keras-unemployment-prediction

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🧠 Project Overview

This project involves building AI models using Python, Scikit-learn, and Keras to predict unemployment trends in Nigeria. It leverages historical employment data to train machine learning models that can forecast future unemployment rates, supporting data-driven economic insights and decision-making.

🎯 Purpose

The primary goal is to analyze and forecast unemployment rates in Nigeria using past employment data. By training regression models on historical data, we aim to build accurate predictors that can assist policymakers, researchers, and analysts in anticipating labor market trends.

🛠️ Technologies Used

1. Keras with TensorFlow Backend

• Built an LSTM-based deep learning model for time series prediction.
• Used TensorFlow as the computational backend.

2. Scikit-learn (Sklearn)

• Implemented classical regression models:

  • GradientBoostingRegressor
  • LinearRegression (referred to as Multiple Regressor)

• Used for feature-based unemployment prediction.

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