Packages

There are several packages and function used in this project.

import folium
import altair as alt
import numpy as np
import pandas as pd
import geopandas as gpd
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
import hvplot.pandas
import esri2gpd
import carto2gpd
import seaborn as sns
import osmnx as ox
import datetime
from IPython.display import IFrame
from matplotlib import pyplot as plt
from shapely.geometry import Point

# Models
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor

# perform the K-Means fit
from sklearn.cluster import KMeans

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()

# Model selection
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV

# Pipelines
from sklearn.pipeline import make_pipeline

# Preprocessing
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, PolynomialFeatures, OneHotEncoder

# Neighbors
from sklearn.neighbors import NearestNeighbors

Reference

LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.

---

A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.

---

The KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares (see below). This algorithm requires the number of clusters to be specified. It scales well to large numbers of samples and has been used across a large range of application areas in many different fields.