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K-Means Clustering Algorithm Implementation in Python

Importing the necessary libraries:

```python

import numpy as np

import pandas as pd

from sklearn.cluster import KMeans

import matplotlib.pyplot as plt

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Loading the dataset:

```python

data = pd.read_csv('data.csv')

```

Preprocessing the data (if required):

Scaling the data if necessary, e.g.:

```python

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()

data = scaler.fit_transform(data)

```

Handling missing values, e.g.:

```python

data = data.dropna()

```

Creating the K-Means object:

```python

kmeans = KMeans(n_clusters=3) Replace 3 with the desired number of clusters

```

Fitting the K-Means model to the data:

```python

kmeans.fit(data)

```

Getting the cluster labels:

```python

labels = kmeans.labels_

```

Visualizing the clusters:

```python

plt.scatter(data[:, 0], data[:, 1], c=labels)

plt.show()

```

Evaluating the K-Means model:

Using the Silhouette Coefficient, e.g.:

```python

from sklearn.metrics import silhouette_score

score = silhouette_score(data, labels)

```

Using the Elbow Method, e.g.:

```python

from sklearn.metrics import calinski_harabasz_score

scores = []

for k in range(2, 10): Replace 10 with the maximum number of clusters to consider

kmeans = KMeans(n_clusters=k)

kmeans.fit(data)

scores.append(calinski_harabasz_score(data, kmeans.labels_))

plt.plot(range(2, 10), scores)

plt.show()

```

Additional customization:

Number of clusters: Adjust the `n_clusters` parameter in the `KMeans` object.

Maximum number of iterations: Set the `max_iter` parameter in the `KMeans` object.

Initialization method: Choose the method for initializing the cluster centroids, e.g., 'k-means++'.

Distance metric: Specify the distance metric used for cluster assignment, e.g., 'euclidean'.

Notes:

The Elbow Method is not foolproof and may not always provide the optimal number of clusters.

Visualizing the clusters can help you understand the distribution of data and identify potential outliers.

The Silhouette Coefficient measures the similarity of a point to its own cluster compared to other clusters.

Experiment with different parameter settings to optimize the performance of the K-Means model.