<jupyter_start><jupyter_text>Lambda School Data Science*Unit 2, Sprint 3, Module 1*--- Define ML problemsYou will use your portfolio project dataset for all assignments this sprint. AssignmentComplete these tasks for your project, and document your decisions.- [ ] Choose your target. Which column in your tabular dataset will you predict?- [ ] Choose which observations you will use to train, validate, and test your model. And which observations, if any, to exclude.- [ ] Determine whether your problem is regression or classification.- [ ] Choose your evaluation metric.- [ ] Begin to clean and explore your data.- [ ] Begin to choose which features, if any, to exclude. Would some features "leak" information from the future?<jupyter_code>import pandas as pd

ufc = pd.read_csv('ufc_combined.csv')
ufc.shape
ufc.head()
# winner is my target to predict

y = ufc['winner']
X = ufc.drop(columns='winner')
# check it's distribution
y.describe()
y.value_counts()
# splitting into test and train

train = ufc[ufc['fight_year'] <= 2016]
test = ufc[ufc['fight_year'] > 2016]

train.shape, test.shape<jupyter_output><empty_output><jupyter_text>Since the y predict is whether Fighter 1 wins or loses it's a binary classification problem<jupyter_code># dropping columns that we won't have info on for predicting in the future

dropped_columns = ['method', 'end_round', 'Unnamed: 0', 'stance_fighter1', 'stance_fighter2']

train = train.drop(columns=dropped_columns)
test = test.drop(columns=dropped_columns)<jupyter_output><empty_output>
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