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ORDER SHEETIN THE HIGH COURT OF SINDH AT KARACHI Suit No.2236 of 2018 ___ DATE ORDER WITH SIGNATURE OF JUDGE ___ Plaintiff:The Shaheen Foundation Through Mr.Syed Mustafa Ali, Advocate. Defendant No.1
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Import necessary libraries like pandas, numpy, nltk, sklearn.
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Read the text data using pandas read_csv function.
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Tokenize the text data using nltk word_tokenize.
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Vectorize the tokenized text data using CountVectorizer or TfidfVectorizer.
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Apply KMeans clustering algorithm on the vectorized text data.
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Obtain the cluster labels and centroids from the KMeans model.

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nikhil130yadavk-means-cluster-on-text-data is a method used in machine learning for clustering text data into different groups based on similarity.
Data scientists, machine learning engineers, or anyone working with text data may be required to use nikhil130yadavk-means-cluster-on-text-data.
To fill out nikhil130yadavk-means-cluster-on-text-data, you would need a dataset of text data and then apply the k-means clustering algorithm to identify similar groups.
The purpose of nikhil130yadavk-means-cluster-on-text-data is to make sense of large amounts of text data by grouping similar data points together.
On nikhil130yadavk-means-cluster-on-text-data, you need to report the clusters formed, the centroid of each cluster, and the text data points assigned to each cluster.
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