ahmatabdelkerim235/African-Migration-Data-Analysis-and-Modeling

Project Description: Your project analyzes migration data, using Python and various machine learning and data visualization libraries. The dataset contains 10,000 entries tracking migrants with features like PersonID, Age, Gender, Country of Birth, Destination Country, Migration Reason, Education Level, and Year of Migration. # African-Migration-Data-Analysis-and-Modeling Project Description: Your project analyzes migration data, using Python and various machine learning and data visualization libraries. The dataset contains 10,000 entries tracking migrants with features like PersonID, Age, Gender, Country of Birth, Destination Country, Migration Reason, Education Level, and Year of Migration. Key Steps and Methods: Data Cleaning & Exploration: You handle missing values, check unique and descriptive statistics, and visualize the distribution of features such as gender, age, migration reason, education level, and migration trends over years. Visualization: Plots include count plots, histograms, bar charts, and a heatmap visualizing migration flows between origins and destinations. Feature Engineering & Preprocessing: Categorical variables are encoded. The dataset is split into features and a target variable (Migration Reason). Classification: You employ a Random Forest Classifier to predict migration reasons. The model’s accuracy is evaluated, with metrics such as precision, recall, and f1-score. Clustering: Using KMeans clustering, you segment migrants based on age, gender, and education level to discover group trends within the data. Regression Analysis: Linear regression predicts the year of migration based on age and gender, assessing the mean squared error (MSE). Core Libraries Used: pandas, numpy (data handling) matplotlib, seaborn (visualization) scikit-learn (machine learning) Summary: This project provides a comprehensive pipeline for migration data analysis, including preprocessing, visualization, supervised classification (Random Forest), unsupervised clustering (KMeans), and regression modeling, offering insights into patterns and factors influencing migration.