r-squared

There are 0 repository under r-squared topic.

LastAncientOne / GDP_Project
GDP Forcasting
excel gdp forecast forecasting data-analysis data-visualization statistics hypothesis-testing r-squared p-value
5
YenLinWu / Daily_Work_of_Data_Science
資料科學的日常研究議題
data-science python machine-learning statistics homogeneity normality-test correlation-coefficient classification-models regression-models chatterjee-correlation kendall-tau pearson-correlation spearman-rank-correlation kernel-density-estimation maape mape mae r-squared model-drift overfitting
Language:Jupyter Notebook 4
nikolas-virionis / linear-regression-model
An npm package to make it easier to deal with a handful of values, and try to model them in one of the most used mathematical models, with an R/Numpy-like accuracy algorithm
linear-regression dataset insight linear linear-models regression correlation pearson-correlation r-squared coefficient-of-determination
Language:JavaScript 3
e-paj / Machine-Learning-Projects-in-Python
This repository contains all the Machine Learning projects I did using different Machine Learning methods. Python being the main software used.
python clustering-algorithm k-means-clustering linear-regression machine-learning r-squared svm-model kaggle-dataset accuracy-score standardization visualization classification deep-learning numpy pandas cosine-similarity train-test-using-sklearn decision-tree-classifier feature-selection webscraping
Language:Jupyter Notebook 2
hamza-mughees / Line-of-Best-Fit
This project calculates the equation of the line of best fit of a given correlation
machine-learning linear-regression line-of-best-fit python numpy r-squared
Language:Python 2
caralifarrell / Beginner-Machine-Learning-in-Python-ChatGPT-Bonus-2023
An introduction into the world of machine learning with a comprehensive Udemy online course, designed for beginners, to learn Python programming fundamentals and gain valuable insights into the practical applications of machine learning.
linear-regression multiple-linear-regression ordinary-least-squares r-squared accuracy-ratio adjusted-r-squared classification clustering confusion-matrix data-science feature-scaling k-means-clustering logistic-regression machine-learning maximum-likelihood python
Language:Jupyter Notebook 1
hardikasnani / diamond-price-and-carat-prediction
I leveraged an algorithmic approach to predict the price and carat of the diamond using Machine Learning. Various regression models have been trained and their performance has been evaluated using the R Squared Score followed by tuning of the hyperparameters of top models. I have also carried out a trade-off based on the R Squared Score and the Run-Time to take a situational decision to select the best model.
cross-validation-grid-search diamonds-carat-prediction diamonds-dataset diamonds-price-prediction hyperparamter-tuning lasso-regression machine-learning multivariate-regression r-squared random-forest-regression ridge-regression run-time trade-off
Language:Jupyter Notebook 1
m-imani / Models-development
developing several models (Linear Regression, Multiple Linear Regression, and Polynomial Regression) that will predict the price of the car using the variables or features. Then evaluating these models (in-sample, and cross-validation) using R-squared and Mean-Squared-Error metrics to find out which model is a better fit for this dataset.
linear-regression polynomial-regression cross-validation multiple-linear-regression r-squared
Language:Jupyter Notebook 1
MahtabEK / Confidence-Intervals-The-Bootstrap
Exploring the confidence-Interval concept and bootstrapping.
confidence-intervals bootstrap estimation r-squared hockey-data
Language:Jupyter Notebook 1
pngo1997 / Learning-to-Rank-Algorithm
Builds a ranking model to predict the relevance score for query-product pairs in HomeDepot’s product search.
information-retrieval inverted-index learning-to-rank python text-processing feature-engineering linear-regression mse neural-network r-squared ranking-algorithm support-vector-regression xgboost point-wise tf-idf
Language:Jupyter Notebook 1
RichaSavant / Stock-Market-Analysis-A-Two-Stage-Comparative-Stacking-Approach-using-Pyspark-Jan_2024
This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.
decision-trees gradient-boosting linear-regression machine-learning mae pyspark r-squared random-forest rmse stacking-ensemble stock-market stock-market-analysis stock-market-prediction
1
SAI-SRINIVASA-SUBRAMANYAM / machine_practices_problems
adjusted-r-squared cost-function ineuron ineuron-ai linear-regression loss-function multivariate-analysis r-squared subramanyam
Language:Jupyter Notebook 1
stdlib-js / stats-incr-mpcorr2
Compute a moving squared sample Pearson product-moment correlation coefficient incrementally.
nodejs javascript stdlib node node-js statistics stats mathematics math covariance sample-covariance variance unbiased var correlation corr pcorr pearson product-moment r-squared
Language:JavaScript 1
stdlib-js / stats-incr-pcorr2
Compute a squared sample Pearson product-moment correlation coefficient.
nodejs javascript stdlib node node-js statistics stats mathematics math covariance sample-covariance variance unbiased var correlation corr pearson product-moment r-squared r2
Language:JavaScript 1
EmmaSui / Business-Analytics-Predicting-Catalog-Demand
Using multiple linear regression model to predict customer demand in order to make business decision
python matplotlib numpy sklearn pandas seaborn business-analytics multiple-linear-regression r-squared statsmodels crisp-dm
Language:Jupyter Notebook 0
jilek / MechaCar_Statistical_Analysis
Statistical analysis to predict the importance of various manufacturing parameters on fuel economy of a prototype car.
statistics r regression rstudio linear-regression t-test p-value variance r-squared matplotlib tidyverse dplyr
Language:HTML 0
Mahsatajik / Statistical-Inference
University of Tehran - Spring 2020
