data science with R/Julia/Python

kuch to shikha

Author

kirit ved

Published

July 8, 2024

Modified

January 22, 2025

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picture of kirit ved

data science with R

R setup

[1] "executing r"
 [1] "forecast"   "quantmod"   "TTR"        "xts"        "zoo"       
 [6] "knitr"      "rmarkdown"  "Boruta"     "vip"        "outliers"  
[11] "mice"       "missForest" "lubridate"  "forcats"    "stringr"   
[16] "dplyr"      "purrr"      "readr"      "tidyr"      "tibble"    
[21] "ggplot2"    "tidyverse"  "janitor"    "pacman"    

loading dataset

sepal_length sepal_width petal_length petal_width species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 NA
4.7 3.2 1.3 0.2 setosa
NA 3.1 1.5 0.2 setosa
5.0 3.6 NA 0.2 NA
5.4 3.9 1.7 0.4 setosa
4.6 3.4 NA 0.3 setosa
5.0 3.4 1.5 0.2 NA
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 NA setosa
5.4 3.7 1.5 0.2 setosa
4.8 3.4 1.6 0.2 setosa
4.8 3.0 NA 0.1 setosa
4.3 3.0 1.1 0.1 setosa
5.8 4.0 1.2 0.2 setosa
5.7 4.4 1.5 0.4 setosa
5.4 3.9 1.3 0.4 setosa
5.1 3.5 1.4 0.3 setosa
5.7 NA 1.7 0.3 setosa
5.1 3.8 1.5 0.3 setosa
5.4 3.4 1.7 0.2 setosa
5.1 3.7 1.5 0.4 setosa
4.6 3.6 1.0 0.2 NA
5.1 3.3 1.7 0.5 setosa
4.8 3.4 1.9 0.2 setosa
5.0 3.0 NA 0.2 setosa
5.0 3.4 1.6 0.4 NA
5.2 3.5 1.5 0.2 setosa
5.2 3.4 1.4 NA NA
4.7 3.2 1.6 0.2 setosa
4.8 3.1 1.6 0.2 setosa
5.4 NA 1.5 0.4 setosa
5.2 4.1 1.5 0.1 setosa
5.5 NA 1.4 0.2 NA
4.9 3.1 1.5 NA setosa
5.0 3.2 1.2 0.2 setosa
5.5 3.5 1.3 0.2 setosa
4.9 3.6 1.4 0.1 setosa
4.4 3.0 1.3 0.2 setosa
5.1 3.4 1.5 0.2 setosa
NA 3.5 1.3 0.3 setosa
4.5 2.3 1.3 0.3 setosa
4.4 3.2 NA 0.2 NA
5.0 3.5 1.6 0.6 setosa
5.1 NA 1.9 NA NA
4.8 3.0 1.4 0.3 setosa
5.1 3.8 1.6 0.2 setosa
4.6 3.2 1.4 0.2 setosa
5.3 3.7 1.5 0.2 setosa
5.0 3.3 1.4 0.2 setosa
7.0 3.2 4.7 1.4 versicolor
6.4 3.2 4.5 1.5 versicolor
6.9 3.1 4.9 1.5 versicolor
5.5 2.3 4.0 1.3 versicolor
6.5 2.8 4.6 1.5 versicolor
5.7 2.8 4.5 1.3 versicolor
6.3 3.3 4.7 1.6 versicolor
4.9 NA NA NA versicolor
6.6 2.9 4.6 1.3 versicolor
5.2 2.7 3.9 NA versicolor
5.0 2.0 3.5 NA NA
5.9 NA 4.2 1.5 versicolor
6.0 2.2 4.0 1.0 versicolor
6.1 2.9 4.7 1.4 versicolor
5.6 2.9 NA 1.3 NA
6.7 3.1 4.4 1.4 versicolor
5.6 3.0 4.5 1.5 NA
5.8 NA 4.1 1.0 versicolor
6.2 2.2 4.5 1.5 versicolor
5.6 2.5 3.9 1.1 versicolor
NA NA 4.8 1.8 versicolor
6.1 2.8 4.0 NA versicolor
6.3 2.5 4.9 1.5 versicolor
6.1 2.8 4.7 1.2 versicolor
6.4 2.9 4.3 1.3 versicolor
6.6 3.0 4.4 1.4 versicolor
6.8 2.8 4.8 1.4 versicolor
6.7 3.0 5.0 1.7 versicolor
NA 2.9 NA 1.5 versicolor
5.7 2.6 3.5 1.0 versicolor
5.5 2.4 3.8 1.1 versicolor
5.5 2.4 NA 1.0 versicolor
5.8 2.7 3.9 1.2 versicolor
6.0 NA 5.1 NA versicolor
5.4 3.0 4.5 1.5 versicolor
6.0 3.4 4.5 1.6 versicolor
6.7 3.1 4.7 1.5 NA
6.3 2.3 4.4 1.3 versicolor
5.6 3.0 4.1 1.3 versicolor
5.5 2.5 4.0 1.3 versicolor
5.5 2.6 4.4 1.2 versicolor
6.1 3.0 4.6 1.4 versicolor
5.8 2.6 4.0 1.2 versicolor
5.0 2.3 3.3 1.0 versicolor
5.6 2.7 4.2 1.3 versicolor
5.7 3.0 4.2 1.2 NA
5.7 2.9 4.2 1.3 versicolor
NA NA 4.3 1.3 versicolor
5.1 2.5 3.0 1.1 versicolor
5.7 2.8 NA 1.3 versicolor
NA 3.3 6.0 2.5 virginica
5.8 2.7 5.1 NA virginica
NA 3.0 5.9 2.1 virginica
6.3 2.9 5.6 1.8 virginica
6.5 3.0 5.8 2.2 virginica
7.6 3.0 6.6 2.1 virginica
4.9 2.5 4.5 1.7 virginica
NA NA 6.3 1.8 virginica
6.7 2.5 5.8 1.8 virginica
7.2 3.6 6.1 2.5 virginica
6.5 3.2 5.1 NA NA
6.4 2.7 5.3 1.9 virginica
6.8 3.0 5.5 2.1 virginica
5.7 2.5 5.0 2.0 virginica
5.8 2.8 5.1 2.4 virginica
6.4 3.2 5.3 2.3 virginica
6.5 3.0 5.5 1.8 virginica
7.7 3.8 6.7 2.2 virginica
7.7 2.6 6.9 NA virginica
6.0 NA 5.0 1.5 NA
6.9 3.2 5.7 2.3 virginica
5.6 2.8 4.9 2.0 virginica
NA 2.8 6.7 NA virginica
6.3 2.7 NA NA virginica
6.7 3.3 5.7 2.1 virginica
7.2 NA 6.0 1.8 NA
6.2 2.8 4.8 1.8 virginica
6.1 3.0 4.9 NA virginica
6.4 2.8 5.6 2.1 virginica
7.2 3.0 5.8 1.6 virginica
NA 2.8 6.1 1.9 virginica
7.9 3.8 6.4 2.0 virginica
6.4 2.8 5.6 2.2 virginica
6.3 NA NA 1.5 virginica
6.1 2.6 5.6 1.4 virginica
NA 3.0 6.1 2.3 virginica
6.3 3.4 5.6 2.4 virginica
6.4 3.1 5.5 1.8 virginica
6.0 3.0 4.8 1.8 virginica
6.9 3.1 5.4 2.1 NA
6.7 3.1 5.6 2.4 NA
6.9 3.1 5.1 2.3 virginica
5.8 2.7 5.1 1.9 virginica
6.8 3.2 5.9 2.3 virginica
NA 3.3 5.7 NA virginica
6.7 3.0 5.2 2.3 virginica
6.3 NA 5.0 1.9 virginica
6.5 NA 5.2 2.0 virginica
6.2 3.4 5.4 2.3 virginica
5.9 3.0 5.1 1.8 virginica
'data.frame':   150 obs. of  5 variables:
 $ sepal_length: num  5.1 4.9 4.7 NA 5 5.4 4.6 5 4.4 4.9 ...
 $ sepal_width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ petal_length: num  1.4 1.4 1.3 1.5 NA 1.7 NA 1.5 1.4 1.5 ...
 $ petal_width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 NA ...
 $ species     : Factor w/ 3 levels "setosa","versicolor",..: 1 NA 1 1 NA 1 1 NA 1 1 ...
sepal_length  sepal_width petal_length  petal_width      species 
          12           16           12           16           19 
  [1] 0 1 0 1 2 0 1 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 2 0 0 1 0 2 1 0 0
 [38] 0 0 0 1 0 2 0 3 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 2 1 0 0 2 0 1 1 0 0 2 1 0 0
 [75] 0 0 0 0 2 0 0 1 0 2 0 0 1 0 0 0 0 0 0 0 0 1 0 2 0 1 1 1 1 0 0 0 0 2 0 0 2
[112] 0 0 0 0 0 0 0 1 2 0 0 2 2 0 2 0 1 0 0 1 0 0 2 0 1 0 0 0 1 1 0 0 0 2 0 1 1
[149] 0 0

forecasting with r

The following package(s) will be installed:
- quantmod [0.4.26]
These packages will be installed into "C:/jr29102024/renv/library/windows/R-4.4/x86_64-w64-mingw32".

# Installing packages --------------------------------------------------------
- Installing quantmod ...                       OK [linked from cache]
Successfully installed 1 package in 18 milliseconds.
The following package(s) will be installed:
- forecast [8.23.0]
These packages will be installed into "C:/jr29102024/renv/library/windows/R-4.4/x86_64-w64-mingw32".

# Installing packages --------------------------------------------------------
- Installing forecast ...                       OK [linked from cache]
Successfully installed 1 package in 21 milliseconds.
[1] "BAJAJHIND.NS"

removing missing values using mice package


 iter imp variable
  1   1  sepal_length  sepal_width  petal_length  petal_width  species
  1   2  sepal_length  sepal_width  petal_length  petal_width  species
  1   3  sepal_length  sepal_width  petal_length  petal_width  species
  1   4  sepal_length  sepal_width  petal_length  petal_width  species
  1   5  sepal_length  sepal_width  petal_length  petal_width  species
  2   1  sepal_length  sepal_width  petal_length  petal_width  species
  2   2  sepal_length  sepal_width  petal_length  petal_width  species
  2   3  sepal_length  sepal_width  petal_length  petal_width  species
  2   4  sepal_length  sepal_width  petal_length  petal_width  species
  2   5  sepal_length  sepal_width  petal_length  petal_width  species
  3   1  sepal_length  sepal_width  petal_length  petal_width  species
  3   2  sepal_length  sepal_width  petal_length  petal_width  species
  3   3  sepal_length  sepal_width  petal_length  petal_width  species
  3   4  sepal_length  sepal_width  petal_length  petal_width  species
  3   5  sepal_length  sepal_width  petal_length  petal_width  species
  4   1  sepal_length  sepal_width  petal_length  petal_width  species
  4   2  sepal_length  sepal_width  petal_length  petal_width  species
  4   3  sepal_length  sepal_width  petal_length  petal_width  species
  4   4  sepal_length  sepal_width  petal_length  petal_width  species
  4   5  sepal_length  sepal_width  petal_length  petal_width  species
  5   1  sepal_length  sepal_width  petal_length  petal_width  species
  5   2  sepal_length  sepal_width  petal_length  petal_width  species
  5   3  sepal_length  sepal_width  petal_length  petal_width  species
  5   4  sepal_length  sepal_width  petal_length  petal_width  species
  5   5  sepal_length  sepal_width  petal_length  petal_width  species
sepal_length sepal_width petal_length petal_width species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
5.0 3.1 1.5 0.2 setosa
5.0 3.6 1.9 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5.0 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 0.2 setosa
5.4 3.7 1.5 0.2 setosa
4.8 3.4 1.6 0.2 setosa
4.8 3.0 1.6 0.1 setosa
4.3 3.0 1.1 0.1 setosa
5.8 4.0 1.2 0.2 setosa
5.7 4.4 1.5 0.4 setosa
5.4 3.9 1.3 0.4 setosa
5.1 3.5 1.4 0.3 setosa
5.7 4.0 1.7 0.3 setosa
5.1 3.8 1.5 0.3 setosa
5.4 3.4 1.7 0.2 setosa
5.1 3.7 1.5 0.4 setosa
4.6 3.6 1.0 0.2 setosa
5.1 3.3 1.7 0.5 setosa
4.8 3.4 1.9 0.2 setosa
5.0 3.0 1.4 0.2 setosa
5.0 3.4 1.6 0.4 setosa
5.2 3.5 1.5 0.2 setosa
5.2 3.4 1.4 0.6 setosa
4.7 3.2 1.6 0.2 setosa
4.8 3.1 1.6 0.2 setosa
5.4 3.9 1.5 0.4 setosa
5.2 4.1 1.5 0.1 setosa
5.5 3.9 1.4 0.2 setosa
4.9 3.1 1.5 0.2 setosa
5.0 3.2 1.2 0.2 setosa
5.5 3.5 1.3 0.2 setosa
4.9 3.6 1.4 0.1 setosa
4.4 3.0 1.3 0.2 setosa
5.1 3.4 1.5 0.2 setosa
5.0 3.5 1.3 0.3 setosa
4.5 2.3 1.3 0.3 setosa
4.4 3.2 1.4 0.2 setosa
5.0 3.5 1.6 0.6 setosa
5.1 2.6 1.9 0.1 setosa
4.8 3.0 1.4 0.3 setosa
5.1 3.8 1.6 0.2 setosa
4.6 3.2 1.4 0.2 setosa
5.3 3.7 1.5 0.2 setosa
5.0 3.3 1.4 0.2 setosa
7.0 3.2 4.7 1.4 versicolor
6.4 3.2 4.5 1.5 versicolor
6.9 3.1 4.9 1.5 versicolor
5.5 2.3 4.0 1.3 versicolor
6.5 2.8 4.6 1.5 versicolor
5.7 2.8 4.5 1.3 versicolor
6.3 3.3 4.7 1.6 versicolor
4.9 2.3 3.3 1.0 versicolor
6.6 2.9 4.6 1.3 versicolor
5.2 2.7 3.9 1.0 versicolor
5.0 2.0 3.5 1.0 versicolor
5.9 3.0 4.2 1.5 versicolor
6.0 2.2 4.0 1.0 versicolor
6.1 2.9 4.7 1.4 versicolor
5.6 2.9 4.2 1.3 versicolor
6.7 3.1 4.4 1.4 versicolor
5.6 3.0 4.5 1.5 versicolor
5.8 2.6 4.1 1.0 versicolor
6.2 2.2 4.5 1.5 versicolor
5.6 2.5 3.9 1.1 versicolor
6.8 3.1 4.8 1.8 versicolor
6.1 2.8 4.0 1.5 versicolor
6.3 2.5 4.9 1.5 versicolor
6.1 2.8 4.7 1.2 versicolor
6.4 2.9 4.3 1.3 versicolor
6.6 3.0 4.4 1.4 versicolor
6.8 2.8 4.8 1.4 versicolor
6.7 3.0 5.0 1.7 versicolor
5.2 2.9 3.5 1.5 versicolor
5.7 2.6 3.5 1.0 versicolor
5.5 2.4 3.8 1.1 versicolor
5.5 2.4 3.8 1.0 versicolor
5.8 2.7 3.9 1.2 versicolor
6.0 2.8 5.1 1.4 versicolor
5.4 3.0 4.5 1.5 versicolor
6.0 3.4 4.5 1.6 versicolor
6.7 3.1 4.7 1.5 versicolor
6.3 2.3 4.4 1.3 versicolor
5.6 3.0 4.1 1.3 versicolor
5.5 2.5 4.0 1.3 versicolor
5.5 2.6 4.4 1.2 versicolor
6.1 3.0 4.6 1.4 versicolor
5.8 2.6 4.0 1.2 versicolor
5.0 2.3 3.3 1.0 versicolor
5.6 2.7 4.2 1.3 versicolor
5.7 3.0 4.2 1.2 versicolor
5.7 2.9 4.2 1.3 versicolor
5.8 2.5 4.3 1.3 versicolor
5.1 2.5 3.0 1.1 versicolor
5.7 2.8 4.0 1.3 versicolor
7.2 3.3 6.0 2.5 virginica
5.8 2.7 5.1 2.0 virginica
6.9 3.0 5.9 2.1 virginica
6.3 2.9 5.6 1.8 virginica
6.5 3.0 5.8 2.2 virginica
7.6 3.0 6.6 2.1 virginica
4.9 2.5 4.5 1.7 virginica
7.6 3.0 6.3 1.8 virginica
6.7 2.5 5.8 1.8 virginica
7.2 3.6 6.1 2.5 virginica
6.5 3.2 5.1 1.8 virginica
6.4 2.7 5.3 1.9 virginica
6.8 3.0 5.5 2.1 virginica
5.7 2.5 5.0 2.0 virginica
5.8 2.8 5.1 2.4 virginica
6.4 3.2 5.3 2.3 virginica
6.5 3.0 5.5 1.8 virginica
7.7 3.8 6.7 2.2 virginica
7.7 2.6 6.9 2.3 virginica
6.0 2.6 5.0 1.5 virginica
6.9 3.2 5.7 2.3 virginica
5.6 2.8 4.9 2.0 virginica
7.2 2.8 6.7 2.3 virginica
6.3 2.7 5.0 1.5 virginica
6.7 3.3 5.7 2.1 virginica
7.2 3.1 6.0 1.8 virginica
6.2 2.8 4.8 1.8 virginica
6.1 3.0 4.9 1.9 virginica
6.4 2.8 5.6 2.1 virginica
7.2 3.0 5.8 1.6 virginica
6.7 2.8 6.1 1.9 virginica
7.9 3.8 6.4 2.0 virginica
6.4 2.8 5.6 2.2 virginica
6.3 2.7 5.0 1.5 virginica
6.1 2.6 5.6 1.4 virginica
6.7 3.0 6.1 2.3 virginica
6.3 3.4 5.6 2.4 virginica
6.4 3.1 5.5 1.8 virginica
6.0 3.0 4.8 1.8 virginica
6.9 3.1 5.4 2.1 virginica
6.7 3.1 5.6 2.4 virginica
6.9 3.1 5.1 2.3 virginica
5.8 2.7 5.1 1.9 virginica
6.8 3.2 5.9 2.3 virginica
6.9 3.3 5.7 2.5 virginica
6.7 3.0 5.2 2.3 virginica
6.3 3.0 5.0 1.9 virginica
6.5 3.1 5.2 2.0 virginica
6.2 3.4 5.4 2.3 virginica
5.9 3.0 5.1 1.8 virginica
sepal_length  sepal_width petal_length  petal_width      species 
           0            0            0            0            0 
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0
species cnt
setosa 50
versicolor 50
virginica 50

removing outliers

  sepal_length    sepal_width     petal_length    petal_width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.300   Median :1.300  
 Mean   :5.837   Mean   :3.054   Mean   :3.759   Mean   :1.198  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       species  
 setosa    :50  
 versicolor:50  
 virginica :50  
                
