Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization
Starting with mlsauce’s next release (v0.9.0, for Python and R), you’ll be able to download a plethora of datasets for your statistical/machine learning experiments (this is a work in progress, it will done from a GitHub branch today). These datasets come from the R-universe, and you’ll be able to use them no matter whether you’re working with Python or R.
In the R-universe (new CRAN in disguise?), among other things, there’s an automated package-building workflow for all the common platforms (Linux, macOS and Windows). There’s also an open data API, whose usage underlies what’s described in this post. Remember to cite datasets’ sources. A good practice in packaging R datasets is to provide their references, but I’m guilty of not having done it everytime ;)
Warning, this paragraph may sound a little bit cryptic, but feel free to skip it: In the examples below, you can pass additional – optional – parameters to the dowload function, which are those used by requests.get and pd.DataFrame. Unfortunately, mlsauce’s documentation is not up-to-date, because keras-autodoc was discontinued, and I need to find a previous version of Sphinx that would work with my keras-autodoc’s fork. * Sigh * … I’m eyeing pdoc or mkdocstrings. Anything Markdown, actually.
Contents
Dowload a dataset in Python
Install
!pip install git+https://github.com/Techtonique/mlsauce.git@feature-branch
Import data
import mlsauce as ms
# `ms.download` parameters
# pkgname="MASS"
# dataset="Boston"
# source="https://cran.r-universe.dev/"
# the controversial Boston data set
df1 = ms.download(dataset="Boston")
print(f"===== df1: \n {df1} \n")
print(f"===== df1.dtypes: \n {df1.dtypes}")
print("\n====================================================== \n")
# the controversial Boston data set
df2 = ms.download(dataset="Insurance")
print(f"===== df2: \n {df2} \n")
print(f"===== df2.dtypes: \n {df2.dtypes}")
===== df1:
crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
0 0.0063 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0
1 0.0273 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6
2 0.0273 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7
3 0.0324 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4
4 0.0690 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2
.. ... ... ... ... ... ... ... ... ... ... ... ... ... ...
501 0.0626 0.0 11.93 0 0.573 6.593 69.1 2.4786 1 273 21.0 391.99 9.67 22.4
502 0.0453 0.0 11.93 0 0.573 6.120 76.7 2.2875 1 273 21.0 396.90 9.08 20.6
503 0.0608 0.0 11.93 0 0.573 6.976 91.0 2.1675 1 273 21.0 396.90 5.64 23.9
504 0.1096 0.0 11.93 0 0.573 6.794 89.3 2.3889 1 273 21.0 393.45 6.48 22.0
505 0.0474 0.0 11.93 0 0.573 6.030 80.8 2.5050 1 273 21.0 396.90 7.88 11.9
[506 rows x 14 columns]
===== df1.dtypes:
crim float64
zn float64
indus float64
chas int64
nox float64
rm float64
age float64
dis float64
rad int64
tax int64
ptratio float64
black float64
lstat float64
medv float64
dtype: object
======================================================
===== df2:
District Group Age Holders Claims
0 1 <1l <25 197 38
1 1 <1l 25-29 264 35
2 1 <1l 30-35 246 20
3 1 <1l >35 1680 156
4 1 1-1.5l <25 284 63
.. ... ... ... ... ...
59 4 1.5-2l >35 344 63
60 4 >2l <25 3 0
61 4 >2l 25-29 16 6
62 4 >2l 30-35 25 8
63 4 >2l >35 114 33
[64 rows x 5 columns]
===== df2.dtypes:
District object
Group object
Age object
Holders int64
Claims int64
dtype: object
Dowload a dataset in R
Install
remotes::install_github("Techtonique/mlsauce_r@dev-branch")
Import data
The controversial Boston dataset.
df <- mlsauce::download(pkgname = "MASS",
dataset = "Boston",
source = "https://cran.r-universe.dev/")
print(head(df))
crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
1 0.0063 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98 24.0
2 0.0273 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14 21.6
3 0.0273 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03 34.7
4 0.0324 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94 33.4
5 0.0690 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33 36.2
6 0.0298 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21 28.7
print(summary(lm(medv ~ ., data = df)))
Call:
lm(formula = medv ~ ., data = df)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958230
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
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