adaboost-classifier

There are 1 repository under adaboost-classifier topic.

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.
customer-satisfaction passenger-satisfaction factors airline-passengers airline-passenger-satisfaction python jupyter-notebook crisp-dm exploratory-data-analysis random-forest adaboost-classifier logistic-regression decision-tree-classifier xgboost-algorithm knn-classification naive-bayes-classifier rapidminer
Language:Jupyter Notebook 25
Snigdho8869 / Multiclass-Text-Classification
Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.
adaboost-classifier bert-fine-tuning cnn-model ensemble-learning gradient-boosting-classifier gru keras-tensorflow logistic-regression natural-language-processing neural-network nlp random-forest-classifier rnn-lstm support-vector-machines xlnet-fine-tuning flask-application
Language:Jupyter Notebook 16
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.
data-preprocessing data-science machine-learning machine-learning-algorithms python adaboost-classifier agglomerative-clustering apriori-algorithm ensemble-learning fuzzy-cmeans-clustering machine-learning-models multilayer-perceptron random-forest-classifier self-organizing-map single-layer-perceptron
Language:Jupyter Notebook 6
celestialtaha / Unbalanced-dataset-Classification
Classification on Unbalanced Datasets using Boost Techniques (AdaBoost M2, SMOTE Boost, RusBoost,..)
unbalanced-data machine-learning classification adaboost-classifier smote-algorithm rusboost
Language:Jupyter Notebook 3
krish1919ls / machine_learning_models
Assignments from Applied Machine Learning Class (UTD BUAN-6341)
python linear-regression logistic-regression gradient-descent-algorithm support-vector-classifier decision-tree-classifier adaboost-classifier knn-classifier artificial-neural-networks k-means-clustering expectation-maximization principal-component-analysis independent-component-analysis random-projections hyperparameter-tuning
Language:Jupyter Notebook 2
Kunal-Attri / Iris-Species-Classification
Iris Species Classification usin various ML models.
adaboost-classifier decision-tree extra-trees-classifier gradient-boosted-trees iris-dataset knn-classifier linear-svm naive-bayes-classifier random-forest rbf-svm voting-classifier
Language:Jupyter Notebook 2
SimranS22 / Heart-Disease-Prediction-Model-SurTech
A ML application(deployed on flask) to detect heart disease in patients based on medical features.
data-analysis data-visualization decision-tree-classifier github-codespaces jupyter-notebook knn-classifier logistic-regression machine-learning naive-bayes-classifier python random-forest-classifier svm-classifier adaboost-classifier bagging-ensemble ensemble-learning xgboost-classifier
Language:Jupyter Notebook 2
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.
pandas numpy seaborn scikit-learn statsmodels ordinary-least-squares ridge-regression lasso-regression random-forest-regression naive-bayes-classifier k-nearest-neighbor-classifier decision-tree-classifier random-forest-classifier support-vector-classifier adaboost-classifier gradient-boosting-classifier jupyter-notebooks python matplotlib-pyplot
Language:Jupyter Notebook 2
amir-rs / Brest-Cancer-Prediction-using-ML
This project aims to predict breast cancer using machine learning and deep learning techniques.
adaboost-classifier area-under-curve catboost decision-tree-classifier gradient-boosting-classifier logistic-regression machine-learning random-forest-classifier xgboost
Language:Jupyter Notebook 1
amir-rs / Diabetes-Detection-Neural-Network
This project aims to predict the occurrence of diabetes using machine learning techniques. The dataset used for this analysis is the "diabetes_prediction_dataset.csv" file, which contains various features related to an individual's health condition.
adaboost-classifier area-under-curve decision-tree-classifier gradient-boosting-classifier logistic-regression machine-learning neural-networks random-forest-classifier receiver-operating-characteristic
Language:Jupyter Notebook 1
jermynyeo / Predict-Automobile-Insurance-Prices
All-in-1 notebook which applies different clustering (K-means, hierarchical, fuzzy, optics) and classification (AdaBoost, RandomForest, XGBoost, Custom) techniques for the best model.
k-means-clustering hierarchical-clustering fuzzy-clustering optics-clustering adaboost-classifier random-forest xg-boost silhouette-score
Language:Jupyter Notebook 1
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.
adaboost-classifier decision-tree-classifier knn-classifier logistic-regression random-forest-classifier svm-classifier xgboost-classifier lightgbm-classifier data-analysis data-science feature-engineering machine-learning python
Language:Jupyter Notebook 1
Neo-Panther / ML-Project-Predicting-Crime-Rate
ML Project implementing decision trees, boosting and svm classification from scratch.
decision-tree-classifier machine-learning python3 support-vector-classifier jupyter-notebook adaboost-classifier classification
Language:Jupyter Notebook 1
SolanaO / Disaster_Response_Texts
Text classification of messages collected during and after a natural disaster. Deploy a Flask app on Heroku .
adaboost-classifier flask-application heroku-app imbalanced-classification machine-learning text-classification
Language:Jupyter Notebook 1
themelinaKz / Ham-Spam-Classifiers
Machine learning binary classification algorithms for classifying mails as spam or ham.
adaboost-classifier naive-bayes-classifier id3-decision-tree logistic-regression machine-learning java
Language:Java 1
Zauverer / Test-TSA-Models
Twitter Sentiment Analisys, comparing different models
naive-bayes-classifier random-forest logistic-regression gradient-boosting-classifier adaboost-classifier gridsearchcv countvectorizer nltk-library glob pickle python3
Language:Jupyter Notebook 1
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.
adaboost-classifier exploratory-data-analysis feature-engineering jupyter-notebook k-nearest-neighbours linear-regression machine-learning-algorithms model-selection python random-forest-regression streamlit support-vector-regression
