The following article describes How to Implement Linear Regression With Multiple variables.

**Problem Statement**

Use the sklearn library to create a linear regression with multiple variables. Load a well known dataset split it into training and testing sets, and then train the model to predict a target variable based on one or more features. For instance, you can use the following dataset.

https://raw.githubusercontent.com/sachinmotwani20/NPTEL-ML_Datasets/main/ScoresPrediction.

**Solution**

In order to create a linear regression model with multiple variables using the scikit-learn (sklearn) library, you can follow these steps.

- Import the necessary libraries.
- Load the dataset from the provided URL.
- Split the dataset into training and testing sets.
- Create and train the linear regression model.
- Evaluate the model’s performance.

The following Python code example does this.

```
# Import necessary libraries
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from urllib.request import urlopen
import matplotlib.pyplot as plt
# Load the dataset from the provided URL
url = "https://raw.githubusercontent.com/sachinmotwani20/NPTEL-ML_Datasets/main/ScoresPrediction.csv"
data = pd.read_csv(urlopen(url))
# Check the first few rows of the dataset
print(data.head())
# Separate the features (X) and target variable (y)
X = data.drop('FinalYrScore', axis=1) # Assuming 'FinalYrScore' is the target variable
y = data['FinalYrScore']
# Split the dataset into training and testing sets (e.g., 80% training, 20% testing)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create and train the linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
# Make predictions on the test set
y_pred = model.predict(X_test)
# Evaluate the model's performance
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"Mean Squared Error (MSE): {mse}")
print(f"R-squared (R2) Score: {r2}")
# Plotting the linear regression line
plt.figure(figsize=(10, 6))
plt.scatter(X_test['FirstYrScore'], y_test, color='blue', label='Actual Scores')
plt.plot(X_test['FirstYrScore'], y_pred, color='red', linewidth=2, label='Linear Regression Line')
plt.xlabel('First Year Score')
plt.ylabel('Final Year Score')
plt.title('Linear Regression for Score Prediction')
plt.legend()
plt.grid(True)
plt.show()
```

**Output**

This code will load the dataset, split it into training and testing sets, train a linear regression model, evaluate its performance, and then plot the linear regression line along with the actual data points.

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