Offered By: IBM

Data Science in Agriculture. Prognostication using by Neural Network

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

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GPXX04P5EN

Data Science

463 Enrolled
(33)

At a Glance

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

About 

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 data
  • Forecasting
  • Artificial Neural Networks
The first stage will show you how to download data and pre-prepare it for the analysis:
  • downloading data
  • changing the data types of columns
  • grouping data
  • DataSet transformation

At the stage of forecasting, we will deal with the methods of building and fitting models, as well as with the automation of statistical information calculation, in particular:
  • hypothesis creation
  • splitting the DataSet into training and test sets
  • creating a linear model using sklearn
  • calculation of basic statistical indicators
  • creating a linear model using statsmodels

At the stage of Artificial Neural Networks, we will deal 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 the https://ec.europa.eu/eurostat/databrowser/view/aact_eaa01/default/table?lang=en. Eurostat has a policy of encouraging 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 DataSet from *.csv files
  • Automatically change data in the DataSet
  • Transform a table
  • Visualize data with pandas and seaborn
  • Make Linear forecast models
  • Build and fit Neural Networks