https://github.com/rajapriya12345/labprojects

https://github.com/rajapriya12345/labprojects

machine-learning pytho r

Last synced: about 1 year ago
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• Host: GitHub
• URL: https://github.com/rajapriya12345/labprojects
• Owner: Rajapriya12345
• Created: 2025-01-28T07:08:48.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-28T07:12:18.000Z (over 1 year ago)
• Last Synced: 2025-03-31T07:44:31.746Z (about 1 year ago)
• Topics: machine-learning, pytho, r
• Language: Python
• Homepage:
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
import pandas as pd

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.metrics.pairwise import cosine_similarity

# Sample dataset of text documents

data = {

    "Document": [

        "Natural Language Processing is a field of AI.",

        "TF-IDF stands for Term Frequency-Inverse Document Frequency.",

        "It is widely used in Information Retrieval and Text Mining.",

        "This is an example of text processing using Python.",

        "Machine Learning and NLP are interconnected fields."

    ]

}

# Create a DataFrame

df = pd.DataFrame(data)

# Step 1: Initialize the TF-IDF Vectorizer

tfidf_vectorizer = TfidfVectorizer()

# Step 2: Fit and transform the text data

tfidf_matrix = tfidf_vectorizer.fit_transform(df["Document"])

# Step 3: Display the TF-IDF matrix as a DataFrame

feature_names = tfidf_vectorizer.get_feature_names_out()

tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=feature_names)

print("TF-IDF Matrix:")

print(tfidf_df)

# Step 4: Query the TF-IDF model

query = "text mining and NLP"

query_vector = tfidf_vectorizer.transform([query])

# Step 5: Compute cosine similarity between the query and documents

similarity_scores = cosine_similarity(query_vector, tfidf_matrix)

# Step 6: Display the similarity scores

df["Similarity"] = similarity_scores[0]

print("\nDocuments ranked by similarity to the query:")

print(df.sort_values(by="Similarity", ascending=False))

# Labprojects