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Fatigue Insights: Comprehensive Analysis for Enhanced Well-being
1. Project Objective:
This project aims to conduct an exhaustive analysis of a diverse dataset collected over a year from an individual experiencing fatigue syndrome.
The main goal of this project is to deepen our understanding of the factors influencing fatigue syndrome and to develop personalized strategies for improving the patient's well-being based on the thorough analysis of collected data.
This primary objective will be accompanied by specific tasks, collectively contributing to the overarching goal of gaining a profound understanding of fatigue syndrome and developing personalized strategies for the patient's well-being based on comprehensive data analysis.
The our tasks will provide a more logical flow, starting with data preprocessing, moving into feature engineering and time series analysis, followed by correlation analysis, exploration of causal relationships, machine learning models, clustering analysis, dimensionality reduction, and finally, leveraging NLP methods. This sequence will enhances the overall coherence and effectiveness of the project.
Data Preprocessing:
- Data Cleaning: Detect and address missing values, outliers, and inconsistencies, ensuring data integrity.
- Normalization or Standardization: Normalize or standardize numeric variables to maintain a consistent scale across all parameters.
Feature Engineering:
- Identification of Fatigue-Related Characteristics: Extract relevant features from raw data contributing to fatigue.
- Creation of New Features: Experiment with crafting new features or combining existing ones to capture temporal patterns.
Time Series Analysis:
- Conduct Time Series Analysis: Leverage time series techniques to unveil trends, seasonality, and temporal patterns.
- Application of Time Series Methods: Explore methods like Autoregressive Integrated Moving Average (ARIMA) or Seasonal-Trend decomposition using LOESS (STL) for in-depth time series analysis.
Correlation Analysis:
- Advanced Correlation Analysis: Use correlation matrices and advanced methods like partial correlation to account for confounding factors.
- Visualization of Strong Correlations: Utilize visualization tools such as heatmaps to identify robust correlations between variables.
Exploration of Causal Relationships:
- Explore Causal Inference Methods: Consider Bayesian Causal Networks or Directed Acyclic Graphs (DAGs) to infer potential causal relationships.
- Exercise Caution: Be mindful when drawing causal conclusions, recognizing that correlation does not always imply causation.
Machine Learning Models:
- Develop Predictive Models: Create regression models or machine learning algorithms to identify key predictors of fatigue.
- Importance Analysis: Utilize feature importance analysis to identify the most influential parameters.
Clustering Analysis:
- Perform Cluster Analysis: Group similar days or patterns through clustering analysis to identify clusters associated with fatigue.
Dimensionality Reduction:
- Implement Dimensionality Reduction: Apply techniques like Principal Component Analysis (PCA) to reduce data dimensionality while preserving variance.
Natural Language Processing (NLP):
- Leverage NLP Methods: If subjective state data includes textual information, employ NLP methods to extract sentiments or key themes, providing additional insights into the patient's emotional state.
Identification of Key Factors:
- Identify and emphasize key parameters associated with fatigue syndrome: Cover aspects such as diet, physical and mental activity, sleep, and health indicators.
2. Description of DataSet (data/sample.xlsx)
This dataset contains information about various aspects of life and health that may impact an individual's physical and mental well-being. Below is a description of the columns:
- Date: Date of the record.
- Morn, Day, Eve, Total: Energy expenditure or consumption, possibly categorized by different parts of the day.
- Carbs, Sugar, Fat, Fish, Bird, Egg, Milk, Meat, Vegies, Fruit, Tea, Other: Quantity of consumed carbohydrates, sugar, fats, fish, poultry, eggs, milk, meat, vegetables, fruits, tea, and other products.
- 1, 2, 3, nan, ind, kfc, curry, whm, B1: Quantity of consumed units of specific food items or nutritional components.
- Wakeup time, Bed time, Sleep duration: Information about waking up and going to bed times, as well as the duration of sleep.
- Quality 0-5: Rating of sleep quality on a scale from 0 to 5.
- Sport, Sex, Masturbation, Meditation, Read, Gaming, Cold exposure, Outdoors, Work, Weather, Sun exposure, Stress, Depression: Information about physical and mental activities, work, and responses to stress and depression.
- nan: Column with missing values and a data type of float64.
