DataScience and Logistic Regression python scripts
jergauth : https://github.com/jeremie-gauthier
rmarracc : https://github.com/0auBSQ
- numpy
- matplotlib
- pandas
- seaborn
dataset
implies that both test_dataset
and train_dataset
are usable here.
Describe :
python3 describe.py dataset
Graphics :
python3 histogram.py train_dataset
python3 scatter_plot.py train_dataset
python3 pair_plot.py dataset
Logistic Regression training :
python3 logreg_train.py [-h] [-i ITER] [-l LEARNING] [-b BATCH] [-s] [-p] [-v]
train_dataset
positional arguments:
train_dataset select a valid train dataset
optional arguments:
-h, --help show this help message and exit
-i ITER, --iter ITER set the number of iterations (default to 1000)
-l LEARNING, --learning LEARNING
set the learning rate (default to 0.1)
-b BATCH, --batch BATCH
set the batch size for mini-batch gradient descent
algorithm
-s, --stochastic use the stochastic gradient descent algorithm
-p, --precision show the precision
-v, --visualizer show the resulting graphs
Logistic Regression prediction :
python3 logreg_predict.py [-h] [-s] test_dataset values
positional arguments:
test_dataset select a valid test dataset
values select a file with trained values
optional arguments:
-h, --help show this help message and exit
-s, --show show a 3D representation of the data