# Nyeri County Milk Production Forecasting
### Faith Wambui Gichuru — SCT213-C002-0003/2022 — JKUAT
A comparative time-series forecasting study applying ARIMA, SARIMA, and Facebook Prophet
to both observed Nyeri County dairy data and a Denton-Cholette simulated monthly dataset,
deployed as an interactive Streamlit dashboard.
---
## Project Structure
```
nyeri-milk-forecasting/
│
├── data/
│ ├── 01_county_annual.csv ← Dataset 1: 9 observed annual records
│ ├── 02_subcounty_population.csv ← Dataset 2: 5yr x 8 sub-county records
│ └── 03_simulated_monthly.csv ← Dataset 3: Generated by simulate.py
│
├── 01_data_preparation.py ← CRISP-DM Phase 1 & 2: understand + prepare
├── 02_simulate.py ← CRISP-DM Phase 3: generate Dataset 3
├── 03_eda.py ← CRISP-DM Phase 4a: exploratory analysis
├── 04_models.py ← CRISP-DM Phase 4b: train all 3 models
├── 05_evaluate.py ← CRISP-DM Phase 5: compare + rank models
├── app.py ← CRISP-DM Phase 6: Streamlit dashboard
│
├── requirements.txt
└── README.md
```
---
## Git Setup — Step by Step
### 1. Install Git
Download from
git-scm.com and install.
Verify: open terminal and type `git --version`
### 2. Configure Git (one time only)
```bash
git config --global user.name
git config --global user.email
```
### 3. Create GitHub Repository
1. Go to
github.com and sign in
2. Click the **+** icon → **New repository**
3. Name: `nyeri-milk-forecasting`
4. Set to **Public**
5. Check **Add a README file**
6. Click **Create repository**
### 4. Clone to your computer
```bash
# Replace YOUR_USERNAME with your actual GitHub username
git clone
github.com
cd nyeri-milk-forecasting
```
### 5. Set up Python virtual environment
```bash
# Create the environment
python -m venv venv
# Activate it — Windows:
venv\Scripts\activate
# Activate …