Description

1. Data Preparation
   • Install the required libraries such as pandas, numpy, matplotlib, seaborn, and sklearn.
2. Data Exploration
   • Read and display the top five rows of the dataset.
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   • Perform Exploratory Data Analysis (EDA) to check and count missing values in the data. Here, the missing values are zero.
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3. Data Splitting
   • Split the dataset into training and testing data with a ratio of 80:20 (training data: testing data).
4. Model Building
   • Build and train the model using Random Forest Regressor.
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5. Model Evaluation
   • Evaluate the model's performance using three metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared.
   • This is the scatter plot that compares the actual body fat percentage with the predicted body fat percentage. The linear pattern of the data points shows a strong correlation between the actual and predicted values.
6. Model Prediction with New Data
   • This is a test of the model using new data by inputting values for density, age, weight, height, neck, chest, and other measurements to predict the body fat percentage.