Exploratory Data Analysis (EDA)
Let's take care of all of the imports, at the top of the script/Jupyter Notebook so we don't have to worry about imports later:
# Scikit-Learn and Shallow Learning from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import ElasticNet from sklearn import metrics # TF and Keras-related imports import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Data manipulation and processing import pandas as pd import numpy as np # Visualization import matplotlib.pyplot as plt import seaborn as sns
Since we'll be starting out with shallow learning techniques for the baseline performance - we've imported a utility function, a scaler (for preprocessing), two regressor models and the
metrics module from Scikit-Learn.
Though TensorFlow, we import Keras, and a commonly used class so we can shorten calls such as
We're naturally importing
numpy for handling and manipulating data, as well as Matplotlib and Seaborn to visualize it.
Loading the Data
The dataset we'll be working with reports sales of residential units between 2006 and 2010 in a city called Ames which is located in Iowa, United States.