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Hair Loss Uncovered: Patterns, Causes, and Insights

Do you want to know why you lose hair?

📖 Background

As we age, hair loss becomes one of the health concerns of many people. The fullness of hair not only affects appearance, but is also closely related to an individual's health.

A survey brings together a variety of factors that may contribute to hair loss, including genetic factors, hormonal changes, medical conditions, medications, nutritional deficiencies, psychological stress, and more. Through data exploration and analysis, the potential correlation between these factors and hair loss can be deeply explored, thereby providing useful reference for the development of individual health management, medical intervention and related industries.

Executive Summary

Hair Loss Analysis

Hair loss is a significant concern for many individuals, impacting both appearance and overall health. This analysis explores the factors contributing to hair loss, providing valuable insights into its causes and potential interventions. By examining a dataset containing various factors such as genetics, medical conditions, lifestyle, and demographics, the study aims to uncover correlations and inform health management strategies.

Key Findings:

  • Age Analysis:

    • The average age of individuals experiencing hair loss is 33.6 years;
    • Hair loss is most prevalent among individuals aged 30-39, making this age group a critical focus for targeted interventions.

  • Factors Contributing to Hair Loss:

    • Highly Associated Factors: Medical conditions and nutritional deficiencies are identified as primary contributors to hair loss;
    • Other significant factors include genetic predisposition, hormonal changes, poor hair care habits, environmental factors, smoking, and weight loss;
    • These findings emphasize the multifactorial nature of hair loss and the need for personalized approaches to prevention and treatment.

  • Psychological Stress:

    • Contrary to common consensus, no definitive correlation between stress and hair loss could be established based on this dataset. This highlights the need for further investigation with larger and more diverse data samples.

  • Recommendations:

    • Focus on Key Age Groups: Interventions targeting the 30-39 age range may have the most significant impact;
    • Explore Interventions for Key Factors: Addressing medical conditions, nutritional deficiencies, and lifestyle factors can be pivotal in mitigating hair loss;
    • Further Research on Stress: Larger datasets could help clarify the role of psychological stress in hair loss.

  • Aditional recommendations:

    • The dataset's size is limited, necessitating cautious interpretation of findings and highlighting the need for additional data to enhance reliability and robustness;
    • Expand the Dataset: Acquiring additional data will allow for more robust statistical analysis and validation of findings.

💾 The data

The survey provides the information you need in the Predict Hair Fall.csv in the data folder.

Data contains information on persons in this survey. Each row represents one person.

  • "Id" - A unique identifier for each person.
  • "Genetics" - Whether the person has a family history of baldness.
  • "Hormonal Changes" - Indicates whether the individual has experienced hormonal changes (Yes/No).
  • "Medical Conditions" - Medical history that may lead to baldness; alopecia areata, thyroid problems, scalp infections, psoriasis, dermatitis, etc.
  • "Medications & Treatments" - History of medications that may cause hair loss; chemotherapy, heart medications, antidepressants, steroids, etc.
  • "Nutritional Deficiencies" - Lists nutritional deficiencies that may contribute to hair loss, such as iron deficiency, vitamin D deficiency, biotin deficiency, omega-3 fatty acid deficiency, etc.
  • "Stress" - Indicates the stress level of the individual (Low/Moderate/High).
  • "Age" - Represents the age of the individual.
  • "Poor Hair Care Habits" - Indicates whether the individual practices poor hair care habits (Yes/No).
  • "Environmental Factors" - Indicates whether the individual is exposed to environmental factors that may contribute to hair loss (Yes/No).
  • "Smoking" - Indicates whether the individual smokes (Yes/No).
  • "Weight Loss" - Indicates whether the individual has experienced significant weight loss (Yes/No).
  • "Hair Loss" - Binary variable indicating the presence (1) or absence (0) of baldness in the individual.

Import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import plotly.express as px
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
from scipy.stats import chi2_contingency

# sklearn
from sklearn import preprocessing
from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB, BernoulliNB
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, recall_score
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

data = pd.read_csv('data/Predict Hair Fall.csv')

Exploratory Data Analysis

data.head()
data.tail()
data.info()
data.shape
data[['Age', 'Hair Loss']].describe().transpose()
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