one-hot-encoding

There are 0 repository under one-hot-encoding topic.

entron / entity-embedding-rossmann
categorical-data categorical-features entity-embedding kaggle one-hot-encoding
Language:Jupyter Notebook 870
Wongi-Choi1014 / Korean-OCR-Model-Design-based-on-Keras-CNN
Korean OCR Model Design(한글 OCR 모델 설계)
keras cnn korean ocr integer-encoding deep-learning recognition activate activation-functions convolutional-neural-networks dataset gpu-tensorflow one-hot-encoding training
Language:Python 73
zouguojian / Travel-time-prediction
When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time
feature-cross feature-fusion-network graph-attention-networks one-hot-encoding travel-time-prediction spatio-temporal-correlation multi-task-learning traffic-speed-prediction holistic-attention travel-time-estimate
Language:Python 26
Multi-label-movie-poster-genre-classification
d-misra / Multi-label-movie-poster-genre-classification
Keras implementation of multi-label classification of movie genres from IMDB posters
keras keras-tutorial fine-tuning-cnns multi-label-image-classification image-classification cnn-keras movie-genre-classification one-hot-encoding imagedatagenerator
Language:Jupyter Notebook 17
iAmKankan / Natural-Language-Processing-NLP-Tutorial
NLP tutorials and guidelines to learn efficiently
bigrams bow cbow glove lemmatization one-hot-encoding stemming stopwords tf-idf-vectorizer tokenization unigram word-embeddings word2vec
8
yammadev / cbrs
Case-based Reasoning (CBR) System
artificial-intelligence machine-learning case-based-reasoning one-hot-encoding mahalanobis-distance jupyter-notebook
Language:Jupyter Notebook 8
imharshag / NIDS-using-ML
This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats
datamining ensemble-learning gaussian-naive-bayes knn label-encoding network-intrusion-detection nsl-kdd one-hot-encoding principal-component-analysis random-forest recursive-feature-elimination svm voting-classifier xgboost matplotlib python sklearn
Language:Jupyter Notebook 6
Heart-Failure-Prediction-
jayachandru001 / Heart-Failure-Prediction-
This project involves training of Machine Learning models to predict the Heart Failure for Heart Disease event. In this KNN gives a high Accuracy of 89%.
machine-learning machine-learning-algorithms machinelearning heart-disease-prediction heart-disease medical heart-disease-dataset knn-classifier one-hot-encoding k-means heart-failure-prediction heart-failure
Language:Jupyter Notebook 6
labrijisaad / Predicting-Student-Admissions-with-Neural-Networks-using-Python
We tried in this notebook to predict student admissions to graduate school at UCLA based on three pieces of data.
neural-networks python jupyter-notebook one-hot-encoding
Language:Jupyter Notebook 6
Abdelrahmanrezk / nlp_in_actions
Natural Language Processing In Actions
nlp machine-learning spacy word2vec bag-of-words one-hot-encoding tf-idf deep-learning neural-network keras sklearn tensorflow text-classification text-processing vectorization
Language:Jupyter Notebook 5
gogundur / Classification
Classification - Term Deposit Opening Decision
classification logistic-regression one-hot-encoding random-forest k-nearest-neighbors python confusion-matrix machine-learning
Language:Jupyter Notebook 5
Hoda233 / Arabic-Text-Diacritization
Using Natural Language Processing techniques, to predict diacritics of an Arabic Text.
arabic-text-diacritization embeddings lstm nlp one-hot-encoding rnn word2vec diacritics tashkeel arabic
Language:Jupyter Notebook 4
Shahrukh2016 / Retail_Sales_Prediction
Revolutionize sales forecasting for Rossmann stores with our high-accuracy XGBoost model, leveraging data analysis, feature engineering, and machine learning to predict sales up to six weeks in advance.
data-analysis data-cleaning data-gathering data-preprocessing decision-trees exploratory-data-analysis feature-engineering lightgbm machine-learning model-evaluation model-selection one-hot-encoding outlier-detection random-forests regression-analysis regularization statistical-analysis transformation visualization xgboost
Language:Jupyter Notebook 4
avestura / scikit-heart-disease-classifier
💚 A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more
svm svm-classifier decision-tree decision-tree-classifier pca roc pruning grid-search-cross-validation confusion-matrix one-hot-encoding cost-complexity-pruning linear-kernel poly-kernel rbf-kernel sigmoid-kernel
Language:Jupyter Notebook 3
mdnuruzzamanKALLOL / One-Hot-Encoding-User-Input-LR
Feature Engineering
linear-regression matplotlib numpy one-hot-encoding pandas seaborn
Language:Jupyter Notebook 3
tanvirnwu / Feature-Engineering--Python
categorical-features ordinary-feature feature-engineering feature-selection one-hot-encoding onehot-encoder onehot-encoding onehotencoder frequency-enconding
Language:Jupyter Notebook 3
Abdulrahmankhaled11 / House-Price-Prediction
Using Regression algorithms for predict houses prices for a dataset
cross-validation regression regression-models data-cleaning data-preprocessing data-science data-visualization dummy-variables-encoding encoding exploratory-data-analysis lasso-regression one-hot-encoding pandas ridge-regression
Language:Jupyter Notebook 2
AIprototype / LogisticRegression-CreditApproval-Python
Create a machine learning model using logistic regression that can predict credit card approvals from the described dataset.
logistic-regression scikit-learn machine-learning credit-card banking polynomial-regression one-hot-encoding pandas artificial-intelligence linear-models confusion-matrix cross-validation data-cleaning classification-model
Language:Python 2
bharatkulmani / Dry-Bean
Project is about predicting Class Of Beans using Supervised Learning Models
classifier confusion-matrix decision-tree-classifier eda label-encoding lazypredict numpy one-hot-encoding outlier-detection outlier-removal pandas seaborn smote smote-sampling standard-scaler dry-beans-dataset
Language:Jupyter Notebook 2
billy-enrizky / predicting_income_project
Practicion of Machine Learning, One-hot encoding, Random Forest, Hyperparameter Tuning, Grid Search CV, With accuracy of 84.92%!
gridsearchcv machine-learning one-hot-encoding random-forest
Language:HTML 2
Real-Estate-Statistical-Modeling
griffinbran / Real-Estate-Statistical-Modeling
Predictive analysis, with feature engineering, and machine learning (ML) algorithms, such as linear regression, applied to predict the final sale price of homes in Ames, IA from 2006-2010.
kaggle-submission correlation-analysis feature-engineering sklearn p-values cross-validation hypothesis-testing one-hot-encoding multiple-regression linear-models imputed-values outlier-detection
Language:Jupyter Notebook 2
mdnuruzzamanKALLOL / One-Hot-Encoding
Feature Engineering
linear-regression matplotlib numpy one-hot-encoding pandas seaborn
