50 skills found · Page 1 of 2
entron / Entity Embedding RossmannNo description available
Wongi-Choi1014 / Korean OCR Model Design Based On Keras CNNKorean OCR Model Design(한글 OCR 모델 설계)
zouguojian / Travel Time PredictionWhen Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time
yashu-seth / DummyPyA python module to transform categorical variables to one hot encoded vectors. It particularly handles categorical variables of data that cannot be fit into memory.
FluxML / OneHotArrays.jlMemory efficient one-hot array encodings
ELHoussineT / AutoDataCleanerSimple and automatic data cleaning in one line of code! It performs one-hot encoding, date & time casting to datetime dtype, detects binary columns, safely convert non-numeric columns to numeric dtypes, cleaning dirty/empty values, normalizing values and removing unwanted columns all in one line of code. Get your data ready for model training and fitting quickly.
d-misra / Multi Label Movie Poster Genre ClassificationKeras implementation of multi-label classification of movie genres from IMDB posters
Flag-C / ThermometerEncodingreproduction of Thermometer Encoding: One Hot Way To Resist Adversarial Examples in pytorch
Pinak-Datta / Wiz CraftA CLI-based dataset preprocessing tool for machine learning tasks. Features include data exploration, null value handling, one-hot encoding, and feature scaling, and download the modified dataset effortlessly.
rungjoo / Emotion Not OneThe Emotion is Not One-hot Encoding: Learning with Grayscale Label for Emotion Recognition in Conversation (INTERSPEECH 2022)
valentineashio / Online Payments Fraud Detection Dataset Case StudyA Data Science/Machine Learning Project. According to Bolster , Global Fraud Index (as at June 2022) is at 10,183 and growing. This is high risk to businesses and customers transacting online. This indicates that traditional rules-based methods of detecting and combating fraud are fast becoming less effective. It becomes imperative for stakeholders to develop innovative means to make transacting online as safe as possible. Artificial intelligence provides viable and efficient solutions via Machine Learning models/algorithms. In this project, I trained a fraud detection model to predict online payment fraud using Blossom Bank PLC as case study. Blosssom Bank ( BB PLC) is a multinational financial services group, that offers retail and investment banking, pension management, assets management and payment services, headquartered in London, UK. Blossom Bank wants to build a machine learning model to predict online payment fraud. Here is the dataset used for this task. With this model, BB PLC will: Keep up with fast evolving technological threats and better prevent the loss of funds (profit) to fraudsters. Accurately detect and identify anomalies in managing online transactions done on its platforms which may go undetected using traditional rules-based methods. 3.Improve quality assurance thus retaining old customers and acquire new ones. This will increase credit/profit base. Improve its policy and decision making. Steps: 1.Loading necessary python libraries. Loading Dataset. Exploratory Data Analysis. Higlighting Relationships and insights. Data Transformation; Using resampling techniques to address Class-imbalace.. Feature Engineering. Model Training. Model Evaluation. Challenges: I encountered a number of challenges during coding which made me run into error reports. these were due to improper documentations, syntax, especially during feature engineering (one-hot encoding: 'fit.transform'). This aspect consumed most of my time I was able to solve these challenges by making extensive research and paying close attention to syntax. I was able to selve the encoding by using 'pd.get_dummies() and making some specifications in the methods.
brendanhasz / Target EncodingComparison between label, one-hot, target, and cross-fold target encoding
DataJenius / NLPEncodingExperimentCompare different encoding methods to see how well they perform on a classification task. Determine if a reddit comment is from /r/StarWars or /r/lotr. Compares one-hot encoding, word2vec, custom word embeddings, and BERT.
imharshag / NIDS Using MLThis project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats
yammadev / CbrsCase-based Reasoning (CBR) System
nickpoorman / One HotOne hot encode vectors using a streaming implementation.
h3ik0th / Clusteringclustering of mixed variables with kmeans, meanshift, kprototypes, one-hot encoding, inertia and silhouette scores
Lovish-Dak / Network Intrusion Detection SystemIn this project, I created a network intrusion detection system using CNN and BiLSTM layers. I trained the model on NSL-KDD & UNSW-NB15 dataset. I used some pre-processing techniques such as Min-Max Normalization, One-Hot Encoding etc. and measured the performance of the model on metrics such as accuracy, FPR, FNR, Precision, Recall & F-1 Score
SannketNikam / Credit Risk AnalysisCredito - Credit Risk Analysis using XGBoost Classifier with RandomizedSearchCV for loan approval decisions.
singhrahuldps / OneHotEncodeA python script to deploy One-Hot encoding in Pandas Dataframes