chisquare-test confidence-interval linear-regression p-value r-squared bar-plot boxplot corrtest ggplot histogram anova-test bootstrap auc roc sensitivity specificity
Language:R 0
NavindaFernando / Heart-Risk-Prediction-Model
Heart Risk Level Predicting Regression Model :broken_heart:
joblib linear-regression machine-learning pandas r-squared regression-models scikit-learn supervised
Language:Jupyter Notebook 0
NgeNXQ / StockX-Deadstock-Price-Predicting
Predicting annual highest of sneakers on StockX
python stockx brands data data-science data-visualization gradient-boosting-regressor lasso-regression-model linear-regression-models python3 r-squared random-forest-regressor sklearn sneakers
Language:Jupyter Notebook 0
Saxsori / ft_linear_regression
An introduction to machine learning
ai gradient-descent linear-regression machine-learning matplotlib mean-square-error normalization numpy pandas r-squared
Language:Python 0
TheDataByte / LinearRegression_Python
Hosts Python content associated with Linear Regression series on YouTube
data-science linear-regression python r-squared statistics youtube
Language:Jupyter Notebook 0
TheDataByte / LinearRegression_R
Hosts R content associated with Linear Regression series on YouTube
data-science linear-regression r-programming r-squared statistics youtube
Language:R 0
Wb-az / pyspark-mlib-soundlevel-prediction
Creates a ML Pipeline leveraging PySpark SQL and PySpark MLib to predict sound level
correlation-analysis data-engineering-pipeline data-science grid-search mean-absolute-error mean-square-error metrics multivariate-regression nasa pyspark pyspark-mllib pyspark-sql python r-squared regressor rmse sound-processing
Language:Jupyter Notebook 0
anilesh-prajapati / Probability-and-Statistics-for-Machine-Learning
Probability and Statistics for Machine Learning
cross-entropy gaussian ordinary-least-squares p-values r-squared standard-deviation standard-error uniform variables variance box-and-whisker-plot covariance-and-correlation mean-median-and-mode z-scores
Language:Jupyter Notebook
ashishyadav24092000 / Multiple-Linear-Regression
Performing multiple linear regression on a simple dataset.
multiple-linear-regression performance-metrics r-squared
Language:Jupyter Notebook
Chirag6525 / Multi-Machine-Learning-Model
The comparison of multiple Machine Learning models refers to training, evaluating, and analyzing the performance of different algorithms on the same dataset to identify which model performs best for a specific predictive task.
decison-tree-regressor gradient-boosting-regressor linear-regression machine-learning mean-square-error r-squared random-forest-regressor
Language:Jupyter Notebook
DougUOT / MechaCar_Statistical_Analysis
Learn how to use R and statistics in order to analyze vehicle data
r statistics data-analysis linear-regression t-test r-studio p-value r-squared lm
Language:R
EmamulHossen / Feature-Transformation-Assignment-6.3-
Feature transformation is a technique in machine learning that is used to modify the original features of a dataset in order to improve the performance of machine learning algorithms.
data-preprocessing linear-regression missing-values mse pandas pandas-dataframe r-squared train-test-split visualization z-score
Language:Jupyter Notebook
IotchulindraRai / MILK_sales_Production
Collect and preprocess historical sales data of Sujal Dairy Pvt. Ltd. to understand trends. Implement regression models like linear regression, decision trees, or advanced models like LSTM, based on data complexity. Validate model accuracy using metrics such as RMSE, MAE, and R-squared.
lstm machine-learning mae milk-production r-squared rmse salesprediction
Language:Jupyter Notebook
Jiya-Patel-JP / Solar-Power-Prediction
Predicting solar power generation using Linear Regression
decision-tree linear-regression machine-learning mean-absolute-error r-squared random-forest solar-energy data jupyter-notebook matplotlib numpy pandas python scikit-learn seaborn
Language:Jupyter Notebook
Jiya-Patel-JP / Wine-Quality-Prediction
Using regression and classification to train and test the model; to gain insights and create a PowerBI dashboard.
accuracy-metrics classification cross-validation f1-score gradient-boosting linear-regression machine-learning-algorithms precision prediction-model python r-squared random-forest rmse xgboost
Language:Jupyter Notebook
Kovenda / A-B-Testing-a-Cotton-Mill
To increase efficiency of a cotton mill. I set up an ANOVA 3 factor analysis model in R to determine best spindle & position that produces the longest roving. The only significant difference in roving length was observed when position was 3 and spindle was 1 or 2. (ANOVA Model in R)
a-b-testing anova-analysis anova-test data-science dplyr emmeans experimental-data-analysis experimental-design ggplot ggplot2 interaction-effects mathematical-modelling multifractal-analysis probability r-squared residuals scale-location-plot statistics
Language:R
OscarTMa / Regression-Project
Regression is a fundamental supervised machine learning technique used to predict continuous numerical outcomes based on input features.
mae mse r-squared
Language:Jupyter Notebook
rudyscav / World-Happiness-Scores
Functional specification to calculate per country's happiness score
happiness-score correlation-matrix descriptive-statistics f-statistics r-squared regression-analysis
Language:R
samir-m0hamed / Ice_Cream_Sales
Predicts ice cream sales based on temperature using Polynomial Regression. The model captures the non-linear relationship between temperature and sales, achieving R² ≈ 0.94 (train) and 0.89 (test). Includes data preprocessing, model training , Evaluation, and interactive results visualization.
polynomial-features polynomial-regression r-squared scikit-learn curve-fitting
Language:Jupyter Notebook
Sanchariii / GoldOracle
Gold Price Prediction
gold r-squared randomforestregressor
Language:Jupyter Notebook

