Overview
The goal of this analysis was to analyze and build a mock predictive model using the Caesarian Section Classification dataset. The analysis conducted revealed the distribution of the data and explored relationships between attributes and the outcome. Predictive models Naive Bases and Decision Tree were trained and assessed to determine the capacity for a functional model.
Data & Tools
The Caesarian Section Classification Dataset was provided by Amin, Muhammad and Ali, Amir (2018) and shared via the UCI Machine Learning Repository. It contains information on 80 pregnant women’s caesarian section outcomes and includes characteristics like age, blood pressure, heart problem, etc. The exploratory data analysis was performed in Google Colab using Python.
Methodology
The analysis was conducted in Google Colab using Python. The main libraries used include pandas, numpy, seaborn, scipy, matplotlib, mpl_toolkits and sklearn. The data was explored by visualizing notable relationships between attributes using Seaborn and Matplotlib. Predictive models Decision Tree and Naive Bases were trained and evaluated using accuracy, precision and recall.
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