Linear Regression Implementation
├── Introduction
│   └── Overview of Linear Regression
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Creating a Sample Dataset
├── Implementing Linear Regression
│   ├── Data Preparation
│   ├── Model Training
│   └── Model Evaluation
├── Visualization
│   └── Regression Line Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of Linear Regression

• Linear Regression is used to model the relationship between a dependent variable and one or more independent variables.

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

Creating a Sample Dataset

# Python code to create a simple dataset
X = np.array([5, 15, 25, 35, 45, 55]).reshape((-1, 1))  # Feature
y = np.array([5, 20, 14, 32, 22, 38])                   # Target

3. Implementing Linear Regression

Data Preparation

# Python code for data preparation
X_train = X
y_train = y

Model Training

# Python code to train the Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)

Model Evaluation

# Python code to evaluate the model
y_pred = model.predict(X_train)
rmse = np.sqrt(mean_squared_error(y_train, y_pred))

4. Visualization

Regression Line Visualization