ML-Model KMeans Example

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import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

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# Load the train and test datasets to create two DataFrames

train_url = "http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv"
train = pd.read_csv(train_url)
test_url = "http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/test.csv"
test = pd.read_csv(test_url)

Let's preview the kind of data you will be working with by printing some samples from both the train and test DataFrames.

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print("***** Train_Set *****")
print(train.head())
print("\n")
print("***** Test_Set *****")
print(test.head())

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print("***** Train_Set *****")
print(train.describe())
print("\n")
print("***** Test_Set *****")
print(test.describe())

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print(train.columns.values)

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# For the train set
train.isna().head()

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test.isna().head()

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print("*****In the train set*****")
print(train.isna().sum())
print("\n")
print("*****In the test set*****")
print(test.isna().sum())

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# Fill missing values with mean column values in the train set
train.fillna(train.mean(), inplace=True)

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# Fill missing values with mean column values in the test set
test.fillna(test.mean(), inplace=True)

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train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)

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train[["Sex", "Survived"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)

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train[["SibSp", "Survived"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)

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g = sns.FacetGrid(train, col='Survived')
g.map(plt.hist, 'Age', bins=20)

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train = train.drop(['Name','Ticket', 'Cabin','Embarked'], axis=1)
test = test.drop(['Name','Ticket', 'Cabin','Embarked'], axis=1)

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labelEncoder = LabelEncoder()
labelEncoder.fit(train['Sex'])
labelEncoder.fit(test['Sex'])
train['Sex'] = labelEncoder.transform(train['Sex'])
test['Sex'] = labelEncoder.transform(test['Sex'])

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train.info()

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X = np.array(train.drop(['Survived'], 1).astype(float))
y = np.array(train['Survived'])

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kmeans = KMeans(n_clusters=2) # You want cluster the passenger records into 2: Survived or Not survived
kmeans.fit(X)

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def func_ac():
    correct = 0
    for i in range(len(X)):
        predict_me = np.array(X[i].astype(float))
        predict_me = predict_me.reshape(-1, len(predict_me))
        prediction = kmeans.predict(predict_me)
        if prediction[0] == y[i]:
            correct += 1
    return correct/len(X) 
        
 
print(func_ac())

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kmeans = kmeans = KMeans(n_clusters=2, max_iter=600, algorithm = 'auto')
kmeans.fit(X)
print(func_ac())

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scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)

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kmeans.fit(X_scaled)
print(func_ac())

Elbow Method Elbow method gives us an idea on what a good k number of clusters would be based on the sum of squared distance (SSE) between data points and their assigned clusters’ centroids. We pick k at the spot where SSE starts to flatten out and forming an elbow. We’ll use the geyser dataset and evaluate SSE for different values of k and see where the curve might form an elbow and flatten out.

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 # clustering dataset
# determine k using elbow method

from sklearn.cluster import KMeans
from sklearn import metrics
from scipy.spatial.distance import cdist
import numpy as np
import matplotlib.pyplot as plt

x1 = np.array([3, 1, 1, 2, 1, 6, 6, 6, 5, 6, 7, 8, 9, 8, 9, 9, 8])
x2 = np.array([5, 4, 5, 6, 5, 8, 6, 7, 6, 7, 1, 2, 1, 2, 3, 2, 3])

plt.plot()
plt.xlim([0, 10])
plt.ylim([0, 10])
plt.title('Dataset')
plt.scatter(x1, x2)
plt.show()

# create new plot and data
plt.plot()
X = np.array(list(zip(x1, x2))).reshape(len(x1), 2)
colors = ['b', 'g', 'r']
markers = ['o', 'v', 's']

# k means determine k
distortions = []
K = range(1,10)
for k in K:
    kmeanModel = KMeans(n_clusters=k).fit(X)
    kmeanModel.fit(X)
    distortions.append(sum(np.min(cdist(X, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / X.shape[0])

# Plot the elbow
plt.plot(K, distortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Distortion')
plt.title('The Elbow Method showing the optimal k')
plt.show()