Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras

DSLR (Datascience X Logistic Regression) : a multi class logistic regression model that sorts Hogwarts students to their houses.

This project utilizes Logistic Regression for breast cancer classification, incorporating data visualization to enhance understanding. It covers the entire workflow, from data import to model evaluation, ensuring a comprehensive analysis of the classification process.

A collection of 8 Applied Data Science projects.

Credit Scoring Project: Perform a Weight of Evidence Logistic Regression Modelling (WoELR) to generate credit scorecard for loan approval.

Classifying Criminal Offenses: Classification Application in Python Using scikit-learn and TensorFlow-Keras

This project explores an IBM telecom dataset, conducting initial EDA and data preprocessing. It examines three genetic algorithm variations for feature selection: one-point, two-point, and uniform crossover. Logistic regression is used to predict customer churn, and performance is evaluated using error bar plots.

Github repo for ML Specialization course on Coursera. Contains notes and practice python notebooks.

The notebook contains Python code for various machine learning tasks and models. Here is an overview of its content:

Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.

Login-registration form, design

Predicting Customer Churn using Data Mining and Machine Learning techniques - Logistic Regression, Decision Trees and Random Forests

Develop a model to predict which retail customers will respond to a marketing campaign. Logistic Regression shows the best performance.

This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.

IoT based Novel Approach for Remote Patient Pulse Rate Monitoring System with Stroke Prediction using Logistic Regression

An in-depth exploration of logistic regression models, including data cleaning, model building, and performance evaluation on various datasets.

HealthAnalytics-SAS: Leveraging SAS Programming to Uncover Health Trends, Correlations, and Predictive Insights from Comprehensive Health Data Analysis.

Exploring disparities in the COMPAS algorithm: an analysis of recidivism predictions among demographic groups.

Machine Learning Algorithms