"Machine Learning with the Neural Network"

florianklimek

Hey guys,

I'm a student and have a particular question for a project at university. We have data about all countries in the world regarding the broadband penetration and the Human Development Index in the years 2000, 2005 and 2010 - 2014.

Now we would like to make a prediction about how the broadband penetration could affect the HDI in the future using the neural network or another method you would recommend. In the end, we would like to see that if for example Brazil risens the broadband penetration by 1% the HDI will rise by the number x.

Can you help us how to manage that?

Thank you and kind regards!

lionelderkrikor

Hi@florianklimek,

I don't know if your project is a regression problem or a time serie problem.

In the first case, you can use theLinear RegressionorVector Linear Regressionoperators (HDI vs broadband penetration).

but can you share your dataset(s) to better understand ?

Regards,

Lionel

florianklimek

Thank you for your reply!

Of course, here is the data. Ignore the other data, first of all we are focused on Broadband and the overall HDI.

Thanks!

lionelderkrikor

Hi@florianklimek

You have little data, however they are well fitted by aLinear Regressionmodel (R2 micro = 0,977) :

hyhhy

1. you can set the country to study by sethis country in the Set country operator

2. I performed this study withHuman Development Index (HDI),if you want to study theHDIsetHDIin theSelect AttributeandSet roleoperators.

3. You can set your future Broadband by setting them in theFuture Broadbandoperator to predict your future HDI.

Here the process :

<运营商激活= " true " class = "过程”兼容ibility="8.0.001" expanded="true" name="Process">

<运营商激活= " true " class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Year 2000" width="90" x="45" y="34">
<参数键=“csv_file”值= " C: \ \莱昂内尔\ D用户ocuments\Formations_DataScience\Rapidminer\Tests_Rapidminer\Broadband_vs_HDI\Year 2000 Gesamt.csv"/>














<运营商激活= " true " class="set_macro" compatibility="8.0.001" expanded="true" height="82" name="Set country" width="90" x="179" y="34">



<运营商激活= " true " class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples" width="90" x="313" y="238">




<运营商激活= " true " class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Year 2005" width="90" x="45" y="187">
<参数键=“csv_file”值= " C: \ \莱昂内尔\ D用户ocuments\Formations_DataScience\Rapidminer\Tests_Rapidminer\Broadband_vs_HDI\Year 2005 Gesamt.csv"/>














<运营商激活= " true " class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples (2)" width="90" x="179" y="136">




<运营商激活= " true " class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Year 2010" width="90" x="45" y="289">
<参数键=“csv_file”值= " C: \ \莱昂内尔\ D用户ocuments\Formations_DataScience\Rapidminer\Tests_Rapidminer\Broadband_vs_HDI\Year 2010 Gesamt.csv"/>














<运营商激活= " true " class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples (3)" width="90" x="179" y="289">




<运营商激活= " true " class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Year 2011" width="90" x="45" y="442">
<参数键=“csv_file”值= " C: \ \莱昂内尔\ D用户ocuments\Formations_DataScience\Rapidminer\Tests_Rapidminer\Broadband_vs_HDI\Year 2011 Gesamt.csv"/>














<运营商激活= " true " class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples (4)" width="90" x="179" y="391">




<运营商激活= " true " class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Year 2012" width="90" x="45" y="544">
<参数键=“csv_file”值= " C: \ \莱昂内尔\ D用户ocuments\Formations_DataScience\Rapidminer\Tests_Rapidminer\Broadband_vs_HDI\Year 2012 Gesamt.csv"/>














<运营商激活= " true " class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples (5)" width="90" x="179" y="544">




<运营商激活= " true " class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Year 2013" width="90" x="45" y="646">
<参数键=“csv_file”值= " C: \ \莱昂内尔\ D用户ocuments\Formations_DataScience\Rapidminer\Tests_Rapidminer\Broadband_vs_HDI\Year 2013 Gesamt.csv"/>














