GitHub topics: label-encoding

Repositories

Sanchemtos/Multi-Label-Emotion-Recognition

This project focuses on detecting multiple emotions from English text using a fine-tuned **BERT** model. It leverages the [GoEmotions](https://huggingface.co/datasets/go_emotions) dataset — a large-scale human-annotated dataset of Reddit comments labeled with 27 emotions + neutral.

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362heavy/Liver_Cirrhosis_Stage_Detection_System

This repository contains a system for detecting the stage of liver cirrhosis using historical patient data. It employs machine learning to analyze key medical indicators and classify patients into three distinct stages. 🦠📈

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saifalibaig/Multi-Label-Emotion-Recognition

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SunnyRao07/stroke-risk-prediction

Predicting stroke risk using machine learning models based on healthcare and demographic data.

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SunnyRao07/Water-Quality-Analysis

A machine learning project that predicts water potability based on chemical and physical attributes, using models like Logistic Regression, Random Forest, and XGBoost.

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imharshag/NIDS-using-ML

This project showcases a Network Intrusion Detection System (NIDS) designed to bolster cybersecurity defenses against evolving threats

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Sameer051022/LabelEncoding_Models

"A comprehensive exploration of machine learning models using label encoding to preprocess categorical data, featuring implementations in Python with sklearn."

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maheshvarade/decision-tree-for-classification-of-penguins-species

🐧 Decision Tree for Classification of Penguin Species This project implements a Decision Tree Classifier to classify different species of penguins using the Palmer Penguins dataset. The dataset contains features such as bill length, bill depth, flipper length, and body mass to predict the species (Adelie, Chinstrap, or Gentoo).

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JaspreetSingh-exe/Vehicle-Price-Prediction

Vehicle Price Prediction is a machine learning project that estimates vehicle prices using features like make, model, year, mileage, and more. It employs multiple regression models, including Linear Regression, Random Forest, Gradient Boosting, CatBoost, and Stacking Regressor, with GridSearchCV for tuning.

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Shailesh-Padhariya/Human-Resources

This project analyzes employee attrition using machine learning models, including Logistic Regression, Random Forest, and XGBoost. The objective is to identify key factors influencing employee turnover and provide insights to improve retention strategies

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LKEthridge/Supervised_Learning

Supervised Learning project from TripleTen

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kingership321/Esophageal_Cancer_Detection

This is an esophageal cancer detection project which uses dataset from Kaggle. The dataset has been attached to this repo. This repo also contains codes in both jupyter notebook and pdf format. The code involves techniques like EDA and feature engineering, LabelEncoding and One-Hot-Encoding and the model used is Linear Regression.

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rubyyy1118/Machine_Learning_Optimization_Study

The Learning From Data - Assignment in my MSc Business Analytics course

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aneeshmurali-n/Project-ML-Data-Preprocessing

The main objective of this project is to design and implement a robust data preprocessing system that addresses common challenges such as missing values, outliers, inconsistent formatting, and noise. By performing effective data preprocessing, the project aims to enhance the quality, reliability, and usefulness of the data for machine learning.

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abinashsahoo007/Project-Resume-Classification

The document classification solution should significantly reduce the manual human effort in the HRM. It should achieve a higher level of accuracy and automation with minimal human intervention.

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areeba0/Image-Classification-using-Gaussian-Naive-Bayes-SVM-and-KNN

the code uses KNN, Gaussian Naive Bayes & SVM to classify images. It preprocesses, normalizes data, applies PCA , computes accuracy, precision etc. It evaluates k-NN using Euclidean distance & cosine similarity, visualizing results with line plots, 3D scatter plots, & confusion matrices to demonstrate classifier performance.

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CRIPAC-DIG/hetgsl

[CIKM 2021] Code and dataset for "Label-informed Graph Structure Learning for Node Classification"

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SannketNikam/Credit-Risk-Analysis

Credito - Credit Risk Analysis using XGBoost Classifier with RandomizedSearchCV for loan approval decisions.

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sorodocosmin/feedbackHHC

This project focuses on analyzing patient feedback regarding the treatment provided by home healthcare service agencies.

Language: Python - Size: 5.52 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

shikhararyan/Text-Classification-Transformer-Model-

This sentiment analysis model utilizes a Transformer architecture to classify text sentiment into positive, negative, or neutral categories with high accuracy. It preprocesses text data, trains the model on the IMDB dataset, and effectively predicts sentiment based on user input.

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Ninad077/Machine_Learning-Logistic_Regression

Content: Machine Learning, Logistic regression steps, Probability matrix, Confusion matrix, Accuracy score, Recall value, Data preprocessing, Label encoding, Scaling the data, Splitting train test data, Running Logistic Regression, Y prediction on test data, Class imbalance, Type 1 & Type 2 errors.

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damaniayesh/Covid_data_Prediction

This project provides the data based on classification to check if the patient is covid +ve or -ve.

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Ishikaaa/ML-Data-Scientist-job-market-in-the-US

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Gopika-S-Nair/Machine-Learning---Classification

This repository contains Machine Learning Classification algorithms implementation

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REAtes/Directing-App-Customers-to-Subscription

The primary goal of this project is to convert free users of a financial tracking app into paid members. This conversion will be achieved by building a model that identifies users who are unlikely to enroll in the paid version of the app.

