PYTHON

decision tree regressor

# Import DecisionTreeRegressor from sklearn.tree
from sklearn.tree import DecisionTreeRegressor

# Instantiate dt
dt = DecisionTreeRegressor(max_depth=8,
                           min_samples_leaf=0.13,
                           random_state=3)

# Fit dt to the training set
dt.fit(X_train, y_train)

scikit decision tree regressor

# import dataset
# dataset = pd.read_csv('Data.csv') 
# alternatively open up .csv file to read data
  
dataset = np.array(
[['Asset Flip', 100, 1000],
['Text Based', 500, 3000],
['Visual Novel', 1500, 5000],
['2D Pixel Art', 3500, 8000],
['2D Vector Art', 5000, 6500],
['Strategy', 6000, 7000],
['First Person Shooter', 8000, 15000],
['Simulator', 9500, 20000],
['Racing', 12000, 21000],
['RPG', 14000, 25000],
['Sandbox', 15500, 27000],
['Open-World', 16500, 30000],
['MMOFPS', 25000, 52000],
['MMORPG', 30000, 80000]
])
  
# print the dataset
print(dataset)