r-squared

LastAncientOne / GDP_Project

YenLinWu / Daily_Work_of_Data_Science

nikolas-virionis / linear-regression-model

e-paj / Machine-Learning-Projects-in-Python

hamza-mughees / Line-of-Best-Fit

caralifarrell / Beginner-Machine-Learning-in-Python-ChatGPT-Bonus-2023

hardikasnani / diamond-price-and-carat-prediction

m-imani / Models-development

MahtabEK / Confidence-Intervals-The-Bootstrap

pngo1997 / Learning-to-Rank-Algorithm

RichaSavant / Stock-Market-Analysis-A-Two-Stage-Comparative-Stacking-Approach-using-Pyspark-Jan_2024

SAI-SRINIVASA-SUBRAMANYAM / machine_practices_problems

stdlib-js / stats-incr-mpcorr2

stdlib-js / stats-incr-pcorr2

EmmaSui / Business-Analytics-Predicting-Catalog-Demand

jilek / MechaCar_Statistical_Analysis

Mahsatajik / Statistical-Inference

NavindaFernando / Heart-Risk-Prediction-Model

NgeNXQ / StockX-Deadstock-Price-Predicting

Saxsori / ft_linear_regression

TheDataByte / LinearRegression_Python

TheDataByte / LinearRegression_R

Wb-az / pyspark-mlib-soundlevel-prediction

anilesh-prajapati / Probability-and-Statistics-for-Machine-Learning

ashishyadav24092000 / Multiple-Linear-Regression

Chirag6525 / Multi-Machine-Learning-Model

DougUOT / MechaCar_Statistical_Analysis

EmamulHossen / Feature-Transformation-Assignment-6.3-

IotchulindraRai / MILK_sales_Production

Jiya-Patel-JP / Solar-Power-Prediction

Jiya-Patel-JP / Wine-Quality-Prediction

Kovenda / A-B-Testing-a-Cotton-Mill

OscarTMa / Regression-Project

rudyscav / World-Happiness-Scores

samir-m0hamed / Ice_Cream_Sales

Sanchariii / GoldOracle