- Energy, Clarity, Muscles, Wibes, Productivity, Health, Stool, Peeing in night: Ratings for energy levels, mental clarity, muscle condition, mood, productivity, health status, stool quality, and incidental information about nighttime urination.
- Note: Notes or comments (seemingly, this column has missing values). This dataset provides a comprehensive view of daily activities, dietary habits, and health-related factors, allowing for potential insights into the relationships between lifestyle and well-being.
This dataset provides a comprehensive view of daily activities, dietary habits, and health-related factors, allowing for potential insights into the relationships between lifestyle and well-being.
3. Reading and Exploring Data
"""
Reading and Exploring Data
1. Import necessary libraries: seaborn, matplotlib.pyplot, and pandas.
2. Read data from an Excel file ('data/sample.xlsx'), skipping the first row as it contains headers.
3. Set column names using the second row.
4. Remove the row with column names to avoid duplication.
5. Display information about the dataset.
6. Print the first few rows of the dataset for initial exploration.
"""
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Read data, starting from the second row
sample_data = pd.read_excel('data/sample.xlsx', header=None, skiprows=1)
# Set column names from the second row
sample_data.columns = sample_data.iloc[0]
# Remove the row with column names
sample_data = sample_data.iloc[1:]
# Display information about the data
sample_data.info()
# Display the first few rows of the data
print(sample_data.head())
4. Numeric Conversion and Correlation Analysis
"""
Numeric Conversion and Correlation Analysis
This code performs the following tasks:
Excludes specified columns ('other', '1', '2', '3') from conversion to numeric.
Converts numeric columns (excluding specified ones) to numeric format, handling errors by coercing them to NaN.
Computes the correlation matrix of the dataset.
Displays the correlation matrix.
Visualizes correlations using a heatmap for better interpretation.
"""
# Exclude specified columns from conversion to numeric
exclude_columns = ['other', '1', '2', '3']
# Convert numeric columns (excluding specified ones)
sample_data.loc[:, sample_data.columns.difference(exclude_columns)] = sample_data.loc[:, sample_data.columns.difference(exclude_columns)].apply(pd.to_numeric, errors='coerce')
# Compute the correlation matrix
correlation_matrix = sample_data.corr()
# Display the correlation matrix
#print(correlation_matrix)
# Visualize correlations using a heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation Heatmap')
plt.show()
4.2 Correlation between nutrition and energy
# Select only necessary columns for analysis
nutrition_columns = ['Carbs', 'Sugar', 'Fat', 'Fish', 'bird', 'Energy']
# Compute the correlation matrix for selected columns
nutrition_correlation = sample_data[nutrition_columns].corr()
# Build a heatmap for correlation visualization
plt.figure(figsize=(10, 8))
sns.heatmap(nutrition_correlation, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation between Nutrition and Energy')
plt.show()
4.3 Correlation between nutrition, energy, and sleep quality
# Specify columns for nutrition and energy analysis
nutrition_energy_columns = ['Carbs', 'Sugar', 'Fat', 'Fish', 'bird', 'Energy', 'quality 0-5']
# Compute the correlation matrix for selected columns
nutrition_energy_correlation = sample_data[nutrition_energy_columns].corr()
# Build a heatmap for correlation visualization
plt.figure(figsize=(12, 10))
sns.heatmap(nutrition_energy_correlation, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation between Nutrition, Energy, and Sleep Quality')
plt.show()
4.4 Correlation between sleep-related parameters (duration, wakeup time) and health indicators
"""
This code block analyzes the correlation between sleep-related parameters (duration, wakeup time) and health indicators, visualizing the results using a heatmap.
"""
# Specify columns for health indicators analysis
health_columns = ['sleep duration', 'wakeup time', 'Energy', 'Clarity', 'Muscles', 'Wibes', 'Productivity', 'Health']
# Compute the correlation matrix for selected columns
health_correlation = sample_data[health_columns].corr()
# Build a heatmap for correlation visualization
plt.figure(figsize=(12, 10))
sns.heatmap(health_correlation, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)
plt.title('Correlation between Sleep, Wakeup Time, and Health Indicators')
plt.show()
4.5 Correlation between Sleep, Wakeup Time, and Health Indicators