Milk-Production-Time-Series-Forecasting-Datascience-Project

This project uses time series forecasting to predict future milk production. The data used in this project is monthly milk production data from January 1962 to December 1975. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The model is evaluated using various metrics.

Problem Statement:

The dairy industry is a major economic sector in many countries. The ability to forecast milk production is important for farmers, processors, and policymakers. The Goal is to build the model that can accurately predict the quantity of milk that will be produced in the future.

Solution Approach:

This project uses time series forecasting to predict future milk production. The ARIMA (autoregressive integrated moving average) model is used to forecast the milk production. The ARIMA model is a powerful statistical model that is used to forecast time series data that exhibits autoregressive (AR) and moving average (MA) properties. The model is fitted to the historical milk production data and then used to forecast future production.

Observations:

The following observations were made during the course of this project:

The monthly milk production data is a non-stationary time series. This means that the mean and variance of the data are not constant over time.
The ARIMA model was able to forecast milk production with a relatively high accuracy.
The model can be evaluated using various metrics such as R2 Score, Mean Squared Error(MSE), Root Mean Squared Error(RMSE), Mean Absolute Error(MAE), Mean Absolute Percentage Error(MAPE).
The model was able to forecast future milk production with a reasonable degree of accuracy.
The model was able to capture the seasonal patterns in the milk production data.

Insights:

The following insights were gained from this project:

Time series forecasting can be a valuable tool for predicting future milk production.
The ARIMA model is a powerful tool that can be used to forecast a variety of time series data.
The accuracy of the ARIMA model can be improved by adjusting the model parameters.
The model can be used to identify trends and patterns in milk production data.

Findings:

The following findings were made from this project: