<jupyter_start><jupyter_text>🔷 PART 2: Predictive Data Modeling 🔷 In this Jupyter notebook, we analyze the processed data through a **predictive** lens: we train and test segmented datasets on various machine learning models (and potentially advanced machine learning and/or deep learning algorithms) to attain a well-performing predictor. --- 🔵 TABLE OF CONTENTS 🔵 Use this **table of contents** to navigate the various sections of the predictive data modeling notebook. 1. [Section A: Imports and Initializations](section-A)    All necessary imports and object instantiations for predictive analytics. 2. [Section B: Data Processing & Finalization](section-B)    Data curation and preparation for directed predictive modeling. 6. [Section C: Introductory Machine Learning](section-D)    Use of classical and basic machine learning algorithms to run predictive modeling. 7. [Section D: Deep Learning & Advanced Modeling](section-E)    Use of deep learning and advanced statistical algorithms to run predictive modeling.     8. [Appendix: Supplementary Custom Objects](appendix)    Custom Python object architectures used throughout the data predictions.    ---  🔹 Section A: Imports and Initializations  General Imports for Data Manipulation and Visualization.<jupyter_code>import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns<jupyter_output><empty_output><jupyter_text>Custom Algorithmic Structures for Processed Data Visualization.<jupyter_code>import sys
sys.path.append("../source/structures")

# TODO: Place custom structures from `../source/structures` here.<jupyter_output><empty_output>
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