                
                
       sepal_length sepal_width petal_length petal_width
  [1,]          5.1         3.5          1.4         0.2
  [2,]          4.9         3.0          1.4         0.2
  [3,]          4.7         3.2          1.3         0.2
  [4,]          5.0         3.1          1.5         0.2
  [5,]          5.0         3.6          1.9         0.2
  [6,]          5.4         3.9          1.7         0.4
  [7,]          4.6         3.4          1.4         0.3
  [8,]          5.0         3.4          1.5         0.2
  [9,]          4.4         2.9          1.4         0.2
 [10,]          4.9         3.1          1.5         0.2
 [11,]          5.4         3.7          1.5         0.2
 [12,]          4.8         3.4          1.6         0.2
 [13,]          4.8         3.0          1.6         0.1
 [14,]          4.3         3.0          1.1         0.1
 [15,]          5.8         4.0          1.2         0.2
 [16,]          5.7         2.8          1.5         0.4
 [17,]          5.4         3.9          1.3         0.4
 [18,]          5.1         3.5          1.4         0.3
 [19,]          5.7         4.0          1.7         0.3
 [20,]          5.1         3.8          1.5         0.3
 [21,]          5.4         3.4          1.7         0.2
 [22,]          5.1         3.7          1.5         0.4
 [23,]          4.6         3.6          1.0         0.2
 [24,]          5.1         3.3          1.7         0.5
 [25,]          4.8         3.4          1.9         0.2
 [26,]          5.0         3.0          1.4         0.2
 [27,]          5.0         3.4          1.6         0.4
 [28,]          5.2         3.5          1.5         0.2
 [29,]          5.2         3.4          1.4         0.6
 [30,]          4.7         3.2          1.6         0.2
 [31,]          4.8         3.1          1.6         0.2
 [32,]          5.4         3.9          1.5         0.4
 [33,]          5.2         2.8          1.5         0.1
 [34,]          5.5         3.9          1.4         0.2
 [35,]          4.9         3.1          1.5         0.2
 [36,]          5.0         3.2          1.2         0.2
 [37,]          5.5         3.5          1.3         0.2
 [38,]          4.9         3.6          1.4         0.1
 [39,]          4.4         3.0          1.3         0.2
 [40,]          5.1         3.4          1.5         0.2
 [41,]          5.0         3.5          1.3         0.3
 [42,]          4.5         2.3          1.3         0.3
 [43,]          4.4         3.2          1.4         0.2
 [44,]          5.0         3.5          1.6         0.6
 [45,]          5.1         2.6          1.9         0.1
 [46,]          4.8         3.0          1.4         0.3
 [47,]          5.1         3.8          1.6         0.2
 [48,]          4.6         3.2          1.4         0.2
 [49,]          5.3         3.7          1.5         0.2
 [50,]          5.0         3.3          1.4         0.2
 [51,]          7.0         3.2          4.7         1.4
 [52,]          6.4         3.2          4.5         1.5
 [53,]          6.9         3.1          4.9         1.5
 [54,]          5.5         2.3          4.0         1.3
 [55,]          6.5         2.8          4.6         1.5
 [56,]          5.7         2.8          4.5         1.3
 [57,]          6.3         3.3          4.7         1.6
 [58,]          4.9         2.3          3.3         1.0
 [59,]          6.6         2.9          4.6         1.3
 [60,]          5.2         2.7          3.9         1.0
 [61,]          5.0         2.8          3.5         1.0
 [62,]          5.9         3.0          4.2         1.5
 [63,]          6.0         2.2          4.0         1.0
 [64,]          6.1         2.9          4.7         1.4
 [65,]          5.6         2.9          4.2         1.3
 [66,]          6.7         3.1          4.4         1.4
 [67,]          5.6         3.0          4.5         1.5
 [68,]          5.8         2.6          4.1         1.0
 [69,]          6.2         2.2          4.5         1.5
 [70,]          5.6         2.5          3.9         1.1
 [71,]          6.8         3.1          4.8         1.8
 [72,]          6.1         2.8          4.0         1.5
 [73,]          6.3         2.5          4.9         1.5
 [74,]          6.1         2.8          4.7         1.2
 [75,]          6.4         2.9          4.3         1.3
 [76,]          6.6         3.0          4.4         1.4
 [77,]          6.8         2.8          4.8         1.4
 [78,]          6.7         3.0          5.0         1.7
 [79,]          5.2         2.9          3.5         1.5
 [80,]          5.7         2.6          3.5         1.0
 [81,]          5.5         2.4          3.8         1.1
 [82,]          5.5         2.4          3.8         1.0
 [83,]          5.8         2.7          3.9         1.2
 [84,]          6.0         2.8          5.1         1.4
 [85,]          5.4         3.0          4.5         1.5
 [86,]          6.0         3.4          4.5         1.6
 [87,]          6.7         3.1          4.7         1.5
 [88,]          6.3         2.3          4.4         1.3
 [89,]          5.6         3.0          4.1         1.3
 [90,]          5.5         2.5          4.0         1.3
 [91,]          5.5         2.6          4.4         1.2
 [92,]          6.1         3.0          4.6         1.4
 [93,]          5.8         2.6          4.0         1.2
 [94,]          5.0         2.3          3.3         1.0
 [95,]          5.6         2.7          4.2         1.3
 [96,]          5.7         3.0          4.2         1.2
 [97,]          5.7         2.9          4.2         1.3
 [98,]          5.8         2.5          4.3         1.3
 [99,]          5.1         2.5          3.0         1.1
[100,]          5.7         2.8          4.0         1.3
[101,]          7.2         3.3          6.0         2.5
[102,]          5.8         2.7          5.1         2.0
[103,]          6.9         3.0          5.9         2.1
[104,]          6.3         2.9          5.6         1.8
[105,]          6.5         3.0          5.8         2.2
[106,]          7.6         3.0          6.6         2.1
[107,]          4.9         2.5          4.5         1.7
[108,]          7.6         3.0          6.3         1.8
[109,]          6.7         2.5          5.8         1.8
[110,]          7.2         3.6          6.1         2.5
[111,]          6.5         3.2          5.1         1.8
[112,]          6.4         2.7          5.3         1.9
[113,]          6.8         3.0          5.5         2.1
[114,]          5.7         2.5          5.0         2.0
[115,]          5.8         2.8          5.1         2.4
[116,]          6.4         3.2          5.3         2.3
[117,]          6.5         3.0          5.5         1.8
[118,]          7.7         3.8          6.7         2.2
[119,]          7.7         2.6          6.9         2.3
[120,]          6.0         2.6          5.0         1.5
[121,]          6.9         3.2          5.7         2.3
[122,]          5.6         2.8          4.9         2.0
[123,]          7.2         2.8          6.7         2.3
[124,]          6.3         2.7          5.0         1.5
[125,]          6.7         3.3          5.7         2.1
[126,]          7.2         3.1          6.0         1.8
[127,]          6.2         2.8          4.8         1.8
[128,]          6.1         3.0          4.9         1.9
[129,]          6.4         2.8          5.6         2.1
[130,]          7.2         3.0          5.8         1.6
[131,]          6.7         2.8          6.1         1.9
[132,]          7.9         3.8          6.4         2.0
[133,]          6.4         2.8          5.6         2.2
[134,]          6.3         2.7          5.0         1.5
[135,]          6.1         2.6          5.6         1.4
[136,]          6.7         3.0          6.1         2.3
[137,]          6.3         3.4          5.6         2.4
[138,]          6.4         3.1          5.5         1.8
[139,]          6.0         3.0          4.8         1.8
[140,]          6.9         3.1          5.4         2.1
[141,]          6.7         3.1          5.6         2.4
[142,]          6.9         3.1          5.1         2.3
[143,]          5.8         2.7          5.1         1.9
[144,]          6.8         3.2          5.9         2.3
[145,]          6.9         3.3          5.7         2.5
[146,]          6.7         3.0          5.2         2.3
[147,]          6.3         3.0          5.0         1.9
[148,]          6.5         3.1          5.2         2.0
[149,]          6.2         3.4          5.4         2.3
[150,]          5.9         3.0          5.1         1.8

variable importance

Boruta performed 9 iterations in 0.30634 secs.
 4 attributes confirmed important: petal_length, petal_width,
sepal_length, sepal_width;
 No attributes deemed unimportant.
              Length Class    Mode     
finalDecision  4     factor   numeric  
ImpHistory    63     -none-   numeric  
pValue         1     -none-   numeric  
maxRuns        1     -none-   numeric  
light          1     -none-   logical  
mcAdj          1     -none-   logical  
timeTaken      1     difftime numeric  
roughfixed     1     -none-   logical  
call           3     -none-   call     
impSource      1     -none-   character
meanImp medianImp minImp maxImp normHits decision
petal_length 31.95207 32.07526 30.31581 33.17381 1 Confirmed
petal_width 30.64759 30.12608 29.29565 33.41465 1 Confirmed
sepal_length 15.34600 15.05829 14.44868 16.39415 1 Confirmed
sepal_width 10.98677 10.65324 10.31884 12.53375 1 Confirmed

Linear regression using R

x y
1 8.366832
2 12.746207
3 17.494133
4 20.141324
5 24.574645
6 23.964041
7 29.850902
8 32.902596
9 35.351320
10 35.838560
          x         y
x 1.0000000 0.9878864
y 0.9878864 1.0000000

Call:
lm(formula = y ~ x, data = d)

Residuals:
   Min     1Q Median     3Q    Max 
-2.222 -1.415  0.526  1.071  2.000 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)    7.088      1.067   6.641 0.000162 ***
x              3.097      0.172  18.006 9.28e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.562 on 8 degrees of freedom
Multiple R-squared:  0.9759,    Adjusted R-squared:  0.9729 
F-statistic: 324.2 on 1 and 8 DF,  p-value: 9.284e-08
List of 12
 $ coefficients : Named num [1:2] 7.09 3.1
  ..- attr(*, "names")= chr [1:2] "(Intercept)" "x"
 $ residuals    : Named num [1:10] -1.819 -0.537 1.114 0.664 2 ...
  ..- attr(*, "names")= chr [1:10] "1" "2" "3" "4" ...
 $ effects      : Named num [1:10] -76.28 28.13 1.56 1.1 2.43 ...
  ..- attr(*, "names")= chr [1:10] "(Intercept)" "x" "" "" ...
 $ rank         : int 2
 $ fitted.values: Named num [1:10] 10.2 13.3 16.4 19.5 22.6 ...
  ..- attr(*, "names")= chr [1:10] "1" "2" "3" "4" ...
 $ assign       : int [1:2] 0 1
 $ qr           :List of 5
  ..$ qr   : num [1:10, 1:2] -3.162 0.316 0.316 0.316 0.316 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:10] "1" "2" "3" "4" ...
  .. .. ..$ : chr [1:2] "(Intercept)" "x"
  .. ..- attr(*, "assign")= int [1:2] 0 1
  ..$ qraux: num [1:2] 1.32 1.27
  ..$ pivot: int [1:2] 1 2
  ..$ tol  : num 1e-07
  ..$ rank : int 2
  ..- attr(*, "class")= chr "qr"
 $ df.residual  : int 8
 $ xlevels      : Named list()
 $ call         : language lm(formula = y ~ x, data = d)
 $ terms        :Classes 'terms', 'formula'  language y ~ x
  .. ..- attr(*, "variables")= language list(y, x)
  .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:2] "y" "x"
  .. .. .. ..$ : chr "x"
  .. ..- attr(*, "term.labels")= chr "x"
  .. ..- attr(*, "order")= int 1
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. ..- attr(*, "predvars")= language list(y, x)
  .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. ..- attr(*, "names")= chr [1:2] "y" "x"
 $ model        :'data.frame':  10 obs. of  2 variables:
  ..$ y: num [1:10] 8.37 12.75 17.49 20.14 24.57 ...
  ..$ x: int [1:10] 1 2 3 4 5 6 7 8 9 10
  ..- attr(*, "terms")=Classes 'terms', 'formula'  language y ~ x
  .. .. ..- attr(*, "variables")= language list(y, x)
  .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:2] "y" "x"
  .. .. .. .. ..$ : chr "x"
  .. .. ..- attr(*, "term.labels")= chr "x"
  .. .. ..- attr(*, "order")= int 1
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= int 1
  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  .. .. ..- attr(*, "predvars")= language list(y, x)
  .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "numeric"
  .. .. .. ..- attr(*, "names")= chr [1:2] "y" "x"
 - attr(*, "class")= chr "lm"
x
5.5
9.3
x py
5.5 24.12306
9.3 35.89250

histogram with normal density curve in R

data science with Julia

Julia setup

load_pkgs (generic function with 1 method)
my_i_pkgsf (generic function with 1 method)
All packages loaded successfully.
All packages loaded successfully.
All packages loaded successfully.
All packages loaded successfully.
glfw initialised

sample project

masti with Julia

2-element Vector{String}:
 "Random"
 "Plots"
All packages loaded successfully.
-5:1:5
my (generic function with 1 method)
Plots.GRBackend()

working with dataframes

TaskLocalRNG()
6×2 DataFrame
 Row │ popat1     popat2
     │ Float64    Float64
─────┼─────────────────────
   1 │ 0.0491718  0.944318
   2 │ 0.119079   0.46105
   3 │ 0.393271   0.830334
   4 │ 0.0240943  0.573132
   5 │ 0.691857   0.176625
   6 │ 0.767518   0.114935
2×7 DataFrame
 Row │ variable  mean      min        median    max       nmissing  eltype
     │ Symbol    Float64   Float64    Float64   Float64   Int64     DataType
─────┼───────────────────────────────────────────────────────────────────────
   1 │ popat1    0.445195  0.0240943  0.527348  0.855718         0  Float64
   2 │ popat2    0.524112  0.114935   0.517091  0.944318         0  Float64
[0.0491718221481211, 0.11907881640750706, 0.3932710232252806, 0.024094310524527707, 0.6918572875342215, 0.7675180540873912, 0.08725304891274233, 0.8557176841095734, 0.8025607099234905, 0.661425351684768]
[0.0491718221481211, 0.11907881640750706, 0.3932710232252806, 0.024094310524527707, 0.6918572875342215, 0.7675180540873912, 0.08725304891274233, 0.8557176841095734, 0.8025607099234905, 0.661425351684768]
["popat1", "popat2"]
10×2 DataFrame
 Row │ popat1     popat2
     │ Float64    Float64
─────┼─────────────────────
   1 │ 0.0491718  0.944318
   2 │ 0.119079   0.46105
   3 │ 0.393271   0.830334
   4 │ 0.0240943  0.573132
   5 │ 0.691857   0.176625
   6 │ 0.767518   0.114935
   7 │ 0.087253   0.7864
   8 │ 0.855718   0.892598
   9 │ 0.802561   0.207253
  10 │ 0.661425   0.254472
10×2 DataFrame
 Row │ popat1     popat2
     │ Float64    Float64
─────┼─────────────────────
   1 │ 0.0491718  0.944318
   2 │ 0.119079   0.46105
   3 │ 0.393271   0.830334
   4 │ 0.0240943  0.573132
   5 │ 0.691857   0.176625
   6 │ 0.767518   0.114935
   7 │ 0.087253   0.7864
   8 │ 0.855718   0.892598
   9 │ 0.802561   0.207253
  10 │ 0.661425   0.254472
"dtmp10215.csv"
true
The file dtmp10215.csv exists.

solving small linear equation

[1 1; 1 -1]
[10, 5]
2-element Vector{Float64}:
 7.5
 2.5
[7.5, 2.5]

solving large linear equation

1-element Vector{String}:
 "LinearAlgebra"
All packages loaded successfully.
4.740436406477113
-2.7048316242181025
8.84445031591571
-12.18760403297759
-10.760104744507274
-0.6370867741423903
2.92433835274419
3.763709603261683
3.188440276898675
18.76174192711995
10×1 Matrix{Float64}:
  2.220446049250313e-16
 -1.6653345369377348e-15
  3.4416913763379853e-15
 -1.1102230246251565e-16
 -1.3877787807814457e-16
 -9.992007221626409e-16
  1.4432899320127035e-15
 -2.220446049250313e-15
  2.2620794126737565e-15
 -6.661338147750939e-16
2.220446049250313e-16
-1.6653345369377348e-15
3.4416913763379853e-15
-1.1102230246251565e-16
-1.3877787807814457e-16
-9.992007221626409e-16
1.4432899320127035e-15
-2.220446049250313e-15
2.2620794126737565e-15
-6.661338147750939e-16

histogram with frequency & density

3-element Vector{String}:
 "Distributions"
 "Plots"
 "KernelDensity"
All packages loaded successfully.
Normal{Float64}(μ=100.0, σ=10.0)
1000-element Vector{Float64}:
 107.14239454177303
 112.15416632163475
 122.50508395669189
  89.69436513203053
  87.6361252843894
  91.1750826426113
 110.72775710668704
  90.05507424672686
  94.24706740996659
 107.40201249783048
   ⋮
 103.02805925456413
 124.58899439180635
  94.98397866596254
  80.01139491277324
  82.92420298648162
 101.55509941876329
  91.3235805424571
  95.6705118100269
 103.08985851332379

Plots.Series(RecipesPipeline.DefaultsDict(:plot_object => Plot{Plots.GRBackend() n=1}, :subplot => Subplot{1}, :markershape => :none, :label => "y1", :fillalpha => nothing, :orientation => :vertical, :linealpha => nothing, :x_extrema => (NaN, NaN), :arrow => nothing, :series_index => 1…))
5.0

Linear Regression

4-element Vector{String}:
 "Random"
 "DataFrames"
 "CSV"
 "GLM"
TaskLocalRNG()
10
1:10
my (generic function with 2 methods)
10-element Vector{Float64}:
  8.366831772346464
 12.746207447785931
 17.49413341845734
 20.14132370171251
 24.574645018314158
 23.964040581229376
 29.85090173942833
 32.902596318375934
 35.35131979172247
 35.838560532354634
[8.366831772346464, 12.746207447785931, 17.49413341845734, 20.14132370171251, 24.574645018314158, 23.964040581229376, 29.85090173942833, 32.902596318375934, 35.35131979172247, 35.838560532354634]

"C:\\jr29102024\\tmp.png"
10×2 Matrix{Float64}:
  1.0   8.36683
  2.0  12.7462
  3.0  17.4941
  4.0  20.1413
  5.0  24.5746
  6.0  23.964
  7.0  29.8509
  8.0  32.9026
  9.0  35.3513
 10.0  35.8386
10×2 DataFrame
 Row │ x        y
     │ Float64  Float64
─────┼───────────────────
   1 │     1.0   8.36683
   2 │     2.0  12.7462
   3 │     3.0  17.4941
   4 │     4.0  20.1413
   5 │     5.0  24.5746
   6 │     6.0  23.964
   7 │     7.0  29.8509
   8 │     8.0  32.9026
   9 │     9.0  35.3513
  10 │    10.0  35.8386
10×2 DataFrame
 Row │ x        y
     │ Float64  Float64
─────┼───────────────────
   1 │     1.0   8.36683
   2 │     2.0  12.7462
   3 │     3.0  17.4941
   4 │     4.0  20.1413
   5 │     5.0  24.5746
   6 │     6.0  23.964
   7 │     7.0  29.8509
   8 │     8.0  32.9026
   9 │     9.0  35.3513
  10 │    10.0  35.8386
"tmp.csv"
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y ~ 1 + x