Language:Jupyter Notebook 0
alef-s / Easy_Visa
Analyze the data of Visa applicants, build a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommend a suitable profile for the applicants for whom the visa should be certified or denied.
adaboost-classifier bagging-classifier customer-profiling data-processing data-visualization exploratory-data-analysis gradient-boosting-classifier gridsearchcv hyperparameter-tuning random-forest-classifier stacking-classifier xgboost
Language:Jupyter Notebook 0
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 )
adaboost-classifier bagging k-nearest-neighbors-k-nn random-forest-classifier data-analysis decision-tree-classifier gradient-boosting monkey-pox roc-curve
Language:Jupyter Notebook 0
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
adaboost-classifier decision-tree gradient-boosting-classifier knn-classification logistic-regression machine machine-learning python random-forest support-vector-classifier xgboost-classifier kneighborsclassifier-model
Language:Jupyter Notebook 0
DataScienceVishal / Breast_Cancer_Prediction
Breast_Cancer_Prediction using XGBoostClassifier & AdaboostClassifier
adaboost-classifier eda gridsearchcv hyperparameter-tuning xgboost-classifier
Language:Jupyter Notebook 0
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 course of Data Science Ms
adaboost-classifier bagging-ensemble decision-tree-classifier extra-trees-classifier gradient-boosting hyperparameter-tuning knn logistic-regression machine-learning ridge-regression stacking-method svm-classifier python
Language:Jupyter Notebook 0
DeepanshuDabas03 / Severity_Index
Machine learning project done during Monsoon Semester 2023 in IIITD.
adaboost-classifier decision-trees gradient-boosting-classifier logistic-regression mlp-classifier python random-forest-classifier road-accident-analysis road-accident-project sckit-learn severity-analysis svm-classifier traffic-analysis xg-boost traffic-severity-index
Language:Jupyter Notebook 0
jhylin / ML2-3_boosted_trees
Machine learning series 2.3 on boosted trees
adaboost-classifier rdkit scikit-learn-python xgboost-classifier
Language:Jupyter Notebook 0
mamta987 / telecom-churn-case-study
Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.
adaboost-classifier churn-prediction data-imbalance eda logistic-regression random-forest
Language:Jupyter Notebook 0
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.
adaboost-classifier data-analysis data-science decision-tree-classifier feature-engineering knn-classifier lightgbm-classifier logistic-regression machine-learning-algorithms python random-forest-classifier xgboost-classifier
Language:Jupyter Notebook 0
PratanuP / Bank-Customer-Churn
A prediction model based on ML as well as DL and compare their performances to find Churned Customers
adaboost-classifier ann churn-prediction data-analysis data-visualization decision-tree-classifier deep-learning deep-learning-algorithms gaussian-naive-bayes-classification gradient-boosting-classifier k-nearest-neighbours logistic-regression machine-learning machine-learning-algorithms random-forest-classifier svc svm-classifier xgboost-classifier
Language:Jupyter Notebook 0
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.
adaboost-classifier classifier-model customer-churn-analysis customer-churn-prediction extra-trees-classifier gradient-boosting-classifier hyperparameter-tuning knn-classification python random-forest-classifier shapley-additive-explanations smote-sampling xgboost-classifier
Language:Jupyter Notebook 0
Snigdho8869 / Natural-Language-Processing-NLP-Projects
Exploring a collection of Jupyter notebooks showcasing a variety of Natural Language Processing (NLP) projects.
adaboost-classifier bert-model cnn deep-learning ensemble-learning exploratory-data-analysis gru keras language-detection logistic-regression lstm machine-learning naive-bayes-classifier natural-language-processing nlp random-forest support-vector-machines tensorflow xgboost-classifier xlnet
Language:Jupyter Notebook 0
vicaaa12 / advanced-machine-learning
Advanced Machine Learning
adaboost-classifier bagging-classifier confusion-matrix correlation-matrix data-preprocessing ensemble-learning exploratory-data-analysis feature-importance gradientboostinclassifier hyperparameter-tuning missing-value-imputation model-comparison model-performance-comparison outlier-detection oversampling-technique randomforest-classification randomsearchcv undersampling-technique xgboost-classifier
Language:Jupyter Notebook 0
akashkriplani / telecom-churn-case-study
upGrad's Telecom Churn Case Study hosted on Kaggle platform
adaboost-classifier decision-trees eda logistic-regression pca random-forest rfe telecom-churn-prediction xgboost-classifier
Language:Jupyter Notebook
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.
adaboost-classifier cat-boost-algorithm decision-tree-classifier gradient-boosting knn-classifier lgbmclassifier logistic-regression matplotlib numpy pandas python3 random-forest-classifier sklearn svm-classifier voting-classifier
Language:Jupyter Notebook
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
adaboost-classifier ensemble-learning python scikit-learn xgboost-classifier
Language:Jupyter Notebook
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
adaboost-classifier data-cleaning data-preprocessing data-processing data-visualization eda model-building model-evaluation
Language:Jupyter Notebook
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
adaboost adaboost-classifier cascade-classifier viola-jones
Language:Jupyter Notebook
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.
adaboost-classifier confusion-matrix data-preparation data-preprocessing decision-tree-classifier dummy-classifier feedforward-neural-network gaussian-naive-bayes-classifier hypothesis-testing kaggle-competition logistic-regression model-evaluation-and-selection model-training predictive-modeling random-forest-classifier support-vector-classifier bernoulli-naive-bayes-classifier titanic-survival-prediction
Language:Jupyter Notebook

