Using Python and Jupyter Notebook create the following script: 1. Exploratory Analysis a) Conduct Exploratory Data Analysis (EDA) using pandas-profiling to help identify key insights from the dataset. 2. Remove Anomalies a) Remove outliers using Isolation Forest 3. Create Learning Curves for both algorithms (Logical Regression and Nave Bayes). a) Please use recall for your scoring (i.e. scoring='recall_weighted') b) Logistical Regression (solver='lbfgs', class_weight='balanced',_max_iter=1000, random_state =100) 4. Create Optimize models (including ROC/AUC Curves) using the following two (2) algorithms to predict the proper label classification: a) Logistical Regression (solver='lbfgs', class_weight='balanced',_max_iter=1000, random_state =100 ) b) Nave Bayes