Coefficients:
───────────────────────────────────────────────────────────────────────
               Coef.  Std. Error      t  Pr(>|t|)  Lower 95%  Upper 95%
───────────────────────────────────────────────────────────────────────
(Intercept)  7.08833     1.06729   6.64    0.0002    4.62715    9.54951
x            3.09722     0.17201  18.01    <1e-07    2.70057    3.49388
───────────────────────────────────────────────────────────────────────
StatsModels.TableRegressionModel{LinearModel{GLM.LmResp{Vector{Float64}}, GLM.DensePredChol{Float64, CholeskyPivoted{Float64, Matrix{Float64}, Vector{Int64}}}}, Matrix{Float64}}

y ~ 1 + x

Coefficients:
───────────────────────────────────────────────────────────────────────
               Coef.  Std. Error      t  Pr(>|t|)  Lower 95%  Upper 95%
───────────────────────────────────────────────────────────────────────
(Intercept)  7.08833     1.06729   6.64    0.0002    4.62715    9.54951
x            3.09722     0.17201  18.01    <1e-07    2.70057    3.49388
───────────────────────────────────────────────────────────────────────
[7.088329718063239, 3.097222966201723]
2×1 DataFrame
 Row │ x
     │ Float64
─────┼─────────
   1 │     5.5
   2 │     9.3
2-element Vector{Union{Missing, Float64}}:
 24.123056032172716
 35.89250330373926
Union{Missing, Float64}[24.123056032172716, 35.89250330373926]
2-element Vector{Union{Missing, Float64}}:
 24.123056032172716
 35.89250330373926
2×2 DataFrame
 Row │ x        py
     │ Float64  Float64?
─────┼───────────────────
   1 │     5.5   24.1231
   2 │     9.3   35.8925

histogram with normal density curve in julia

hwnc (generic function with 1 method)
100
10
100
Normal{Float64}(μ=100.0, σ=10.0)
100-element Vector{Float64}:
 111.7802591371556
 103.85511601627927
 104.04152107206976
  98.96424017031497
 105.95319156684396
 104.55470483254561
  92.98082482726603
  87.3291108433959
  98.92129655504161
 107.1833151703558
   ⋮
 108.25303693370215
 102.46148788764755
 110.76246060033249
  89.85041368927503
 100.07460362971408
 112.08620015351264
  92.46866102892267
  94.02111638978063
  98.50938982305627
100
"C:\\jr29102024\\tmp1.png"
100

bhankas with julia

kbv_hwnc (generic function with 1 method)
7-element Vector{String}:
 "StatsBase"
 "Images"
 "ImageView"
 "Plots"
 "MLJ"
 "RDatasets"
 "MLJModels"
All packages loaded successfully.
-5:1:5
kbv1 (generic function with 1 method)
11-element Vector{Int64}:
 -208
 -115
  -52
  -13
    8
   17
   20
   23
   32
   53
   92
Plots.GRBackend()

"C:\\jr29102024\\jup1.png"
TaskLocalRNG()
10000
50
10
10000-element Vector{Float64}:
 50.6193274031408
 52.78405814164
 44.04175584635948
 50.466593895733816
 60.85794021543276
 34.23435077414016
 51.759399913010746
 58.653808054093254
 22.09718994450693
 31.079844417740873
  ⋮
 52.413115928596625
 49.025474847116975
 76.3170646324677
 59.048108777995076
 48.00603105315709
 43.491790609401015
 48.29383789460438
 48.4884742176607
 36.51482096334351

Julia variable importance

method -1

5-element Vector{String}:
 "RDatasets"
 "FeatureSelectors"
 "DataFrames"
 "CategoricalArrays"
 "CSV"
All packages loaded successfully.
UnivariateFeatureSelector(FeatureSelectors.pearson_correlation, 5, nothing)
5-element Vector{String}:
 "LStat"
 "Rm"
 "PTRatio"
 "Indus"
 "Tax"
5×7 DataFrame
 Row │ variable     mean     min     median  max        nmissing  eltype
     │ Symbol       Union…   Any     Union…  Any        Int64     DataType
─────┼────────────────────────────────────────────────────────────────────────────────────────────
   1 │ SepalLength  5.84333  4.3     5.8     7.9               0  Float64
   2 │ SepalWidth   3.05733  2.0     3.0     4.4               0  Float64
   3 │ PetalLength  3.758    1.0     4.35    6.9               0  Float64
   4 │ PetalWidth   1.19933  0.1     1.3     2.5               0  Float64
   5 │ Species               setosa          virginica         0  CategoricalValue{String, UInt8}
UnivariateFeatureSelector(FeatureSelectors.pearson_correlation, 4, nothing)
4-element Vector{String}:
 "PetalWidth"
 "PetalLength"
 "SepalLength"
 "SepalWidth"
["PetalWidth", "PetalLength", "SepalLength", "SepalWidth"]

method-2

8-element Vector{String}:
 "RDatasets"
 "MLJ"
 "MLJBase"
 "MLJModels"
 "DataFrames"
 "MLJDecisionTreeInterface"
 "Random"
 "DecisionTree"
All packages loaded successfully.
150×5 DataFrame
 Row │ SepalLength  SepalWidth  PetalLength  PetalWidth  Species
     │ Float64      Float64     Float64      Float64     Cat…
─────┼─────────────────────────────────────────────────────────────
   1 │         5.1         3.5          1.4         0.2  setosa
   2 │         4.9         3.0          1.4         0.2  setosa
   3 │         4.7         3.2          1.3         0.2  setosa
   4 │         4.6         3.1          1.5         0.2  setosa
   5 │         5.0         3.6          1.4         0.2  setosa
   6 │         5.4         3.9          1.7         0.4  setosa
   7 │         4.6         3.4          1.4         0.3  setosa
   8 │         5.0         3.4          1.5         0.2  setosa
  ⋮  │      ⋮           ⋮            ⋮           ⋮           ⋮
 144 │         6.8         3.2          5.9         2.3  virginica
 145 │         6.7         3.3          5.7         2.5  virginica
 146 │         6.7         3.0          5.2         2.3  virginica
 147 │         6.3         2.5          5.0         1.9  virginica
 148 │         6.5         3.0          5.2         2.0  virginica
 149 │         6.2         3.4          5.4         2.3  virginica
 150 │         5.9         3.0          5.1         1.8  virginica
                                                   135 rows omitted
150×4 DataFrame
 Row │ SepalLength  SepalWidth  PetalLength  PetalWidth
     │ Float64      Float64     Float64      Float64
─────┼──────────────────────────────────────────────────
   1 │         5.1         3.5          1.4         0.2
   2 │         4.9         3.0          1.4         0.2
   3 │         4.7         3.2          1.3         0.2
   4 │         4.6         3.1          1.5         0.2
   5 │         5.0         3.6          1.4         0.2
   6 │         5.4         3.9          1.7         0.4
   7 │         4.6         3.4          1.4         0.3
   8 │         5.0         3.4          1.5         0.2
  ⋮  │      ⋮           ⋮            ⋮           ⋮
 144 │         6.8         3.2          5.9         2.3
 145 │         6.7         3.3          5.7         2.5
 146 │         6.7         3.0          5.2         2.3
 147 │         6.3         2.5          5.0         1.9
 148 │         6.5         3.0          5.2         2.0
 149 │         6.2         3.4          5.4         2.3
 150 │         5.9         3.0          5.1         1.8
                                        135 rows omitted
150-element CategoricalArray{String,1,UInt8}:
 "setosa"
 "setosa"
 "setosa"
 "setosa"
 "setosa"
 "setosa"
 "setosa"
 "setosa"
 "setosa"
 "setosa"
 ⋮
 "virginica"
 "virginica"
 "virginica"
 "virginica"
 "virginica"
 "virginica"
 "virginica"
 "virginica"
 "virginica"
TaskLocalRNG()
RandomForestClassifier(
  max_depth = -1, 
  min_samples_leaf = 1, 
  min_samples_split = 2, 
  min_purity_increase = 0.0, 
  n_subfeatures = -1, 
  n_trees = 100, 
  sampling_fraction = 0.7, 
  feature_importance = :impurity, 
  rng = Random._GLOBAL_RNG())
untrained Machine; caches model-specific representations of data
  model: RandomForestClassifier(max_depth = -1, …)
  args: 
    1:  Source @095 ⏎ Table{AbstractVector{ScientificTypesBase.Continuous}}
    2:  Source @299 ⏎ AbstractVector{Multiclass{3}}
trained Machine; caches model-specific representations of data
  model: RandomForestClassifier(max_depth = -1, …)
  args: 
    1:  Source @095 ⏎ Table{AbstractVector{ScientificTypesBase.Continuous}}
    2:  Source @299 ⏎ AbstractVector{Multiclass{3}}
Vector{Pair{Symbol, Float64}} (alias for Array{Pair{Symbol, Float64}, 1})
Feature Importances:
:PetalWidth => 0.45098651744544205
:PetalLength => 0.42261825648864204
:SepalLength => 0.10388331996352827
:SepalWidth => 0.022511906102387645

test julia

5.6---60.16

data visualisation with julia

150×5 DataFrame
 Row │ SepalLength  SepalWidth  PetalLength  PetalWidth  Species
     │ Float64      Float64     Float64      Float64     Cat…
─────┼─────────────────────────────────────────────────────────────
   1 │         5.1         3.5          1.4         0.2  setosa
   2 │         4.9         3.0          1.4         0.2  setosa
   3 │         4.7         3.2          1.3         0.2  setosa
   4 │         4.6         3.1          1.5         0.2  setosa
   5 │         5.0         3.6          1.4         0.2  setosa
   6 │         5.4         3.9          1.7         0.4  setosa
   7 │         4.6         3.4          1.4         0.3  setosa
   8 │         5.0         3.4          1.5         0.2  setosa
  ⋮  │      ⋮           ⋮            ⋮           ⋮           ⋮
 144 │         6.8         3.2          5.9         2.3  virginica
 145 │         6.7         3.3          5.7         2.5  virginica
 146 │         6.7         3.0          5.2         2.3  virginica
 147 │         6.3         2.5          5.0         1.9  virginica
 148 │         6.5         3.0          5.2         2.0  virginica
 149 │         6.2         3.4          5.4         2.3  virginica
 150 │         5.9         3.0          5.1         1.8  virginica
                                                   135 rows omitted
Plots.GRBackend()
"C:\\jr29102024\\q1.png"

3×2 DataFrame
 Row │ Species     Count
     │ Cat…        Int64
─────┼───────────────────
   1 │ setosa         50
   2 │ versicolor     50
   3 │ virginica      50
3-element Vector{Float64}:
 33.333333333333336
 33.333333333333336
 33.333333333333336
3×3 DataFrame
 Row │ Species     Count  pct
     │ Cat…        Int64  Float64
─────┼────────────────────────────
   1 │ setosa         50  33.3333
   2 │ versicolor     50  33.3333
   3 │ virginica      50  33.3333
my_round (generic function with 2 methods)
3-element CategoricalArray{String,1,UInt8}:
 "setosa"
 "versicolor"
 "virginica"
3-element Vector{Int64}:
 50
 50
 50
3-element Vector{Float64}:
 33.333333333333336
 33.333333333333336
 33.333333333333336
3-element Vector{Float64}:
 33.33
 33.33
 33.33

3-element Vector{Float64}:
 33.333333333333336
 33.333333333333336
 33.333333333333336
3-element Vector{Float64}:
 33.33
 33.33
 33.33
3-element Vector{Float64}:
 0.0
 0.0
 0.0
3-element Vector{Float64}:
 0.0
 0.0
 0.0

3-element Vector{Float64}:
  60.0
 180.00000000000003
 300.0
3-element Vector{Tuple{Float64, Float64}}:
 (0.8660254037844386, 0.5)
 (-4.960524086056721e-16, -1.0)
 (-0.8660254037844386, 0.5)
zip([33.33, 33.33, 33.33], [(0.8660254037844386, 0.5), (-4.960524086056721e-16, -1.0), (-0.8660254037844386, 0.5)])

lp with julia

A JuMP Model
├ solver: GLPK
├ objective_sense: FEASIBILITY_SENSE
├ num_variables: 0
├ num_constraints: 0
└ Names registered in the model: none
x
y
12 x + 20 y
x + y <= 100
2 x + 3 y <= 120
y <= 30
Max 12 x + 20 y
Subject to
 x + y <= 100
 2 x + 3 y <= 120
 y <= 30
 x >= 0
 y >= 0
Optimal solution:
objective value is 780.0
x = 15.0
y = 30.0

rndom forest dt mining with julia

[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3][1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3][0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, -1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

0.9333333333333333

data mining using MLJ julia Package

All packages loaded successfully.
All packages loaded successfully.
All packages loaded successfully.
All packages loaded successfully.
glfw initialised
All packages loaded successfully.

