Pinned Repositories
credit-card-fraud-detection
Code to prevent credit card fraud based on the data located at https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
daily-climate-ts
Forecasting of the dataset located at https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-series-data
employee_attrition_analysis
Code in R to describe and analyse employee attrition and predict it using statistical models.
fake-news-detector
API REST for detecting if a text correspond to a fake news or to a legitimate one. It's served using Flask and uses a fine-tuned BERT model.
movielens-recommender
MovieLens 100K dataset exploration and recommender system building
twitter-sentiment-analysis
ML web app that can be deployed through Docker to analyse the sentiment of tweets.
josumsc's Repositories
josumsc/employee_attrition_analysis
Code in R to describe and analyse employee attrition and predict it using statistical models.
josumsc/credit-card-fraud-detection
Code to prevent credit card fraud based on the data located at https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud
josumsc/daily-climate-ts
Forecasting of the dataset located at https://www.kaggle.com/datasets/sumanthvrao/daily-climate-time-series-data
josumsc/fake-news-detector
API REST for detecting if a text correspond to a fake news or to a legitimate one. It's served using Flask and uses a fine-tuned BERT model.
josumsc/movielens-recommender
MovieLens 100K dataset exploration and recommender system building
josumsc/twitter-sentiment-analysis
ML web app that can be deployed through Docker to analyse the sentiment of tweets.
josumsc/airlines-sentiment-analysis
Analysis of the information located at https://www.kaggle.com/crowdflower/twitter-airline-sentiment combined with the official dataset at https://www.transtats.bts.gov/Fields.asp?gnoyr_VQ=FGJ
josumsc/automobile_cleaning
Cleaning, preparation and analysis of the data from https://archive.ics.uci.edu/ml/datasets/Automobile
josumsc/classic-ml-sentiment-analysis
Exercise to use simple ML models to perform sentiment analysis and show they are still relevant to the task
josumsc/CovidBCN
Project based on getting data from public sources and combine it into a single DDBB to explore
josumsc/darts-forecasting
General project to learn about Darts module in Python
josumsc/dogs-vs-cats
Resolution of the classification problem stated at https://www.kaggle.com/uysimty/keras-cnn-dog-or-cat-classification using Keras and ConvNets
josumsc/helm-charts
Community Helm Charts
josumsc/house-prices-regression-kaggle
Attempt at the Kaggle competition based on https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview
josumsc/hr-analytics-ds
Data Discovery and Modeling of the set located at https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists
josumsc/josumsc
josumsc/josumsc.github.io
Personal blog about Data and AI related topics
josumsc/m1_max_testing
Tensorflow testing on m1 Mac
josumsc/multi30k-machine-translation
Training of a machine translation system from English to German from the ground up. It uses the dataset contained at https://github.com/multi30k/dataset/tree/master/data/task1/raw
josumsc/names-analysis-ny
Analysis made to discover patterns in baby names on the NY state. Data to be located at https://health.data.ny.gov/Health/Baby-Names-Beginning-2007/jxy9-yhdk
josumsc/retrocket-implicit-recommender
Implicit Collaborative Filtering trained on the RetailRocket dataset based on the ideas from arXiv:2009.08950
josumsc/search_with_machine_learning_course
Public repository for the Search with Machine Learning course taught by Daniel Tunkelang and Grant Ingersoll. Available at https://corise.com/course/search-with-machine-learning?utm_source=daniel.
josumsc/time-series-datasets-forecasting
Quick and dirty forecasting of the time series datasets located at https://www.kaggle.com/shenba/time-series-datasets?select=sales-of-shampoo-over-a-three-ye.csv
josumsc/titanic_ml_python
Attempt at the Kaggle competition https://www.kaggle.com/c/titanic/ based on the famous Titanic dataset
josumsc/web_scrapping_datosmacro
Scrapes the website https://datosmacro.expansion.com/ to get a dataset describing the evolution of the 3 main indicators for the petroleum price between 2000-01 and 2020-10.