DSLR

DataScience x Logistic Regression - School-42 project

Goals:

• Learn how to read a dataset, to visualize it in different ways, to select and clean unnecessary information from your data.
• Implement one-vs-all logistic regression that will solve classification problem

Look at subject.pdf for more information

Requirements:

• Python 3
• PyTorch
• NumPy
• Pandas
• Matplotlib

Setup:

git clone https://github.com/Gleonett/DSLR.git
cd DSLR
pip3 install -r requirements.txt

Get started:

For information about arguments each executable has -h flag

Data description

This are some visualization utils for dataset:

histogram.py	scatter_plot.py	clusters_3d_plot.py
Show course marks distribution	Show values for two courses using Cartesian coordinates	3D scatter plot of clusters

pair_plot.py is scatter_plot.py + histogram.py for all courses

describe.py is implementation of pandas.DataFrame.describe

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Training and Evaluating:

Accuracy with standard parameters is 0.99

Parameters for training, including batch size, are stored in config.yaml

• logreg_train.py saves data/weights.pt (use -v flag for loss history visualization)

stochastic GD	batch GD	GD

• logreg_predict.py takes data/weights.pt and saves data/houses.csv
• evaluate.py - logreg_train.py + logreg_predict.py and evaluating on dataset_truth.csv
• random_evaluate.py - training and evaluating on random splitted dataset_train.csv