########################
difeerent models in mlj 
########################
231×3 DataFrame
 Row │ name                               package_name                  is_supervised
     │ String                             String                        Bool
─────┼────────────────────────────────────────────────────────────────────────────────
   1 │ ABODDetector                       OutlierDetectionNeighbors             false
   2 │ ABODDetector                       OutlierDetectionPython                false
   3 │ ARDRegressor                       MLJScikitLearnInterface                true
   4 │ AdaBoostClassifier                 MLJScikitLearnInterface                true
   5 │ AdaBoostRegressor                  MLJScikitLearnInterface                true
   6 │ AdaBoostStumpClassifier            DecisionTree                           true
   7 │ AffinityPropagation                MLJScikitLearnInterface               false
   8 │ AgglomerativeClustering            MLJScikitLearnInterface               false
   9 │ AutoEncoder                        BetaML                                false
  10 │ BM25Transformer                    MLJText                               false
  11 │ BaggingClassifier                  MLJScikitLearnInterface                true
  12 │ BaggingRegressor                   MLJScikitLearnInterface                true
  13 │ BayesianLDA                        MLJScikitLearnInterface                true
  14 │ BayesianLDA                        MultivariateStats                      true
  15 │ BayesianQDA                        MLJScikitLearnInterface                true
  16 │ BayesianRidgeRegressor             MLJScikitLearnInterface                true
  17 │ BayesianSubspaceLDA                MultivariateStats                      true
  18 │ BernoulliNBClassifier              MLJScikitLearnInterface                true
  19 │ Birch                              MLJScikitLearnInterface               false
  20 │ BisectingKMeans                    MLJScikitLearnInterface               false
  21 │ BorderlineSMOTE1                   Imbalance                             false
  22 │ CBLOFDetector                      OutlierDetectionPython                false
  23 │ CDDetector                         OutlierDetectionPython                false
  24 │ COFDetector                        OutlierDetectionNeighbors             false
  25 │ COFDetector                        OutlierDetectionPython                false
  26 │ COPODDetector                      OutlierDetectionPython                false
  27 │ CatBoostClassifier                 CatBoost                               true
  28 │ CatBoostRegressor                  CatBoost                               true
  29 │ ClusterUndersampler                Imbalance                             false
  30 │ ComplementNBClassifier             MLJScikitLearnInterface                true
  31 │ ConstantClassifier                 MLJModels                              true
  32 │ ConstantRegressor                  MLJModels                              true
  33 │ ContinuousEncoder                  MLJModels                             false
  34 │ CountTransformer                   MLJText                               false
  35 │ DBSCAN                             Clustering                            false
  36 │ DBSCAN                             MLJScikitLearnInterface               false
  37 │ DNNDetector                        OutlierDetectionNeighbors             false
  38 │ DecisionTreeClassifier             BetaML                                 true
  39 │ DecisionTreeClassifier             DecisionTree                           true
  40 │ DecisionTreeRegressor              BetaML                                 true
  41 │ DecisionTreeRegressor              DecisionTree                           true
  42 │ DeterministicConstantClassifier    MLJModels                              true
  43 │ DeterministicConstantRegressor     MLJModels                              true
  44 │ DummyClassifier                    MLJScikitLearnInterface                true
  45 │ DummyRegressor                     MLJScikitLearnInterface                true
  46 │ ECODDetector                       OutlierDetectionPython                false
  47 │ ENNUndersampler                    Imbalance                             false
  48 │ ElasticNetCVRegressor              MLJScikitLearnInterface                true
  49 │ ElasticNetRegressor                MLJLinearModels                        true
  50 │ ElasticNetRegressor                MLJScikitLearnInterface                true
  51 │ EpsilonSVR                         LIBSVM                                 true
  52 │ EvoLinearRegressor                 EvoLinear                              true
  53 │ EvoSplineRegressor                 EvoLinear                              true
  54 │ EvoTreeClassifier                  EvoTrees                               true
  55 │ EvoTreeCount                       EvoTrees                               true
  56 │ EvoTreeGaussian                    EvoTrees                               true
  57 │ EvoTreeMLE                         EvoTrees                               true
  58 │ EvoTreeRegressor                   EvoTrees                               true
  59 │ ExtraTreesClassifier               MLJScikitLearnInterface                true
  60 │ ExtraTreesRegressor                MLJScikitLearnInterface                true
  61 │ FactorAnalysis                     MultivariateStats                     false
  62 │ FeatureAgglomeration               MLJScikitLearnInterface               false
  63 │ FeatureSelector                    MLJModels                             false
  64 │ FillImputer                        MLJModels                             false
  65 │ GMMDetector                        OutlierDetectionPython                false
  66 │ GaussianMixtureClusterer           BetaML                                false
  67 │ GaussianMixtureImputer             BetaML                                false
  68 │ GaussianMixtureRegressor           BetaML                                 true
  69 │ GaussianNBClassifier               MLJScikitLearnInterface                true
  70 │ GaussianNBClassifier               NaiveBayes                             true
  71 │ GaussianProcessClassifier          MLJScikitLearnInterface                true
  72 │ GaussianProcessRegressor           MLJScikitLearnInterface                true
  73 │ GeneralImputer                     BetaML                                false
  74 │ GradientBoostingClassifier         MLJScikitLearnInterface                true
  75 │ GradientBoostingRegressor          MLJScikitLearnInterface                true
  76 │ HBOSDetector                       OutlierDetectionPython                false
  77 │ HDBSCAN                            MLJScikitLearnInterface               false
  78 │ HierarchicalClustering             Clustering                            false
  79 │ HistGradientBoostingClassifier     MLJScikitLearnInterface                true
  80 │ HistGradientBoostingRegressor      MLJScikitLearnInterface                true
  81 │ HuberRegressor                     MLJLinearModels                        true
  82 │ HuberRegressor                     MLJScikitLearnInterface                true
  83 │ ICA                                MultivariateStats                     false
  84 │ IForestDetector                    OutlierDetectionPython                false
  85 │ INNEDetector                       OutlierDetectionPython                false
  86 │ ImageClassifier                    MLJFlux                                true
  87 │ InteractionTransformer             MLJModels                             false
  88 │ KDEDetector                        OutlierDetectionPython                false
  89 │ KMeans                             Clustering                            false
  90 │ KMeans                             MLJScikitLearnInterface               false
  91 │ KMeans                             ParallelKMeans                        false
  92 │ KMeansClusterer                    BetaML                                false
  93 │ KMedoids                           Clustering                            false
  94 │ KMedoidsClusterer                  BetaML                                false
  95 │ KNNClassifier                      NearestNeighborModels                  true
  96 │ KNNDetector                        OutlierDetectionNeighbors             false
  97 │ KNNDetector                        OutlierDetectionPython                false
  98 │ KNNRegressor                       NearestNeighborModels                  true
  99 │ KNeighborsClassifier               MLJScikitLearnInterface                true
 100 │ KNeighborsRegressor                MLJScikitLearnInterface                true
 101 │ KPLSRegressor                      PartialLeastSquaresRegressor           true
 102 │ KernelPCA                          MultivariateStats                     false
 103 │ KernelPerceptronClassifier         BetaML                                 true
 104 │ LADRegressor                       MLJLinearModels                        true
 105 │ LDA                                MultivariateStats                      true
 106 │ LGBMClassifier                     LightGBM                               true
 107 │ LGBMRegressor                      LightGBM                               true
 108 │ LMDDDetector                       OutlierDetectionPython                false
 109 │ LOCIDetector                       OutlierDetectionPython                false
 110 │ LODADetector                       OutlierDetectionPython                false
 111 │ LOFDetector                        OutlierDetectionNeighbors             false
 112 │ LOFDetector                        OutlierDetectionPython                false
 113 │ LarsCVRegressor                    MLJScikitLearnInterface                true
 114 │ LarsRegressor                      MLJScikitLearnInterface                true
 115 │ LassoCVRegressor                   MLJScikitLearnInterface                true
 116 │ LassoLarsCVRegressor               MLJScikitLearnInterface                true
 117 │ LassoLarsICRegressor               MLJScikitLearnInterface                true
 118 │ LassoLarsRegressor                 MLJScikitLearnInterface                true
 119 │ LassoRegressor                     MLJLinearModels                        true
 120 │ LassoRegressor                     MLJScikitLearnInterface                true
 121 │ LinearBinaryClassifier             GLM                                    true
 122 │ LinearCountRegressor               GLM                                    true
 123 │ LinearRegressor                    GLM                                    true
 124 │ LinearRegressor                    MLJLinearModels                        true
 125 │ LinearRegressor                    MLJScikitLearnInterface                true
 126 │ LinearRegressor                    MultivariateStats                      true
 127 │ LinearSVC                          LIBSVM                                 true
 128 │ LogisticCVClassifier               MLJScikitLearnInterface                true
 129 │ LogisticClassifier                 MLJLinearModels                        true
 130 │ LogisticClassifier                 MLJScikitLearnInterface                true
 131 │ MCDDetector                        OutlierDetectionPython                false
 132 │ MeanShift                          MLJScikitLearnInterface               false
 133 │ MiniBatchKMeans                    MLJScikitLearnInterface               false
 134 │ MultiTaskElasticNetCVRegressor     MLJScikitLearnInterface                true
 135 │ MultiTaskElasticNetRegressor       MLJScikitLearnInterface                true
 136 │ MultiTaskLassoCVRegressor          MLJScikitLearnInterface                true
 137 │ MultiTaskLassoRegressor            MLJScikitLearnInterface                true
 138 │ MultinomialClassifier              MLJLinearModels                        true
 139 │ MultinomialNBClassifier            MLJScikitLearnInterface                true
 140 │ MultinomialNBClassifier            NaiveBayes                             true
 141 │ MultitargetGaussianMixtureRegres…  BetaML                                 true
 142 │ MultitargetKNNClassifier           NearestNeighborModels                  true
 143 │ MultitargetKNNRegressor            NearestNeighborModels                  true
 144 │ MultitargetLinearRegressor         MultivariateStats                      true
 145 │ MultitargetNeuralNetworkRegressor  BetaML                                 true
 146 │ MultitargetNeuralNetworkRegressor  MLJFlux                                true
 147 │ MultitargetRidgeRegressor          MultivariateStats                      true
 148 │ MultitargetSRRegressor             SymbolicRegression                     true
 149 │ NeuralNetworkClassifier            BetaML                                 true
 150 │ NeuralNetworkClassifier            MLJFlux                                true
 151 │ NeuralNetworkRegressor             BetaML                                 true
 152 │ NeuralNetworkRegressor             MLJFlux                                true
 153 │ NuSVC                              LIBSVM                                 true
 154 │ NuSVR                              LIBSVM                                 true
 155 │ OCSVMDetector                      OutlierDetectionPython                false
 156 │ OPTICS                             MLJScikitLearnInterface               false
 157 │ OneClassSVM                        LIBSVM                                false
 158 │ OneHotEncoder                      MLJModels                             false
 159 │ OneRuleClassifier                  OneRule                                true
 160 │ OrthogonalMatchingPursuitCVRegre…  MLJScikitLearnInterface                true
 161 │ OrthogonalMatchingPursuitRegress…  MLJScikitLearnInterface                true
 162 │ PCA                                MultivariateStats                     false
 163 │ PCADetector                        OutlierDetectionPython                false
 164 │ PLSRegressor                       PartialLeastSquaresRegressor           true
 165 │ PPCA                               MultivariateStats                     false
 166 │ PartLS                             PartitionedLS                          true
 167 │ PassiveAggressiveClassifier        MLJScikitLearnInterface                true
 168 │ PassiveAggressiveRegressor         MLJScikitLearnInterface                true
 169 │ PegasosClassifier                  BetaML                                 true
 170 │ PerceptronClassifier               BetaML                                 true
 171 │ PerceptronClassifier               MLJScikitLearnInterface                true
 172 │ ProbabilisticNuSVC                 LIBSVM                                 true
 173 │ ProbabilisticSGDClassifier         MLJScikitLearnInterface                true
 174 │ ProbabilisticSVC                   LIBSVM                                 true
 175 │ QuantileRegressor                  MLJLinearModels                        true
 176 │ RANSACRegressor                    MLJScikitLearnInterface                true
 177 │ RODDetector                        OutlierDetectionPython                false
 178 │ ROSE                               Imbalance                             false
 179 │ RandomForestClassifier             BetaML                                 true
 180 │ RandomForestClassifier             DecisionTree                           true
 181 │ RandomForestClassifier             MLJScikitLearnInterface                true
 182 │ RandomForestImputer                BetaML                                false
 183 │ RandomForestRegressor              BetaML                                 true
 184 │ RandomForestRegressor              DecisionTree                           true
 185 │ RandomForestRegressor              MLJScikitLearnInterface                true
 186 │ RandomOversampler                  Imbalance                             false
 187 │ RandomUndersampler                 Imbalance                             false
 188 │ RandomWalkOversampler              Imbalance                             false
 189 │ RidgeCVClassifier                  MLJScikitLearnInterface                true
 190 │ RidgeCVRegressor                   MLJScikitLearnInterface                true
 191 │ RidgeClassifier                    MLJScikitLearnInterface                true
 192 │ RidgeRegressor                     MLJLinearModels                        true
 193 │ RidgeRegressor                     MLJScikitLearnInterface                true
 194 │ RidgeRegressor                     MultivariateStats                      true
 195 │ RobustRegressor                    MLJLinearModels                        true
 196 │ SGDClassifier                      MLJScikitLearnInterface                true
 197 │ SGDRegressor                       MLJScikitLearnInterface                true
 198 │ SMOTE                              Imbalance                             false
 199 │ SMOTEN                             Imbalance                             false
 200 │ SMOTENC                            Imbalance                             false
 201 │ SODDetector                        OutlierDetectionPython                false
 202 │ SOSDetector                        OutlierDetectionPython                false
 203 │ SRRegressor                        SymbolicRegression                     true
 204 │ SVC                                LIBSVM                                 true
 205 │ SVMClassifier                      MLJScikitLearnInterface                true
 206 │ SVMLinearClassifier                MLJScikitLearnInterface                true
 207 │ SVMLinearRegressor                 MLJScikitLearnInterface                true
 208 │ SVMNuClassifier                    MLJScikitLearnInterface                true
 209 │ SVMNuRegressor                     MLJScikitLearnInterface                true
 210 │ SVMRegressor                       MLJScikitLearnInterface                true
 211 │ SelfOrganizingMap                  SelfOrganizingMaps                    false
 212 │ SimpleImputer                      BetaML                                false
 213 │ SpectralClustering                 MLJScikitLearnInterface               false
 214 │ StableForestClassifier             SIRUS                                  true
 215 │ StableForestRegressor              SIRUS                                  true
 216 │ StableRulesClassifier              SIRUS                                  true
 217 │ StableRulesRegressor               SIRUS                                  true
 218 │ Standardizer                       MLJModels                             false
 219 │ SubspaceLDA                        MultivariateStats                      true
 220 │ TSVDTransformer                    TSVD                                  false
 221 │ TfidfTransformer                   MLJText                               false
 222 │ TheilSenRegressor                  MLJScikitLearnInterface                true
 223 │ TomekUndersampler                  Imbalance                             false
 224 │ UnivariateBoxCoxTransformer        MLJModels                             false
 225 │ UnivariateDiscretizer              MLJModels                             false
 226 │ UnivariateFillImputer              MLJModels                             false
 227 │ UnivariateStandardizer             MLJModels                             false
 228 │ UnivariateTimeTypeToContinuous     MLJModels                             false
 229 │ XGBoostClassifier                  XGBoost                                true
 230 │ XGBoostCount                       XGBoost                                true
 231 │ XGBoostRegressor                   XGBoost                                true
146×3 DataFrame
 Row │ name                               package_name                  is_supervised
     │ String                             String                        Bool
─────┼────────────────────────────────────────────────────────────────────────────────
   1 │ DecisionTreeClassifier             BetaML                                 true
   2 │ DecisionTreeRegressor              BetaML                                 true
   3 │ GaussianMixtureRegressor           BetaML                                 true
   4 │ KernelPerceptronClassifier         BetaML                                 true
   5 │ MultitargetGaussianMixtureRegres…  BetaML                                 true
   6 │ MultitargetNeuralNetworkRegressor  BetaML                                 true
   7 │ NeuralNetworkClassifier            BetaML                                 true
   8 │ NeuralNetworkRegressor             BetaML                                 true
   9 │ PegasosClassifier                  BetaML                                 true
  10 │ PerceptronClassifier               BetaML                                 true
  11 │ RandomForestClassifier             BetaML                                 true
  12 │ RandomForestRegressor              BetaML                                 true
  13 │ CatBoostClassifier                 CatBoost                               true
  14 │ CatBoostRegressor                  CatBoost                               true
  15 │ AdaBoostStumpClassifier            DecisionTree                           true
  16 │ DecisionTreeClassifier             DecisionTree                           true
  17 │ DecisionTreeRegressor              DecisionTree                           true
  18 │ RandomForestClassifier             DecisionTree                           true
  19 │ RandomForestRegressor              DecisionTree                           true
  20 │ EvoLinearRegressor                 EvoLinear                              true
  21 │ EvoSplineRegressor                 EvoLinear                              true
  22 │ EvoTreeClassifier                  EvoTrees                               true
  23 │ EvoTreeCount                       EvoTrees                               true
  24 │ EvoTreeGaussian                    EvoTrees                               true
  25 │ EvoTreeMLE                         EvoTrees                               true
  26 │ EvoTreeRegressor                   EvoTrees                               true
  27 │ LinearBinaryClassifier             GLM                                    true
  28 │ LinearCountRegressor               GLM                                    true
  29 │ LinearRegressor                    GLM                                    true
  30 │ EpsilonSVR                         LIBSVM                                 true
  31 │ LinearSVC                          LIBSVM                                 true
  32 │ NuSVC                              LIBSVM                                 true
  33 │ NuSVR                              LIBSVM                                 true
  34 │ ProbabilisticNuSVC                 LIBSVM                                 true
  35 │ ProbabilisticSVC                   LIBSVM                                 true
  36 │ SVC                                LIBSVM                                 true
  37 │ LGBMClassifier                     LightGBM                               true
  38 │ LGBMRegressor                      LightGBM                               true
  39 │ ImageClassifier                    MLJFlux                                true
  40 │ MultitargetNeuralNetworkRegressor  MLJFlux                                true
  41 │ NeuralNetworkClassifier            MLJFlux                                true
  42 │ NeuralNetworkRegressor             MLJFlux                                true
  43 │ ElasticNetRegressor                MLJLinearModels                        true
  44 │ HuberRegressor                     MLJLinearModels                        true
  45 │ LADRegressor                       MLJLinearModels                        true
  46 │ LassoRegressor                     MLJLinearModels                        true
  47 │ LinearRegressor                    MLJLinearModels                        true
  48 │ LogisticClassifier                 MLJLinearModels                        true
  49 │ MultinomialClassifier              MLJLinearModels                        true
  50 │ QuantileRegressor                  MLJLinearModels                        true
  51 │ RidgeRegressor                     MLJLinearModels                        true
  52 │ RobustRegressor                    MLJLinearModels                        true
  53 │ ConstantClassifier                 MLJModels                              true
  54 │ ConstantRegressor                  MLJModels                              true
  55 │ DeterministicConstantClassifier    MLJModels                              true
  56 │ DeterministicConstantRegressor     MLJModels                              true
  57 │ ARDRegressor                       MLJScikitLearnInterface                true
  58 │ AdaBoostClassifier                 MLJScikitLearnInterface                true
  59 │ AdaBoostRegressor                  MLJScikitLearnInterface                true
  60 │ BaggingClassifier                  MLJScikitLearnInterface                true
  61 │ BaggingRegressor                   MLJScikitLearnInterface                true
  62 │ BayesianLDA                        MLJScikitLearnInterface                true
  63 │ BayesianQDA                        MLJScikitLearnInterface                true
  64 │ BayesianRidgeRegressor             MLJScikitLearnInterface                true
  65 │ BernoulliNBClassifier              MLJScikitLearnInterface                true
  66 │ ComplementNBClassifier             MLJScikitLearnInterface                true
  67 │ DummyClassifier                    MLJScikitLearnInterface                true
  68 │ DummyRegressor                     MLJScikitLearnInterface                true
  69 │ ElasticNetCVRegressor              MLJScikitLearnInterface                true
  70 │ ElasticNetRegressor                MLJScikitLearnInterface                true
  71 │ ExtraTreesClassifier               MLJScikitLearnInterface                true
  72 │ ExtraTreesRegressor                MLJScikitLearnInterface                true
  73 │ GaussianNBClassifier               MLJScikitLearnInterface                true
  74 │ GaussianProcessClassifier          MLJScikitLearnInterface                true
  75 │ GaussianProcessRegressor           MLJScikitLearnInterface                true
  76 │ GradientBoostingClassifier         MLJScikitLearnInterface                true
  77 │ GradientBoostingRegressor          MLJScikitLearnInterface                true
  78 │ HistGradientBoostingClassifier     MLJScikitLearnInterface                true
  79 │ HistGradientBoostingRegressor      MLJScikitLearnInterface                true
  80 │ HuberRegressor                     MLJScikitLearnInterface                true
  81 │ KNeighborsClassifier               MLJScikitLearnInterface                true
  82 │ KNeighborsRegressor                MLJScikitLearnInterface                true
  83 │ LarsCVRegressor                    MLJScikitLearnInterface                true
  84 │ LarsRegressor                      MLJScikitLearnInterface                true
  85 │ LassoCVRegressor                   MLJScikitLearnInterface                true
  86 │ LassoLarsCVRegressor               MLJScikitLearnInterface                true
  87 │ LassoLarsICRegressor               MLJScikitLearnInterface                true
  88 │ LassoLarsRegressor                 MLJScikitLearnInterface                true
  89 │ LassoRegressor                     MLJScikitLearnInterface                true
  90 │ LinearRegressor                    MLJScikitLearnInterface                true
  91 │ LogisticCVClassifier               MLJScikitLearnInterface                true
  92 │ LogisticClassifier                 MLJScikitLearnInterface                true
  93 │ MultiTaskElasticNetCVRegressor     MLJScikitLearnInterface                true
  94 │ MultiTaskElasticNetRegressor       MLJScikitLearnInterface                true
  95 │ MultiTaskLassoCVRegressor          MLJScikitLearnInterface                true
  96 │ MultiTaskLassoRegressor            MLJScikitLearnInterface                true
  97 │ MultinomialNBClassifier            MLJScikitLearnInterface                true
  98 │ OrthogonalMatchingPursuitCVRegre…  MLJScikitLearnInterface                true
  99 │ OrthogonalMatchingPursuitRegress…  MLJScikitLearnInterface                true
 100 │ PassiveAggressiveClassifier        MLJScikitLearnInterface                true
 101 │ PassiveAggressiveRegressor         MLJScikitLearnInterface                true
 102 │ PerceptronClassifier               MLJScikitLearnInterface                true
 103 │ ProbabilisticSGDClassifier         MLJScikitLearnInterface                true
 104 │ RANSACRegressor                    MLJScikitLearnInterface                true
 105 │ RandomForestClassifier             MLJScikitLearnInterface                true
 106 │ RandomForestRegressor              MLJScikitLearnInterface                true
 107 │ RidgeCVClassifier                  MLJScikitLearnInterface                true
 108 │ RidgeCVRegressor                   MLJScikitLearnInterface                true
 109 │ RidgeClassifier                    MLJScikitLearnInterface                true
 110 │ RidgeRegressor                     MLJScikitLearnInterface                true
 111 │ SGDClassifier                      MLJScikitLearnInterface                true
 112 │ SGDRegressor                       MLJScikitLearnInterface                true
 113 │ SVMClassifier                      MLJScikitLearnInterface                true
 114 │ SVMLinearClassifier                MLJScikitLearnInterface                true
 115 │ SVMLinearRegressor                 MLJScikitLearnInterface                true
 116 │ SVMNuClassifier                    MLJScikitLearnInterface                true
 117 │ SVMNuRegressor                     MLJScikitLearnInterface                true
 118 │ SVMRegressor                       MLJScikitLearnInterface                true
 119 │ TheilSenRegressor                  MLJScikitLearnInterface                true
 120 │ BayesianLDA                        MultivariateStats                      true
 121 │ BayesianSubspaceLDA                MultivariateStats                      true
 122 │ LDA                                MultivariateStats                      true
 123 │ LinearRegressor                    MultivariateStats                      true
 124 │ MultitargetLinearRegressor         MultivariateStats                      true
 125 │ MultitargetRidgeRegressor          MultivariateStats                      true
 126 │ RidgeRegressor                     MultivariateStats                      true
 127 │ SubspaceLDA                        MultivariateStats                      true
 128 │ GaussianNBClassifier               NaiveBayes                             true
 129 │ MultinomialNBClassifier            NaiveBayes                             true
 130 │ KNNClassifier                      NearestNeighborModels                  true
 131 │ KNNRegressor                       NearestNeighborModels                  true
 132 │ MultitargetKNNClassifier           NearestNeighborModels                  true
 133 │ MultitargetKNNRegressor            NearestNeighborModels                  true
 134 │ OneRuleClassifier                  OneRule                                true
 135 │ KPLSRegressor                      PartialLeastSquaresRegressor           true
 136 │ PLSRegressor                       PartialLeastSquaresRegressor           true
 137 │ PartLS                             PartitionedLS                          true
 138 │ StableForestClassifier             SIRUS                                  true
 139 │ StableForestRegressor              SIRUS                                  true
 140 │ StableRulesClassifier              SIRUS                                  true
 141 │ StableRulesRegressor               SIRUS                                  true
 142 │ MultitargetSRRegressor             SymbolicRegression                     true
 143 │ SRRegressor                        SymbolicRegression                     true
 144 │ XGBoostClassifier                  XGBoost                                true
 145 │ XGBoostCount                       XGBoost                                true
 146 │ XGBoostRegressor                   XGBoost                                true

####################################
difeerent classifiers models in mlj 
####################################
1---AdaBoostClassifier---MLJScikitLearnInterface
2---AdaBoostStumpClassifier---DecisionTree
3---BaggingClassifier---MLJScikitLearnInterface
4---BayesianLDA---MLJScikitLearnInterface
5---BayesianLDA---MultivariateStats
6---BayesianQDA---MLJScikitLearnInterface
7---BayesianSubspaceLDA---MultivariateStats
8---CatBoostClassifier---CatBoost
9---ConstantClassifier---MLJModels
10---DecisionTreeClassifier---BetaML
11---DecisionTreeClassifier---DecisionTree
12---DeterministicConstantClassifier---MLJModels
13---DummyClassifier---MLJScikitLearnInterface
14---EvoTreeClassifier---EvoTrees
15---ExtraTreesClassifier---MLJScikitLearnInterface
16---GaussianNBClassifier---MLJScikitLearnInterface
17---GaussianNBClassifier---NaiveBayes
18---GaussianProcessClassifier---MLJScikitLearnInterface
19---GradientBoostingClassifier---MLJScikitLearnInterface
20---HistGradientBoostingClassifier---MLJScikitLearnInterface
21---KNNClassifier---NearestNeighborModels
22---KNeighborsClassifier---MLJScikitLearnInterface
23---KernelPerceptronClassifier---BetaML
24---LDA---MultivariateStats
25---LGBMClassifier---LightGBM
26---LinearSVC---LIBSVM
27---LogisticCVClassifier---MLJScikitLearnInterface
28---LogisticClassifier---MLJLinearModels
29---LogisticClassifier---MLJScikitLearnInterface
30---MultinomialClassifier---MLJLinearModels
31---NeuralNetworkClassifier---BetaML
32---NeuralNetworkClassifier---MLJFlux
33---NuSVC---LIBSVM
34---PassiveAggressiveClassifier---MLJScikitLearnInterface
35---PegasosClassifier---BetaML
36---PerceptronClassifier---BetaML
37---PerceptronClassifier---MLJScikitLearnInterface
38---ProbabilisticNuSVC---LIBSVM
39---ProbabilisticSGDClassifier---MLJScikitLearnInterface
40---ProbabilisticSVC---LIBSVM
41---RandomForestClassifier---BetaML
42---RandomForestClassifier---DecisionTree
43---RandomForestClassifier---MLJScikitLearnInterface
44---RidgeCVClassifier---MLJScikitLearnInterface
45---RidgeClassifier---MLJScikitLearnInterface
46---SGDClassifier---MLJScikitLearnInterface
47---SVC---LIBSVM
48---SVMClassifier---MLJScikitLearnInterface
49---SVMLinearClassifier---MLJScikitLearnInterface
50---SVMNuClassifier---MLJScikitLearnInterface
51---StableForestClassifier---SIRUS
52---StableRulesClassifier---SIRUS
53---SubspaceLDA---MultivariateStats
54---XGBoostClassifier---XGBoost

