Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

Related tags

Machine Learningend_to_end_hitters_ml
Overview

END TO END MACHINE LEARNING PROJECT ON HITTERS DATASET

Can a machine learning project be implemented to estimate the salaries of baseball players whose salary information and career statistics for 1986 are shared?

DATA SET STORY:

  • This dataset was originally taken from the StatLib library at Carnegie Mellon University.
  • This is part of the data that was used in the 1988 ASA Graphics Section Poster Session.
  • The salary data were originally from Sports Illustrated, April 20, 1987.
  • The 1986 and career statistics were obtained from The 1987 Baseball Encyclopedia Update published by Collier Books, Macmillan Publishing Company, New York.

ATTRIBUTES: A data frame with 322 observations of major league players on the following 20 variables.

  • AtBat: Number of times at bat in 1986-1987 season
  • Hits: Number of hits in 1986-1987 season
  • HmRun: Number of home runs in 1986-1987 season
  • Runs: Number of runs in 1986-1987 season
  • RBI: Number of runs batted in 1986-1987 season
  • Walks: Number of walks in 1986-1987 season
  • Years: Number of years in the major leagues
  • CAtBat: Number of times at bat during his career
  • CHits: Number of hits during his career
  • CHmRun: Number of home runs during his career
  • CRuns: Number of runs during his career
  • CRBI: Number of runs batted in during his career
  • CWalks: Number of walks during his career
  • League: A factor with levels A and N indicating player's league at the end of 1986
  • Division: A factor with levels E and W indicating player's division at the end of 1986
  • PutOuts: Number of put outs in 1986-1987 season
  • Assists: Number of assists in 1986-1987 season
  • Errors: Number of errors in 1986-1987 season
  • Salary: 1996-1987 annual salary on opening day in thousands of dollars
  • NewLeague: A factor with levels A and N indicating player's league at the beginning of 1987
Owner
Pinar Oner
Data Engineer | Project Coordinator
Pinar Oner
GitHub Repository
Mixing up the Invariant Information clustering architecture, with self supervised concepts from SimCLR and MoCo approaches

Self Supervised clusterer Combined IIC, and Moco architectures, with some SimCLR notions, to get state of the art unsupervised clustering while retain

Bendidi Ihab 9 Feb 13, 2022
TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models

538 Jan 01, 2023
A library of extension and helper modules for Python's data analysis and machine learning libraries.

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks. Sebastian Raschka 2014-2021 Links Doc

Sebastian Raschka 4.2k Dec 29, 2022
Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

Payment-Date-Prediction Machine Learning Model to predict the payment date of an invoice when it gets created in the system.

15 Sep 09, 2022
ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions

A library for debugging/inspecting machine learning classifiers and explaining their predictions

154 Dec 17, 2022
Retrieve annotated intron sequences and classify them as minor (U12-type) or major (U2-type)

(intron I nterrogator and C lassifier) intronIC is a program that can be used to classify intron sequences as minor (U12-type) or major (U2-type), usi

Graham Larue 4 Jul 26, 2022
QML: A Python Toolkit for Quantum Machine Learning

QML is a Python2/3-compatible toolkit for representation learning of properties of molecules and solids.

176 Dec 09, 2022
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

Petastorm Contents Petastorm Installation Generating a dataset Plain Python API Tensorflow API Pytorch API Spark Dataset Converter API Analyzing petas

Uber Open Source 1.6k Dec 31, 2022
Fourier-Bayesian estimation of stochastic volatility models

fourier-bayesian-sv-estimation Fourier-Bayesian estimation of stochastic volatility models Code used to run the numerical examples of "Bayesian Approa

15 Jun 20, 2022
AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications.

AutoTabular AutoTabular automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just

wenqi 2 Jun 26, 2022
Decision tree is the most powerful and popular tool for classification and prediction

Diabetes Prediction Using Decision Tree Introduction Decision tree is the most powerful and popular tool for classification and prediction. A Decision

Arjun U 1 Jan 23, 2022
Decision Tree Regression algorithm implemented on Python from scratch.

Decision_Tree_Regression I implemented the decision tree regression algorithm on Python. Unlike regular linear regression, this algorithm is used when

1 Dec 22, 2021
BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.

Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi

BentoML 4.4k Jan 04, 2023
Spark development environment for k8s

Local Spark Dev Env with Docker Development environment for k8s. Using the spark-operator image to ensure it will be the same environment. Start conta

Otacilio Filho 18 Jan 04, 2022
Upgini : data search library for your machine learning pipelines

Automated data search library for your machine learning pipelines → find & deliver relevant external data & features to boost ML accuracy :chart_with_upwards_trend:

Upgini 175 Jan 08, 2023
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. 10x Larger Models 10x Faster Trainin

Microsoft 8.4k Dec 30, 2022
Learning --> Numpy January 2022 - winter'22

Numerical-Python Numpy NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along

Shahzaneer Ahmed 0 Mar 12, 2022
A Multipurpose Library for Synthetic Time Series Generation in Python

TimeSynth Multipurpose Library for Synthetic Time Series Please cite as: J. R. Maat, A. Malali, and P. Protopapas, “TimeSynth: A Multipurpose Library

278 Dec 26, 2022
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

Seldon Core: Blazing Fast, Industry-Ready ML An open source platform to deploy your machine learning models on Kubernetes at massive scale. Overview S

Seldon 3.5k Jan 01, 2023
Price forecasting of SGB and IRFC Bonds and comparing there returns

Project_Bonds Project Title : Price forecasting of SGB and IRFC Bonds and comparing there returns. Introduction of the Project The 2008-09 global fina

Tishya S 1 Oct 28, 2021