Numerical Predictions Using Ridge and Lasso Regression

Getting Started

Before diving in, the process/pipeline will be the following:

[Flow chart here]

If you are interested in reproducing this work, here are the versions of Python and Python packages used:

import sys
print(sys.version)

## 3.9.1 (v3.9.1:1e5d33e9b9, Dec  7 2020, 12:10:52) 
## [Clang 6.0 (clang-600.0.57)]

# !pip install "numpy==1.20.0"
# !pip install "pandas==1.2.2"
# !pip install "matplotlib==3.3.4"
# !pip install "seaborn==0.11.1"
# !pip install "scikit-learn==0.24.1"

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import datasets
from sklearn import linear_model

sns.set()
plt.style.use("ggplot")
iris = datasets.load_iris()
type(iris)

## <class 'sklearn.utils.Bunch'>

print(iris.keys())

## dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])

type(iris.data), type(iris.target)

## (<class 'numpy.ndarray'>, <class 'numpy.ndarray'>)

iris.data.shape

## (150, 4)

iris.target_names

## array(['setosa', 'versicolor', 'virginica'], dtype='<U10')

x = iris.data
y = iris.target
df = pd.DataFrame(x, columns=iris.feature_names)
print(df.head())

##    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
## 0                5.1               3.5                1.4               0.2
## 1                4.9               3.0                1.4               0.2
## 2                4.7               3.2                1.3               0.2
## 3                4.6               3.1                1.5               0.2
## 4                5.0               3.6                1.4               0.2

_ = pd.plotting.scatter_matrix(df, c = y, figsize = [8, 8], s = 150, marker = "D")
plt.show()