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Prognostication using Neural Network in Agriculture

IntermediateGuided Project

In this lab, we will learn the basic methods of forecasting using Linear Regression and Neural Networks.

4.4 (55 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Agriculture

Enrollment Count

  • 609

Skills You Will Learn

  • Data Science, Machine Learning, Artificial Intelligence

Offered By

  • IBM

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • May 10, 2025
About this Guided Project
In this lab, we will learn the basic methods of forecasting using linear regression and neural networks. The lab consists of three stages:
  • Download and preliminary analysis of the data
  • Forecasting
  • Artificial neural networks
The first stage will show you how to download data and prepare it for analysis.
  • Downloading data
  • Changing the data types of columns
  • Grouping data
  • Data set transformation
The second stage deals with forecasting. This stage includes methods of building and fitting models as well as the automation of statistical information calculation.
  • Hypothesis creation
  • Splitting the data set into training and test sets
  • Creating a linear model using sklearn
  • Calculation of basic statistical indicators
  • Creating a linear model using statsmodels
The third stage focuses on artificial neural networks and deals with the methods of building and fitting models based on artificial intelligence.
  • Creating a linear model using scikit-learn
  • Creating a linear model using Keras
The statistical data was obtained from https://ec.europa.eu/eurostat/databrowser/view/aact_eaa01/default/table?lang=en. Eurostat has a policy of encouraging the free re-use of its data, both for non-commercial and commercial purposes.

Prerequisites

  • Python - basic level
  • Pandas - basic level
  • SeaBorn - basic level
  • Statistics - basic level
  • Scikit-learn - basic level
  • Keras - basic level
 

After completing this lab, you will be able to:

  • Download a data set from *.csv files.
  • Automatically change the data in the set.
  • Transform a table
  • Visualize data with pandas and seaborn
  • Make linear forecast models
  • Build and fit neural networks.
 
 

Instructors

Yaroslav Vyklyuk

Full Professor, Doctor of Computer Science, PhD

Dr. Yaroslav Vyklyuk is a full professor at the Lviv Polytechnic National University, Department of Artificial Intelligence Systems. He is an author of over 210 scientific works, 10 monographs, and books, a member of the Editorial Board of 6 international scientific journals, member of the Academic Councils on protection Ph.D. and DrSc thesis in "Mathematical modeling and computational methods". Research Interests: Data Science, Applied System Analysis, Mathematical Modeling, and Decision Making of Complex Dynamic Systems (socio-economic, geographical, tourist, and crisis systems) using Artificial Intelligence Technology, DataMining, Big Data, Parallel Calculations, Statistics, Econometrics, Econophysics and other Advanced Mathematical Methods with implementation into information, WEB, and geographic information systems.

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Kateryna Hazdiuk

PhD of Software Engineering

I am an assistant professor at the Yuriy Fedcovych Chernivtsi National University, Software of Computer Systems Department; an author of over 40 scientific works and 10 training manuals. Research Interests: Mathematical Modeling of Complex Dynamic Systems (bio-like systems, socio-economic, geographical systems), Data Science, Decision Making using Artificial Intelligence Technology, DataMining, Big Data, Parallel Calculations, Statistics, and other methods.

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