Height Prediction using Linear Regression 📏🤖

This project implements a Linear Regression model to predict a person’s height based on their weight using a simple height–weight dataset.

📌 Overview

• Input Feature: Weight
• Target Variable: Height
• Algorithm Used: Linear Regression
• Language: Python
• Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn

📂 Dataset

The dataset contains two columns:

• Weight – Weight of the person
• Height – Height of the person

The data is used to learn a linear relationship between weight and height.

⚙️ Workflow

1. Load and explore the dataset
2. Visualize the relationship using a scatter plot
3. Split data into training and testing sets
4. Train a Linear Regression model
5. Make height predictions
6. Cross-Validation and Residual Analysis
7. Evaluate model performance

📈 Visualization

A scatter plot is used to show the correlation between weight and height, along with the regression line for better understanding.

🚀 How to Run

1. Clone the repository
2. Install required libraries:

   pip install numpy pandas matplotlib scikit-learn seaborn