<运营商激活= " true " class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples (6)" width="90" x="179" y="697">




<运营商激活= " true " class="read_csv" compatibility="8.0.001" expanded="true" height="68" name="Year 2014" width="90" x="45" y="748">
<参数键=“csv_file”值= " C: \ \莱昂内尔\ D用户ocuments\Formations_DataScience\Rapidminer\Tests_Rapidminer\Broadband_vs_HDI\Year 2014 Gesamt.csv"/>














<运营商激活= " true " class="filter_examples" compatibility="8.0.001" expanded="true" height="103" name="Filter Examples (7)" width="90" x="179" y="799">




<运营商激活= " true " class="operator_toolbox:create_exampleset_from_doc" compatibility="0.7.000" expanded="true" height="68" name="Year" width="90" x="45" y="952">


<运营商激活= " true " class="append" compatibility="8.0.001" expanded="true" height="208" name="Append" width="90" x="447" y="391"/>
<运营商激活= " true " class="generate_id" compatibility="8.0.001" expanded="true" height="82" name="Generate ID" width="90" x="581" y="493"/>
<运营商激活= " true " class="generate_id" compatibility="8.0.001" expanded="true" height="82" name="Generate ID (2)" width="90" x="179" y="952"/>
<运营商激活= " true " class="join" compatibility="8.0.001" expanded="true" height="82" name="Join" width="90" x="715" y="493">


<运营商激活= " true " class="select_attributes" compatibility="8.0.001" expanded="true" height="82" name="Select Attributes" width="90" x="849" y="493">



<运营商激活= " true " class="multiply" compatibility="8.0.001" expanded="true" height="103" name="Multiply" width="90" x="983" y="493"/>
<运营商激活= " true " class="select_attributes" compatibility="8.0.001" expanded="true" height="82" name="Select Attributes (2)" width="90" x="1117" y="544">




<运营商激活= " true " class="set_role" compatibility="8.0.001" expanded="true" height="82" name="Set Role" width="90" x="1117" y="442">




<运营商激活= " true " class="concurrency:cross_validation" compatibility="8.0.001" expanded="true" height="145" name="Cross Validation" width="90" x="1251" y="442">


<运营商激活= " true " class="linear_regression" compatibility="8.0.001" expanded="true" height="103" name="Linear Regression" width="90" x="179" y="34"/>







<运营商激活= " true " class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">


<运营商激活= " true " class="performance_regression" compatibility="8.0.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">














<运营商激活= " true " class="multiply" compatibility="8.0.001" expanded="true" height="103" name="Multiply (2)" width="90" x="1318" y="646"/>
<运营商激活= " true " class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="1385" y="544">


<运营商激活= " true " class="operator_toolbox:create_exampleset_from_doc" compatibility="0.7.000" expanded="true" height="68" name="Future Broadband" width="90" x="1117" y="748">


<运营商激活= " true " class="numerical_to_real" compatibility="8.0.001" expanded="true" height="82" name="Numerical to Real" width="90" x="1251" y="748"/>
<运营商激活= " true " class="apply_model" compatibility="8.0.001" expanded="true" height="82" name="Apply Model (3)" width="90" x="1452" y="748">

































<连接from_op =“未来宽带”from_port = "put" to_op="Numerical to Real" to_port="example set input"/>

I hope it will be helpful,

Regards,

Lionel

florianklimek

Thank you very much, Lionel!

Really a great community here! But we have two more questions. So the process calculates all the numbers from each year right? So how can we interpretate the results?

Thanks a lot!

lionelderkrikor

Hi@florianklimek,

The process fit your data withLinear RegressionModel.

In your case, the equation of the model is :

Human Developement Index (HDI) = 0,006 * BroadBand Penetration + 0.685

So you can use this equation to predict theHuman Developement Index (HDI)for a givenBroadBand Penetration.

NB : Connect one of theMultiplier(2)output (which is a model output) to theresult (res)to see the parameters of the regression model.

I hope it will be helpful,

Regards,

Lionel