Case Studies and Projects in Machine Learning/EDA/DL

Embark on a journey of data-driven insights with our diabetes research project. Leveraging Python's pandas, matplotlib, and scikit-learn, we preprocess, visualize, and analyze 330 health features. Employing logistic regression, decision trees, KNN, and SVM, we predict diabetes with precision.

Telecom Churn prediction with multiple machine learning models

This GitHub project implements a logistic regression model to analyze and predict credit risk for a lending company. Explore comprehensive training, testing, and evaluation scripts to enhance the accuracy and reliability of risk assessments. Empower your lending decisions with robust, transparent, and customizable machine learning solutions.

This is a trained ML model that analyzes an email to distinguish between legitimate messages (i.e. ham) and pesky spam.

This repository will contain all the stuffs required for beginners in ML and DL do follow and star this repo for regular updates

Predicting the type of cancer class with the help of categorical and text data.

This repository contains all the code developed with the aim of training a machine learning model useful for recognizing whether a fingerprint image is a spoofed or original one.

ML classifier using computer vision to classify photos of dogs, frogs, and hogs.

Eric-Simon-Supervised-ML-Challenge

This project helps to make prediction of fake news by developing a machine learning model using the logistic regression algorithm. The project provides a reliable solution to identify and predict the authenticity of news articles, helping users distinguish between real and fake news sources.

Investigation into creating a model to predict MLB All-Stars

Using Logistic Regression to predict whether or not a given star will have an Exoplanet in orbit, using data from HYG3 and the open exoplanet archive.

Why do employees leave? This project first compares the predictive performance of three different models, then uses the best model to help reveal the top contributing factors.

Final Project - In Vehicle Coupon Recommendation

Exploratory data analysis was performed on a dataset heart disease dataset, constructing various visualisations, to gain useful insights about the data. Various machine learning algorithms were then trained to classify whether someone has heart disease or not based on various features, scoring each algorithm based on their accuracy and deciding …

Predicting the volcanic eruption using ML Algorithms.

This repository contains the final report drafted on the replication of the Caruana and Niculescu-Mizil paper on the comparison of supervised learning algorithms. Here, I compare Logistic Regression, Random Forest, and Artificial Neural Networks over 4 different datasets measured over 3 different metrics. This project was done for Cogs 118A. The…

Data Analysis and Binary Classification of 4 popular datasets using Logistic Regression and Naive Bayes built from scratch