Learn machine learning the fun way, with Oracle and RedBull Racing

Overview

Red Bull Racing Analytics Hands-On Labs

License: UPL Quality gate

Introduction

Are you interested in learning machine learning (ML)? How about doing this in the context of the exciting world of F1 racing?! Get your ML skills bootstrapped here with Oracle and Red Bull Racing!

Red Bull F1 Race Car

This tutorial teaches ML analytics with a series of hands-on labs (HOLs) using the Data Science service in Oracle Cloud Infrastructure.

You'll learn how to get data from some public data sources, then how to analyze this data using some of the latest ML techniques. In the process you'll build ML models and test them out in a predictor app.

Getting Started

There is some infrastructure that must be deployed before you can enjoy this tutorial. See the Terraform documentation for more information.

After the OCI infrastructure is deployed, proceed with the beginner's tutorial to start through the ML labs.

Prerequisites

You must have an OCI account. Click here to create a new cloud account.

This solution is designed to work with several OCI services, allowing you to quickly be up-and-running:

There are required OCI resources (see the Terraform documentation for more information) that are needed for this tutorial.

Notes/Issues

None at this time.

URLs

Contributing

This project is open source. Please submit your contributions by forking this repository and submitting a pull request! Oracle appreciates any contributions that are made by the open source community.

License

Copyright (c) 2021 Oracle and/or its affiliates.

Licensed under the Universal Permissive License (UPL), Version 1.0.

See LICENSE for more details.

Comments
  • Refactored Terraform code

    Refactored Terraform code

    • Compatible with ORM, Cloud Shell and Terraform CLI
    • Updated README to include instructions for all three methods
    • Refactored, removing unnecessary resources (Vault, public Subnet, etc.).
    • Added a nerd knob so that it could use an existing Group (rather than create a new one)
    • Fixed ORM RegEx filters to allow dashes (-) and underscores (_), for the names
    opened by timclegg 2
  • Issue with hands on lab guide - launchapp.sh missing

    Issue with hands on lab guide - launchapp.sh missing

    https://github.com/oracle-devrel/redbull-analytics-hol/tree/main/beginners#beginners-hands-on-lab

    In Starting The Web Application it reads:

    cd /home/opc/redbull-analytics-hol/beginners/web ./launchapp.sh start

    However is launchapp.sh is missing, for example

    (redbullenv) cd /home/opc/redbull-analytics-hol/beginners/web (redbullenv) ./launchapp.sh start bash: ./launchapp.sh: No such file or directory

    opened by raekins 1
  • fix: Updating schema.yaml syntax

    fix: Updating schema.yaml syntax

    Making the variable notation follow what the doc syntax shows (https://docs.oracle.com/en-us/iaas/Content/ResourceManager/Concepts/terraformconfigresourcemanager_topic-schema.htm)

    opened by timclegg 1
  • Exploratory Data Analysis Merge Issue

    Exploratory Data Analysis Merge Issue

    Hello I have been encountering an issue while running the lab. The Jupyter notebook 03.f1_analysis_EDA.ipynb has the following issue on cell number 5:


    ValueError Traceback (most recent call last) in ----> 1 df1 = pd.merge(races,results,how='inner',on=['raceId']) 2 df2 = pd.merge(df1,quali,how='inner',on=['raceId','driverId','constructorId']) 3 df3 = pd.merge(df2,drivers,how='inner',on=['driverId']) 4 df4 = pd.merge(df3,constructors,how='inner',on=['constructorId']) 5 df5 = pd.merge(df4,circuit,how='inner',on=['circuitId'])

    ~/redbullenv/lib64/python3.6/site-packages/pandas/core/reshape/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate) 85 copy=copy, 86 indicator=indicator, ---> 87 validate=validate, 88 ) 89 return op.get_result()

    ~/redbullenv/lib64/python3.6/site-packages/pandas/core/reshape/merge.py in init(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate) 654 # validate the merge keys dtypes. We may need to coerce 655 # to avoid incompatible dtypes --> 656 self._maybe_coerce_merge_keys() 657 658 # If argument passed to validate,

    ~/redbullenv/lib64/python3.6/site-packages/pandas/core/reshape/merge.py in _maybe_coerce_merge_keys(self) 1163 inferred_right in string_types and inferred_left not in string_types 1164 ): -> 1165 raise ValueError(msg) 1166 1167 # datetimelikes must match exactly

    ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat

    I’m using an oracle automatic deployment provided by oracle as part of their environment. I do not have a lot of experience with Python but one possible ible solution is to read the numeric values form the csv file as integer or float but I’m almost certain the solution might be a little more elaborated than that 😉. Anyway thanks for your time. I’m really excited to test your solution and finish the lab. Thanks again.

    opened by yankodavila 2
  • Has the PAR for the stack deploy image expired.

    Has the PAR for the stack deploy image expired.

    Cannot deploy stack as getting PAR expired message.

