In this tutorial we will learn how to match two strings with matching score using Machine Learning in python. In many situations you might require this type of question. let’s see how to solve this question…
Match two strings with matching score using Machine Learning in python
here we see the step-by-step explanation for calculating matching score…
Steps to calculate Matching Score
1.Initialize Strings
we start by defining two strings that we want to compare.
For example, let’s consider
string1
'machine learning'
string2
'machine learning techniques'
these strings will be used to calculate the matching score.
2. Preprocessing
Before we calculate the matching score, we might need to preprocess the strings.
3. Choose matching algorithm
Various algorithms can be used to calculate the matching score, such as Levenshtein distance, cosine similarity, or Jaccard similarity. In this example, we will use cosine similarity.
4. Compute Matching Score
Using a Machine Learning approach, we’ll calculate the matching score between the two strings. This score will quantify the similarity between ‘string1’ and ‘string2’.
Here’s an example of how to implement this in Python:
#import required libraries
from sklearn.feature_extraction.text import TfidVectorizer
from sklearn.metrics.pairwise import cosine_similarity
#intialize the strings
string1="machine learning"
string2="machine learning techniques"
#by using Cosine Similarity method
#convert strings to TF-IDF vectors
vectorizer=TfidVectorizer(analyzer ='char',ngram_range=(2,3)).fit_transform([string1,string2])
vetors=vectorizer.toarray()
#compute the cosine similarity
cosine_sim=cosine_similarity(vectors)
matching_score=cosine_sim[0][1]
print(f"The matchine score between '{string1}' and '{string2}' is {matching_score:.2f}")
Output
The matching score between 'machine learning' and 'machine learning techniques' is 0.71
In this tutorial, we explored how to match strings and calculate similarity scores using python. The method I used, cosine similarity, provides a score based on the vector space model by converting strings into numerical vectors. This approach is particularly useful when you want to measure the similarity between two prices of text based on their content. Depending on your specific requirements, this method can be effective way to compare strings and determine their similarity.
For better learning and coding, you can check this link –> click here
Have a Happy and Great Coding!