Body Fat Prediction

This repository contains a Python implementation of a machine learning model for predicting body fat percentage from various anthropometric measurements. The model utilizes a Random Forest regressor and is trained on a dataset of over 10,000 individuals.

Getting Started

These instructions will help you set up the project on your local machine for development and testing purposes.

Prerequisites

• Python 3.7 or later
• Pandas 1.0.3 or later
• Numpy 1.18.2 or later
• Scikit-learn 0.22.1 or later
• Matplotlib 3.2.1 or later

Installation

1. Clone the repository:

bash git clone https://github.com/yourusername/bodyfatprediction.git ​

2. Change to the project directory:

bash cd bodyfatprediction ​

3. Install the required Python packages:

bash pip install -r requirements.txt ​

Usage

To use the model for predicting body fat percentage, follow these steps:

1. Import the necessary libraries:

python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error import matplotlib.pyplot as plt ​

2. Load the dataset:

url = https://github.com/imranainds/body_fat_prediction/blob/main/bodyfat.csv df = pd.read_csv(url) ​

3. Preprocess the data:

Split the dataset into features (X) and target (y)

X = df.drop('bodyfat', axis=1) y = df['bodyfat'] ​

Split the dataset into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ​

4. Train the model:

Create a Random Forest regressor

model = RandomForestRegressor(n_estimators=100, random_state=42) ​

Train the model on the training data

model.fit(X_train, y_train) ​

5. Make predictions:

Make predictions on the test data

y_pred = model.predict(X_test) ​

6. Evaluate the model:

Calculate the mean squared error

mse = mean_squared_error(y_test, y_pred) print(f"Mean Squared Error: {mse}") ​

Plot the actual vs predicted body fat percentage

plt.scatter(y_test, y_pred) plt.xlabel('Actual Body Fat Percentage') plt.ylabel('Predicted Body Fat Percentage') plt.title('Actual vs Predicted Body Fat Percentage') plt.show() ​

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• The dataset used in this project is sourced from the Body Composition Data Sets page at the University of California, Irvine.
• The model's performance can be further improved by experimenting with different machine learning algorithms, feature engineering techniques, and hyperparameter tuning.
• Please note that this project is for educational purposes only and should not be used for medical or health-related advice. Consult a healthcare professional for personalized advice.