2401PTDS_Regression_Project

Table of contents

1.Project overview

The aim is to analyse and predict average temperature from the agri-food sector, using data from the FAO and IPCC, to understand climate impacts and develop sustainable strategies for stakeholders including policymakers and agricultural businesses.

2. Dataset

By the end of this project, you will have a thorough understanding of the impact of agricultural activities on CO2 emissions and climate change. Your findings and recommendations will contribute to the ongoing efforts to promote sustainability within the agri-food sector, providing valuable insights for the stakeholders involved in this initiative.

3. Packages

You please assist to put this in a bullet point for a readme

To carry out all the objectives for this repo, the following necessary dependencies were loaded:

import pandas as pd import numpy as np import matplotlib.pyplot as plt import itertools import random import seaborn as sns import matplotlib.cm as cm import json import pingouin as pg import missingno as msno import statsmodels.api as sm import pmdarima as pm

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.graphics.tsaplots import plot_predict from statsmodels.tsa.statespace.tools import diff from statsmodels.tsa.arima.model import ARIMA from statsmodels.tsa.stattools import adfuller from sklearn.metrics import mean_squared_error from math import sqrt

import warnings warnings.filterwarnings("ignore") plt.rcParams["figure.figsize"] = (15,10) Copilot To carry out all the objectives for this repo, the following necessary dependencies were loaded: import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import itertools

import random

import seaborn as sns

import matplotlib.cm as cm

import json

import pingouin as pg

import missingno as msno

import statsmodels.api as sm

import pmdarima as pm

from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

from statsmodels.graphics.tsaplots import plot_predict

from statsmodels.tsa.statespace.tools import diff

from statsmodels.tsa.arima.model import ARIMA

from statsmodels.tsa.stattools import adfuller

from sklearn.metrics import mean_squared_error

from math import sqrt

import warnings

warnings.filterwarnings("ignore")

plt.rcParams["figure.figsize"] = (15,10) import warnings warnings.filterwarnings("ignore") plt.rcParams["figure.figsize"] = (15,10)

4. Environment

It's highly recommended to use a virtual environment or Jupytor for your projects, there are many ways to do this; we've outlined one such method below. Make sure to regularly update this section. This way, anyone who clones your repository will know exactly what steps to follow to prepare the necessary environment. The instructions provided here should enable a person to clone your repo and quickly get started.

Create the new evironment - you only need to do this once

create the conda environment

conda create

Exporting your conda environment

conda activate conda install pip pip freeze > requirements.txt pip list --format=freeze > requirements.txt

This is how you activate the virtual environment in a terminal and install the project dependencies

activate the virtual environment

conda activate

install the pip package

conda install pip

install the requirements for this project

pip install -r requirements.txt

5. Contributors

Data Engineers:

(1) Lizaan Botha

(2) Susheila Naick

(3) Amukelani Khosa

(4) Lutho Ntsepe

(5) Richard Marais

(6) Thabiso Nyokolodi