MLB - Decision Tree Practice.ipynb

Kernel: Python [conda env:Anaconda3]

Import the MLB Hitters Data Set

import numpy as np
import pandas as pd
df = pd.read_csv('data/hitters.csv')

df.head()

df.shape

(322, 20)

Create a Few New Features for the Data Set (i.e. batting average)

Create a train test split

from sklearn.model_selection import train_test_split

Use SkLearn to build a decision tree regressor to predict Salary

Does it work well? What is the MSE? Plot your predictions against the actual salaries