marcuskiama97-ai/Kenya-Power-Consumption-Cost-Analysis-KPLC-Data-Science-Project-

This project analyzes Kenya Power (KPLC) energy consumption data across major regions and customer types. The goal is to uncover regional consumption trends, identify distribution inefficiencies. # **Kenya Power Consumption Cost Analysis (KPLC Data Science Project)** [ ] [ ] [ ] [ ] [ ] [ ] [ ] --- ## **Project Summary** This project analyzes Kenya Power (KPLC) energy consumption data across major regions and customer types. The goal is to uncover **regional consumption trends**, identify **distribution inefficiencies**, and simulate realistic **customer level, county level, and national trends** to inform operational decisions and policy planning. The project combines **data cleaning, transformation, visualization, simulation, and dashboarding** using Python, SQL, Excel, and Power BI. --- ## **Objectives** - Analyze energy consumption patterns across counties and customer types - Identify high consumption areas and distribution inefficiencies - Provide actionable insights for energy management and cost optimization - Simulate realistic trends at multiple levels: customer, county, and national --- ## **Tools & Technologies** - **Python** – data cleaning, transformation, and simulation - **Pandas** – data manipulation and aggregation - **NumPy** – numerical computations - **Matplotlib** – visualizations and trend plotting - **Power BI** – interactive dashboards and KPIs - **SQL** – data extraction and querying - **Excel / CSV** – handling raw datasets and intermediate files --- ## **Project Structure** - **data/** - **raw/** – original KPLC datasets - **cleaned/** – processed datasets ready for analysis - **notebooks/** - `kplc_analysis.ipynb` – main Jupyter notebook for data analysis - **scripts/** - `data_cleaning.py` – Python script for cleaning and transforming data - `simulation.py` – script for generating synthetic consumption trends - **images/** - screenshots used in README or reports - `README.md` – project documentation --- ## **Key Insights** - Regional consumption patterns and trends across Kenya - Identification of high-consumption counties and distribution inefficiencies - Comparison of consumption across customer types (residen …