# Performing-Linear-Regression-Using-Tensor-Flow

**Repository Path**: mirrors_ibm/Performing-Linear-Regression-Using-Tensor-Flow

## Basic Information

- **Project Name**: Performing-Linear-Regression-Using-Tensor-Flow
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
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- **Created**: 2020-11-23
- **Last Updated**: 2025-08-09

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**Tags**: None

## README

# Performing Linear Regression Using Tensor Flow

## Abstract
In this developer tutorial, we will create a Jupyter Notebook that contains Python code for defining linear regression and then using Tensor Flow to implement it. The notebook will be running on IBM Cloud Pak for Data as a Service on IBM Cloud. The IBM Cloud Pak for Data platform provides additional support, such as integration with multiple data sources, built-in analytics, Jupyter Notebooks, and machine learning. It also offers Scalability by distributing processes across multiple computing resources.

## Introduction
Defining a linear regression in simple terms, is the approximation of a linear model used to describe the relationship between two or more variables. In a simple linear regression there are two variables, the dependent variable, which can be seen as the "state" or "final goal" that we study and try to predict, and the independent variables, also known as explanatory variables, which can be seen as the "causes" of the "states".

When more than one independent variable is present the process is called multiple linear regression.
When multiple dependent variables are predicted the process is known as multivariate linear regression.

The equation of a simple linear model is
<p align="center">
𝑌=𝑎𝑋+𝑏
</p>

Where Y is the dependent variable and X is the independent variable, and a and b being the parameters we adjust. a is known as "slope" or "gradient" and b is the "intercept". You can interpret this equation as Y being a function of X, or Y being dependent on X.

#### Let’s get started!

## Incuded components
1.	[IBM Cloud Pak for Data](https://www.ibm.com/in-en/products/cloud-pak-for-data): Development platform for AI Applications.
2.	[Jupyter Notebook](https://jupyter.org/): An open source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text.

## Featured technologies
1.	[Python](https://www.python.org/): Python is a programming language that lets you work more quickly and integrate your systems more effectively.
2.	[TensorFlow](https://www.tensorflow.org/): The core open source library to help you develop and train ML models

## Steps:
Following steps are involved in the process and are explained below in detail with screenshots for convenience:
1.	Create your IBM Cloud Account and access the IBM Cloud Pak for Data as a Service.
2.	Create a new project.
3.	Associate the Watson Machine Learning service with the project.
4.	Add a Notebook to your project.
5.	Run the Notebook.

### Step 1:
-	Sign up or Sign in to [IBM Cloud](https://ibm.biz/BdqtVY). 
-	Search for Watson Studio / CPDaaS Service.
-	Create the service by selecting the region and pricing plan of your preference
<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/Searchforcpd.png?raw=true"  width="800">
</p>

<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/createcpd.png?raw=true"  width="800">
</p>

### Step 2:
-	Launch the Watson Studio service. 
<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/createaproject.png?raw=true"  width="800">
</p>

-	Click on create a project and create an empty project. Make sure you name your project and add a storage service as shown in the picture.
<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/createnewproject.png?raw=true"  width="800">
</p>

-	After your project is created, you will be directed to a project dashboard.

### Step 3:
-	Go to settings of the project.
-	click on the add service button in the associated service tab. Select Watson in the drop-down menu.

<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/settingsandassociate.png?raw=true"  width="800">
</p>

-	Add the Watson Machine Learning Service. Mark the service and Associate it with the Project as shown below.

<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/selectmlservice.png?raw=true"  width="800">
</p>
<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/associatemlservice.png?raw=true"  width="800">
</p>

### Step 4:
-	Add a Jupyter notebook to your project by clicking on Add to Project and selecting Notebook in the pop-up menu.
<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/addjupyter.png?raw=true"  width="800">
</p>

-	Select ‘From URL’ option and paste the notebook url from the github repo:
```
https://github.com/IBM/dl-learning-path-assets/tree/main/fundamentals-of-deeplearning/notebooks/PerformingLinearRegressionUsingTensorFlow.ipynb
```
- Name your notebook and click create.

<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/pasteurl.png?raw=true"  width="800">
</p>

### Step 5:
Once the notebook is loaded. Click on Cell and Select Run All to run the notebook. The rest of the tutorial is explained in the notebook in detail.

<p align="center">
<img src="https://github.com/IBM/Performing-Linear-Regression-Using-Tensor-Flow/blob/master/doc/source/images/runnotebook.png?raw=true"  width="800">
</p>

## Summary
In this tutorial, we learned the basics of linear regression and how tensorflow is used to implement ML algorithms. You learned how to run a Jupyter Notebook using Watson Studio on IBM Cloud Pak for Data as a Service and how to use open source frameworks in the IBM Cloud Pak for Data as a Service.