Simple-Linear-Regression

Weight-Height Prediction using Linear Regression

This project demonstrates how to predict height based on weight using a simple linear regression model in Python. It utilizes libraries like Pandas, Matplotlib, NumPy, Seaborn, and Scikit-learn.

Dataset

The project uses the "Weight-Height Polynomial Dataset.csv" file, which contains weight and height data.

Libraries Used

• Pandas: For data manipulation and analysis.
• Matplotlib: For creating visualizations.
• NumPy: For numerical computations.
• Seaborn: For enhanced visualizations.
• Scikit-learn: For building and evaluating the linear regression model.

Methodology

1. Data Loading and Exploration: The dataset is loaded using Pandas, and basic exploratory data analysis is performed.
2. Data Visualization: Scatter plots and pair plots are used to visualize the relationship between weight and height.
3. Data Preprocessing: The data is split into training and testing sets using train_test_split. The weight feature is standardized using StandardScaler.
4. Model Building: A linear regression model is created and trained using the training data.
5. Model Evaluation: The model's performance is evaluated using metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared.
6. Prediction: The trained model is used to predict height for new weight values.

Usage

1. Clone the repository: git clone <repository_url>
2. Install dependencies: pip install pandas matplotlib numpy seaborn scikit-learn
3. Run the Jupyter Notebook: Open and run the notebook to see the code and results.

Results

The model achieves an R-squared score of [insert R-squared score here], indicating a [good/moderate/poor] fit to the data.