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: 3 months 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 (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-23T00:07:12.000Z (about 1 year ago)
- Last Synced: 2024-05-23T00:52:02.281Z (about 1 year ago)
- Topics: apache-spark, databricks, mllib, pyspark, python, sql
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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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