Life Expectancy Prediction Exploration Using Neural Networks

This mini project incorporates various factors other than traditional demographic information to train a neural network to predict life expectancies.

🛠️ Technologies

• Python
• TensorFlow/Keras
• Pandas
• Scikit-learn

📘 What I Have Learned

Data is Key

• Data Preprocessing: One of the first lessons was the importance of clean and meaningful data. Working with Pandas, I employed techniques for data cleaning and transformation, which are crucial for training a reliable model.
• Dataset Analysis: Understanding the dataset was vital. It helped me recognize patterns, anomalies, and the significance of different features in predicting life expectancy.

Machine Learning

• Model Selection: Experimenting with different models using TensorFlow/Keras and Scikit-learn was interesting to see how both libraries equip the everyday user with powerful models to explore machine learning.
• Model Tuning: This process highlighted the delicate balance between model complexity, overfitting, and underfitting.

💡 Improvements

• Feature Engineering: Incorporate more diverse features and data to improve the model's accuracy and predictability.
• Further Tunining of Hyperparameters: Further experimentation of hyperparameters used during the model building process can be explored.
• Data Analysis: More thorough data analysis of the results can be done to derive more insights.