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This repository will contain all the stuffs required for beginners in ML and DL do follow and star this repo for regular updates
Case Studies and Projects in Machine Learning/EDA/DL
Machine learning model to detect heart attack. Various techinques applied data cleaning, visualization, and modeling
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.
Classifying Breast Cancer Tumors
Predicting the volcanic eruption using ML Algorithms.
Predictive model that divides the customers into groups based on common characteristics so companies can market to each group effectively and appropriately
A Machine Learning Workshop for HackCU III
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.
Telecom Churn prediction with multiple machine learning models
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.
Code store for custom implementation of some machine learning algorithms from scratch.
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.
Final Project - In Vehicle Coupon Recommendation
Machine Learning concepts and models like SMOTE, RandomForest Classifier, Decision Tree, K-NN, and Logistic Regression were first implemented without any ML libraries.
ML classifier using computer vision to classify photos of dogs, frogs, and hogs.
An investigation of San Francisco Fire Incidents using open data - exploratory analysis and modelling logit and multinomial logit regressions
Eric-Simon-Supervised-ML-Challenge
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.
Investigation into creating a model to predict MLB All-Stars
This is a trained ML model that analyzes an email to distinguish between legitimate messages (i.e. ham) and pesky spam.
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 on which algorithm was the best to use for future predictions.
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 datasets were taken from the UCI ML repository.
Data Analysis and Binary Classification of 4 popular datasets using Logistic Regression and Naive Bayes built from scratch
Classification of IMDB Reviews dataset and News Group dataset using Logistic Regression, Decision Trees, Support Vector Machines, Ada Boost and Random Forest. Methods and Accuracy of each model were compared and reported
Implementation of Logistic Regression for getting intuition : how neural network works
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.
Predicting the type of cancer class with the help of categorical and text data.
In this repository, we dive into a famous natural language processing problem, where we classify a piece of text as hate speech or not.
Linear Regression and Logistic Regression
Portfolio of Machine Learning Projects
Algorithm Of Convex Optimizer