How to handle nutrition data

[ajama01]

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Hello everyone, I'm currently working on a data from an FFQ (Food Frequency Questionnaire). I've tried to perform PCA on this data, the results of the PCA are not really interpretable (overlapping of variables and individuals) the problem is that some columns corresponding to foods are mostly 0, I don't know how I can handle this kind of data. What do you suggest?

[MostlyKIGuess]

• Log Transformation: ( log(x+1) to handle zeros) can sometimes stabilize the variance and make the data more standerd.
• Normalization/Standardization: PCA is sensitive to the scale of the data, so it might be beneficial to standardize your data (e.g., using z-scores).

import pandas as pd
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
import numpy as np
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('ffq_data.csv')

# Log transformation
data_log_transformed = np.log1p(data)

# Standardize
scaler = StandardScaler()
data_scaled = scaler.fit_transform(data_log_transformed)

# PCA time
pca = PCA(n_components=2)
principal_components = pca.fit_transform(data_scaled)

# Convert to DataFrame
pca_df = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])

# Plot it
plt.scatter(pca_df['PC1'], pca_df['PC2'])
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.title('PCA of FFQ Data')
plt.show()

[ajama01]

Thank u !

[MostlyKIGuess]

Hope that helps! :3

[LiteBrite82]

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