Kyphosis disease prediction using Fully Connected Neural Networks (FCNNs) model and XGBoost model with GridSearchCV

The goal is to create a model predicting the grade of an essay

This repository contains the resources and codebase for a research project aimed at predicting breast cancer cases using data from the KNUST hospital.

In this analysis, I will demonstrate how PCA works in different tasks and how much time and resources we save in our daily analysis.

solution https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud. Xgboost is an efficient method of gradient boosting that makes a random initial prediction then calculates similarity scores and gain to build the trees and decrease the gap between the actual value and the predicted value.Gridsearch was used to get the best parameters tuning.

Supervised Machine Learning and Credit Risk

Course Dropout Prediction, Datathon Spring'24

using machine learning to assess credit risk

A Deep Learning analysis to predict success of charity campaigns

Here is the repository for sharing jupyter notebooks discussed in 'AI-1402' class.

The objective of this analysis was to use machine learning models to accurately predict credit risk.

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

Supervised Machine Learning Project

This project predicts hotel booking cancellations using Machine Learning techniques, benefiting both travelers and hotels.

Data analysis, visualization and prediction for the prevention of heart disease

Built a model to determine the risk associated with extending credit to a borrower. Performed Univariate and Bivariate exploration using various methods such as pair-plot and heatmap to detect outliers and to monitor the behaviour and correlation of the features. Imputed the missing values using KNN Imputer and implemented SMOTE to address the i…

Machine learning for credit card default. Precision-recalls are calculated due to imbalanced data. Confusion matrices and test statistics are compared with each other based on Logit over and under-sampling methods, decision tree, SVM, ensemble learning using Random Forest, Ada Boost and Gradient Boosting. Easy Ensemble AdaBoost classifier appear…