Implementation of some famous machine learning algorithm from scratch
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Updated
Apr 30, 2020 - Jupyter Notebook
Implementation of some famous machine learning algorithm from scratch
Text mining in Python
A multi-class classification problem where the objective is to read a question posted on the popular reference website, StackOverflow and predict the primary topics it deals with, i.e. tags which the question will be associated with.
To understand the impact on stock price based on the various news headlines.
Indexing and Retrieval Models
Extract text content from an HTML page, process it, and extract unique words from the processed text. This notebook utilizes various text processing techniques including cleaning, normalization, tokenization, lemmatization or stemming, and stop words removal.
A Naive Bayes classifier was used to predict the probability of salary being above certain threshold according to certain job descriptions
Natural language processing for Tamazight language
Simpel aplikasi untuk Tokenisasi, Stopword Removal, dan Stemming pada Information Retrieval dengan Codeigniter
ML model for Fake news detection
In this project I used different text preprocessing techniques to preprocess my text data. The I used different classification algorithm to get the best results as it was a classification problem. I used Kaggles free GPUs and Datasets in this competion.
Spam classifier using NLP
Python implementation of a web crawler that, from a set of seed urls, retrieves the most similar pages.
Text Processing performed on the Apple Macbook for feature extraction
All Lab assignments of NLP Course covered in IIIT
Resume classification is the task that automatically categorizes resumes or CVs into predefined domain categories or classes based on their content. This task is essential for the job recruitment process, particularly when organizations receive a large number of applications for various positions.
Customer sentiment analysis is the process of using natural language processing (NLP) and machine learning techniques to analyze and understand the feelings, opinions, and attitudes expressed by customers in textual data, such as reviews, feedback, and social media posts.
Long english text passages are given, a genuine topic is needed to be assigned to the particular text passage. After cleaning the dataset, features were learnt using thidf approach, Linear SVC is used to get the final prediction
Add a description, image, and links to the stemming topic page so that developers can more easily learn about it.
To associate your repository with the stemming topic, visit your repo's landing page and select "manage topics."