https://github.com/niteshchawla/delhivery-featureengineering

The company wants to understand and process the data coming out of data engineering pipelines: • Clean, sanitize and manipulate data to get useful features out of raw fields • Make sense out of the raw data and help the data science team to build forecasting models on it
https://github.com/niteshchawla/delhivery-featureengineering

feature-engineering hypothesis-testing matplotlib-pyplot numpy pandas-python scipy-stats seaborn

Last synced: 8 days ago
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The company wants to understand and process the data coming out of data engineering pipelines: • Clean, sanitize and manipulate data to get useful features out of raw fields • Make sense out of the raw data and help the data science team to build forecasting models on it

• Host: GitHub
• URL: https://github.com/niteshchawla/delhivery-featureengineering
• Owner: Niteshchawla
• Created: 2024-06-28T10:24:09.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-28T10:26:25.000Z (5 months ago)
• Last Synced: 2024-06-28T11:44:39.353Z (5 months ago)
• Topics: feature-engineering, hypothesis-testing, matplotlib-pyplot, numpy, pandas-python, scipy-stats, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 3.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Delhivery-FeatureEngineering

About Delhivery

Delhivery is the largest and fastest-growing fully integrated player in India by revenue in Fiscal 2021. They aim to build the operating system for commerce, through a combination of world-class infrastructure, logistics operations of the highest quality, and cutting-edge engineering and technology capabilities.

The Data team builds intelligence and capabilities using this data that helps them to widen the gap between the quality, efficiency, and profitability of their business versus their competitors.

Objective:

The company wants to understand and process the data coming out of data engineering pipelines:

• Clean, sanitize and manipulate data to get useful features out of raw fields

• Make sense out of the raw data and help the data science team to build forecasting models on it