############
mlj1 running
############

#############################
mlj deecision tree classifier
#############################
start mlj deecision tree classifier
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 17 2; 0 1 16])
[14 0 0; 0 17 2; 0 1 16]

###########
mlj2running
###########

#################################
starting random forest classifier
#################################
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 17 2; 0 1 16])
[14 0 0; 0 17 2; 0 1 16]

############
mlj3 running
############

######################
mlj xgboost classifier
######################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]

############
mlj4 running
############

#######################
mlj adaboost classifier
#######################
Accuracy: 0.9
ConfusionMatrix{3}([14 0 0; 0 17 4; 0 1 14])
[14 0 0; 0 17 4; 0 1 14]

############
ml51 running
############

#############################
mlj adaboost stump classifier
#############################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]

############
mlj6 running
############

############################
mlj NuSVC libsvm  classifier
############################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 18 2; 0 0 16])
[14 0 0; 0 18 2; 0 0 16]

############
mlj7 running
############

##############################
mlj Neural network  classifier
##############################
***
*** Training  for 200 epochs with algorithm ADAM.
Training..   avg loss on epoch 1 (1):    1.8944182288019402
Training of 200 epoch completed. Final epoch error: 2.1567533608672576.
Accuracy: 0.28
ConfusionMatrix{3}([14 18 18; 0 0 0; 0 0 0])
[14 18 18; 0 0 0; 0 0 0]

############
mlj8 running
############

######################################################
mlj random forest classifer from decision tree package
######################################################
machine(RandomForestClassifier(max_depth = -1, …), …)
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]

############
mlj9 running
############

###############################
KNeighborsClassifier Classifier
###############################
Accuracy: 0.98
ConfusionMatrix{3}([14 0 0; 0 18 1; 0 0 17])
[14 0 0; 0 18 1; 0 0 17]

#############
mlj10 running
#############

################################
using SVC classifier from libsvm
################################
Accuracy: 0.94
ConfusionMatrix{3}([14 0 0; 0 18 3; 0 0 15])
[14 0 0; 0 18 3; 0 0 15]

#############
mlj11 running
#############

#########################
using Catboost classifier
#########################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]

#############
mlj12 running
#############

#######################
using PegasosClassifier
#######################
Accuracy: 0.96
ConfusionMatrix{3}([14 0 0; 0 17 1; 0 1 17])
[14 0 0; 0 17 1; 0 1 17]

#############
mlj13 running
#############
All packages loaded successfully.
[0.9666666666666667, 0.9, 0.9666666666666667, 0.9666666666666667, 0.9]
0.9400000000000001----0.036514837167011066

#############
mlj14 running
#############
All packages loaded successfully.
[0.9666666666666667, 1.0, 0.9, 0.8666666666666667, 1.0]
0.9466666666666667----0.060553007081949814

###########################
mlj16 with cross validation
###########################
[1.0, 0.95, 0.95, 1.0, 1.0]
0.9800000000000001
0.02738612787525833

#####################################
ek aur mlj11911 with cross validation
#####################################
[1.0, 1.0, 0.8666666666666667, 0.9333333333333333, 0.8333333333333334]
0.9266666666666665
0.07601169500660918

##########################
ek aur mljtune with tuning
##########################
---------------
RandomForestRegressor(max_depth = -1, …)
---------------
(best_model = RandomForestRegressor(max_depth = -1, …), best_history_entry = (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]]), history = @NamedTuple{model::MLJDecisionTreeInterface.RandomForestRegressor, measure::Vector{MLJBase.RootMeanSquaredError}, measurement::Vector{Float64}, per_fold::Vector{Vector{Float64}}}[(model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [5.021924787822756], per_fold = [[3.3741287546478103, 4.103380639578459, 4.568646619152445, 6.897883607841374, 5.424281993823224]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.6774372882045085], per_fold = [[3.003770053048402, 3.9444903644734066, 4.693197597093959, 6.789751223142656, 4.084560417092651]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.772483574701619], per_fold = [[2.9948640131926414, 3.9887668115821007, 4.959489325759872, 6.674681289880182, 4.455964713222702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.703944145986171], per_fold = [[3.04157986553557, 3.9504312360165246, 4.642152478502118, 6.7973135687913695, 4.245619651796751]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.675715674831727], per_fold = [[3.0668645869369326, 3.9048956622481676, 4.652229167983413, 6.541823741045831, 4.496556765052191]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.7051750843571725], per_fold = [[2.936317439249649, 3.8544894797266087, 4.801542464920288, 6.611152096789043, 4.522407713773929]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.707833964624099], per_fold = [[3.1468033499933568, 3.855049055177091, 4.498632912528334, 7.071324837883725, 3.9766060411405664]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.765659732014386], per_fold = [[3.1668394215003435, 3.9383442316700448, 4.8052750099023545, 6.669651078201205, 4.521418225764702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.844822691796512], per_fold = [[2.9870176796825936, 3.6120895128809996, 4.852258918448313, 7.0890030269701265, 4.646900986558451]])], best_report = (features = [:Crim, :Zn, :Indus, :NOx, :Rm, :Age, :Dis, :Rad, :Tax, :PTRatio, :Black, :LStat],), plotting = (parameter_names = ["n_trees"], parameter_scales = [:linear], parameter_values = Any[20; 60; 90; 100; 80; 70; 40; 50; 10; 30;;], measurements = [5.021924787822756, 4.6774372882045085, 4.772483574701619, 4.703944145986171, 4.675715674831727, 4.7051750843571725, 4.707833964624099, 4.765659732014386, 4.621983720432108, 4.844822691796512]))
---------------
(best_model = RandomForestRegressor(max_depth = -1, …), best_fitted_params = (forest = Ensemble of Decision Trees
Trees:      10
Avg Leaves: 244.8
Avg Depth:  18.1,))
---------------
[4.621983720432108]
---------------
(model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]])
---------------
@NamedTuple{model::MLJDecisionTreeInterface.RandomForestRegressor, measure::Vector{MLJBase.RootMeanSquaredError}, measurement::Vector{Float64}, per_fold::Vector{Vector{Float64}}}[(model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [5.021924787822756], per_fold = [[3.3741287546478103, 4.103380639578459, 4.568646619152445, 6.897883607841374, 5.424281993823224]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.6774372882045085], per_fold = [[3.003770053048402, 3.9444903644734066, 4.693197597093959, 6.789751223142656, 4.084560417092651]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.772483574701619], per_fold = [[2.9948640131926414, 3.9887668115821007, 4.959489325759872, 6.674681289880182, 4.455964713222702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.703944145986171], per_fold = [[3.04157986553557, 3.9504312360165246, 4.642152478502118, 6.7973135687913695, 4.245619651796751]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.675715674831727], per_fold = [[3.0668645869369326, 3.9048956622481676, 4.652229167983413, 6.541823741045831, 4.496556765052191]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.7051750843571725], per_fold = [[2.936317439249649, 3.8544894797266087, 4.801542464920288, 6.611152096789043, 4.522407713773929]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.707833964624099], per_fold = [[3.1468033499933568, 3.855049055177091, 4.498632912528334, 7.071324837883725, 3.9766060411405664]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.765659732014386], per_fold = [[3.1668394215003435, 3.9383442316700448, 4.8052750099023545, 6.669651078201205, 4.521418225764702]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.621983720432108], per_fold = [[3.1498851519679625, 3.307942437273786, 5.265947866309833, 6.583592191092477, 3.856878900287789]]), (model = RandomForestRegressor(max_depth = -1, …), measure = [RootMeanSquaredError()], measurement = [4.844822691796512], per_fold = [[2.9870176796825936, 3.6120895128809996, 4.852258918448313, 7.0890030269701265, 4.646900986558451]])]
RandomForestRegressor(max_depth = -1, …)

###################
mlj15 curve fitting
###################
All packages loaded successfully.
1:15 --- [37.06068977676915, 46.34033787266742, 69.54393490463906, 44.54533495128814, 73.28311957710237, 80.5294137715672, 79.24090025087456, 141.66766436385825, 144.02799909372706, 153.36189396281418, 187.34979593984932, 228.81142875260755, 217.04530680138464, 268.26007526906113, 295.1324663209221]
1.67556 + 50.7076*x - 18.5013*x^2 + 3.14092*x^3 - 0.216981*x^4 + 0.00537528*x^5
110.83570187826422
[42.61172335542394, 44.76709048817514, 50.223752217648915, 58.76091874225561, 70.15780026040558, 84.19360697050917, 100.64754907097672, 119.29883676021862, 139.92668023664515, 162.3102896986667, 186.22887534469362, 211.4616473731363, 237.78781598240496, 264.9865913709101, 292.83718373706193]
end main

data mining with decision tree

Test set accuracy: 0.94
Feature 3 < 2.6 ?
├─ 1 : 33/33
└─ Feature 4 < 1.75 ?
    ├─ Feature 3 < 5.35 ?
        ├─ 2 : 31/31
        └─ 3 : 2/2
    └─ Feature 3 < 4.85 ?
        ├─ Feature 2 < 3.1 ?
            ├─ 3 : 2/2
            └─ 2 : 1/1
        └─ 3 : 31/31

plotting with makie

6×3 DataFrame
 Row │ x        y1        y2
     │ Float64  Float64   Float64
─────┼───────────────────────────────
   1 │    -5.0  0.958924   0.283662
   2 │    -4.9  0.982453   0.186512
   3 │    -4.8  0.996165   0.087499
   4 │    -4.7  0.999923  -0.0123887
   5 │    -4.6  0.993691  -0.112153
   6 │    -4.5  0.97753   -0.210796

symbolics math symbolics


#############################
using Symbolics julia package
#############################
using symbolics
-35 + 3x + 5y = 0
x - y = 0
[4.375, 4.375]
-123 - 7x - 5(x^2) + x^3
Differential(x)(-123 - 7x - 5(x^2) + x^3)
-7 - 10x + 3(x^2)
8x + 4(x^2)
x^6 + 6(x^5)*y + 15(x^4)*(y^2) + 20(x^3)*(y^3) + 15(x^2)*(y^4) + 6x*(y^5) + y^6

symbolics math sympy


#########################
using SymPy julia package
#########################

############################
solving non linear equations
############################
1
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 0)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 1)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 2)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 3)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 4)
CRootOf(x^6 + x^5 + x^4 + x^3 + x^2 + x + 3, 5)
1
-1.1280707714543692 - 0.5876060832247132im
-1.1280707714543692 + 0.5876060832247132im
-0.16403887800672787 - 1.21306844998484im
-0.16403887800672787 + 1.21306844998484im
0.7921096494610971 - 0.7810724308829708im
0.7921096494610971 + 0.7810724308829708im

################################
solving derivative of a function
################################
3*x^2 - 7

#################################
solving integration of a function
#################################
x^4/4 - 7*x^2/2

###########################
solving non linear equation
###########################
-3/((-1/2 - sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3)) - (-1/2 - sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3)/3
-(-1/2 + sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3)/3 - 3/((-1/2 + sqrt(3)*I/2)*(27*sqrt(35) + 162)^(1/3))
-(27*sqrt(35) + 162)^(1/3)/3 - 3/(27*sqrt(35) + 162)^(1/3)
1.3609461421185716 + 1.5989131324879002im
1.3609461421185716 - 1.5989131324879002im
-2.721892284237143

##################################
substituting values in  a function
##################################
2*y^2 + y + 4

####################
expanding expression
####################
x^5 + 5*x^4*y + 10*x^3*y^2 + 10*x^2*y^3 + 5*x*y^4 + y^5
x^4 + 6*x^3 + 11*x^2 + 6*x

#######################
solving three variables
#######################
2
2

trial & error calculations


#####################
iterative calculation
#####################
f1 = 0
1    0.0    -1.0e7    1.0e7    -145.0
2    5.0e6    0.0    1.0e7    1.2499992499997e20
3    2.5e6    0.0    5.0e6    1.5624981249985e19
4    1.25e6    0.0    2.5e6    1.9531203124924997e18
5    625000.0    0.0    1.25e6    2.4413945312124982e17
6    312500.0    0.0    625000.0    3.0517285154374856e16
7    156250.0    0.0    312500.0    3.814624022499855e15
8    78125.0    0.0    156250.0    4.76818847187355e14
9    39062.5    0.0    78125.0    5.9600066904151875e13
10    19531.25    0.0    39062.5    7.449436070411641e12
11    9765.625    0.0    19531.25    9.310364135818066e11
12    4882.8125    0.0    9765.625    1.1634376681132935e11
13    2441.40625    0.0    4882.8125    1.4534019041496735e10
14    1220.703125    0.0    2441.40625    1.8145115859689522e9
15    610.3515625    0.0    1220.703125    2.262522812443185e8
16    305.17578125    0.0    610.3515625    2.8140336603331864e7
17    152.587890625    0.0    305.17578125    3.48180395836059e6
18    76.2939453125    0.0    152.587890625    426024.1479041474
19    38.14697265625    0.0    76.2939453125    50771.69482681027
20    19.073486328125    0.0    38.14697265625    5588.0593438109645
21    9.5367431640625    0.0    19.073486328125    392.2928684721501
22    4.76837158203125    0.0    9.5367431640625    -133.40211487660667
23    7.152557373046875    4.76837158203125    9.5367431640625    24.525658051394885
24    5.9604644775390625    4.76837158203125    7.152557373046875    -75.58596041567432
25    6.556510925292969    5.9604644775390625    7.152557373046875    -31.452358893347537
26    6.854534149169922    6.556510925292969    7.152557373046875    -5.023311687583373
27    7.003545761108398    6.854534149169922    7.152557373046875    9.351256697903352
28    6.92903995513916    6.854534149169922    7.003545761108398    2.065234155078201
29    6.891787052154541    6.854534149169922    6.92903995513916    -1.5035682574081761
30    6.910413503646851    6.891787052154541    6.92903995513916    0.2746811890004892
31    6.901100277900696    6.891787052154541    6.910413503646851    -0.6159790507817888
32    6.905756890773773    6.901100277900696    6.910413503646851    -0.17103311295772983
33    6.908085197210312    6.905756890773773    6.910413503646851    0.05172795463928992
34    6.9069210439920425    6.905756890773773    6.908085197210312    -0.059676595271554334
35    6.907503120601177    6.9069210439920425    6.908085197210312    -0.003980324935838553
36    6.9077941589057446    6.907503120601177    6.908085197210312    0.02387231362286002
37    6.907648639753461    6.907503120601177    6.9077941589057446    0.009945619045481635
38    6.907575880177319    6.907503120601177    6.907648639753461    0.002982553231476004
39    6.907539500389248    6.907503120601177    6.907575880177319    -0.0004989093078791029
40    6.907557690283284    6.907539500389248    6.907575880177319    0.0012418160978882042
41    6.907548595336266    6.907539500389248    6.907557690283284    0.00037145192897014567
42    6.907544047862757    6.907539500389248    6.907548595336266    -6.372905590978917e-5
43    6.907546321599511    6.907544047862757    6.907548595336266    0.00015386134492700876
44    6.907545184731134    6.907544047862757    6.907546321599511    4.5066121600711995e-5
45    6.907544616296946    6.907544047862757    6.907545184731134    -9.331472881513037e-6
46    6.90754490051404    6.907544616296946    6.907545184731134    1.7867322924303153e-5
47    6.907544758405493    6.907544616296946    6.90754490051404    4.2679246519128355e-6
48    6.907544687351219    6.907544616296946    6.907544758405493    -2.531774185854374e-6
49    6.907544722878356    6.907544687351219    6.907544758405493    8.680752330292307e-7
50    6.907544705114788    6.907544687351219    6.907544722878356    -8.318495190451358e-7
51    6.907544713996572    6.907544705114788    6.907544722878356    1.811281435948331e-8
52    6.90754470955568    6.907544705114788    6.907544713996572    -4.068683097102621e-7
53    6.907544711776126    6.90754470955568    6.907544713996572    -1.9437777609709883e-7
54    6.907544712886349    6.907544711776126    6.907544713996572    -8.813245244709833e-8
55    6.90754471344146    6.907544712886349    6.907544713996572    -3.5009804832952796e-8
56    6.907544713719016    6.90754471344146    6.907544713996572    -8.448466815025313e-9
57    6.907544713857794    6.907544713719016    6.907544713996572    4.832173772228998e-9
58    6.907544713788405    6.907544713719016    6.907544713857794    -1.8081323105434421e-9
59    6.9075447138231    6.907544713788405    6.907544713857794    1.5120633634069236e-9
60    6.907544713805752    6.907544713788405    6.9075447138231    -1.4807710613240488e-10
61    6.907544713814426    6.907544713805752    6.9075447138231    6.819789177825442e-10
62    6.907544713810089    6.907544713805752    6.907544713814426    2.6696511667978484e-10
63    6.907544713807921    6.907544713805752    6.907544713810089    5.951505954726599e-11
converged to 6.907544713807921 after 63 iteration

makie plot

All packages loaded successfully.

