pgshandino/unemployment-in-Africa-Nigeria-

This is a submission to the 10alytics hackathon 2023. ### Unemployment in Africa (Nigeria) Project **Project Description** This project analyzes unemployment trends in Nigeria using data science techniques. It includes data collection, cleaning, preprocessing, and exploratory data analysis (EDA). Advanced machine learning models are used to predict future unemployment rates, providing actionable insights for policymakers to address unemployment challenges. **Features** - **Data Collection and Cleaning:** Gathering and preprocessing unemployment data. - **Exploratory Data Analysis (EDA):** Identifying patterns and correlations. - **Machine Learning Models:** Predicting future unemployment rates. - **Visualization:** Visual representation of data and results. **Project Structure** - **Datasets/**: Contains the dataset used for analysis. - **submission.ipynb**: Jupyter notebooks with EDA, model training, and predictions. - **submission.slides.html**: A HTML file containing the final output. **Installation** 1. Clone the repository: ```sh git clone github.com ``` 2. Navigate to the project directory: ```sh cd unemployment-in-Africa-Nigeria- ``` **Usage** Run the Jupyter notebook to see the data analysis, model training, and predictions. Open the slides in a browser to see the final output slide of the presentation. **Contributing** Contributions are welcome! Please submit a pull request or open an issue to discuss any changes or improvements. For more details, visit the project repository: Unemployment in Africa (Nigeria). Thank you.