Language:Jupyter Notebook 2
MUHAMMADAKMAL137 / IMDB-Dataset-Classification-using-Pre-trained-Word-Embedding-with-GloVec-6B
In this project, I worked with a small corpus consisting of simple sentences. I tokenized the words using n-grams from the NLTK library and performed word-level and character-level one-hot encoding. Additionally, I utilized the Keras Tokenizer to tokenize the sentences and implemented word embedding using the Embedding layer. For sentiment analysis
one-hot-encoding word-embeddings pre-trained-glove-6b pre-trained-word-embedding character-level character-level-language-model word-level-language-model word-level corpus ngrams seq2seq
Language:Jupyter Notebook 2
prushh / data-analytics-exercises
Exercises for the "Data Analytics" course, University of Bologna (2021/2022)
bayesian-learning data-aquisition data-visualization decision-tree linear-regression pandas clustering svm one-hot-encoding
Language:Python 2
sayo2rule / bank-churning-with-machine-learning
This repository covers my code using regression models to predict if a customer would be exiting a bank or not. It also capture the use classification models to classify if a customer has left the bank or not (binary classification).
data-preprocessing data-visualization decision-tree-classifier decision-tree-regression exploratory-data-analysis feature-selection label-encoding linear-regression logistic-regression machine-learning one-hot-encoding random-forest-classifier random-forest-regression
Language:Jupyter Notebook 2
vaitybharati / Assignment-06-Logistic-Regression
Assignment-06-Logistic-Regression. Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no") Attribute information For bank dataset Input variables: # bank client data: 1 - age (numeric) 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student", "blue-collar","self-employed","retired","technician","services") 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed) 4 - education (categorical: "unknown","secondary","primary","tertiary") 5 - default: has credit in default? (binary: "yes","no") 6 - balance: average yearly balance, in euros (numeric) 7 - housing: has housing loan? (binary: "yes","no") 8 - loan: has personal loan? (binary: "yes","no") # related with the last contact of the current campaign: 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") 10 - day: last contact day of the month (numeric) 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec") 12 - duration: last contact duration, in seconds (numeric) # other attributes: 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted) 15 - previous: number of contacts performed before this campaign and for this client (numeric) 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success") Output variable (desired target): 17 - y - has the client subscribed a term deposit? (binary: "yes","no") 8. Missing Attribute Values: None
python logistic-regression pandas numpy matplotlib-pyplot seaborn linear-model confusion-matrix roc-curve roc-auc-score eda one-hot-encoding concatenation classifier logit-model logistic-regression-algorithm
Language:Jupyter Notebook 2
arbaazkhaan / FIFA-Dataset-Refinement
Welcome to the FIFA Dataset Data Cleaning and Transformation project! This initiative focuses on refining and enhancing the FIFA dataset to ensure it is well-prepared for in-depth analysis. The project involves a comprehensive data cleaning process and transformation of key features to improve data quality and usability.
clean-dataset data-cleaning data-cleaning-and-preprocessing data-transformation feature-engineering fifa fifa-dataset handling-missing-value numpy one-hot-encoding pandas python handling-duplicate-values prepare-for-analysis
Language:Jupyter Notebook 1
HarshitWaldia / Churn_Modelling
Churn Modelling with Bank Customer Prediction using ANN: Utilizing Artificial Neural Networks for predicting customer churn in banking scenarios.
ann churn-modelling machine-learning one-hot-encoding
Language:Jupyter Notebook 1
mohadeseh-ghafoori / Coursera-Deep-Learning-Specialization
adam-optimizer he-initializer lstm-neural-networks rmsprop rnn-tensorflow u-net yolo l2-regularization regularization-dropout bidirectional-rnn one-hot-encoding batch-normalization iou semantic-segmentation data-augmentation transfer-learning gram-matrix neural-machine-translation neural-style-transfer triplet-loss
Language:Jupyter Notebook 1
parsa-abbasi / intro-to-nlp
An Introduction to Natural Language Processing (NLP)
bag-of-words byte-pair-encoding lemmatization naive-bayes nlp one-hot-encoding stemming text-classification text-processing tf-idf tokenization tokenizer word2vec
Language:Jupyter Notebook 1
PraveenHurakadli / Heart-Disease-Prediction-Using-PCA
Utilizing Principal Component Analysis (PCA) for insightful feature reduction and predictive modeling, this GitHub repository offers a comprehensive approach to forecasting heart disease risks. Explore detailed data analysis, PCA implementation, and machine learning algorithms to predict and understand factors contributing to heart health.
heart-disease heart-disease-dataset heart-failure heart-failure-prediction kmeans-clustering knn-classifier machine-learning machine-learning-algorithms machinelearning one-hot-encoding
Language:Jupyter Notebook 1
sef007 / Neural-Network-Email-Classifier-Numpy-Only
Neural Network using NumPy, V1: Built from scratch. V2: Optimised with hyperparameter search.
activation-functions backward-propagation forward-propagation gradient-descent hyperparameter-search one-hot-encoding weight weights-and-biases
Language:Python 1
sef007 / NN-Numpy-Only-HOG-Feature-Extraction-and-ML-Library-Integration
Digit Recognition Neural Network: Built from scratch using only NumPy. Optimised version includes HOG feature extraction. Third version utilises prebuilt ML libraries.
activation-functions backward-propagation gradient-descent hog-features one-hot-encoding weights-and-biases forward-propogation neural-network-numpy-only keras numpy pandas sklearn
Language:Python 1
shimolina-polina / processing-of-data
Processing of data gaps, coding of categorical features, data scaling.
minmaxscaler one-hot-encoding simple-imputer missing-indicator
Language:Jupyter Notebook 1
Skygers / Multiclass-U-Net-for-liver-tumor-segmentation
Liver Tumor Detection using Multiclass Semantic Segmentation with U-Net Model Architecture. CT-Scan images processed with Window Leveling and Window Blending Method, also CT-Scan Mask processed with One Hot Semantic Segmentation (OHESS)
cv2 liver-segmentation multiclass-segmentation one-hot one-hot-encoding pyhton3 semantic-segmentation tensorflow2 tumor-segmentation
Language:Jupyter Notebook 1
Tikhon-Radkevich / DynamicGridworld
Here, you'll find my solution to the Dynamic Gridworld Sales Prediction challenge
kaggle-competition kaggle one-hot-encoding
Language:Jupyter Notebook 1

