gmayuri1904
Python | Machine Learning | Data Science | NLP | AI A python developer excited to concur the data revolution.
SURE TrustMaharashtra,India
gmayuri1904's Stars
gmayuri1904/ML-Roadmap
A curated list of Machine learning videos, links, projects and datasets to help you conquer the ML landscape in 6 months
alexhusted/100119-Lending-Club-Loan-Data
Building a machine learning algorithm for the purpose of correctly identifying whether a person, given certain characteristics, has a high likelihood to default on a loan.
amogh9594/lending-club-loan-data-analysis
Create a model that predicts whether or not a loan will be default using the historical data.
The-Assembly/Turn-Any-PDF-into-an-Audiobook-
In this session, we'll show you how to use Python to automagically turn a PDF into an audiobook, without anyone needing to read the contents out loud to procure the audio. To achieve this, we'll use a few separate Python libraries—namely Pyttsx3 (for speech to text) and PyPDF2 (to parse PDF files)—and show you how to put it all together to obtain downloadable audio from your PDF input in a single command. We'll also demonstrate how you can customize this process to modulate output voice and speed. This technique can easily be then further refined for nuances of text and speech using other libraries and programming (including NLP/machine learning-based ones) Prerequisites: —Python (https://www.python.org/downloads/) —Visual Studio Code (https://code.visualstudio.com/download) ----------------------------------------- To learn more about The Assembly’s workshops, visit our website, social media or email us at workshops@theassembly.ae Our website: http://theassembly.ae Instagram: http://instagram.com/makesmartthings Facebook: http://fb.com/makesmartthings Twitter: http://twitter.com/makesmartthings #Python #Tutorial
girishksahu/INSAID2021-ML-Foundation-Customer_Classification
INSAID 2021 ML Foundation Term Project
girishksahu/INSAID2022-DL-NLP-Classify-Whether-Real-or-Fake-News
INSAID 2022 Deep Learning NLP Term Project
campusx-official/linear-regression-assumptions
A python code snippet to test assumptions of linear regression
anarabiyev/EDA_Streamlit_App
Repo for Exploratory Data Analysis Streamlit App
Devansharma/Health-App
AnushaMadapati/Fraudulent-Firms-Prediction
Improving the efficacy of fraudulent transaction alerts
ParthRaj22/Fraudulent-Firm-Classification
The goal of the research is to help the auditors by building a classification model that can predict the fraudulent firm on the basis the present and historical risk factors.
jgcorliss/lending-club
Applying machine learning to predict loan charge-offs on LendingClub.com
sanjakamer/Healthcare-Data-Analysis
Medical Billing Data Science in Python
girishksahu/INSAID2021-ML-Intermediate-Drug_Prediction
INSAID 2021 ML Intermediate Term Project
girishksahu/Demand_Forecasting
Part of Job-a-Thon by Analytics Vidhya
harishpuvvada/LoanDefault-Prediction
Lending Club Loan data analysis
apoorva-dave/WineQualityPrediction
Predicts quality of wine
pligor/predicting_quality_of_red_wine
Predicting Quality of Red Wine using Machine Learning
arpit3043/Extractive-Text-Summerization
Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.