    2021/11/07 10:50:11[TERRAFORM_CONSOLE] [INFO] Error Message: work request did not succeed, workId: ocid1.coreservicesworkrequest.oc1.eu-amsterdam-1.abqw2ljrwz2n7qqj7ghdwtnlrqol355oumc7a6coushvgdrebskspaewh7ea, entity: image, action: CREATED. Message: Import image not found: PAR is invalid (maybe is expired or deleted), please check.

    PAR in stack file is https://objectstorage.eu-frankfurt-1.oraclecloud.com/p/khhPjc_IMuyBOMfZUcJajIzCpoZ5aC-D7VMCU__GVZRlIQueXLIIcaaqLOZIuT1a/n/emeasespainsandbox/b/publichol/o/redbullhol-20210809-1523

    opened by Mel-A-M 1
Releases(v0.1.8)
Owner
Oracle DevRel
Oracle DevRel
:truck: Agile Data Preparation Workflows made easy with dask, cudf, dask_cudf and pyspark

To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges: Optimus is the missing framework to prof

Iron 1.3k Dec 30, 2022
Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video.

Datashredder is a simple data corruption engine written in python. You can corrupt anything text, images and video. You can chose the cha

2 Jul 22, 2022
AWS Glue ETL Code Samples

AWS Glue ETL Code Samples This repository has samples that demonstrate various aspects of the new AWS Glue service, as well as various AWS Glue utilit

AWS Samples 1.2k Jan 03, 2023
Shot notebooks resuming the main functions of GeoPandas

Shot notebooks resuming the main functions of GeoPandas, 2 notebooks written as Exercises to apply these functions.

1 Jan 12, 2022
Maximum Covariance Analysis in Python

xMCA | Maximum Covariance Analysis in Python The aim of this package is to provide a flexible tool for the climate science community to perform Maximu

Niclas Rieger 39 Jan 03, 2023
Data processing with Pandas.

Processing-data-with-python This is a simple example showing how to use Pandas to create a dataframe and the processing data with python. The jupyter

1 Jan 23, 2022
A lightweight, hub-and-spoke dashboard for multi-account Data Science projects

A lightweight, hub-and-spoke dashboard for cross-account Data Science Projects Introduction Modern Data Science environments often involve many indepe

AWS Samples 3 Oct 30, 2021
A Numba-based two-point correlation function calculator using a grid decomposition

A Numba-based two-point correlation function (2PCF) calculator using a grid decomposition. Like Corrfunc, but written in Numba, with simplicity and hackability in mind.

Lehman Garrison 3 Aug 24, 2022
A columnar data container that can be compressed.

Unmaintained Package Notice Unfortunately, and due to lack of resources, the Blosc Development Team is unable to maintain this package anymore. During

944 Dec 09, 2022
NFCDS Workshop Beginners Guide Bioinformatics Data Analysis

Genomics Workshop FIXME: overview of workshop Code of Conduct All participants s

Elizabeth Brooks 2 Jun 13, 2022
Multiple Pairwise Comparisons (Post Hoc) Tests in Python

scikit-posthocs is a Python package that provides post hoc tests for pairwise multiple comparisons that are usually performed in statistical data anal

Maksim Terpilowski 264 Dec 30, 2022
DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN

DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN. Allowing for both categorical and numerical data, DenseClus makes it possible to incorporate all features in cluste

Amazon Web Services - Labs 53 Dec 08, 2022
Data Analytics on Genomes and Genetics

Data Analytics performed on On genomes and Genetics dataset to predict genetic disorder and disorder subclass. DONE by TEAM SIGMA!

1 Jan 12, 2022
Catalogue data - A Python Scripts to prepare catalogue data

catalogue_data Scripts to prepare catalogue data. Setup Clone this repo. Install

BigScience Workshop 3 Mar 03, 2022
Investigating EV charging data

Investigating EV charging data Introduction: Got an opportunity to work with a home monitoring technology company over the last 6 months whose goal wa

Yash 2 Apr 07, 2022
A Big Data ETL project in PySpark on the historical NYC Taxi Rides data

Processing NYC Taxi Data using PySpark ETL pipeline Description This is an project to extract, transform, and load large amount of data from NYC Taxi

Unnikrishnan 2 Dec 12, 2021
A stock analysis app with streamlit

StockAnalysisApp A stock analysis app with streamlit. You select the ticker of the stock and the app makes a series of analysis by using the price cha

Antonio Catalano 50 Nov 27, 2022
Produces a summary CSV report of an Amber Electric customer's energy consumption and cost data.

Amber Electric Usage Summary This is a command line tool that produces a summary CSV report of an Amber Electric customer's energy consumption and cos

Graham Lea 12 May 26, 2022
This is an analysis and prediction project for house prices in King County, USA based on certain features of the house

This is a project for analysis and estimation of House Prices in King County USA The .csv file contains the data of the house and the .ipynb file con

Amit Prakash 1 Jan 21, 2022
Hidden Markov Models in Python, with scikit-learn like API

hmmlearn hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and

2.7k Jan 03, 2023