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.
Continue readingGuided Project
Data Science
567 EnrolledAt a Glance
In this lab, we will learn the basic methods of forecasting using Linear Regression and Neural Networks.
- Download and preliminary analysis of the data
- Forecasting
- Artificial neural networks
- Downloading data
- Changing the data types of columns
- Grouping data
- Data set transformation
- 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
- Creating a linear model using scikit-learn
- Creating a linear model using Keras
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.
Estimated Effort
1 Hour
Level
Intermediate
Industries
Agriculture
Skills You Will Learn
Artificial Intelligence, Data Science, Machine Learning
Language
English
Course Code
GPXX04P5EN