joannewanjikunyaga/remote-productivity-predictive-model

Predictive Analytics Model for Monitoring Remote Productivity in Kenya’s ICT Sector (MSc IT Thesis, Strathmore University) # Predictive Analytics Model for Monitoring Remote Productivity in Kenya's ICT Sector **Author:** Joanne Wanjiku Nyaga **Student ID:** 102410 **Institution:** Strathmore University **Program:** MSc Information Technology **Supervisor:** Dr. Allan Omondi **Year:** 2025/2026 ## Project Overview This research develops a predictive analytics model to monitor and forecast productivity levels among remote employees in Kenya's ICT sector using machine learning algorithms (Random Forest, SVM, and ANN). ## Dataset - **Source:** Kaggle - Remote Work Productivity Dataset - **Location:** `data/raw/` - **Note:** Download from Kaggle and place in `data/raw/` folder ## Project Structure ``` remote-productivity-model/ ├── data/ │ ├── raw/ # Original Kaggle dataset │ └── processed/ # Cleaned and prepared data ├── notebooks/ # Jupyter notebooks for analysis ├── src/ # Python scripts ├── results/ │ ├── figures/ # Visualizations and plots │ └── models/ # Trained models ├── .gitignore ├── README.md └── requirements.txt ``` ## Setup Instructions 1. Clone this repository 2. Create virtual environment: `python3 -m venv venv` 3. Activate environment: `source venv/bin/activate` 4. Install dependencies: `pip install -r requirements.txt` 5. Download dataset from Kaggle and place in `data/raw/` 6. Open VS Code: `code .` ## Methodology Following CRISP-DM framework: 1. Business Understanding 2. Data Understanding 3. Data Preparation 4. Modeling (RF, SVM, ANN) 5. Evaluation (RMSE, R², SHAP/LIME) 6. Deployment ## License Academic use only - MSc Thesis Project