adaboost-classifier

yogeshwaran-shanmuganathan / Airline-Passenger-Satisfaction

Snigdho8869 / Multiclass-Text-Classification

Ruban2205 / Machine_learning_fundamentals

celestialtaha / Unbalanced-dataset-Classification

krish1919ls / machine_learning_models

Kunal-Attri / Iris-Species-Classification

SimranS22 / Heart-Disease-Prediction-Model-SurTech

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

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

amir-rs / Diabetes-Detection-Neural-Network

jermynyeo / Predict-Automobile-Insurance-Prices

Navneet2409 / cardiovascular-risk-prediction

Neo-Panther / ML-Project-Predicting-Crime-Rate

SolanaO / Disaster_Response_Texts

themelinaKz / Ham-Spam-Classifiers

Zauverer / Test-TSA-Models

Afrid1045 / Car_Price_Prediction

alef-s / Easy_Visa

anandanraju / Monkey_Pox_Classification_Model-

AqilaFadia / Diabetes-Prediction

DataScienceVishal / Breast_Cancer_Prediction

davidocmartins / Machine_Learning_Project

DeepanshuDabas03 / Severity_Index

jhylin / ML2-3_boosted_trees

mamta987 / telecom-churn-case-study

Navneet2409 / credit-card-default-prediction

PratanuP / Bank-Customer-Churn

razamehar / Predicting-Bank-Customer-Churn

Snigdho8869 / Natural-Language-Processing-NLP-Projects

vicaaa12 / advanced-machine-learning

akashkriplani / telecom-churn-case-study

GunturWibawa / TelecomChurnPrediction

nourhenehanana / Diabetes-Diagnosis

PoojaP-atil / Cancer-Prediction-Model

siddikayyappa / SMAI-end-project

virchan / predictive_modeling_workflow