FaithMnisi/Maji-Ndogo-Agricultural-Analytics

An exploratory data analysis project that transforms agricultural and environmental data into actionable insights. Built with Python and SQL to support crop selection and farming automation in the fictional country of Maji Ndogo. # Maji Ndogo Agricultural Analytics ## Overview Maji Ndogo is a fictional African country working to modernize its agricultural sector through data-driven decision-making. Before farming processes can be automated, it is essential to understand which crops grow best under different environmental conditions. This project uses Python and SQL to analyze agricultural, climate, and geographical data to identify the factors that influence crop performance. Through exploratory data analysis, data transformation, and statistical aggregation, the project uncovers patterns that can be used to recommend suitable locations for crop cultivation and support future farming automation. ## Objectives This project aims to: * Explore how environmental factors influence crop production. * Identify the conditions under which different crops perform best. * Compare crop performance across regions and soil types. * Discover relationships between climate, geography, and agricultural yield. * Generate data-driven insights to support agricultural planning. ## Analysis Performed Throughout this project, I answered several key analytical questions, including: ### 1. Crop-Specific Analysis * Investigated the environmental conditions required for successful tea cultivation. * Calculated the average rainfall and elevation for tea-growing regions. * Compared growing conditions across different crop types. ### 2. Soil Analysis * Grouped agricultural data by soil type. * Evaluated soil fertility across different regions. * Identified fertile soils suitable for future agricultural expansion. ### 3. Climate and Geographic Analysis * Explored the relationship between rainfall, elevation, and temperature. * Calculated average environmental conditions across different categories. * Investigated how climate influences crop suitability. ### 4. Yield Analysis * Identified crops producing above-average yields. * Ranked crop performance using grouped statistical summaries. * Determined which c …