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.

**Further Reading**

Examples of OpenCV Library in Python

A Brief Introduction of Pandas Library in Python