Offered By: IBM
Prognostication using Neural Network in Agriculture
In this lab, we will learn the basic methods of forecasting using Linear Regression and Neural Networks.
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Data Science
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At a Glance
In this lab, we will learn the basic methods of forecasting using Linear Regression and Neural Networks.
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
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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.
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