Predictive programming with Python

In the field of data science and machine learning, Python is a very popular programming language that provides various libraries and tools for data prediction and building predictive models. In this article, I will show you how to use Python for predictive programming and provide the corresponding source code.

1. Importing the Necessary Libraries
   Before we begin, we need to import some necessary Python libraries. Commonly used libraries include NumPy, Pandas, Scikit-learn, etc. NumPy is used to handle numerical calculations, Pandas is used for data processing and analysis, and Scikit-learn is used for the implementation of machine learning algorithms.

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

2. Preparing the data set
   Before doing predictive programming, we need to prepare a data set. This data set can be a CSV file or the results of a database query. In this example we will use a simple example data set with two variables: X