Project 12 - Predicting Baseball Outcomes with Machine Learning in Python | Case Study
242 بار بازدید -
2 ماه پیش
-
Welcome to our in-depth case
Welcome to our in-depth case study on predicting baseball outcomes using machine learning algorithms in Python! ⚾📊
In this video, we will:
Introduce the concept of predicting baseball game results using data science.
Walk you through the dataset selection and preprocessing steps.
Demonstrate feature engineering and selection for building effective models.
Explore different machine learning algorithms and evaluate their performance.
Show you how to implement these algorithms in Python using popular libraries like pandas, scikit-learn, and more.
Analyze the results and discuss the potential improvements and future work.
Whether you are a data science enthusiast, a baseball fan, or both, this video will provide valuable insights into how machine learning can be applied to sports analytics. Don’t forget to like, comment, and subscribe for more exciting content on data science and machine learning!
Chapters:
0:00 Introduction
1:30 Data Collection and Preprocessing
6:45 Feature Engineering
12:20 Model Selection and Training
18:35 Model Evaluation
24:10 Results Analysis
29:05 Conclusion and Future Work
In this video, we will:
Introduce the concept of predicting baseball game results using data science.
Walk you through the dataset selection and preprocessing steps.
Demonstrate feature engineering and selection for building effective models.
Explore different machine learning algorithms and evaluate their performance.
Show you how to implement these algorithms in Python using popular libraries like pandas, scikit-learn, and more.
Analyze the results and discuss the potential improvements and future work.
Whether you are a data science enthusiast, a baseball fan, or both, this video will provide valuable insights into how machine learning can be applied to sports analytics. Don’t forget to like, comment, and subscribe for more exciting content on data science and machine learning!
Chapters:
0:00 Introduction
1:30 Data Collection and Preprocessing
6:45 Feature Engineering
12:20 Model Selection and Training
18:35 Model Evaluation
24:10 Results Analysis
29:05 Conclusion and Future Work
2 ماه پیش
در تاریخ 1403/03/23 منتشر شده
است.
242
بـار بازدید شده