Heart Attack Prediction Using Machine Learning Algorithm

This is a Breast Cancer Detection project with unsupervised learning algorithmic approaches alongside Naive Bayes Classifier Algorithm, Logistic Regression and GaussianNB.

This repository contains the Iris Classification Machine Learning Project. Which is a comprehensive exploration of machine learning techniques applied to the classification of iris flowers into different species based on their physical characteristics.

Logistic Regression Project

The goal of this project is to develop a machine learning model that can help banks to identify customers who are likely to churn and take appropriate measures to retain them

Loan Prediction using Classification Techniques

Data analysis of concerning prevalence of heart disease and stroke given various health variables, such as Age, Smoking habits, Weight, among others.

Machine Learning C++

The goal of this project is to predict whether a loan would be approved or not for the clients.

The Optimizing crop production project is a cutting-edge solution aimed at enhancing crop yield and productivity by leveraging data-driven insights. Through the use of advanced machine learning algorithms, this project helps farmers make decisions on various aspects of agriculture based on the certain climatic conditions.

Heart Failure Prediction by developing various Supervised Learning algorithms in Python

Used R to create a series of models to predict whether or not an individual is likely to have diabetes given a set of predictors, using a Diabetes dataset (768 obs. of 9 variables). Cleansed the data, created several visuals, and then selected the best model.

X Education Organization wants to identify if a customer registered on their website for enquiry is a potential customer or not. Using past data to build a machine learning algorithm

Churn-Prediction-in-Telecom-Industry-using-Logistic-Regression

Laboratory with random forest, logistic regression and SVM. The dataset used for this test is a set of points generated randomly with the following specification: • Number of Samples: 1200 • Number of Classes: 3 • Number of Features: 2 (Length and Width).

Used libraries and functions as follows:

Deep learning projects using Pytorch and tensorflow.

This is a sample code repository to leverage classic "Pima Indians Diabetes" from UCI to perform diabetes classification by Logistic Regression & Gradient Boosting algorithms.

Assumptions of Logistic Regression, Clearly Explained