https://github.com/jabhij/apple_dataanalyis_apachespark

Analyzed Apple's dataset to check how many people bought Airpods after buying Mac or iPhone. Thereafter, using ML and predictive analytics to check future outcomes.
https://github.com/jabhij/apple_dataanalyis_apachespark

apache-spark databricks mllib pyspark python sql

Last synced: about 21 hours ago
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Analyzed Apple's dataset to check how many people bought Airpods after buying Mac or iPhone. Thereafter, using ML and predictive analytics to check future outcomes.

• Host: GitHub
• URL: https://github.com/jabhij/apple_dataanalyis_apachespark
• Owner: jabhij
• Created: 2024-05-22T23:53:13.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-05-23T00:07:12.000Z (6 months ago)
• Last Synced: 2024-05-23T00:52:02.281Z (6 months ago)
• Topics: apache-spark, databricks, mllib, pyspark, python, sql
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Apple Data Analysis using Apache Spark

### (End-to-end Data Engineering and ML Project)

Analyzed Apple's dataset to check how many people bought Airpods after buying a Mac or iPhone. Thereafter, using ML and predictive analytics to check future outcomes. Therefore, as we can see this project has 3 phases-

#### Phase 1 | Data Analysis

  - Data Cleaning and Preprocessing

  - Exploratory Data Analysis

    

#### Phase 2 | Data Engeenering

  - Development of an end-to-end ETL/ELT data pipeline

    

#### Phase 3 | Machine Learning

  - Application of Statistical and mathematical models (Predictive Analytics)

## What I've learned?

I've learned in-depth application of different concepts and techniques like-

  - Databricks

    - Delta Tables

    - Parquet

  - Apache Spark

    - PySpark

    - Apache SQL

  - SQL

    - Broadcast Join

    -  Windows Functions- LAG and LEAD in SQL

  -  Factory Patterns

  -  MlLib

## Catch me

For any query, ping me on 

- LinkedIn: [@jabhij](https://www.linkedin.com/in/jabhij/)

- Twitter: [@jabhij](https://twitter.com/jabhij)

- Web: [LetUsTweak](http://letustweak.com)

Hope, it helps!! ヅ