Lambert-01/Maji_Ndogo_Farm_Survey

# Maji_Ndogo_Farm_SurveyMaji Ndogo – Smart Farming Automation 🌱 Hey there, I'm glad you're on board for the Maji Ndogo project AGAIN! Let me walk you through what we're up against and how we'll tackle it. 🌍 The Challenge I’m working on an ambitious project aimed at automating farming in Maji Ndogo, a place with diverse and challenging agricultural landscapes. But before diving into automation, I need to answer two key questions: Where should specific crops be planted? What factors influence these decisions? It’s not just about deploying technology; it’s about making informed and data-driven decisions based on: ✅ Rainfall ✅ Soil fertility ✅ Climate conditions ✅ Geographical data 🔍 The Approach This analysis is the foundation of the entire automation project. Think of it like solving a complex puzzle—each piece of data helps complete the bigger picture. Data Extraction – My dataset is stored in an SQLite database, split into multiple tables. Unlike Power BI or SQL queries, data analysis in Python happens in a single table. That means I need to brush off my SQL skills to import, merge, and structure the data properly. Data Cleaning & Reshaping – The data is messy, and that’s expected. I'll be cleaning, restructuring, and filtering it to ensure we only work with meaningful information. Pattern & Correlation Analysis – Once the dataset is structured, I’ll analyze patterns and relationships between variables to determine the best farming strategies for Maji Ndogo. 🚀 Let’s Get Started! This is more than just a coding challenge—it’s about impact. The insights I uncover will directly improve farming decisions, making agriculture in Maji Ndogo more efficient, productive, and sustainable. I'll be sharing findings, discussing strategies, and refining my approach as I go. 🔧 Tech Stack Python 🐍 Pandas for data manipulation SQLite for database management Matplotlib / Seaborn for visualization Geospatial Analysis (TBD) 💡 I’m ready to gear up and make a real difference. Let …