allan-bit-web/data_driven_journey_in_Maji_ndogo

# data_driven_journey_in_Maji_ndogo ## Project Overview This project explores a large water services database to understand water sources, visit patterns, water quality, and pollution issues in Maji Ndogo. I used MySQL to query and analyze multiple tables, extract key insights, and identify areas that require attention. The goal is to practice real-world data exploration and support data-driven decision-making for improving access to safe water. ## Data Exploration - Listed all tables using `SHOW TABLES`. | Table Name | Description | |----------------------|--------------------------------------| | water_source | Stores information about water sources | | visits | Logs visits to water sources | | water_quality | Contains water quality assessments | | well_pollution | Stores pollution test results | | location | Geographic location data | | global_water_access | National and regional water access data | | employee | Survey staff information | | data_dictionary | Metadata about the dataset | - Identified 8 tables including water_source, visits, water_quality, and well_pollution. - Retrieved the first 5 records from each table using SELECT statements. - Used DESCRIBE to understand column names and data types. - Interpreted what each table represents (water sources, visits, quality, pollution, locations, etc.). - Queried the `location`, `visits`, and `water_source` tables using SELECT and LIMIT to understand the dataset structure. - Identified key columns such as location_id, source_id, and time_of_record. - Observed that visits and water_source tables are linked using foreign keys. - Used the data_dictionary table to understand column definitions and metadata. ## Water Source Types Analysis - Identified the `water_source` table as the source of water type information. - Used `SELECT DISTINCT` …