| IndrajeetPatil/ggstatsplot |
1,860 |
|
1 |
4 |
about 2 years ago |
40 |
August 07, 2023 |
62 |
gpl-3.0 |
R |
| Enhancing {ggplot2} plots with statistical analysis 📊📣 |
| przemek83/volbx |
227 |
|
0 |
0 |
over 2 years ago |
0 |
|
5 |
lgpl-3.0 |
C++ |
| Graphical tool for data manipulation written in C++/Qt. |
| MonashBioinformaticsPlatform/LFQ-Analyst |
27 |
|
0 |
0 |
over 2 years ago |
0 |
|
4 |
gpl-3.0 |
HTML |
| The repo for LFQ-Analyst |
| tirthajyoti/mlr |
11 |
|
1 |
0 |
over 6 years ago |
1 |
August 02, 2019 |
0 |
gpl-3.0 |
Python |
| Multiple linear regression with statistical inference, residual analysis, direct CSV loading, and other features |
| piever/PlugAndPlot.jl |
10 |
|
0 |
0 |
about 6 years ago |
0 |
|
5 |
other |
Julia |
| GUI for interactive statistical analysis in Julia |
| NikhilaThota/CapstoneProject_House_Prices_Prediction |
7 |
|
0 |
0 |
about 8 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis. |
