marianovila/Pro.Fin.-Proces.Datos

Proyecto de Finalización - Procesamiento de Datos - ISPC

Comisión Sala 1 - Cohorte 2022

GRUPO 2

Integrantes:

• Alvarez, Agustín
• Bolatti Cristofaro, María Carolina
• de Rosario Boero, Aldana Mailén
• Galván, Sebastián Alejandro
• Octtinger, Martina
• Siccardi, Luis
• Vila, Mariano Raúl

Datasets:

(2 tipos)

• MBTI https://www.kaggle.com/datasets/zeyadkhalid/mbti-personality-types-500-dataset
• Employee https://www.kaggle.com/datasets/tejashvi14/employee-future-prediction

Video Presentación:

• https://drive.google.com/file/d/1gvLA33aP4U0kzeeIOpRLlaFr18mwXo7W/view?usp=share_link

Reportes:

• MBTI https://drive.google.com/file/d/1rsTAg3ZC2-ggwnlNGQqK6enVQ9DvdPpK/view?usp=share_link
• Employee https://drive.google.com/file/d/1DzzCGrHSEerQkIOSMfsUE9UbhXGQcbuG/view?usp=share_link

Notebooks:

• MBTI https://colab.research.google.com/drive/19yV5MfRZb6b0bf7sHLlZ2B5xU25KrXeX?usp=sharing
• Employee https://colab.research.google.com/drive/13mrasACPF8qh-MNNnCNzgk9Nx8Y50wVJ?usp=sharing

Requerimientos:

Como el dataset de mbti supera los 100 MB hay instalar el git lfs para usar archivos grandes:

• https://docs.github.com/en/repositories/working-with-files/managing-large-files/configuring-git-large-file-storage

Colab

# instalacion de pandas
!python -V
!pip -V
!pip install pandas==1.5.3

# Clonacion de repositorio
from google.colab import drive
drive.mount('/content/drive')
! ls 'drive/MyDrive/Colab Notebooks/ProcesamientoDeDatos'
! cd 'drive/MyDrive/Colab Notebooks/ProcesamientoDeDatos'
! git clone https://github.com/Carolina-Bolatti/Pro.Fin.-Proces.Datos.git

# Actualizacion con github
from google.colab import drive
drive.mount('/content/drive')
%cd 'Pro.Fin.-Proces.Datos'
!git lfs install
!git lfs fetch
!git lfs pull
!git pull
!ls -lisa

# Chequeo que tenemos los datos
from google.colab import drive
drive.mount('/content/drive')
%cd 'Pro.Fin.-Proces.Datos'
!git lfs install
!ls -lrt data
!head -2 data/*

# Carga de datos de empleo
from google.colab import drive
drive.mount('/content/drive')
!ls
import pandas as pd
empleados = pd.read_csv('data/Employee.csv')
print(empleados)

VsCode

Crear entorono vitrual y activarlo:

python -m venv venv

venv\Script\activate

Insatar las dependencias:

pip install -r requirements.txt