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

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.

Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

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.

Demystify Cuisine and Culture From Ingredients using Natural Language Processing and Machine Learning | Python, Pandas, Matplotlib, NLTK, scikit-learn, webscraping, Python Flask-powered API backed by PostgreSQL, Front-end with HTML, CSS, and Javascript

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.

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

Machine learning models to classify exoplanets from the raw dataset. Using Python: Sklearn, Joblib, NumPy, Pandas, Matplotlib.

Predicts Forsyth–Edwards Chess Notation from a chess board image, using a C-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.