#This may help:

#Import the necessary libraries:

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

#Load the data:

data = pd.read_csv("your_data_file.csv")

#Split the data into training and testing sets:

from sklearn.model_selection import train_test_split

X = data['vegetation_index'].values.reshape(-1, 1)

y = data['soil_moisture'].values.reshape(-1, 1)

#Train the machine learning model:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = LinearRegression()

model.fit(X_train, y_train)

#Make predictions:

y_pred = model.predict(X_test)

from sklearn.metrics import mean_squared_error, r2_score

#Evaluate the model:

print('Mean squared error: %.2f'

% mean_squared_error(y_test, y_pred))

print('Coefficient of determination: %.2f'

% r2_score(y_test, y_pred))

To introduce the training and test data to your machine learning algorithm, you can follow these general steps:

Here are some specific suggestions for each of these steps:

Hope it helps