Thorreznou.
Project description
Thorreznou Library 🖥
Bootcamp Data Science | Full Time Class - November 2021
Library for machine learning projects with Python. The aim is to speed up the work process to the repetitive tasks that the data scientist usually carries out. The library has three parts: EDA, ML and VIZ. Each part focuses on a field of work, exploratory data analysis, machine learning, visualisation.
Visualization
- visualizeME_palettes_or_colors
- visualizeME_and_describe_violinbox
- visualizeME_and_describe_barplot
- visualizeME_FigureWords
- visualizeME_bagel_look_top
- visualizeME_and_describe_Spidey
- visualizeME_c_matrix
- visualizeME_scores_models
Exploratory Data Analysis (EDA)
- resize_image
- standarize_numbers
- coordinates
- reduce_img
- process_color
- reduce_col_palette
- math_expect
- overview
- outlier_removal
- datetime_into_columns
- missing_data
Machine Learning
- divide
- prepare_data
- model_scoring_classification
- model_scoring_regression
- random_forest_classif
- SVC1
- LogistRegress
- corr_target
- knn
- poly_reg
- lin_reg
Launch library
pip install thorreznou
Authors 🕶
- Group VIS: Marta, Natalia, Óscar
- Group EDA: David, Isabella, Jorge
- Group ML: Dani, Fer, Ana
- Group LAUNCH: Javi, Fede, Erik
- Project Manager: Miguel
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Thorreznou-0.1.8.tar.gz
(143.2 kB
view details)
File details
Details for the file Thorreznou-0.1.8.tar.gz.
File metadata
- Download URL: Thorreznou-0.1.8.tar.gz
- Upload date:
- Size: 143.2 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.8.0 pkginfo/1.8.2 readme-renderer/32.0 requests/2.27.1 requests-toolbelt/0.9.1 urllib3/1.26.8 tqdm/4.63.0 importlib-metadata/3.10.1 keyring/23.4.0 rfc3986/2.0.0 colorama/0.4.4 CPython/3.7.12
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
d016469f8d66d9b6cb01a3c1919b6e4f6b66d92096a41c5c02f72d0017a6783e
|
|
| MD5 |
4716bdf6baedc0fd7f1ec2da7c27834d
|
|
| BLAKE2b-256 |
551aaf0f3386e22b5e9a42907a7d95359bbe9aa64229b90bd94ad201f497f9e2
|
