To create a model that uses the percentage of individuals with bachelor's degrees as input and returns the percentage of internet users in a state as output, you can use a linear regression model. Linear regression is a statistical method that allows you to model the relationship between a dependent variable (in this case, the percentage of internet users in a state) and one or more independent variables (in this case, the percentage of individuals with bachelor's degrees).
Here is an example of how you can implement a linear regression model in Python using the scikit-learn library:
# Import necessary libraries
from sklearn.linear_model import LinearRegression
import pandas as pd
# Load the dataset
data = pd.read_csv("internetusage.csv")
# Select the percentage of individuals with bachelor's degrees and percentage of internet users as the input and output variables
X = data[["percent_bachelor_degrees"]]
y = data["percent_internet_users"]
# Split the data into training and test sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Create the linear regression model
model = LinearRegression()
# Train the model on the training data
model.fit(X_train, y_train)
# Use the model to make predictions on the test data
y_pred = model.predict(X_test)
# Calculate the accuracy of the model
from sklearn.metrics import r2_score
accuracy = r2_score(y_test, y_pred)
print("Accuracy: ", accuracy)
This code will create a linear regression model that uses the percentage of individuals with bachelor's degrees as the input and returns the predicted percentage of internet users in a state as the output. The model will be trained on a portion of the data, and the accuracy of the model will be calculated using the remaining data as the test set. The accuracy of the model will be printed to the console.
You can then use the model to make predictions on new data by using the predict method, like this:
# Make a prediction using the model
percent_bachelor_degrees = 60
prediction = model.predict([[percent_bachelor_degrees]])
print("Predicted percentage of internet users: ", prediction[0])
This code will make a prediction for a state with a 60% percentage of individuals with bachelor's degrees and print the predicted percentage of internet users to the console.