When it comes to deciding whether the applicant’s profile is relevant to be granted with loan or not,banks have to look after many aspects. Predicting loan approval is a common application of machine learning in the financial industry.

Linear regression, retraining models, regularization, SVC, RandomForest, K-means, PCA, Recommender systems, Neural Network, Keras

Stress Detection Using Natural Language Processing

This repository exhibits fundamental theory and algorithms with codes and explainations that frequently applied in the field Machine Learning

Predict an individual’s belief in climate change based on historical tweet data

A project focused on anomaly detection within web authentication systems, employing both supervised and unsupervised machine learning techniques to enhance security by pinpointing and analyzing unusual activities.

Project that uses the Howell data set to train decision tree, SVC, and NN models and compare their performance

Given a set of text movie reviews that have been labeled positive or negative, a machine learning classification model should be trained and tested for predicting the nature of future movie reviews.

Loan approval prediction means using credit history data of the loan applicants and algorithms to build an intelligent system that can determine loan approvals.

SVC model predictions on cancer dataset of sklearn and also used GridSearchCV to find the best parameters for the SVC model

Predicting the type of language in South Africa using machine learning

Classify consumers' complaints into the product category it belongs to using the description of the complaint.

Credit Card Default Prediction is a machine learning project that involves predicting the likelihood of a credit card user defaulting on their payments. The project aims to help credit card companies mitigate the risk of default by identifying potential default accounts in advance and taking preventive actions.

Brain Tumor Detection

SVC and KNN methods were used to predict whether mushrooms are poisonous or edible according to their properties. Random forest and chi-square variable selection methods were applied and the 10-fold cross validation method was used and f1 scores were calculated by re-estimating. Finally, the models were compared.

Hotel Review Classification

This project aims to predict if a cancer diagnosis is benign or malignant using Support Vector Machine (SVM) model. The model utilizes several features related to cancer cells to make predictions.

The project provides information about breast cancer to help doctors predict if a person has it.

Backend server for the Wine Quality Predictor.