grpahs using GLMakie ## more with dataframe

["year,language", "1951,Regional Assembly Language", "1952,Autocode", "1954,IPL", "1955,FLOW-MATIC", "1957,FORTRAN", "1957,COMTRAN", "1958,LISP", "1958,ALGOL 58", "1959,FACT", "1959,COBOL", "1959,RPG", "1962,APL", "1962,Simula", "1962,SNOBOL", "1963,CPL", "1964,Speakeasy", "1964,BASIC", "1964,PL/I", "1966,JOSS", "1967,BCPL", "1968,Logo", "1969,B", "1970,Pascal", "1970,Forth", "1972,C", "1972,Smalltalk", "1972,Prolog", "1973,ML", "1975,Scheme", "1978,SQL ", "1980,C++ ", "1983,Ada", "1984,Common Lisp", "1984,MATLAB", "1984,dBase III", "1985,Eiffel", "1986,Objective-C", "1986,LabVIEW ", "1986,Erlang", "1987,Perl", "1988,Tcl", "1988,Wolfram Language ", "1989,FL ", "1990,Haskell", "1991,Python", "1991,Visual Basic", "1993,Lua", "1993,R", "1994,CLOS ", "1995,Ruby", "1995,Ada 95", "1995,Java", "1995,Delphi ", "1995,JavaScript", "1995,PHP", "1997,Rebol", "2000,ActionScript", "2001,C#", "2001,D", "2002,Scratch", "2003,Groovy", "2003,Scala", "2005,F#", "2006,PowerShell", "2007,Clojure", "2009,Go", "2010,Rust", "2011,Dart", "2011,Kotlin", "2011,Red", "2011,Elixir", "2012,Julia", "2014,Swift"]
73×2 DataFrame
 Row │ year   language
     │ Int64  String31
─────┼───────────────────────────────────
   1 │  1951  Regional Assembly Language
   2 │  1952  Autocode
   3 │  1954  IPL
   4 │  1955  FLOW-MATIC
   5 │  1957  FORTRAN
   6 │  1957  COMTRAN
   7 │  1958  LISP
   8 │  1958  ALGOL 58
   9 │  1959  FACT
  10 │  1959  COBOL
  11 │  1959  RPG
  12 │  1962  APL
  13 │  1962  Simula
  14 │  1962  SNOBOL
  15 │  1963  CPL
  16 │  1964  Speakeasy
  17 │  1964  BASIC
  18 │  1964  PL/I
  19 │  1966  JOSS
  20 │  1967  BCPL
  21 │  1968  Logo
  22 │  1969  B
  23 │  1970  Pascal
  24 │  1970  Forth
  25 │  1972  C
  26 │  1972  Smalltalk
  27 │  1972  Prolog
  28 │  1973  ML
  29 │  1975  Scheme
  30 │  1978  SQL
  31 │  1980  C++
  32 │  1983  Ada
  33 │  1984  Common Lisp
  34 │  1984  MATLAB
  35 │  1984  dBase III
  36 │  1985  Eiffel
  37 │  1986  Objective-C
  38 │  1986  LabVIEW
  39 │  1986  Erlang
  40 │  1987  Perl
  41 │  1988  Tcl
  42 │  1988  Wolfram Language
  43 │  1989  FL
  44 │  1990  Haskell
  45 │  1991  Python
  46 │  1991  Visual Basic
  47 │  1993  Lua
  48 │  1993  R
  49 │  1994  CLOS
  50 │  1995  Ruby
  51 │  1995  Ada 95
  52 │  1995  Java
  53 │  1995  Delphi
  54 │  1995  JavaScript
  55 │  1995  PHP
  56 │  1997  Rebol
  57 │  2000  ActionScript
  58 │  2001  C#
  59 │  2001  D
  60 │  2002  Scratch
  61 │  2003  Groovy
  62 │  2003  Scala
  63 │  2005  F#
  64 │  2006  PowerShell
  65 │  2007  Clojure
  66 │  2009  Go
  67 │  2010  Rust
  68 │  2011  Dart
  69 │  2011  Kotlin
  70 │  2011  Red
  71 │  2011  Elixir
  72 │  2012  Julia
  73 │  2014  Swift
(73, 2)
2×7 DataFrame
 Row │ variable  mean     min       median  max        nmissing  eltype
     │ Symbol    Union…   Any       Union…  Any        Int64     DataType
─────┼────────────────────────────────────────────────────────────────────
   1 │ year      1982.99  1951      1986.0  2014              0  Int64
   2 │ language           ALGOL 58          dBase III         0  String31
4×2 DataFrame
 Row │ year   language
     │ Int64  String31
─────┼─────────────────
   1 │  2011  Dart
   2 │  2011  Kotlin
   3 │  2011  Red
   4 │  2011  Elixir
2×2 DataFrame
 Row │ year   language
     │ Int64  String31
─────┼─────────────────
   1 │  1993  R
   2 │  2011  Red
73×2 DataFrame
 Row │ language                    cnt
     │ String31                    Int64
─────┼───────────────────────────────────
   1 │ Regional Assembly Language      1
   2 │ Autocode                        1
   3 │ IPL                             1
   4 │ FLOW-MATIC                      1
   5 │ FORTRAN                         1
   6 │ COMTRAN                         1
   7 │ LISP                            1
   8 │ ALGOL 58                        1
   9 │ FACT                            1
  10 │ COBOL                           1
  11 │ RPG                             1
  12 │ APL                             1
  13 │ Simula                          1
  14 │ SNOBOL                          1
  15 │ CPL                             1
  16 │ Speakeasy                       1
  17 │ BASIC                           1
  18 │ PL/I                            1
  19 │ JOSS                            1
  20 │ BCPL                            1
  21 │ Logo                            1
  22 │ B                               1
  23 │ Pascal                          1
  24 │ Forth                           1
  25 │ C                               1
  26 │ Smalltalk                       1
  27 │ Prolog                          1
  28 │ ML                              1
  29 │ Scheme                          1
  30 │ SQL                             1
  31 │ C++                             1
  32 │ Ada                             1
  33 │ Common Lisp                     1
  34 │ MATLAB                          1
  35 │ dBase III                       1
  36 │ Eiffel                          1
  37 │ Objective-C                     1
  38 │ LabVIEW                         1
  39 │ Erlang                          1
  40 │ Perl                            1
  41 │ Tcl                             1
  42 │ Wolfram Language                1
  43 │ FL                              1
  44 │ Haskell                         1
  45 │ Python                          1
  46 │ Visual Basic                    1
  47 │ Lua                             1
  48 │ R                               1
  49 │ CLOS                            1
  50 │ Ruby                            1
  51 │ Ada 95                          1
  52 │ Java                            1
  53 │ Delphi                          1
  54 │ JavaScript                      1
  55 │ PHP                             1
  56 │ Rebol                           1
  57 │ ActionScript                    1
  58 │ C#                              1
  59 │ D                               1
  60 │ Scratch                         1
  61 │ Groovy                          1
  62 │ Scala                           1
  63 │ F#                              1
  64 │ PowerShell                      1
  65 │ Clojure                         1
  66 │ Go                              1
  67 │ Rust                            1
  68 │ Dart                            1
  69 │ Kotlin                          1
  70 │ Red                             1
  71 │ Elixir                          1
  72 │ Julia                           1
  73 │ Swift                           1
45×2 DataFrame
 Row │ year   cnt
     │ Int64  Int64
─────┼──────────────
   1 │  1951      1
   2 │  1952      1
   3 │  1954      1
   4 │  1955      1
   5 │  1957      2
   6 │  1958      2
   7 │  1959      3
   8 │  1962      3
   9 │  1963      1
  10 │  1964      3
  11 │  1966      1
  12 │  1967      1
  13 │  1968      1
  14 │  1969      1
  15 │  1970      2
  16 │  1972      3
  17 │  1973      1
  18 │  1975      1
  19 │  1978      1
  20 │  1980      1
  21 │  1983      1
  22 │  1984      3
  23 │  1985      1
  24 │  1986      3
  25 │  1987      1
  26 │  1988      2
  27 │  1989      1
  28 │  1990      1
  29 │  1991      2
  30 │  1993      2
  31 │  1994      1
  32 │  1995      6
  33 │  1997      1
  34 │  2000      1
  35 │  2001      2
  36 │  2002      1
  37 │  2003      2
  38 │  2005      1
  39 │  2006      1
  40 │  2007      1
  41 │  2009      1
  42 │  2010      1
  43 │  2011      4
  44 │  2012      1
  45 │  2014      1
16×2 DataFrame
 Row │ year   cnt
     │ Int64  Int64
─────┼──────────────
   1 │  1957      2
   2 │  1958      2
   3 │  1959      3
   4 │  1962      3
   5 │  1964      3
   6 │  1970      2
   7 │  1972      3
   8 │  1984      3
   9 │  1986      3
  10 │  1988      2
  11 │  1991      2
  12 │  1993      2
  13 │  1995      6
  14 │  2001      2
  15 │  2003      2
  16 │  2011      4

ggplot graphs from julia

transferring dataframe to R

forecasting

scikit learn

All packages loaded successfully.

########################################
using logistic regression of scikitlearn
########################################
accuracy: 0.94
[0.9090909090909091, 0.9090909090909091, 0.9, 0.8888888888888888, 1.0]
0.9214141414141415 --- 0.044709950748680026

####################################
using Random forest from scikitlearn
####################################
[0.9090909090909091, 0.8181818181818182, 0.9, 0.8888888888888888, 1.0]
0.9032323232323233 --- 0.06490007073737401

#############################
using gradient boosting model
#############################
[0.9090909090909091, 0.8181818181818182, 0.9, 0.8888888888888888, 1.0]
0.9032323232323233 --- 0.06490007073737401

#########################
using decision tree model
#########################
[0.9090909090909091, 0.8181818181818182, 0.9, 0.8888888888888888, 1.0]
0.9032323232323233 --- 0.06490007073737401

##
hi
##

fitting data using curvefit package

fitted equation with polynomial :- 24.2809 + 5.00783*x - 2.77871*x^2 + 0.913072*x^3 - 0.0887008*x^4 + 0.00284204*x^5
given value:- 8.456
predicted value:- 89.37775043707526
16×5 DataFrame
 Row │ x        y         yp        diff        diff2
     │ Float64  Float64   Float64   Float64     Float64
─────┼─────────────────────────────────────────────────────
   1 │     0.0   23.2988   24.2809    0.982084    0.964489
   2 │     1.0   32.4526   27.3373   -5.1153     26.1663
   3 │     2.0   15.9293   29.1581   13.2287    174.999
   4 │     3.0   51.7123   32.4548  -19.2574    370.849
   5 │     4.0   28.6556   38.4924    9.83678    96.7623
   6 │     5.0   36.7128   47.4297   10.717     114.853
   7 │     6.0   72.6562   58.6614  -13.9948    195.856
   8 │     7.0   74.7773   71.1582   -3.61915    13.0983
   9 │     8.0   71.7125   83.8084   12.0959    146.312
  10 │     9.0   98.4208   95.7589   -2.66182     7.08529
  11 │    10.0  105.912   106.756     0.844395    0.713003
  12 │    11.0  131.397   117.487   -13.9102    193.494
  13 │    12.0  115.372   129.919    14.5467    211.607
  14 │    13.0  146.969   147.645     0.676121    0.457139
  15 │    14.0  182.962   176.219    -6.7432     45.4708
  16 │    15.0  221.127   223.501     2.3742      5.63681
1604.3242241468913
1:15 --- [37.06068977676915, 46.34033787266742, 69.54393490463906, 44.54533495128814, 73.28311957710237, 80.5294137715672, 79.24090025087456, 141.66766436385825, 144.02799909372706, 153.36189396281418, 187.34979593984932, 228.81142875260755, 217.04530680138464, 268.26007526906113, 295.1324663209221]
1.67556 + 50.7076*x - 18.5013*x^2 + 3.14092*x^3 - 0.216981*x^4 + 0.00537528*x^5
110.83570187826422
[42.61172335542394, 44.76709048817514, 50.223752217648915, 58.76091874225561, 70.15780026040558, 84.19360697050917, 100.64754907097672, 119.29883676021862, 139.92668023664515, 162.3102896986667, 186.22887534469362, 211.4616473731363, 237.78781598240496, 264.9865913709101, 292.83718373706193]
"C:\\jr29102024\\j135b.png"

load images using quarto

## mlj revised

7×4 DataFrame
 Row │ name                package_name  is_supervised  abstract_type
     │ String              String        Bool           DataType
─────┼────────────────────────────────────────────────────────────────
   1 │ EpsilonSVR          LIBSVM                 true  Deterministic
   2 │ LinearSVC           LIBSVM                 true  Deterministic
   3 │ NuSVC               LIBSVM                 true  Deterministic
   4 │ NuSVR               LIBSVM                 true  Deterministic
   5 │ ProbabilisticNuSVC  LIBSVM                 true  Probabilistic
   6 │ ProbabilisticSVC    LIBSVM                 true  Probabilistic
   7 │ SVC                 LIBSVM                 true  Deterministic
Accuracy: 0.92
MLJBase.ConfusionMatrixObject{3}([17 0 0; 0 16 2; 0 2 13], ["setosa", "versicolor", "virginica"])
Accuracy: 0.92
MLJBase.ConfusionMatrixObject{3}([17 0 0; 0 16 2; 0 2 13], ["setosa", "versicolor", "virginica"])
NeuralNetworkClassifier(layers = nothing, …)
***
*** Training  for 200 epochs with algorithm ADAM.
Training..   avg loss on epoch 1 (1):    1.2475527752165407
Training of 200 epoch completed. Final epoch error: 1.301778717558459.
Accuracy: 0.33999999999999997
MLJBase.ConfusionMatrixObject{3}([17 18 15; 0 0 0; 0 0 0], ["setosa", "versicolor", "virginica"])
KNeighborsClassifier(n_neighbors = 5, …)
Accuracy: 0.92
MLJBase.ConfusionMatrixObject{3}([17 0 0; 0 15 1; 0 3 14], ["setosa", "versicolor", "virginica"])

calling jupyter notebook & executing it

x
x^6 - 1 = 0
Sym{PyObject}[-1, 1, -1/2 - sqrt(3)*I/2, -1/2 + sqrt(3)*I/2, 1/2 - sqrt(3)*I/2, 1/2 + sqrt(3)*I/2]
-1
1
-1/2 - sqrt(3)*I/2
-1/2 + sqrt(3)*I/2
1/2 - sqrt(3)*I/2
1/2 + sqrt(3)*I/2
-1
1
-0.5 - 0.8660254037844386im
-0.5 + 0.8660254037844386im
0.5 - 0.8660254037844386im
0.5 + 0.8660254037844386im
(x - 1)*(x + 1)*(x^2 - x + 1)*(x^2 + x + 1)
"# This file is machine-generated - editing it directly is not advised\njulia_version = \"1.10.5\"\nmanifest_format = \"2.0\"\nproject_hash = \"b5deeec79cbe7ef7ec3532a388bf78a5a547b2fb\"\n[[deps.ArgTools]]\nuuid = \"0dad84c5-d112-42e6-8d28-ef12dabb789f\"\nversion = \"1.1.1\"\n[[deps.Arti" ⋯ 39194 bytes ⋯ "l]]\ndeps = [\"Artifacts\", \"JLLWrappers\", \"Libdl\", \"Pkg\", \"Wayland_jll\", \"Wayland_protocols_jll\", \"Xorg_libxcb_jll\", \"Xorg_xkeyboard_config_jll\"]\ngit-tree-sha1 = \"9c304562909ab2bab0262639bd4f444d7bc2be37\"\nuuid = \"d8fb68d0-12a3-5cfd-a85a-d49703b185fd\"\nversion = \"1.4.1+1\"\n"

classification using cross validation

[1.0, 1.0, 0.8333333333333334, 0.9, 0.8]
0.9066666666666666
0.09249624617007737
true

calling qt.html

learning scikit learn julia package

learning scikit learn julia package

using scikitlearn julia package

PyObject LogisticRegression(max_iter=2000)
accuracy: 0.9733333333333334
[0.9666666666666667, 1.0, 0.9333333333333333, 0.9666666666666667, 1.0]
0.9733333333333334---0.027888667551135848
PyObject DecisionTreeClassifier()
accuracy: 1.0
[0.9666666666666667, 0.9666666666666667, 0.9, 1.0, 1.0]
0.9666666666666668---0.0408248290463863
PyObject RandomForestClassifier()
accuracy: 1.0
[0.9666666666666667, 0.9666666666666667, 0.9333333333333333, 0.9666666666666667, 1.0]
0.9666666666666668---0.02357022603955158
PyObject AdaBoostClassifier()
accuracy: 0.96
[0.9666666666666667, 0.9333333333333333, 0.9, 0.9333333333333333, 1.0]
0.9466666666666667---0.03800584750330459
PyObject SVC()
accuracy: 0.9733333333333334
[0.9666666666666667, 0.9666666666666667, 0.9666666666666667, 0.9333333333333333, 1.0]
0.9666666666666666---0.02357022603955158

using mlj julia package

DecisionTreeClassifier(max_depth = -1, …)

#############################################
simple decision tree without cross validation
#############################################
Accuracy: 0.92

##########################################
simple decision tree with cross validation
##########################################
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

