<jupyter_start><jupyter_text>Define ML problems I. Wrangle Data **Question:** What issues do we need to address in our `wrangle` function?- "Weird values," eg. Yes/No/True/False/X/x/0/1- High cardinality categorical features<jupyter_code><jupyter_output><empty_output><jupyter_text>II. Split Data<jupyter_code><jupyter_output><empty_output><jupyter_text>III. Establish Baseline<jupyter_code><jupyter_output><empty_output><jupyter_text>IV. Build Model - `LogisticRegression`    - `SimpleImputer`    - `StandardScaler`<jupyter_code><jupyter_output><empty_output><jupyter_text>- `RandomForestClassifier`    - `SimpleImputer`<jupyter_code><jupyter_output><empty_output><jupyter_text>V. Check Metrics**Accuracy**<jupyter_code><jupyter_output><empty_output><jupyter_text>**Precision, Recall, F1**<jupyter_code><jupyter_output><empty_output><jupyter_text>**ROC curve**- To evaluate models for binary classification.- Decide what probability threshold you should use when making your predictions.<jupyter_code><jupyter_output><empty_output>
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