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
Candlestick Pattern Recognition with Python and TA-Lib

Candlestick-Pattern-Recognition-with-Python-and-TA-Lib Goal Look at the S&P500 to try and get a better understanding of these candlestick patterns and

Ganesh Jainarain 11 Oct 07, 2022
Making the DAEN information accessible.

The purpose of this repository is to make the information on Australian COVID-19 adverse events accessible. The Therapeutics Goods Administration (TGA) keeps a database of adverse reactions to medica

10 May 10, 2022
PySpark bindings for H3, a hierarchical hexagonal geospatial indexing system

h3-pyspark: Uber's H3 Hexagonal Hierarchical Geospatial Indexing System in PySpark PySpark bindings for the H3 core library. For available functions,

Kevin Schaich 12 Dec 24, 2022
A python package which can be pip installed to perform statistics and visualize binomial and gaussian distributions of the dataset

GBiStat package A python package to assist programmers with data analysis. This package could be used to plot : Binomial Distribution of the dataset p

Rishikesh S 4 Oct 17, 2022
Stream-Kafka-ELK-Stack - Weather data streaming using Apache Kafka and Elastic Stack.

Streaming Data Pipeline - Kafka + ELK Stack Streaming weather data using Apache Kafka and Elastic Stack. Data source: https://openweathermap.org/api O

Felipe Demenech Vasconcelos 2 Jan 20, 2022
A Python and R autograding solution

Otter-Grader Otter Grader is a light-weight, modular open-source autograder developed by the Data Science Education Program at UC Berkeley. It is desi

Infrastructure Team 93 Jan 03, 2023
INF42 - Topological Data Analysis

TDA INF421(Conception et analyse d'algorithmes) Projet : Topological Data Analysis SphereMin Etant donné un nuage des points, ce programme contient de

2 Jan 07, 2022
Picka: A Python module for data generation and randomization.

Picka: A Python module for data generation and randomization. Author: Anthony Long Version: 1.0.1 - Fixed the broken image stuff. Whoops What is Picka

Anthony 108 Nov 30, 2021
Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

Dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

dbt Labs 6.3k Jan 08, 2023
Deep universal probabilistic programming with Python and PyTorch

Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab

7.7k Dec 30, 2022
Import, connect and transform data into Excel

xlwings_query Import, connect and transform data into Excel. Description The concept is to apply data transformations to a main query object. When the

George Karakostas 1 Jan 19, 2022
Feature Detection Based Template Matching

Feature Detection Based Template Matching The classification of the photos was made using the OpenCv template Matching method. Installation Use the pa

Muhammet Erem 2 Nov 18, 2021
DefAP is a program developed to facilitate the exploration of a material's defect chemistry

DefAP is a program developed to facilitate the exploration of a material's defect chemistry. A large number of features are provided and rapid exploration is supported through the use of autoplotting

6 Oct 25, 2022
DaCe is a parallel programming framework that takes code in Python/NumPy and other programming languages

aCe - Data-Centric Parallel Programming Decoupling domain science from performance optimization. DaCe is a parallel programming framework that takes c

SPCL 330 Dec 30, 2022
In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift.

ETL Pipeline for AWS Project Description In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift. The data is loaded from S3 t

Mobeen Ahmed 1 Nov 01, 2021
An orchestration platform for the development, production, and observation of data assets.

Dagster An orchestration platform for the development, production, and observation of data assets. Dagster lets you define jobs in terms of the data f

Dagster 6.2k Jan 08, 2023
An extension to pandas dataframes describe function.

pandas_summary An extension to pandas dataframes describe function. The module contains DataFrameSummary object that extend describe() with: propertie

Mourad 450 Dec 30, 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
Vaex library for Big Data Analytics of an Airline dataset

Vaex-Big-Data-Analytics-for-Airline-data A Python notebook (ipynb) created in Jupyter Notebook, which utilizes the Vaex library for Big Data Analytics

Nikolas Petrou 1 Feb 13, 2022
BErt-like Neurophysiological Data Representation

BENDR BErt-like Neurophysiological Data Representation This repository contains the source code for reproducing, or extending the BERT-like self-super

114 Dec 23, 2022