correlation logic similar words for jyothi sastra
sriLakshmi:
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Sriniratna
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In H4:
- SUK (negative effect) with H4 properties, correlation score 0.40: Marriage: The planet signifies the importance of marriage and harmonious relationships.
In H5:
- GUR (positive effect) with H5 properties, correlation score 0.44: Wealth of the World: Represents material prosperity and riches,
In H9:
- SUN (positive effect) with H9 properties, correlation score 0.36: Father , Represents authority, guidance, and protection,
- GUR (positive effect) with H9 properties, correlation score 0.45: Wealth of the World: Represents material prosperity and riches,
- SUK (negative effect) with H9 properties, correlation score 0.39: Marriage: The planet signifies the importance of marriage and harmonious relationships.
In H10:
- SUK (negative effect) with H10 properties, correlation score 0.32: Marriage: The planet signifies the importance of marriage and harmonious relationships.
sarvati
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In H3:
- SAN (negative effect) with H3 properties, correlation score 0.35: Life Span: Represents the duration and phases of life,
In H5:
- GUR (positive effect) with H5 properties, correlation score 0.44: Wealth of the World: Represents material prosperity and riches,
- SUK (negative effect) with H5 properties, correlation score 0.34: Marriage: The planet signifies the importance of marriage and harmonious relationships.
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import nltk
from nltk.corpus import wordnet
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Sample word lists
list1 = ["apple", "orange", "banana", "grape", "pear"]
list2 = ["fruit", "citrus", "berry", "apple", "banana"]
# Create TF-IDF vectors
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(list1 + list2)
# Calculate cosine similarity
similarity_matrix = cosine_similarity(X)
# ... (Further processing to group words and compare groups)