Postpartum Health Analysis

Understanding Postpartum Depression Through Data-Driven Insights

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🌟 Inspiration

The inspiration for this project came from the movie Nightb*tch, which explores the emotional and psychological challenges of motherhood, including feelings of isolation, anxiety, and identity shifts. The film’s portrayal of postpartum struggles resonated deeply, highlighting the need for better understanding and awareness of postpartum depression (PPD). This project aims to use data science and machine learning to analyze key factors contributing to PPD, with the hope of offering insights that could aid in early diagnosis and support.

📚 What I Learned

Throughout this project, I gained valuable insights into:

  • The emotional and psychological complexities of postpartum depression.
  • The importance of data preprocessing and feature engineering in healthcare analytics.
  • How exploratory data analysis (EDA) can reveal meaningful trends in mental health.
  • The role of machine learning in building predictive models for early risk detection.

🛠 How I Built It

  1. Data Collection & Preprocessing

    • Acquired a dataset with 1,503 records related to postpartum depression symptoms.
    • Handled missing values, outliers, and categorical encoding to improve data quality.
    • Standardized numerical features to enhance model accuracy.

  2. Exploratory Data Analysis (EDA)

    • Visualized depression symptom distribution using histograms, box plots, and scatter plots.
    • Analyzed correlations between emotional states, age, and socio-economic factors.

  3. Feature Engineering & Selection

    • Identified key predictors contributing to postpartum depression.
    • Reduced multicollinearity using correlation analysis.

  4. Machine Learning Model Implementation

    • Trained multiple models (Logistic Regression, Decision Tree, Random Forest).
    • Evaluated models using accuracy, precision, recall, and F1-score.
    • Achieved 99.34% accuracy with the Random Forest model.

  5. Deployment & Visualization

    • Deployed the model using Streamlit Cloud for easy accessibility.
    • Built interactive visualizations to present findings.

🚧 Challenges Faced

  • Data Quality Issues: Handling missing and inconsistent data required careful preprocessing.
  • Feature Selection: Identifying the most relevant attributes without overfitting was a key challenge.
  • Balancing the Dataset: Ensuring fair model performance across different demographic groups.
  • Deployment Optimization: Streamlining the model for real-time predictions and smooth UI integration.

🚀 Final Thoughts

This project highlights the power of data science in mental health, providing insights into postpartum depression. Inspired by Nightbitch and its raw depiction of motherhood struggles, this analysis aims to contribute to early diagnosis and better support for new mothers.

🔗 Live App: Postpartum Health Analysis App
📂 GitHub Repository: Link to Repo

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