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

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.

A research study on How do factors like alcohol consumption, age, ethnic background, and medical history affect the risk of developing Alzheimer's disease?

Machine learning models to predict customer churn in a bank using features like age, credit score, and account activity, implemented with Random Forest and GridSearchCV for optimal performance (Python)

Machine Learning Trading Bot (Algorithmic Trading)

Heart Failure Prediction in Humans

Machine Learning Approach to classify different types of dry beans. Data Cleaning , Encoding, Feature Scaling ,Data visualization, Predictive Modelling applied.

Identifies footballers among a group of 5

A Medical Recommendation app where user can get medical information such as: Medication, Precautions, Diets, etc.. which depends on the Symptoms

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

The Seed Classification ML Model aims to classify different types of wheat seeds into three categories: Kama, Rosa, and Canadian. The model uses various features related to the geometric properties of the seeds to accurately distinguish between these types.

Machine learning models

Support Vector Machines (SVM) are supervised learning models used for classification and regression analysis. They are particularly effective in high-dimensional spaces and situations where the number of dimensions exceeds the number of samples. SVMs are also memory efficient as they use a subset of training points called support vectors.

Diabetes is a medical disorder that affects how the body uses food for energy. When blood sugar levels rise, the pancreas releases insulin. If diabetes is not managed, blood sugar levels can rise, increasing the risk of heart attack and stroke. We used Python machine learning to forecast diabetes.

Predicting market stock prices using various Machine Learning models. Data trained and tested up until 2017. Data found of Kaggle.

Heart Disease Predictor using Linear regression, Logistics and Support Vector Algorithm in Python.

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

Data fetched by wafers is to be passed through the machine learning pipeline and it is to be determined whether the wafer at hand is faulty or not apparently obliterating the need and thus cost of hiring manual labour.

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.

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