ngangawairimu/Automating_Farming_Analysis

This project automates farming in Maji Ndogo, analyzing soil fertility, climate, and geography to recommend optimal crop locations. Python-based data analysis I uncovers patterns guiding farming practices. Project Overview Introduction This project to automate farming in Maji Ndogo, an area with diverse and challenging agricultural conditions. Before implementing technology, I need to understand where and what to plant. This involves analyzing various factors such as rainfall, soil type, and climate to make informed decisions on crop placement. The goal is to use data to recommend the best locations for different crops. This is like solving a puzzle, where each piece of information helps us see the full picture. Data Analysis By importing our data from an SQLite database into a DataFrame. This data is split into tables, and we'll combine them into one for analysis. I’ll analyze the data to find patterns and correlations that will help us determine the best farming practices for Maji Ndogo. Data Handling I'm working with an SQLite database named Maji_Ndogo_farm_survey.db, which contains multiple tables.