This project is an image classification project using a deep-learning based on Convolutional Neural Networks (CNNs) with Keras. The Dogs vs. Cats is a classic problem for anyone who wants to dive deeper into deep-learning.

Graphs, plots and maps made using python.

Better multi-class confusion matrix plots for Scikit-Learn, incorporating per-class and overall evaluation measures.

Classification Models for Practice

R scripts for RF and RBF-SVM for Acute Myeloid Leukemia subtypes multiclass classification using gene expression profiles. LASSO feature selection, SMOTE sampling, 10-fold cross-validation, variable importance plot, PCA plot, normalized Confusion Matrix, GSE13159.

Streamlit application to predict risk of cardiovascular disease

This research work summarized different machine learning algorithms to create models for predicting diabetes patients utilizing the Diabetes Dataset (PIDD) from the UCI repository. The classifiers were K-Nearest Neighbors, Naïve Bayes, Support Vector, Decision Tree, Random Forest, Logistic Regression and Ensemble Model using a voting classifier.

Objective: Build a CNN classifier that will be able to accurately predict the species of plant seedling based on an image of that seedling taken from the top.

The aim of this project is to predict whether a credit card transaction is fraudulent or not, based on the transaction amount, time and other transaction related data.It aims to track down credit card transaction data, which is done by detecting anomalies in the transaction data.

Deep learning application for term deposit prediction on imbalanced dataset

The displayed project constitutes a Placement_prediction_model. Which can be used to predict the Probability of getting hired for a job with an accuracy of 83% on testing data. This model comprises of an ensemble of decision tree classifiers.

This repository contains a Convolutional Neural Network which classifies whether a person is suffering from COVID-19 or not with the help of Chest X-rays.

An LSTM model to label articles into 5 categories.

ML_Logistic_Regression

Fraud Detection in Mobile Money Transactions using Machine Learning . A binary classification project comparing six models (Logistic Regression, Naive Bayes, Decision Tree, Random Forest, KNN, SVM) on the PaySim dataset. Includes data preprocessing, class balancing, feature importance analysis, and model evaluation (accuracy, precision, recall, F1-

A hybrid machine learning & deep learning pipeline for predicting heartdisease from clinical data. Includes EDA,preprocessing with ColumnTransformer, models (Logistic Regression, Random Forest, XGBoost) and a Keras ANN. Evaluation covers ROC-AUC, Precision-Recall, calibration, with SHAP explainability ensuring transparency and trust in healthcareAI

In simple, a Loan (borrowing money from a bank) is the sum of money that you borrow from the bank or lending financial institution in order to meet needs. These needs could result from planned or unplanned events, and by borrowing, you incur a debt that you have to pay within the agreed duration on your contract.

In this project, we aimed to develop a deep learning model for accurately classifying X-ray images as either pneumonia or COVID cases. The objective was to compare accuracy of different types of CNN, where we used two: DenseNet and MobileNetV2 and acheived an accuracy of something close to 54.5% and later using feature scaling it came close to 85%.

This project builds a calibrated Random Forest classifier to predict whether a client will subscribe to a term deposit following a direct marketing campaign. It uses a clean, reproducible pipeline with feature engineering, stratified cross-validation & tuning, and probability calibration.