testbase.jl

installing & updating packages if needed
Installing XGBoost...
Status `C:\Users\DELL\.julia\environments\v1.10\Project.toml`
  [009559a3] XGBoost v2.5.1
XGBoost was already installed & it is up to date...
packages loaded for use...
┌──────────────────────────────────────┬──────────────────────────────┬───────────────┐
│                                 name │                 package_name │ is_supervised │
│                               String │                       String │          Bool │
├──────────────────────────────────────┼──────────────────────────────┼───────────────┤
│                         ABODDetector │    OutlierDetectionNeighbors │         false │
│                         ABODDetector │       OutlierDetectionPython │         false │
│                         ARDRegressor │      MLJScikitLearnInterface │          true │
│                   AdaBoostClassifier │      MLJScikitLearnInterface │          true │
│                    AdaBoostRegressor │      MLJScikitLearnInterface │          true │
│              AdaBoostStumpClassifier │                 DecisionTree │          true │
│                  AffinityPropagation │      MLJScikitLearnInterface │         false │
│              AgglomerativeClustering │      MLJScikitLearnInterface │         false │
│                          AutoEncoder │                       BetaML │         false │
│                      BM25Transformer │                      MLJText │         false │
│                    BaggingClassifier │      MLJScikitLearnInterface │          true │
│                     BaggingRegressor │      MLJScikitLearnInterface │          true │
│                          BayesianLDA │      MLJScikitLearnInterface │          true │
│                          BayesianLDA │            MultivariateStats │          true │
│                          BayesianQDA │      MLJScikitLearnInterface │          true │
│               BayesianRidgeRegressor │      MLJScikitLearnInterface │          true │
│                  BayesianSubspaceLDA │            MultivariateStats │          true │
│                BernoulliNBClassifier │      MLJScikitLearnInterface │          true │
│                                Birch │      MLJScikitLearnInterface │         false │
│                      BisectingKMeans │      MLJScikitLearnInterface │         false │
│                     BorderlineSMOTE1 │                    Imbalance │         false │
│                        CBLOFDetector │       OutlierDetectionPython │         false │
│                           CDDetector │       OutlierDetectionPython │         false │
│                          COFDetector │    OutlierDetectionNeighbors │         false │
│                          COFDetector │       OutlierDetectionPython │         false │
│                        COPODDetector │       OutlierDetectionPython │         false │
│                   CatBoostClassifier │                     CatBoost │          true │
│                    CatBoostRegressor │                     CatBoost │          true │
│                  ClusterUndersampler │                    Imbalance │         false │
│               ComplementNBClassifier │      MLJScikitLearnInterface │          true │
│                   ConstantClassifier │                    MLJModels │          true │
│                    ConstantRegressor │                    MLJModels │          true │
│                    ContinuousEncoder │                    MLJModels │         false │
│                     CountTransformer │                      MLJText │         false │
│                               DBSCAN │                   Clustering │         false │
│                               DBSCAN │      MLJScikitLearnInterface │         false │
│                          DNNDetector │    OutlierDetectionNeighbors │         false │
│               DecisionTreeClassifier │                       BetaML │          true │
│               DecisionTreeClassifier │                 DecisionTree │          true │
│                DecisionTreeRegressor │                       BetaML │          true │
│                DecisionTreeRegressor │                 DecisionTree │          true │
│      DeterministicConstantClassifier │                    MLJModels │          true │
│       DeterministicConstantRegressor │                    MLJModels │          true │
│                      DummyClassifier │      MLJScikitLearnInterface │          true │
│                       DummyRegressor │      MLJScikitLearnInterface │          true │
│                         ECODDetector │       OutlierDetectionPython │         false │
│                      ENNUndersampler │                    Imbalance │         false │
│                ElasticNetCVRegressor │      MLJScikitLearnInterface │          true │
│                  ElasticNetRegressor │              MLJLinearModels │          true │
│                  ElasticNetRegressor │      MLJScikitLearnInterface │          true │
│                           EpsilonSVR │                       LIBSVM │          true │
│                   EvoLinearRegressor │                    EvoLinear │          true │
│                   EvoSplineRegressor │                    EvoLinear │          true │
│                    EvoTreeClassifier │                     EvoTrees │          true │
│                         EvoTreeCount │                     EvoTrees │          true │
│                      EvoTreeGaussian │                     EvoTrees │          true │
│                           EvoTreeMLE │                     EvoTrees │          true │
│                     EvoTreeRegressor │                     EvoTrees │          true │
│                 ExtraTreesClassifier │      MLJScikitLearnInterface │          true │
│                  ExtraTreesRegressor │      MLJScikitLearnInterface │          true │
│                       FactorAnalysis │            MultivariateStats │         false │
│                 FeatureAgglomeration │      MLJScikitLearnInterface │         false │
│                      FeatureSelector │                    MLJModels │         false │
│                          FillImputer │                    MLJModels │         false │
│                          GMMDetector │       OutlierDetectionPython │         false │
│             GaussianMixtureClusterer │                       BetaML │         false │
│               GaussianMixtureImputer │                       BetaML │         false │
│             GaussianMixtureRegressor │                       BetaML │          true │
│                 GaussianNBClassifier │      MLJScikitLearnInterface │          true │
│                 GaussianNBClassifier │                   NaiveBayes │          true │
│            GaussianProcessClassifier │      MLJScikitLearnInterface │          true │
│             GaussianProcessRegressor │      MLJScikitLearnInterface │          true │
│                       GeneralImputer │                       BetaML │         false │
│           GradientBoostingClassifier │      MLJScikitLearnInterface │          true │
│            GradientBoostingRegressor │      MLJScikitLearnInterface │          true │
│                         HBOSDetector │       OutlierDetectionPython │         false │
│                              HDBSCAN │      MLJScikitLearnInterface │         false │
│               HierarchicalClustering │                   Clustering │         false │
│       HistGradientBoostingClassifier │      MLJScikitLearnInterface │          true │
│        HistGradientBoostingRegressor │      MLJScikitLearnInterface │          true │
│                       HuberRegressor │              MLJLinearModels │          true │
│                       HuberRegressor │      MLJScikitLearnInterface │          true │
│                                  ICA │            MultivariateStats │         false │
│                      IForestDetector │       OutlierDetectionPython │         false │
│                         INNEDetector │       OutlierDetectionPython │         false │
│                      ImageClassifier │                      MLJFlux │          true │
│               InteractionTransformer │                    MLJModels │         false │
│                          KDEDetector │       OutlierDetectionPython │         false │
│                               KMeans │                   Clustering │         false │
│                               KMeans │      MLJScikitLearnInterface │         false │
│                               KMeans │               ParallelKMeans │         false │
│                      KMeansClusterer │                       BetaML │         false │
│                             KMedoids │                   Clustering │         false │
│                    KMedoidsClusterer │                       BetaML │         false │
│                        KNNClassifier │        NearestNeighborModels │          true │
│                          KNNDetector │    OutlierDetectionNeighbors │         false │
│                          KNNDetector │       OutlierDetectionPython │         false │
│                         KNNRegressor │        NearestNeighborModels │          true │
│                 KNeighborsClassifier │      MLJScikitLearnInterface │          true │
│                  KNeighborsRegressor │      MLJScikitLearnInterface │          true │
│                        KPLSRegressor │ PartialLeastSquaresRegressor │          true │
│                            KernelPCA │            MultivariateStats │         false │
│           KernelPerceptronClassifier │                       BetaML │          true │
│                         LADRegressor │              MLJLinearModels │          true │
│                                  LDA │            MultivariateStats │          true │
│                       LGBMClassifier │                     LightGBM │          true │
│                        LGBMRegressor │                     LightGBM │          true │
│                         LMDDDetector │       OutlierDetectionPython │         false │
│                         LOCIDetector │       OutlierDetectionPython │         false │
│                         LODADetector │       OutlierDetectionPython │         false │
│                          LOFDetector │    OutlierDetectionNeighbors │         false │
│                          LOFDetector │       OutlierDetectionPython │         false │
│                      LarsCVRegressor │      MLJScikitLearnInterface │          true │
│                        LarsRegressor │      MLJScikitLearnInterface │          true │
│                     LassoCVRegressor │      MLJScikitLearnInterface │          true │
│                 LassoLarsCVRegressor │      MLJScikitLearnInterface │          true │
│                 LassoLarsICRegressor │      MLJScikitLearnInterface │          true │
│                   LassoLarsRegressor │      MLJScikitLearnInterface │          true │
│                       LassoRegressor │              MLJLinearModels │          true │
│                       LassoRegressor │      MLJScikitLearnInterface │          true │
│               LinearBinaryClassifier │                          GLM │          true │
│                 LinearCountRegressor │                          GLM │          true │
│                      LinearRegressor │                          GLM │          true │
│                      LinearRegressor │              MLJLinearModels │          true │
│                      LinearRegressor │      MLJScikitLearnInterface │          true │
│                      LinearRegressor │            MultivariateStats │          true │
│                            LinearSVC │                       LIBSVM │          true │
│                 LogisticCVClassifier │      MLJScikitLearnInterface │          true │
│                   LogisticClassifier │              MLJLinearModels │          true │
│                   LogisticClassifier │      MLJScikitLearnInterface │          true │
│                          MCDDetector │       OutlierDetectionPython │         false │
│                            MeanShift │      MLJScikitLearnInterface │         false │
│                      MiniBatchKMeans │      MLJScikitLearnInterface │         false │
│       MultiTaskElasticNetCVRegressor │      MLJScikitLearnInterface │          true │
│         MultiTaskElasticNetRegressor │      MLJScikitLearnInterface │          true │
│            MultiTaskLassoCVRegressor │      MLJScikitLearnInterface │          true │
│              MultiTaskLassoRegressor │      MLJScikitLearnInterface │          true │
│                MultinomialClassifier │              MLJLinearModels │          true │
│              MultinomialNBClassifier │      MLJScikitLearnInterface │          true │
│              MultinomialNBClassifier │                   NaiveBayes │          true │
│  MultitargetGaussianMixtureRegressor │                       BetaML │          true │
│             MultitargetKNNClassifier │        NearestNeighborModels │          true │
│              MultitargetKNNRegressor │        NearestNeighborModels │          true │
│           MultitargetLinearRegressor │            MultivariateStats │          true │
│    MultitargetNeuralNetworkRegressor │                       BetaML │          true │
│    MultitargetNeuralNetworkRegressor │                      MLJFlux │          true │
│            MultitargetRidgeRegressor │            MultivariateStats │          true │
│               MultitargetSRRegressor │           SymbolicRegression │          true │
│              NeuralNetworkClassifier │                       BetaML │          true │
│              NeuralNetworkClassifier │                      MLJFlux │          true │
│               NeuralNetworkRegressor │                       BetaML │          true │
│               NeuralNetworkRegressor │                      MLJFlux │          true │
│                                NuSVC │                       LIBSVM │          true │
│                                NuSVR │                       LIBSVM │          true │
│                        OCSVMDetector │       OutlierDetectionPython │         false │
│                               OPTICS │      MLJScikitLearnInterface │         false │
│                          OneClassSVM │                       LIBSVM │         false │
│                        OneHotEncoder │                    MLJModels │         false │
│                    OneRuleClassifier │                      OneRule │          true │
│ OrthogonalMatchingPursuitCVRegressor │      MLJScikitLearnInterface │          true │
│   OrthogonalMatchingPursuitRegressor │      MLJScikitLearnInterface │          true │
│                                  PCA │            MultivariateStats │         false │
│                          PCADetector │       OutlierDetectionPython │         false │
│                         PLSRegressor │ PartialLeastSquaresRegressor │          true │
│                                 PPCA │            MultivariateStats │         false │
│                               PartLS │                PartitionedLS │          true │
│          PassiveAggressiveClassifier │      MLJScikitLearnInterface │          true │
│           PassiveAggressiveRegressor │      MLJScikitLearnInterface │          true │
│                    PegasosClassifier │                       BetaML │          true │
│                 PerceptronClassifier │                       BetaML │          true │
│                 PerceptronClassifier │      MLJScikitLearnInterface │          true │
│                   ProbabilisticNuSVC │                       LIBSVM │          true │
│           ProbabilisticSGDClassifier │      MLJScikitLearnInterface │          true │
│                     ProbabilisticSVC │                       LIBSVM │          true │
│                    QuantileRegressor │              MLJLinearModels │          true │
│                      RANSACRegressor │      MLJScikitLearnInterface │          true │
│                          RODDetector │       OutlierDetectionPython │         false │
│                                 ROSE │                    Imbalance │         false │
│               RandomForestClassifier │                       BetaML │          true │
│               RandomForestClassifier │                 DecisionTree │          true │
│               RandomForestClassifier │      MLJScikitLearnInterface │          true │
│                  RandomForestImputer │                       BetaML │         false │
│                RandomForestRegressor │                       BetaML │          true │
│                RandomForestRegressor │                 DecisionTree │          true │
│                RandomForestRegressor │      MLJScikitLearnInterface │          true │
│                    RandomOversampler │                    Imbalance │         false │
│                   RandomUndersampler │                    Imbalance │         false │
│                RandomWalkOversampler │                    Imbalance │         false │
│                    RidgeCVClassifier │      MLJScikitLearnInterface │          true │
│                     RidgeCVRegressor │      MLJScikitLearnInterface │          true │
│                      RidgeClassifier │      MLJScikitLearnInterface │          true │
│                       RidgeRegressor │              MLJLinearModels │          true │
│                       RidgeRegressor │      MLJScikitLearnInterface │          true │
│                       RidgeRegressor │            MultivariateStats │          true │
│                      RobustRegressor │              MLJLinearModels │          true │
│                        SGDClassifier │      MLJScikitLearnInterface │          true │
│                         SGDRegressor │      MLJScikitLearnInterface │          true │
│                                SMOTE │                    Imbalance │         false │
│                               SMOTEN │                    Imbalance │         false │
│                              SMOTENC │                    Imbalance │         false │
│                          SODDetector │       OutlierDetectionPython │         false │
│                          SOSDetector │       OutlierDetectionPython │         false │
│                          SRRegressor │           SymbolicRegression │          true │
│                                  SVC │                       LIBSVM │          true │
│                        SVMClassifier │      MLJScikitLearnInterface │          true │
│                  SVMLinearClassifier │      MLJScikitLearnInterface │          true │
│                   SVMLinearRegressor │      MLJScikitLearnInterface │          true │
│                      SVMNuClassifier │      MLJScikitLearnInterface │          true │
│                       SVMNuRegressor │      MLJScikitLearnInterface │          true │
│                         SVMRegressor │      MLJScikitLearnInterface │          true │
│                    SelfOrganizingMap │           SelfOrganizingMaps │         false │
│                        SimpleImputer │                       BetaML │         false │
│                   SpectralClustering │      MLJScikitLearnInterface │         false │
│               StableForestClassifier │                        SIRUS │          true │
│                StableForestRegressor │                        SIRUS │          true │
│                StableRulesClassifier │                        SIRUS │          true │
│                 StableRulesRegressor │                        SIRUS │          true │
│                         Standardizer │                    MLJModels │         false │
│                          SubspaceLDA │            MultivariateStats │          true │
│                      TSVDTransformer │                         TSVD │         false │
│                     TfidfTransformer │                      MLJText │         false │
│                    TheilSenRegressor │      MLJScikitLearnInterface │          true │
│                    TomekUndersampler │                    Imbalance │         false │
│          UnivariateBoxCoxTransformer │                    MLJModels │         false │
│                UnivariateDiscretizer │                    MLJModels │         false │
│                UnivariateFillImputer │                    MLJModels │         false │
│               UnivariateStandardizer │                    MLJModels │         false │
│       UnivariateTimeTypeToContinuous │                    MLJModels │         false │
│                    XGBoostClassifier │                      XGBoost │          true │
│                         XGBoostCount │                      XGBoost │          true │
│                     XGBoostRegressor │                      XGBoost │          true │
└──────────────────────────────────────┴──────────────────────────────┴───────────────┘

┌──────────────────────────────────────┬──────────────────────────────┬───────────────┐
│                                 name │                 package_name │ is_supervised │
│                               String │                       String │          Bool │
├──────────────────────────────────────┼──────────────────────────────┼───────────────┤
│                         ARDRegressor │      MLJScikitLearnInterface │          true │
│                   AdaBoostClassifier │      MLJScikitLearnInterface │          true │
│                    AdaBoostRegressor │      MLJScikitLearnInterface │          true │
│              AdaBoostStumpClassifier │                 DecisionTree │          true │
│                    BaggingClassifier │      MLJScikitLearnInterface │          true │
│                     BaggingRegressor │      MLJScikitLearnInterface │          true │
│                          BayesianLDA │      MLJScikitLearnInterface │          true │
│                          BayesianLDA │            MultivariateStats │          true │
│                          BayesianQDA │      MLJScikitLearnInterface │          true │
│               BayesianRidgeRegressor │      MLJScikitLearnInterface │          true │
│                  BayesianSubspaceLDA │            MultivariateStats │          true │
│                BernoulliNBClassifier │      MLJScikitLearnInterface │          true │
│                   CatBoostClassifier │                     CatBoost │          true │
│                    CatBoostRegressor │                     CatBoost │          true │
│               ComplementNBClassifier │      MLJScikitLearnInterface │          true │
│                   ConstantClassifier │                    MLJModels │          true │
│                    ConstantRegressor │                    MLJModels │          true │
│               DecisionTreeClassifier │                       BetaML │          true │
│               DecisionTreeClassifier │                 DecisionTree │          true │
│                DecisionTreeRegressor │                       BetaML │          true │
│                DecisionTreeRegressor │                 DecisionTree │          true │
│      DeterministicConstantClassifier │                    MLJModels │          true │
│       DeterministicConstantRegressor │                    MLJModels │          true │
│                      DummyClassifier │      MLJScikitLearnInterface │          true │
│                       DummyRegressor │      MLJScikitLearnInterface │          true │
│                ElasticNetCVRegressor │      MLJScikitLearnInterface │          true │
│                  ElasticNetRegressor │              MLJLinearModels │          true │
│                  ElasticNetRegressor │      MLJScikitLearnInterface │          true │
│                           EpsilonSVR │                       LIBSVM │          true │
│                   EvoLinearRegressor │                    EvoLinear │          true │
│                   EvoSplineRegressor │                    EvoLinear │          true │
│                    EvoTreeClassifier │                     EvoTrees │          true │
│                         EvoTreeCount │                     EvoTrees │          true │
│                      EvoTreeGaussian │                     EvoTrees │          true │
│                           EvoTreeMLE │                     EvoTrees │          true │
│                     EvoTreeRegressor │                     EvoTrees │          true │
│                 ExtraTreesClassifier │      MLJScikitLearnInterface │          true │
│                  ExtraTreesRegressor │      MLJScikitLearnInterface │          true │
│             GaussianMixtureRegressor │                       BetaML │          true │
│                 GaussianNBClassifier │      MLJScikitLearnInterface │          true │
│                 GaussianNBClassifier │                   NaiveBayes │          true │
│            GaussianProcessClassifier │      MLJScikitLearnInterface │          true │
│             GaussianProcessRegressor │      MLJScikitLearnInterface │          true │
│           GradientBoostingClassifier │      MLJScikitLearnInterface │          true │
│            GradientBoostingRegressor │      MLJScikitLearnInterface │          true │
│       HistGradientBoostingClassifier │      MLJScikitLearnInterface │          true │
│        HistGradientBoostingRegressor │      MLJScikitLearnInterface │          true │
│                       HuberRegressor │              MLJLinearModels │          true │
│                       HuberRegressor │      MLJScikitLearnInterface │          true │
│                      ImageClassifier │                      MLJFlux │          true │
│                        KNNClassifier │        NearestNeighborModels │          true │
│                         KNNRegressor │        NearestNeighborModels │          true │
│                 KNeighborsClassifier │      MLJScikitLearnInterface │          true │
│                  KNeighborsRegressor │      MLJScikitLearnInterface │          true │
│                        KPLSRegressor │ PartialLeastSquaresRegressor │          true │
│           KernelPerceptronClassifier │                       BetaML │          true │
│                         LADRegressor │              MLJLinearModels │          true │
│                                  LDA │            MultivariateStats │          true │
│                       LGBMClassifier │                     LightGBM │          true │
│                        LGBMRegressor │                     LightGBM │          true │
│                      LarsCVRegressor │      MLJScikitLearnInterface │          true │
│                        LarsRegressor │      MLJScikitLearnInterface │          true │
│                     LassoCVRegressor │      MLJScikitLearnInterface │          true │
│                 LassoLarsCVRegressor │      MLJScikitLearnInterface │          true │
│                 LassoLarsICRegressor │      MLJScikitLearnInterface │          true │
│                   LassoLarsRegressor │      MLJScikitLearnInterface │          true │
│                       LassoRegressor │              MLJLinearModels │          true │
│                       LassoRegressor │      MLJScikitLearnInterface │          true │
│               LinearBinaryClassifier │                          GLM │          true │
│                 LinearCountRegressor │                          GLM │          true │
│                      LinearRegressor │                          GLM │          true │
│                      LinearRegressor │              MLJLinearModels │          true │
│                      LinearRegressor │      MLJScikitLearnInterface │          true │
│                      LinearRegressor │            MultivariateStats │          true │
│                            LinearSVC │                       LIBSVM │          true │
│                 LogisticCVClassifier │      MLJScikitLearnInterface │          true │
│                   LogisticClassifier │              MLJLinearModels │          true │
│                   LogisticClassifier │      MLJScikitLearnInterface │          true │
│       MultiTaskElasticNetCVRegressor │      MLJScikitLearnInterface │          true │
│         MultiTaskElasticNetRegressor │      MLJScikitLearnInterface │          true │
│            MultiTaskLassoCVRegressor │      MLJScikitLearnInterface │          true │
│              MultiTaskLassoRegressor │      MLJScikitLearnInterface │          true │
│                MultinomialClassifier │              MLJLinearModels │          true │
│              MultinomialNBClassifier │      MLJScikitLearnInterface │          true │
│              MultinomialNBClassifier │                   NaiveBayes │          true │
│  MultitargetGaussianMixtureRegressor │                       BetaML │          true │
│             MultitargetKNNClassifier │        NearestNeighborModels │          true │
│              MultitargetKNNRegressor │        NearestNeighborModels │          true │
│           MultitargetLinearRegressor │            MultivariateStats │          true │
│    MultitargetNeuralNetworkRegressor │                       BetaML │          true │
│    MultitargetNeuralNetworkRegressor │                      MLJFlux │          true │
│            MultitargetRidgeRegressor │            MultivariateStats │          true │
│               MultitargetSRRegressor │           SymbolicRegression │          true │
│              NeuralNetworkClassifier │                       BetaML │          true │
│              NeuralNetworkClassifier │                      MLJFlux │          true │
│               NeuralNetworkRegressor │                       BetaML │          true │
│               NeuralNetworkRegressor │                      MLJFlux │          true │
│                                NuSVC │                       LIBSVM │          true │
│                                NuSVR │                       LIBSVM │          true │
│                    OneRuleClassifier │                      OneRule │          true │
│ OrthogonalMatchingPursuitCVRegressor │      MLJScikitLearnInterface │          true │
│   OrthogonalMatchingPursuitRegressor │      MLJScikitLearnInterface │          true │
│                         PLSRegressor │ PartialLeastSquaresRegressor │          true │
│                               PartLS │                PartitionedLS │          true │
│          PassiveAggressiveClassifier │      MLJScikitLearnInterface │          true │
│           PassiveAggressiveRegressor │      MLJScikitLearnInterface │          true │
│                    PegasosClassifier │                       BetaML │          true │
│                 PerceptronClassifier │                       BetaML │          true │
│                 PerceptronClassifier │      MLJScikitLearnInterface │          true │
│                   ProbabilisticNuSVC │                       LIBSVM │          true │
│           ProbabilisticSGDClassifier │      MLJScikitLearnInterface │          true │
│                     ProbabilisticSVC │                       LIBSVM │          true │
│                    QuantileRegressor │              MLJLinearModels │          true │
│                      RANSACRegressor │      MLJScikitLearnInterface │          true │
│               RandomForestClassifier │                       BetaML │          true │
│               RandomForestClassifier │                 DecisionTree │          true │
│               RandomForestClassifier │      MLJScikitLearnInterface │          true │
│                RandomForestRegressor │                       BetaML │          true │
│                RandomForestRegressor │                 DecisionTree │          true │
│                RandomForestRegressor │      MLJScikitLearnInterface │          true │
│                    RidgeCVClassifier │      MLJScikitLearnInterface │          true │
│                     RidgeCVRegressor │      MLJScikitLearnInterface │          true │
│                      RidgeClassifier │      MLJScikitLearnInterface │          true │
│                       RidgeRegressor │              MLJLinearModels │          true │
│                       RidgeRegressor │      MLJScikitLearnInterface │          true │
│                       RidgeRegressor │            MultivariateStats │          true │
│                      RobustRegressor │              MLJLinearModels │          true │
│                        SGDClassifier │      MLJScikitLearnInterface │          true │
│                         SGDRegressor │      MLJScikitLearnInterface │          true │
│                          SRRegressor │           SymbolicRegression │          true │
│                                  SVC │                       LIBSVM │          true │
│                        SVMClassifier │      MLJScikitLearnInterface │          true │
│                  SVMLinearClassifier │      MLJScikitLearnInterface │          true │
│                   SVMLinearRegressor │      MLJScikitLearnInterface │          true │
│                      SVMNuClassifier │      MLJScikitLearnInterface │          true │
│                       SVMNuRegressor │      MLJScikitLearnInterface │          true │
│                         SVMRegressor │      MLJScikitLearnInterface │          true │
│               StableForestClassifier │                        SIRUS │          true │
│                StableForestRegressor │                        SIRUS │          true │
│                StableRulesClassifier │                        SIRUS │          true │
│                 StableRulesRegressor │                        SIRUS │          true │
│                          SubspaceLDA │            MultivariateStats │          true │
│                    TheilSenRegressor │      MLJScikitLearnInterface │          true │
│                    XGBoostClassifier │                      XGBoost │          true │
│                         XGBoostCount │                      XGBoost │          true │
│                     XGBoostRegressor │                      XGBoost │          true │
└──────────────────────────────────────┴──────────────────────────────┴───────────────┘


