Linear regression is a foundational tool in data science and machine learning, offering a simple yet powerful way to predict outcomes and understand relationships between variables. Python, a leading programming language in these fields, provides the scikit-learn library, an efficient tool for implementing linear regression models. This article will guide you through the steps of using scikit-learn to create a linear regression model.

**Understanding Linear regression**

Linear regression models the relationship between a dependent variable and one or more independent variables using a linear approach. It’s commonly used for forecasting, time series modeling, and finding causal effect relationships between variables.

**Setting environment**

To get started, ensure you have Python installed, along with the scikit-learn library. If you haven’t installed scikit-learn yet, you can do so using pip:

```
pip install scikit-learn
```

**Importing Necessary Libraries:**

Begin by importing the required libraries:

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

**Preparing Your Data:**

Load your dataset and prepare your independent (features) and dependent (target) variables. Here’s an example using a pandas DataFrame:

### Sample Dataset (`your_dataset.csv`

)

Here’s an example of what the dataset (`your_dataset.csv`

) might look like:

```
feature1,feature2,target
1.2,3.4,10.5
2.3,4.5,12.7
3.4,1.2,14.1
4.5,2.3,18.3
5.6,3.4,20.5
```

```
df = pd.read_csv('your_dataset.csv')
X = df[['feature1', 'feature2']] # Independent variables
y = df['target'] # Dependent variable
```

**Splitting the Dataset:**

Split your data into training and testing sets to validate the model’s performance:

```
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```

**Creating and Training the Model:**

Initialize the Linear Regression model and fit it to your training data:

```
model = LinearRegression()
model.fit(X_train, y_train)
```

**Making Predictions and Evaluating the Model:**

Use the trained model to make predictions on the test set and evaluate its performance:

```
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
```

### Python Script for Linear Regression

The Python script to apply linear regression on this dataset using scikit-learn is as follows:

```
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# Load the dataset
df = pd.read_csv('your_dataset.csv')
# Prepare the data
X = df[['feature1', 'feature2']]
y = df['target']
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train the model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions and evaluate
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
# Output the Mean Squared Error
print("Mean Squared Error:", mse)
```

`1.0`

. This value quantifies the average squared difference between the predicted values and the actual values in the dataset.