one-hot-encoding

entron / entity-embedding-rossmann

Wongi-Choi1014 / Korean-OCR-Model-Design-based-on-Keras-CNN

zouguojian / Travel-time-prediction

d-misra / Multi-label-movie-poster-genre-classification

iAmKankan / Natural-Language-Processing-NLP-Tutorial

yammadev / cbrs

imharshag / NIDS-using-ML

jayachandru001 / Heart-Failure-Prediction-

labrijisaad / Predicting-Student-Admissions-with-Neural-Networks-using-Python

Abdelrahmanrezk / nlp_in_actions

gogundur / Classification

Hoda233 / Arabic-Text-Diacritization

Shahrukh2016 / Retail_Sales_Prediction

avestura / scikit-heart-disease-classifier

mdnuruzzamanKALLOL / One-Hot-Encoding-User-Input-LR

tanvirnwu / Feature-Engineering--Python

Abdulrahmankhaled11 / House-Price-Prediction

AIprototype / LogisticRegression-CreditApproval-Python

bharatkulmani / Dry-Bean

billy-enrizky / predicting_income_project

griffinbran / Real-Estate-Statistical-Modeling

mdnuruzzamanKALLOL / One-Hot-Encoding

MUHAMMADAKMAL137 / IMDB-Dataset-Classification-using-Pre-trained-Word-Embedding-with-GloVec-6B

prushh / data-analytics-exercises

sayo2rule / bank-churning-with-machine-learning

vaitybharati / Assignment-06-Logistic-Regression

arbaazkhaan / FIFA-Dataset-Refinement

HarshitWaldia / Churn_Modelling

mohadeseh-ghafoori / Coursera-Deep-Learning-Specialization

parsa-abbasi / intro-to-nlp

PraveenHurakadli / Heart-Disease-Prediction-Using-PCA

sef007 / Neural-Network-Email-Classifier-Numpy-Only

sef007 / NN-Numpy-Only-HOG-Feature-Extraction-and-ML-Library-Integration

shimolina-polina / processing-of-data

Skygers / Multiclass-U-Net-for-liver-tumor-segmentation

Tikhon-Radkevich / DynamicGridworld