Linear Regression serves as a cornerstone in the realm of machine learning, specifically for predictive modeling. In this comprehensive blog post, we will not only introduce the concept of Simple Linear Regression but also provide an in-depth walkthrough of its implementation using Python. We'll leverage powerful libraries such as NumPy for numerical operations, Matplotlib for data visualization, Pandas for data manipulation, and scikit-learn for machine learning algorithms.
Understanding Simple Linear Regression
Definition:
Simple Linear Regression is a statistical method that models the relationship between a dependent variable (target) and a single independent variable (feature). The objective is to find the best-fitting linear relationship to predict the dependent variable based on the independent variable.
Formula:
The simple linear regression equation is given by:
Y = mX + b
Where:
- Y is the dependent variable (target),
- X is the independent variable (feature),
- m is the slope or coefficient,
- b is the intercept.
Implementation in Python
Importing the Libraries
The initial step involves importing the necessary libraries:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pdImporting the Dataset
Load the dataset using Pandas:
dataset = pd.read_csv('/content/Salary_Data.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].valuesHere, X represents the independent variable (Years of Experience), and y represents the dependent variable (Salary).
Splitting the Dataset
Evaluate the model's performance by splitting the dataset into training and testing 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, random_state=0)Training the Simple Linear Regression Model
Train the model using scikit-learn's `LinearRegression` class:
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)Predicting and Visualizing Results
Make predictions and visualize the results:
y_pred = regressor.predict(X_test)Visualizing the Training set results
plt.scatter(X_train, y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experience (Training Set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show().png)
Visualizing the Test set results
plt.scatter(X_test, y_test, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Salary vs Experience (Test Set)')
plt.xlabel('Years of Experience')
plt.ylabel('Salary')
plt.show().png)
Making Predictions and Obtaining the Regression Equation
Make a single prediction and reveal the coefficients of the regression equation:
# Making a single prediction (e.g., salary of an employee with 12 years of experience)
print(regressor.predict([[12]]))Getting the final linear regression equation with the values of the coefficients
print(regressor.coef_)
print(regressor.intercept_)The model predicts that the salary of an employee with 12 years of experience is $138967.5. The final linear regression equation is expressed as Salary = 9312.57 * YearsExperience + 26780.09.
Conclusion
Mastering Simple Linear Regression empowers you to understand and model relationships between variables. With Python and the mentioned libraries, the process becomes not only accessible but also insightful, allowing you to make informed predictions based on data.
