Dhilip Subramanian's repositories
speech-to-text
Speech to Text with Hugging Face and Wav2vec 2.0
Content-Based-Recommendation---Good-Reads-data
Content Based Recommendation using Good read data
Twitter-Scraping-Sentiment-Analysis
SNSCRAPE / Sentiment Analysis
IPL-Data-Visualization-using-Tableau
IPL Data Visualisation Challenge from Machinehack.com
26-Weeks-Of-Data-Science
Email Newsletter
IRIS-datasets
K-Means Clustering in SAS
Spotify-Analysis---Azure-Synapse
Spotify Data Analysis
BERT-SQuAD
SQuAD Question Answering Using BERT, PyTorch
Data-Analysis-
Data Analysis projects
How-to-mine-newsfeed-data-and-extract-interactive-insights-in-Python
A practical guide to topic mining and interactive visualizations
Machine-Learning-with-Python
Python code for common Machine Learning Algorithms
pandas_exercises
Practice your pandas skills!
Salary-Analysis--Australia---New-Zealand
Salary Analysis for Data Scientists
deep-learning
Repo for the Deep Learning Nanodegree Foundations program.
Deep_and_Machine_Learning_Projects
This Repository contains the list of various Machine and Deep Learning related projects. Related code and data files are available inside this folder. One can go through these projects to implement them in real life for specific use cases.
google-images-download
Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!
Machine-Translation-using-mBART-50-and-Hugging-Face
Machine Translation using Facebook mBART50 model and Hugging Face Transformer library
Telecom-Churn-Project
Due to new players in the market, telecom industry has become very competitive and retaining customers becoming a challenge. In spite of reducing tariffs and promoting more offers, the churn rate ( percentage of customers migrating to competitors) is well above 10%. This project is explore possibility of applying Machine Learning to predict the churn rate .
Titanic
Titanic Analysis - Prediction using Supervised Machine Learning Algorithm
Unsupervised-Machine-learning
Credit Card Segmentation - Unsupervised Machine Learning in Python