This project develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

The ML-based Breast Cancer Prediction Model classifies breast tumors as benign or malignant by analyzing key medical features from biopsy data. It offers healthcare professionals a reliable tool for diagnosis and treatment, showcasing the power of AI in healthcare with a focus on precision in detecting malignant tumors to improve patient outcomes.

This is the Data Mining Project for predicting the student's grade before the final and Mid-2 examination. I use Python and Jupyter Notebook for this Project.

This project explores the Framingham Heart disease dataset with the objective to predict its risk in 10 years. Various methods for handling missing values and outliers are explored as iterations. After analysing the dataset, important and necessary features are selected. Seven ML models are implemented, with evaluation on the basis of Test Recall.

Performed EDA, Data Pre-processing, Imbalance data and Supervised Machine learning to predict customer transaction is fraud using features such as services that customer has signed up for, customer account information, and demographic information about the customer.

Improving a Machine Learning Model

A python GUI application that uses a Convolutional Neural Network built in Tensorflow and Keras to classify chest x-rays into NORMAL or PNEUMONIC. The model has been trained on the dataset obtained from Kaggle and produces a good recall score of 94% on the test set.

Supervised Classfication models - Logistic Regression & Decision Tree, AUC-ROC Curve

Data Project

A machine learning project that predicts the probability of loan default using customer financial and credit data, leveraging classification algorithms to identify high-risk applicants.

The motive of this project is to find out the customer satisfaction of some residential hotels of Dhaka.

Classification problems with imbalance datasets, a SMOTE approach

Machine learning pipeline for predicting traffic accident severity using Logistic Regression and SVM (Linear, Polynomial, RBF) classifiers on road accident data with features like speed, weather, road condition, driving experience, junction type, and light conditions.

Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning

This machine learning model classifies breast tumors as benign or malignant by analyzing medical features from biopsy data. It serves as a diagnostic support tool for healthcare professionals, helping improve cancer detection accuracy and patient care through AI-driven tumor classification.

A machine learning project designed to predict the likelihood of heart disease based on a set of health indicators.

An attempt to study various ML models for predicting the quality of Red Wine using various performance measures.