TFIDF_Custom_Implementation: SkLearn Implementation and Custom Implementation of TF-IDF This repository contains implementations of the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction technique using both SkLearn library and a custom implementation. TF-IDF is a popular technique in Natural Language Processing (NLP) for converting text documents into numerical representations that can be used for various machine learning tasks.

SkLearn Implementation: In the SkLearn section, the code demonstrates how to use SkLearn's built-in TfidfVectorizer to calculate TF-IDF values for a collection of string documents. The following steps are covered: Importing necessary libraries and defining the document corpus. Creating a TfidfVectorizer object and fitting it to the corpus. Printing the feature names sorted in alphabetical order by default. Displaying the IDF values calculated by SkLearn. Displaying the shape of the TF-IDF matrix. Printing the TF-IDF values for the first line of the corpus.

Custom Implementation: The custom implementation showcases a step-by-step approach to calculating TF-IDF values. The key components covered are: Vocabulary Creation (fit): Defining a function that takes the input text and generates a vocabulary of unique words. Bag-of-Words (BoW) Representation (get_BoW): Creating a function that generates a sparse Bag-of-Words representation using Compressed Sparse Row (CSR) matrix format. Term Frequency (myTF): Calculating the Term Frequency matrix using the Bag-of-Words representation. Inverse Document Frequency (myIDF): Implementing the Inverse Document Frequency calculation for each term. TF-IDF Transformation (transform): Combining the TF and IDF matrices to calculate the final TF-IDF matrix.

Additional Functionality: The repository also extends the custom implementation to include: Ordering words in the vocabulary based on their IDF values. Loading a larger dataset using Google Colab and a saved corpus. Implementing a function to return either the vocabulary, IDF values, or both based on user preferences.

Instructions for Use: To use the custom TF-IDF implementation, follow these steps: Import necessary libraries (e.g., Counter, csr_matrix, math, etc.). Define the document corpus. Call the fit(corpus) function to generate the vocabulary and prepare the IDF calculation. Call the transform(corpus) function to compute the TF-IDF matrix. Utilize the returned TF-IDF matrix for further analysis.