#############################
using decisiontree classifier
#############################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         17 │         2 │
│    virginica │      0 │          1 │        11 │
└──────────────┴────────┴────────────┴───────────┘


#####################################
using decisiontree classifier with cv
#####################################
machine(DecisionTreeClassifier(max_depth = -1, …), …)
Cross-validation results: PerformanceEvaluation(0.0,)
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

#############################
using randomforest classifier
#############################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         17 │         2 │
│    virginica │      0 │          1 │        11 │
└──────────────┴────────┴────────────┴───────────┘


#####################################
using randomforest classifier with cv
#####################################
machine(RandomForestClassifier(n_estimators = 100, …), …)
Cross-validation results: PerformanceEvaluation(0.0,)
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

#########################
using adaboost classifier
#########################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         17 │         2 │
│    virginica │      0 │          1 │        11 │
└──────────────┴────────┴────────────┴───────────┘


#################################
using adaboost classifier with cv
#################################
machine(AdaBoostClassifier(estimator = nothing, …), …)
Cross-validation results: PerformanceEvaluation(0.0,)
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

####################
using svm classifier
####################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         16 │         3 │
│    virginica │      0 │          0 │        12 │
└──────────────┴────────┴────────────┴───────────┘


############################
using svm classifier with cv
############################
machine(SVMClassifier(C = 1.0, …), …)
Cross-validation results: PerformanceEvaluation(0.893,)
[1.0, 1.0, 0.8333333333333334, 0.9333333333333333, 0.7]
Cross-Validation Results: PerformanceEvaluation(0.893,)
0.8933333333333333----0.12780193008453877

####################################
using logistic regression classifier
####################################
0.96
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         17 │         2 │
│    virginica │      0 │          0 │        12 │
└──────────────┴────────┴────────────┴───────────┘


#############################################
using logistic regression classifier with cv 
#############################################
machine(LogisticClassifier(penalty = l2, …), …)
Cross-validation results: PerformanceEvaluation(0.0,)
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

########################
using xgboost classifier
########################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         17 │         2 │
│    virginica │      0 │          1 │        11 │
└──────────────┴────────┴────────────┴───────────┘


#####################################
using decisiontree classifier with cv
#####################################
machine(XGBoostClassifier(test = 1, …), …)
Cross-validation results: PerformanceEvaluation(0.0,)
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

#######################
using ridged classifier
#######################
0.78
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         12 │         7 │
│    virginica │      0 │          4 │         8 │
└──────────────┴────────┴────────────┴───────────┘


###############################
using ridge classifier with cv 
###############################
machine(RidgeClassifier(alpha = 1.0, …), …)
Cross-validation results: PerformanceEvaluation(0.667,)
[1.0, 0.8, 0.23333333333333334, 0.7666666666666667, 0.5333333333333333]
Cross-Validation Results: PerformanceEvaluation(0.667,)
0.6666666666666666----0.29344694769431684

###################################
using knearest neighbour classifier
###################################
0.92
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         15 │         4 │
│    virginica │      0 │          0 │        12 │
└──────────────┴────────┴────────────┴───────────┘


###########################################
using knearest neighbour classifier with cv
###########################################
machine(KNeighborsClassifier(n_neighbors = 5, …), …)
Cross-validation results: PerformanceEvaluation(0.0,)
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

########################
using bagging classifier
########################
0.94
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     19 │          0 │         0 │
│   versicolor │      0 │         16 │         3 │
│    virginica │      0 │          0 │        12 │
└──────────────┴────────┴────────────┴───────────┘


################################
using bagging classifier with cv
################################
machine(BaggingClassifier(estimator = nothing, …), …)
Cross-validation results: PerformanceEvaluation(0.0,)
[0.0, 0.0, 0.0, 0.0, 0.0]
Cross-Validation Results: PerformanceEvaluation(0.0,)
0.0----0.0

hypertuning with MLJ1

1    1    1    1    1    1   0.667
1    1    2    1    1    1   0.667
1    1    3    1    1    1   0.667
1    1    4    1    1    1   0.667
1    1    5    1    1    1   0.667
1    2    1    1    1    1   0.667
1    2    2    1    1    1   0.667
1    2    3    1    1    1   0.667
1    2    4    1    1    1   0.667
1    2    5    1    1    1   0.667
1    3    1    1    1    1   0.667
1    3    2    1    1    1   0.667
1    3    3    1    1    1   0.667
1    3    4    1    1    1   0.667
1    3    5    1    1    1   0.667
1    4    1    1    1    1   0.667
1    4    2    1    1    1   0.667
1    4    3    1    1    1   0.667
1    4    4    1    1    1   0.667
1    4    5    1    1    1   0.667
1    5    1    1    1    1   0.667

1    5    2    1    1    1   0.667
1    5    3    1    1    1   0.667
1    5    4    1    1    1   0.667
1    5    5    1    1    1   0.667
2    1    1    2    1    1   0.96
2    1    2    2    1    1   0.96
2    1    3    2    1    1   0.96
2    1    4    2    1    1   0.96
2    1    5    2    1    1   0.96

2    2    1    2    1    1   0.96
2    2    2    2    1    1   0.96
2    2    3    2    1    1   0.96
2    2    4    2    1    1   0.96
2    2    5    2    1    1   0.96
2    3    1    2    1    1   0.96
2    3    2    2    1    1   0.96
2    3    3    2    1    1   0.96
2    3    4    2    1    1   0.96
2    3    5    2    1    1   0.96
2    4    1    2    1    1   0.96
2    4    2    2    1    1   0.96
2    4    3    2    1    1   0.96
2    4    4    2    1    1   0.96
2    4    5    2    1    1   0.96

2    5    1    2    1    1   0.96
2    5    2    2    1    1   0.96
2    5    3    2    1    1   0.96
2    5    4    2    1    1   0.96
2    5    5    2    1    1   0.96
3    1    1    3    1    1   0.973

3    1    2    3    1    2   0.973
3    1    3    3    1    3   0.973
3    1    4    3    1    4   0.973
3    1    5    3    1    5   0.973
3    2    1    3    2    1   0.973
3    2    2    3    2    2   0.973
3    2    3    3    2    3   0.973
3    2    4    3    2    4   0.973
3    2    5    3    2    5   0.973
3    3    1    3    3    1   0.973
3    3    2    3    3    2   0.973
3    3    3    3    3    3   0.973
3    3    4    3    3    4   0.973
3    3    5    3    3    5   0.973
3    4    1    3    4    1   0.973
3    4    2    3    4    2   0.973
3    4    3    3    4    3   0.973
3    4    4    3    4    4   0.973
3    4    5    3    4    5   0.973
3    5    1    3    5    1   0.973
3    5    2    3    5    2   0.973
3    5    3    3    5    3   0.973
3    5    4    3    5    4   0.973
3    5    5    3    5    5   0.973
4    1    1    4    1    1   0.993
4    1    2    4    1    1   0.993

4    1    3    4    1    1   0.993
4    1    4    4    1    1   0.993
4    1    5    4    1    1   0.993
4    2    1    4    2    1   0.993
4    2    2    4    2    1   0.993

4    2    3    4    2    1   0.993
4    2    4    4    2    1   0.993
4    2    5    4    2    1   0.993

4    3    1    4    3    1   0.993
4    3    2    4    3    1   0.993
4    3    3    4    3    1   0.993
4    3    4    4    3    1   0.993
4    3    5    4    3    1   0.993
4    4    1    4    3    1   0.993
4    4    2    4    3    1   0.993
4    4    3    4    3    1   0.993
4    4    4    4    3    1   0.993
4    4    5    4    3    1   0.993
4    5    1    4    3    1   0.993
4    5    2    4    3    1   0.993

4    5    3    4    3    1   0.993
4    5    4    4    3    1   0.993
4    5    5    4    3    1   0.993
5    1    1    5    1    1   1.0
5    1    2    5    1    1   1.0
5    1    3    5    1    1   1.0
5    1    4    5    1    1   1.0
5    1    5    5    1    1   1.0
5    2    1    5    1    1   1.0
5    2    2    5    1    1   1.0
5    2    3    5    1    1   1.0
5    2    4    5    1    1   1.0
5    2    5    5    1    1   1.0
5    3    1    5    1    1   1.0

5    3    2    5    1    1   1.0
5    3    3    5    1    1   1.0
5    3    4    5    1    1   1.0

5    3    5    5    1    1   1.0
5    4    1    5    1    1   1.0
5    4    2    5    1    1   1.0
5    4    3    5    1    1   1.0
5    4    4    5    1    1   1.0
5    4    5    5    1    1   1.0
5    5    1    5    1    1   1.0

5    5    2    5    1    1   1.0
5    5    3    5    1    1   1.0
5    5    4    5    1    1   1.0
5    5    5    5    1    1   1.0
6    1    1    5    1    1   1.0
6    1    2    5    1    1   1.0
6    1    3    5    1    1   1.0

6    1    4    5    1    1   1.0
6    1    5    5    1    1   1.0
6    2    1    5    1    1   1.0
6    2    2    5    1    1   1.0
6    2    3    5    1    1   1.0
6    2    4    5    1    1   1.0
6    2    5    5    1    1   1.0
6    3    1    5    1    1   1.0
6    3    2    5    1    1   1.0
6    3    3    5    1    1   1.0
6    3    4    5    1    1   1.0
6    3    5    5    1    1   1.0
6    4    1    5    1    1   1.0
6    4    2    5    1    1   1.0
6    4    3    5    1    1   1.0

6    4    4    5    1    1   1.0
6    4    5    5    1    1   1.0
6    5    1    5    1    1   1.0
6    5    2    5    1    1   1.0
6    5    3    5    1    1   1.0
6    5    4    5    1    1   1.0
6    5    5    5    1    1   1.0

7    1    1    5    1    1   1.0
7    1    2    5    1    1   1.0
7    1    3    5    1    1   1.0

7    1    4    5    1    1   1.0
7    1    5    5    1    1   1.0

7    2    1    5    1    1   1.0
7    2    2    5    1    1   1.0
7    2    3    5    1    1   1.0
7    2    4    5    1    1   1.0
7    2    5    5    1    1   1.0
7    3    1    5    1    1   1.0
7    3    2    5    1    1   1.0
7    3    3    5    1    1   1.0
7    3    4    5    1    1   1.0
7    3    5    5    1    1   1.0
7    4    1    5    1    1   1.0
7    4    2    5    1    1   1.0
7    4    3    5    1    1   1.0

7    4    4    5    1    1   1.0
7    4    5    5    1    1   1.0
7    5    1    5    1    1   1.0
7    5    2    5    1    1   1.0
7    5    3    5    1    1   1.0
7    5    4    5    1    1   1.0

7    5    5    5    1    1   1.0
8    1    1    5    1    1   1.0
8    1    2    5    1    1   1.0
8    1    3    5    1    1   1.0
8    1    4    5    1    1   1.0
8    1    5    5    1    1   1.0
8    2    1    5    1    1   1.0
8    2    2    5    1    1   1.0
8    2    3    5    1    1   1.0
8    2    4    5    1    1   1.0
8    2    5    5    1    1   1.0
8    3    1    5    1    1   1.0
8    3    2    5    1    1   1.0

8    3    3    5    1    1   1.0
8    3    4    5    1    1   1.0
8    3    5    5    1    1   1.0
8    4    1    5    1    1   1.0
8    4    2    5    1    1   1.0
8    4    3    5    1    1   1.0

8    4    4    5    1    1   1.0
8    4    5    5    1    1   1.0

8    5    1    5    1    1   1.0
8    5    2    5    1    1   1.0
8    5    3    5    1    1   1.0
8    5    4    5    1    1   1.0
8    5    5    5    1    1   1.0
9    1    1    5    1    1   1.0
9    1    2    5    1    1   1.0
9    1    3    5    1    1   1.0
9    1    4    5    1    1   1.0
9    1    5    5    1    1   1.0
9    2    1    5    1    1   1.0
9    2    2    5    1    1   1.0
9    2    3    5    1    1   1.0
9    2    4    5    1    1   1.0
9    2    5    5    1    1   1.0
9    3    1    5    1    1   1.0
9    3    2    5    1    1   1.0
9    3    3    5    1    1   1.0
9    3    4    5    1    1   1.0
9    3    5    5    1    1   1.0

9    4    1    5    1    1   1.0
9    4    2    5    1    1   1.0
9    4    3    5    1    1   1.0
9    4    4    5    1    1   1.0
9    4    5    5    1    1   1.0

9    5    1    5    1    1   1.0
9    5    2    5    1    1   1.0
9    5    3    5    1    1   1.0
9    5    4    5    1    1   1.0
9    5    5    5    1    1   1.0
10    1    1    5    1    1   1.0
10    1    2    5    1    1   1.0
10    1    3    5    1    1   1.0
10    1    4    5    1    1   1.0
10    1    5    5    1    1   1.0

10    2    1    5    1    1   1.0
10    2    2    5    1    1   1.0
10    2    3    5    1    1   1.0
10    2    4    5    1    1   1.0
10    2    5    5    1    1   1.0
10    3    1    5    1    1   1.0

10    3    2    5    1    1   1.0
10    3    3    5    1    1   1.0
10    3    4    5    1    1   1.0
10    3    5    5    1    1   1.0
10    4    1    5    1    1   1.0
10    4    2    5    1    1   1.0
10    4    3    5    1    1   1.0
10    4    4    5    1    1   1.0
10    4    5    5    1    1   1.0
10    5    1    5    1    1   1.0
10    5    2    5    1    1   1.0
10    5    3    5    1    1   1.0
10    5    4    5    1    1   1.0
10    5    5    5    1    1   1.0
5    1    1    1.0

best model accuracy:-1.0
┌──────────────┬────────┬────────────┬───────────┐
│ Actual_Class │ setosa │ versicolor │ virginica │
│       String │  Int64 │      Int64 │     Int64 │
├──────────────┼────────┼────────────┼───────────┤
│       setosa │     50 │          0 │         0 │
│   versicolor │      0 │         50 │         0 │
│    virginica │      0 │          0 │        50 │
└──────────────┴────────┴────────────┴───────────┘
accuracy by using function in jm module

accuracy:-1.0

xirr calculations

installing & updating packages if needed
Installing Dates...
Status `C:\Users\DELL\.julia\environments\v1.10\Project.toml`
  [ade2ca70] Dates
Dates was already installed & it is up to date...
Installing ForwardDiff...
Status `C:\Users\DELL\.julia\environments\v1.10\Project.toml`
  [f6369f11] ForwardDiff v0.10.38
ForwardDiff was already installed & it is up to date...
Installing Roots...
Status `C:\Users\DELL\.julia\environments\v1.10\Project.toml`
⌃ [f2b01f46] Roots v2.2.1
Info Packages marked with ⌃ have new versions available and may be upgradable.
Roots was already installed & it is up to date...
packages loaded for use...
2023-11-23   -11000
2023-12-08   -1000
2024-04-02   -38000
2025-03-31   53870
XIRR: 7.142322773631976
Adjusted Cash Flows: [-11000.0, -1000.0, -38000.0, 53846.98808073147]
Calculated IRR: 0.07100000000000024

data science with Python

sample

5.0 12.0 13.0

import csv file using panda

     sepal_length  sepal_width  petal_length  petal_width    species
0             5.1          3.5           1.4          0.2     setosa
1             4.9          3.0           1.4          0.2        NaN
2             4.7          3.2           1.3          0.2     setosa
3             NaN          3.1           1.5          0.2     setosa
4             5.0          3.6           NaN          0.2        NaN
..            ...          ...           ...          ...        ...
145           6.7          3.0           5.2          2.3  virginica
146           6.3          NaN           5.0          1.9  virginica
147           6.5          NaN           5.2          2.0  virginica
148           6.2          3.4           5.4          2.3  virginica
149           5.9          3.0           5.1          1.8  virginica

[150 rows x 5 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150 entries, 0 to 149
Data columns (total 5 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   sepal_length  138 non-null    float64
 1   sepal_width   134 non-null    float64
 2   petal_length  138 non-null    float64
 3   petal_width   134 non-null    float64
 4   species       131 non-null    object 
dtypes: float64(4), object(1)
memory usage: 6.0+ KB
       sepal_length  sepal_width  petal_length  petal_width
count    138.000000   134.000000    138.000000   134.000000
mean       5.786957     3.058955      3.821739     1.182090
std        0.789329     0.423770      1.776519     0.760871
min        4.300000     2.000000      1.000000     0.100000
25%        5.100000     2.800000      1.600000     0.300000
50%        5.700000     3.000000      4.400000     1.300000
75%        6.400000     3.300000      5.100000     1.800000
max        7.900000     4.400000      6.900000     2.500000
150
5
array(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',
       'species'], dtype=object)

how to remove missing values in python using pandas

sepal_length    12
sepal_width     16
petal_length    12
petal_width     16
species         19
dtype: int64
     sepal_length  sepal_width  petal_length  petal_width    species
0             5.1          3.5           1.4          0.2     setosa
1             4.9          3.0           1.4          0.2     setosa
2             4.7          3.2           1.3          0.2     setosa
3             4.7          3.1           1.5          0.2     setosa
4             5.0          3.6           1.5          0.2     setosa
..            ...          ...           ...          ...        ...
145           6.7          3.0           5.2          2.3  virginica
146           6.3          3.0           5.0          1.9  virginica
147           6.5          3.0           5.2          2.0  virginica
148           6.2          3.4           5.4          2.3  virginica
149           5.9          3.0           5.1          1.8  virginica

[150 rows x 5 columns]
sepal_length    0
sepal_width     0
petal_length    0
petal_width     0
species         0
dtype: int64