Atlantic: Automated Data Preprocessing Framework for Machine Learning
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training
To predict whether booked appointment will be completed or it will be no show.
Machine Learning Exercise: Exploring categorical plots, LabelEncoder, pipelines and GridSearchCV using Telco Customer Churn data from Kaggle
Machine learning algorithm is used to detect whether the person will suffer from chronic kidney disease or not.
This project aims to predict the salary of employees based on various features using a decision tree classifier algorithm. By analyzing the provided dataset, the model can make accurate predictions if the salaries of new employees is above 100K or not.
Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model
[CIKM 2021] The source code of "Fully Hyperbolic Graph Convolution Network for Recommendation"
Here are some fun projects to learn ML using Handson approach
Diamond Price Prediction Using Machine Learning (Regression Use Case)
This repository contains introductory notebooks for support vector machine
Encoding is the process of converting non-numerical data (such as categories or text) into a numerical format that can be understood and processed by algorithms.
Exploratory Data Analysis Part-2
End-to-end ML pipeline for Titanic survival prediction — data preprocessing, Random Forest classifier, and a FastAPI web app with prediction history!
Showcasing data science skills for a dataset provided by State Farm for a coding interview.
Feature Engineering on Time Series Dataset (Flight Price Prediction)
Pré-processamento de dados aplicado a um conjunto de dados (dataset) utilizando métodos para tratamento de dados nulos, normalização e padronização de escalas e conversão de dados categóricos.
Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.
This notebook implements a structured machine learning pipeline to classify cosmic data using the CatBoost Classifier, known for its efficiency with categorical features and minimal preprocessing requirements.
Prepare a classification model using Naive Bayes for salary data
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