task is to predict the class to which these plants belong. There are three classes in the dataset: Iris-setosa, Iris-versicolor and Iris-virginica.</h4>
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    <p>import pandas ad pd</p>
	<p>import matplotlib.plyplot as plt</p>
	<p>import numpy as np</p>
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		<h3><b>Import and Preprocess the Dataset<b></h3><br>
		<h4>To import the dataset and load it into our pandas dataframe, execute the following code</h4>
		<h4>To see what the dataset actually looks like, execute the following command - dataset.head()</h4>
		<h4>Executing the above script will display the first five rows of our dataset</h4>
		<h4> To preprocess split our dataset into its attributes and labels. The X variable contains the first four columns of the dataset (i.e. attributes) while y contains the labels.</h4>
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    <p>url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"</p>
	<p>names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']</p>
	<p>dataset = pd.read_csv(url, names=names)</p>
	<p>dataset.head()</p></br>
	<p>X = dataset.iloc[:, :-1].values</p>
	<p>Y = dataset.iloc[:, 4].values</p>
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		<h3><b>Train Test Split<b></h3><br>
		<h4>To avoid over-fitting, we will divide our dataset into training and test splits, which gives us a better idea as to how our algorithm performed during the testing phase. This way our algorithm is tested on un-seen data, as it would be in a production application.</h4>
		<h4>To create training and test splits, execute the following script as below</h4>
		<h4>The above script splits the dataset into 80% train data and 20% test data. This means that out of total 150 records, the training set will contain 120 records and the test set contains 30 of those records.</h4>
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    <p>from sklearn.model_selection import train_test_split</p>
	<p>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)</p>
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		<h3><b>Feature Scaling<b></h3><br>
		<h4>Before making any actual predictions, it is always a good practice to scale the features so that all of them can be uniformly evaluated.
		The gradient descent algorithm (which is used in neural network training and other machine learning algorithms) also converges faster with normalized features.</h4>
		<h4>Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization. For example, the majority of classifiers calculate the distance between two points by the Euclidean distance. If one of the features has a broad range of values, the distance will be governed by this particular feature. Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance.</h4>
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    <p>from sklearn.preprocessing import StandardScaler</p>
	<p>scaler = StandardScaler()</p>
	<p>scaler.fit(X_train)</p></br>
	<p>X_train = scaler.transform(X_train)</p>
	<p>X_test = scaler.transform(X_test)</p>
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		<h3><b>Training and Predictions<b></h3><br>
		<h4>The next step is to import the KNeighborsClassifier class from the sklearn.neighbors library. In the second line, this class is initialized with one parameter, i.e. n_neigbours. This is basically the value for the K. There is no ideal value for K and it is selected after testing and evaluation, however to start out, 5 seems to be the most commonly used value for KNN algorithm.</h4>
		<h4>The final step is to make predictions on our test data. To do so, execute the following script - y_pred = classifier.predict(X_test)</h4>
		<h4>For evaluating an algorithm, confusion matrix, precision, recall and f1 score are the most commonly used metrics. The confusion_matrix and classification_report methods of the sklearn.metrics can be used to calculate these metrics.</h4>
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	<p>from sklearn.neighbors import KNeighborsClassifier</p>
	<p>classifier = KNeighborsClassifier(n_neighbors=5)</p>
	<p>classifier.fit(X_train, y_train)</p></br>
	<p>y_pred = classifier.predict(X_test)</p></br>
	<p>from sklearn.metrics import classification_report, confusion_matrix</p>
	<p>print(confusion_matrix(y_test, y_pred))</p>
	<p>print(classification_report(y_test, y_pred))</p>
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		<h3><b>Comparing Error Rate with the K value</b></h3><br>
		<h4>In the training and prediction section we said that there is no way to know beforehand which value of K that yields the best results in the first go. We randomly chose 5 as the K value and it just happen to result in 100% accuracy.</h4>
		<h4>One way to help you find the best value of K is to plot the graph of K value and the corresponding error rate for the dataset.</h4>
		<h4>The next step is to plot the error values against K values.</h4>
		<h4>From the output we can see that the mean error is zero when the value of the K is between 5 and 18. I would advise you to play around with the value of K to see how it impacts the accuracy of the predictions.</h4>
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        <h3>Question 1</h3>
        <p>KNN is <input style= "color:black; text-decoration: underline; border:#000;" id='answer1'class="panel-footer" type="text" name="" value=""> algorithm and therefore requires no training prior to making real time predictions.</p>
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    <p>error = []</p>
	<p>for i in range(1, 40):</p>
	<p>knn = KNeighborsClassifier(n_neighbors=i)</p>
	<p>knn.fit(X_train, y_train)</p>
	<p>pred_i = knn.predict(X_test)</p>
	<p>error.append(np.mean(pred_i != y_test))</p></br>
	<p>plt.figure(figsize=(12, 6))</p>
	<p>plt.plot(range(1, 40), error, color='red', linestyle='dashed', marker='o', markerfacecolor='blue', markersize=10)</p>
	<p>plt.title('Error Rate K Value')</p>
	<p>plt.xlabel('K Value')</p>
	<p>plt.ylabel('Mean Error')</p>
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