kfold-cross-validation

vikrantarora25/Car-Price-Prediction-Highly-Comprehensive-Linear-Regression-Project-

A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions validity.

LorranSutter/PredictStock-SVM

ML model for stock trend prediction using Python

sourcecode369/ml-algorithms-on-scikit-and-keras

Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..

LaxmiChaudhary/Modeling-of-strength-of-high-performance-concrete-using-Machine-Learning

satishgunjal/House-Price-Prediction-Project

Contains all my data science projects.

yu9824/kennard_stone

This is an algorithm for evenly partitioning.

Ashutosh27ind/PGDDS-Capstone-Project

Credit Card Fraud Detection Project

wanderly0501/Architectural-Picture-Classification

Classify pictures by architectural style and recognize objects with CNNs and YOLO

ogunlao/foundations_of_ml

Machine Learning algorithms built from scratch for AMMI Machine Learning course

ozlemkorpe/Machine-Learning-with-MATLAB

Machine Learning and Data Mining cheatsheet and example operations prepared in MATLAB

rudrajit1729/Machine-Learning-Codes-And-Templates

Codes and templates for ML algorithms created, modified and optimized in Python and R.

alicevillar/titanic-kaggle

Titanic rescue prediction using Decision Tree, SVM, Logistic Regression, Random Forest and KNN. The best accuracy score was from Random Forest: 84.35%

alicevillar/student_admission_prediction

Predicting students admission with Logistic Regression, Decision Tree, SVM (SVC) and Random Forest

arasgungore/multiclass-classification-using-ensemble-learning

Two ensemble models made from ensembles of LightGBM and CNN for a multiclass classification problem.

imanirajian/Machine-Learning

Machine learning: Practical applications

liangzihao8301/Fraud-Detection-in-Insurance-Claims

保险反欺诈预测

prakHr/NeuralNetworksAndFuzzyLogic

[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic

AryaKoureshi/EEG-Signal-Classification-using-CSP-Features

This repository contains a Python implementation for solving a two-class classification problem using CSP features extracted from EEG data. The classification task involves discriminating between mental tasks, specifically imagining foot movement and performing mental discrimination.

nathanntg/lin-train

Linear Regression Feature Selection and Trainer

RoxyDiya/BABY-SIGN-LANGUAGE-RECOGNITION

A Baby Sign Live Recognition Model

saadhaxxan/Optimizing-Hyperparameters-Using-Grid-Search

Optimizing-Hyperparameters-Using-Grid-Search-Deep-Learning

tanvirtin/mnist-ann

An Artificial Neural Network with weight decay created using python using the Numpy library which can read handwritten digits. Uses K-Folds cross validation for training the Neural Network.

efsiatras/breast-cancer-classification-evaluation

Breast cancer classification and evaluation of classifiers

Elbig-exe/Machine-Learning-Project

Artificial intelligence model that detects intrusions in a network by analyzing TCP / IP packets by machine learning algorithms

J4NN0/machine-learning-pca-svm

PCA applied on images and Naive Bayes Classifier to classify them. Validation, cross validation and grid search with multi class SVM

LiadzZ/Anime-Machine-Learning-Recommendation-System

Collaborative filtering, word Embedding dense vector representation - NeuralNetwork regression model and K-Fold Evaluation.

shubhi126/Predicting-who-wins-2008-Democratic-Primaries-Elections--Clinton-vs.-Obama

Imagine you are the front runner for democratic party primaries in 2008 - 1 week into elections you have won a few states(Obama) and your opponent (Hillary) is catching up. How you can use analytics to predict which of the remaining seats will you win using demographic data from states you won and lost. Can we accurately classify win or lose for the remaining seats using data. Can we use this prediction to find out factors which impact our chances of winning and improve our appeal to places that are predicted loss thus improving our chances of winning? Well , Let's find out!

StarlangSoftware/Sampling-Py

Data sampling library

cedoula/Austin_Driver_Score_Predictor

Used Python Scikit-Learn to analyze Austin car crash data from 2018 to 2020 and created an interactive dashboard using a Random Forest Classifier algorithm to calculate a driver score from user features.

MahalavanyaSriram/Natural-Language-Processing-with-Disaster-Tweets

Kaggle Competition - Natural Language Processing with Disaster Tweets

mrzaizai2k/COVID-19-Cough-Classification-phase-1-

This project was made for classifying Covid and non-covid patients through cough sound. This is a non-invasive, inexpensive, and easy method so we can use that as the first filter

saminens/Women-in-Data-Science-2020

WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.

sudhan-bhattarai/Machine_Learning_01Classification_scikit-learn

Comparing Logistic regression, Support vector machine, Decision tree, Random forest and Gradient Boosting models for binary classification ML problem using scikit-learn.

ugurcanerdogan/Cross-Validation-with-Imbalanced-Dataset

BBM467*SDSP - Small Data Science Project - Things to consider in cross validation and resampling when dealing with Imbalanced Data : What is the right way?

ugurcanerdogan/KNN-Classification-Regression

BBM409 Machine Learning Laboratory - Assignment 1 : KNN Classification and KNN Regression using k-Fold cross validation (OOP design for classifiers)