iamRichardCN/machine_learning_Immunization

This repository contains the code used to develop a machine learning model for predicting immunization completion in a rural community in Nigeria. This repository contains the code used to develop a machine learning model for predicting immunization completion in a rural community in Nigeria. In this README file, we will provide an overview of the project, highlight its significance, and discuss valuable lessons learned during its development. ## Project Overview Immunization is a crucial healthcare intervention to prevent the spread of infectious diseases and protect public health. However, immunization completion rates can vary significantly across different regions and communities, particularly in rural areas. This project focuses on predicting immunization completion in a specific rural community in Nigeria. ### Code Overview The code in this repository is organized into several sections, each serving a specific purpose: #### 1. Data Loading and Preprocessing - We start by loading the dataset from an Excel file (`ajet data update.xlsx`) into a pandas DataFrame. - Data preprocessing steps include handling missing values and encoding categorical variables. #### 2. Exploratory Data Analysis (EDA) - We perform exploratory data analysis to visualize patterns in the dataset. - Plots include pair plots to understand feature relationships and count plots to analyze immunization completion by various factors, such as gender, income level, and mother's age. - We also calculate percentages and create crosstabs to gain insights into the distribution of immunization completion. #### 3. Feature Engineering - We convert categorical variables into numerical format using one-hot encoding. - This step prepares the data for machine learning model training. #### 4. Principal Component Analysis (PCA) - We apply Principal Component Analysis (PCA) to reduce the dimensionality of the dataset while retaining important information. - The explained variance ratio is calculated to understand the contribution of each principal component. #### 5. Model Training and Evaluation ##### Logistic Regression - We train a logist …