GitHub topics: adaboost-classifier

Repositories

razamehar/Predicting-Bank-Customer-Churn

This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.

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Snigdho8869/Multiclass-Text-Classification

Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.

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galihpraditya/customer-churn-prediction

This project focuses on predicting customer churn in the telecom industry using machine learning. Customer churn, or the rate at which customers stop using a service, is a critical metric for businesses. By predicting churn, companies can take proactive measures to retain customers and improve customer satisfaction.

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FrienDotJava/adult-census-income

A machine learning project classifying whether someone has income >$50K or <$50K using several models.

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benami171/ML_EX_02

2nd Assignment in machine learning course, Implementing and using Perceptron and Adaboost algorithms.

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reillyc421/Predict-Post-shelter-Outcomes-for-Dogs

Use classification models to predict post-shelter outcomes for dogs (adoption, euthanasia, rescue)

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SimranS22/Heart-Disease-Prediction-Model-SurTech

A ML application(deployed on flask) to detect heart disease in patients based on medical features.

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meinhere/ta-psd

Tugas Akhir Mata Kuliah Proyek Sain Data menggunakan Metode Ensemble Learning

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Bockslunch/Supervised-Learning---Breast-Cancer

Building supervised learning models on a breast cancer dataset with features describing attributes of the tumors

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Computer-Vision-Spring-2024/exVision-FacialSys

Desktop application incorporating face detection using Viola-Jones classical technique as well as face recognition using PCA analysis.

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Ruban2205/Machine_learning_fundamentals

This repository contains a collection of fundamental topics and techniques in machine learning. It aims to provide a comprehensive understanding of various aspects of machine learning through simplified notebooks. Each topic is covered in a separate notebook, allowing for easy exploration and learning.

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manjugovindarajan/EasyVisa-US-visa-applicant-profiling-using-ML

Analyze data of US work Visa applicants, build a predictive model to facilitate approvals, and based on factors that significantly influence visa status, recommend profiles for whom visa should be certified or denied.

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PoojaP-atil/Cancer-Prediction-Model

To analyze the provided cancer.csv data and predict whether or not a patient has breast cancer using ensemble techniques

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AmbreenMahhoor/AdaBoost-Classifier

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tboudart/Life-Expectancy-Regression-Analysis-and-Classification

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

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siddikayyappa/SMAI-end-project

This is the source code for the end project of Statistical Methods in AI, 5th Semester, IIITH, '22. The project involves implementation of a research paper. The research paper is the Paper of Viola Jones Algorithm

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amir-rs/Brest-Cancer-Prediction-using-ML

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inregards2pluto/credit-risk-analysis

Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.

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DataScienceVishal/Breast_Cancer_Prediction

Breast_Cancer_Prediction using XGBoostClassifier & AdaboostClassifier

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Mark-Rozenberg/Credit-Card-Default

Credit Card Clients (CCC) Default Prediction Using various Machine learning Algorithms

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vicaaa12/advanced-machine-learning

Advanced Machine Learning

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DeepanshuDabas03/Severity_Index

Machine learning project done during Monsoon Semester 2023 in IIITD.

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Snigdho8869/Natural-Language-Processing-NLP-Projects

Exploring a collection of Jupyter notebooks showcasing a variety of Natural Language Processing (NLP) projects.

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nourhenehanana/Diabetes-Diagnosis

The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the datsaset

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Afrid1045/Car_Price_Prediction

Predicting the Car prices based on its features and to help maintain transparency between car sellers and buyers. We can use the website to check the car price without third party interaction.

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AqilaFadia/Diabetes-Prediction

This is about how to make Diabetes Prediction with Machine Learning. We are developing a machine learning model capable of predicting whether someone may have diabetes based on health data and specific parameters. Using the right machine learning algorithms, we will process this data to provide valuable predictions for patients and medical

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yogeshwaran-shanmuganathan/Airline-Passenger-Satisfaction

Determining the important factors that influences the customer or passenger satisfaction of an airlines using CRISP-DM methodology in Python and RapidMiner.

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Bharati2301/Algorithms

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celestialtaha/Unbalanced-dataset-Classification

Classification on Unbalanced Datasets using Boost Techniques (AdaBoost M2, SMOTE Boost, RusBoost,..)

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GunturWibawa/TelecomChurnPrediction

Interconnect seeks to forecast customer churn by analyzing package choices and contracts. If a customer plans to leave, they're offered unique codes and special packages to foster loyalty.

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siddharthtelang/Face-and-Pose-Classification

Face detection (class and pose) using various Classifiers - Own Implementation

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virchan/predictive_modeling_workflow

This project explores the predictive modeling workflow using the Kaggle competition "Titanic - Machine Learning from Disaster." It emphasizes key stages like data analysis and model evaluation, aiming to identify the optimal model. Through a real-world approach, we enhance our understanding of the workflow and emphasize rigorous model evaluation.

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Kunal-Attri/Iris-Species-Classification

Iris Species Classification usin various ML models.

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davidocmartins/Machine_Learning_Project

Applied various algorithm models to solve a binary classification problem of predicting if a patient will suffer from a disease. Project done for Machine Learning class of Data Science Ms

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Navneet2409/credit-card-default-prediction

This project is aimed at predicting the case of customer's default payments. This dataset (30000,25) contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan is used to build a classification model.

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Navneet2409/cardiovascular-risk-prediction

The project aims to predict the 10-year risk of future coronary heart disease (CHD) for patients in Framingham, Massachusetts. A dataset (3390,16) containing demographic, behavioral, and medical risk factors of patients is used to build a classification model.

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PratanuP/Bank-Customer-Churn

A prediction model based on ML as well as DL and compare their performances to find Churned Customers

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Zauverer/Test-TSA-Models

Twitter Sentiment Analisys, comparing different models

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anandanraju/Monkey_Pox_Classification_Model-

This Prediction is a research analysis process on data using classification algorithms to compare the accuracy rate for each algorithm given below on this Monkey Pox data such as ( K-Neighbors Classifier, RandomForest Classifier, AdaBoost Classifier, Bagging Classifier, Gradient Boosting Classifier, Decision Tree Classifier )

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anandanraju/Titanic_Survival_Prediction_Model

The sinking of the RMS Titanic is one of the most infamous shipwrecks in world history. In this model, need to analyse what sorts of people were likely to survive. We also need to apply the tools of machine learning to predict which passengers survived in this tragedy.

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