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kfold-cross-validation

Here are 151 public repositories matching this topic...

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 vali…

Updated Jul 28, 2020
Jupyter Notebook

LorranSutter / PredictStock-SVM

ML model for stock trend prediction using Python

python machine-learning random-forest numpy scikit-learn sklearn pandas python3 matplotlib support-vector-machine k-means kfold-cross-validation trend-prediction stock-trend-prediction

Updated Sep 15, 2020
Python

brainhack-school2020 / abide-fmri

Repository for the Brainhack School 2020 team working with fMRI and ABIDE data to train machine learning models.

plotly cross-validation reproducible-science project-management autism fmri jupyter-notebooks nilearn scikitlearn-machine-learning leave-one-out-cross-validation abide-data kfold-cross-validation resting-state-fmri preparation-script cv-classifier brainhack-school support-vector-classification group-kfold-cross-validation

Updated Jun 17, 2024
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sourcecode369 / ml-algorithms-on-scikit-and-keras

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

Updated Jul 22, 2018
Jupyter Notebook

Ashutosh27ind / PGDDS-Capstone-Project

Credit Card Fraud Detection Project

random-forest svm xgboost logistic-regression decision-trees knn smote adasyn kfold-cross-validation credit-risk

Updated Jul 19, 2020
Jupyter Notebook

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

bootstrap random-forest exploratory-data-analysis feature-selection supervised-learning adaboost feature-engineering gradient-boosting supervised-machine-learning decisiontree bagging knn-regression support-vector-regression regression-algorithms kfold-cross-validation ensemble-machine-learning

Updated Oct 26, 2019
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satishgunjal / House-Price-Prediction-Project

Contains all my data science projects.

Updated May 1, 2020
Jupyter Notebook

imanirajian / Machine-Learning

Machine learning: Practical applications

Updated Jan 7, 2022
Jupyter Notebook

nathanntg / lin-train

Linear Regression Feature Selection and Trainer

python linear-regression prediction feature-selection trainer kfold-cross-validation

Updated Jul 3, 2020
Python

ogunlao / foundations_of_ml

Machine Learning algorithms built from scratch for AMMI Machine Learning course

linear-regression naive-bayes-classifier kmeans-clustering coordinate-descent kl-divergence perceptron-learning-algorithm kfold-cross-validation gaussian-discriminant-analysis

Updated Jul 5, 2020
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cloud2010 / dl-models

Collection of RNN GAN SNN CNN in Tensorflow

deep-learning cnn gan rnn tensorflow-models snn kfold-cross-validation

Updated Jun 2, 2024
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Malayanil / Breast-Cancer-Prediction

A Python script that implements Machine Learning Algorithm to predict if a female is affected by Breast Cancer after considering a certain set of features. The credit of the Dataset goes to UCI Repository of ML.

python machine-learning cancer breast-cancer kfold-cross-validation

Updated Apr 30, 2019
Python

prakHr / NeuralNetworksAndFuzzyLogic

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

Updated Nov 20, 2022
Jupyter Notebook

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.

javascript shell api flask aws sqlalchemy dashboard pipeline random-forest scikit-learn postgresql pandas neural-networks hyperparameter-optimization html-css car-accident heroku-deployment austin kfold-cross-validation

Updated Oct 12, 2022
Python

ozlemkorpe / Machine-Learning-with-MATLAB

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

machine-learning matlab naive-bayes ensemble decision-trees support-vector-machines knearest-neighbor-algorithm kfold-cross-validation discriminant-analysis

Updated Aug 10, 2020
MATLAB

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

Kaggle Competition - Natural Language Processing with Disaster Tweets

nlp natural-language-processing tweets svm naive-bayes disaster lstm glove fasttext count-vectorizer kfold-cross-validation bert-model scattertext

Updated Jan 28, 2021
HTML

rudrajit1729 / Machine-Learning-Codes-And-Templates

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

feature-selection datascience feature-extraction thompson-sampling dimensionality-reduction ucb ann regression-models nlp-machine-learning kmeans-clustering apriori-algorithm hierarchical-clustering classification-algorithims parameter-tuning regression-algorithms xgboost-model kfold-cross-validation cnn-classification eclat-algorithm

Updated Mar 28, 2020
Python

HoHasby / Overcoming-overfitting-and-underfitting

Overcoming overfitting and underfitting

python data-science numpy sklearn pandas python3 seaborn feature-extraction regularization resampling polynomial-regression kfold-cross-validation cross-validation-score

Updated Sep 21, 2022
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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…

clustering cross-validation logistic-regression kmeans binary-analysis elections predictive-modeling explanation knearest-neighbor-algorithm classification-algorithm kfold-cross-validation

Updated Feb 28, 2018
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%

machine-learning random-forest numpy sklearn pandas seaborn titanic-kaggle logistic-regression matplotlib decision-trees kfold-cross-validation machine-learning-projects knn-algorithm support-vector-classification performance-measurements

Updated Jun 9, 2021
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