ChachaMarwaDev/ALX-solutions

Analysing geography, soil, climate, and yield data to find where crops thrive in Maji Ndogo — groundwork for a farming automation initiative. # 🌱 Maji Ndogo Agricultural Analysis ### Integrated Project — ALX Data Analytics Programme > Exploratory data analysis to identify optimal crop-growing conditions across five provinces of Maji Ndogo, as groundwork for an agricultural automation initiative. --- ## 📌 Project Overview Maji Ndogo is an ambitious farming automation project. Before any technology can be deployed, the right decisions need to be made about **where** to plant **what**. This analysis answers exactly that — using survey data from 5,654 fields across five provinces, covering geography, weather, soil chemistry, and crop performance. The work involves loading data from a multi-table SQLite database, cleaning it, and running structured analyses to surface the conditions under which each crop performs best. --- ## 📁 Repository Structure ``` ALX-solutions/ │ ├── app.py # Streamlit interactive dashboard ├── Code_challenge_Integrated_Project_P1_student_version.ipynb # Main analysis notebook ├── Clean-coding-with-PEP-8.ipynb # PEP 8 coding standards exercise ├── Introduction_to_NumPy_Exercise.ipynb # NumPy fundamentals exercise ├── Maji_Ndogo_farm_survey_small.db # SQLite database (4 tables, 5,654 fields) ├── pyproject.toml # Project dependencies (uv) ├── uv.lock # Locked dependency versions ├── commands.txt # Useful dev commands reference ├── .gitignore └── README.md ``` --- ## 🗄️ Database Schema The database contains four tables, all joined on `Field_ID`: | Table | Key Columns | Description | |---|---|---| | `geographic_features` | Elevation, Latitude, Longitude, Location, Slope | Where each field is located | | `weather_features` | Rainfall, Min/Max/Ave temperat …