DorrisAkello/Kenya-Weather-Dashboard

Cleaning, analyzing, and visualizing Kenya weather station data using Python and Power BI. # Kenya-Weather-Dashboard Cleaning, analyzing, and visualizing Kenya weather station data using Python and Power BI. # Kenya Weather Dashboard ## Project Overview This project demonstrates an end-to-end data analysis workflow using weather station data collected across Kenya. The project involved cleaning raw weather data in Python, performing exploratory data analysis, and building an interactive Power BI dashboard to support weather monitoring and decision-making. --- ## Objectives * Clean and prepare raw weather data. * Handle missing values and inconsistencies. * Explore weather patterns using Python. * Build an interactive Power BI dashboard. * Present key insights through visualizations. --- ## Tools Used * Python * Google Colab * Pandas * NumPy * Matplotlib * Power BI * GitHub --- ## Dataset The dataset contains weather observations from multiple stations across Kenya between 2023 and 2025. Variables include: * Timestamp * Station Name * Air Temperature (°C) * Humidity (%) * Rainfall (mm) * Wind Speed (m/s) * Wind Direction * Barometric Pressure (hPa) * UV Index * Light Intensity --- ## Data Cleaning Process The following preprocessing steps were completed: * Converted timestamps to datetime format. * Checked for missing values. * Imputed missing values where appropriate. * Removed duplicate records. * Standardized data types. * Performed outlier analysis. * Verified data quality before visualization. --- ## Dashboard Features The Power BI dashboard includes: * Total Rainfall * Average Temperature * Average Humidity * Average Wind Speed * Rainfall Trends * Temperature Trends * Weather Station Comparison * Interactive Filters --- ## Key Insights * Rainfall varied considerably across stations and time periods. * Temperature and humidity showed noticeable seasonal patterns. * Some stations consistently recorded higher rainfall than others. * Interactive filters allow users to compare weather conditions by station and time. --- ## Reposi …