Language: Python - Size: 1.84 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

RimTouny/Credit-Card-Fraud-Detection

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

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katreparitosh/Water-Quality-Analysis

Analytical understanding and applying parameter optimization, regression with gradient descent to predict water quality levels across Indian waters.

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Deepshikha05/Customer_Behavior_Analysis

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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).

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Himanshuachwaan/Diamonds

Diamonds Price prediction using Polars and Ski-learn

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m-enesyilmaz/Feature_Engineering_with_Python

📶In this repository, we will do feature engineering with Python.

Language: Python - Size: 364 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

chiragn888/Data-Science-Projects

This repository consists of various projects based on Machine Learning and NLP.

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RazHoshia/unofficial_encoders

Unofficial but extremely useful Label and One Hot encoders.

Language: Python - Size: 80.1 KB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0

EmamulHossen/Feature_Engineering

Feature engineering or feature extraction or feature discovery is the process of extracting features from raw data.

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NirmalKanagasabai/Reddit_TopicClassification__RandomForest_SGDClassifier

Supervised Machine Learning methods (Random Forest and SGD Classifier) to classify short conversations extracted from Reddit

Language: Python - Size: 242 KB - Last synced at: almost 2 years ago - Pushed at: over 7 years ago - Stars: 0 - Forks: 1

prasadposture/Data-Preparation

There are lot of things that need to be done on the given dataset before we feed it to the machine, these things come under data preprocessing. In this repository I have tried to explain those things with some examples.

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maggieezzat/Visual-History-of-Nobel-Prize-Winners

This is an implementation for a DataCamp project: A Visual History of Nobel Prize Laureates. We try to answer the proposed questions and visualize the results.

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deepakrameshgowda/CREDIT-CARD-FRAUD-DETECTION

Building predictive models to detect and prevent the fraudulent transactions happening on cerdit cards and debit cards. Implementation of 2nd factor authentication for safe and secure transactions.

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S-CHAN11/Insurance-Claim-Prediction

In this project, I use 3 machine learning models (CART, Random Forest and ANN) to predict the claim frequency for a travel insurance firm. I also evaluate which of the three models is most suitable for our dataset.

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Manjari-99/MLP_Perceptron

To implement the internal workings of perceptron and testing the accuracy of in train and test dataset.

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NavindaFernando/Feature-Extraction

Heart Risk Level Predicting Regression Model & Web using Feature Engineering and Data Preprocessing :baby_chick:

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bharatkulmani/Dry-Bean

Project is about predicting Class Of Beans using Supervised Learning Models

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shanuhalli/Assignment-Decision-Trees

Use decision trees to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

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MZayed47/CS-GO_KD-Classifier

This classification task is specifically dependent on a video dataset that includes video clips of kill and death scenes from the first-person shooting game “CS Go”. I have used the ResNet-50 model for image classification and then turn it into a more accurate video classifier by employing the rolling averaging method.

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Related Keywords

label-encoding 55 one-hot-encoding 14 feature-engineering 14 machine-learning 13 python 13 random-forest 9 logistic-regression 9 pandas 9 data-cleaning 9 data-preprocessing 8 matplotlib 7 random-forest-classifier 7 numpy 7 decision-tree-classifier 7 feature-scaling 6 confusion-matrix 6 sklearn 6 data-visualization 6 missing-values 5 standard-scaler 5 hyperparameter-tuning 5 seaborn 4 outliers 4 decision-trees 4 smote 4 xgboost 4 exploratory-data-analysis 4 linear-regression 4 regression-models 4 principal-component-analysis 3 outlier-removal 3 outlier-detection 3 model-evaluation 3 hyperparameter-optimization 3 ordinal-encoding 3 data-science 3 eda 3 python3 3 feature-selection 3 artificial-intelligence 3 xgboost-classifier 3 jupyter-notebook 3 accuracy-score 3 fraud-detection 2 encoder 2 svm 2 classification 2 decision-tree 2 tensorflow 2 gradient-descent 2 transformation 2 scaling 2 handling-missing-value 2 sigmoid-function 2 shapely 2 huggingface 2 standardscaler 2 classification-report 2 machine-learning-algorithms 2 scikit-learn 2 tokenization 2 sgd-classifier 2 ai 2 text-preprocessing 2 class-imbalance 2 linear-discriminant-analysis 2 predictive-modeling 2 bert-model 2 gaussian-naive-bayes 2 knn 2 neural-network 2 knn-classification 2 joblib 2 quantile-transformer 1 classifier 1 auc 1 artificial-neural-networks 1 quadratic-discriminant-analysis 1 cart 1 data-cleaning-and-preprocessing 1 polynomial-features 1 gridsearchcv 1 multivariate-analysis 1 neural-network-classifier 1 html5 1 handling-outlier 1 predictive-analysis 1 univariate-analysis 1 variable-importance 1 keras-tensorflow 1 perceptron-learning-algorithm 1 flask 1 categorical-encoding 1 data-binning 1 topic-classification 1 sgd 1 scikitlearn-machine-learning 1 reddit 1 missing-data 1 sklearn-compatible 1