Here I will discuss Python Program for Linear Regression With Multiple Variables.
The following program demonstrates how to use Linear Regression for multiple variables.
import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression # Load data into a pandas DataFrame data = pd.read_csv('data.csv') # Split the data into independent and dependent variables X = data.iloc[:, :-1] y = data.iloc[:, -1] # Fit a linear regression model to the data model = LinearRegression() model.fit(X, y) # Print the coefficients of the model print('Coefficients:', model.coef_) # Predict the target values for new data new_data = np.array([[3, 5, 7]]) prediction = model.predict(new_data) print('Prediction:', prediction)
This program uses the
LinearRegression class from the
sklearn library to fit a linear regression model to the data. The data is loaded into a
pandas DataFrame, and the independent and dependent variables are split using the
iloc method. The
fit method is used to fit the model to the data, and the
coef_ attribute is used to print the coefficients of the model. The
predict method is used to make predictions for new data.
Note that this is just a basic example, and you may need to modify or add additional code to handle missing values, outlier detection, or feature scaling.
- Dot Net Framework
- Power Bi
- Scratch 3.0