You create a model like this very easily with keras. Follow these steps.
Data Exploring
Before building a machine learning model, you must explore your data first. An approach is to use libraries to visualize the data as graphs. You can use the tool pandas-profiling
https://towardsdatascience.com/exploring-your-data-with-just-1-line-of-python-4b35ce21a82d
Points to look for when exploring the data
You should look for the following:
- Distribution of labels and input features
- Range of input features and labels
- Missing label or input feature
- Nan on value
- Outliers
You should look out for them and use appropriate data cleaning or filtering method to clean them. For example, Nan or missing value may be substituted with 0 and outliers will be removed. Input features may also be normalized.
Building the model
Keras is a easy tool for building machinea learning model. For how to build a basic MLP ( Multi-Layer Perceptron), you can refer to the example code and the resource.
https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
Experimenting
After creating the basic model, you can start experimenting with stuff like:
- Different model structure like adding layers and changing hidden layer size
- Different Optimizer
- Different hyperparameters like learning rate and batch size
- Different input features
You will gain more experience as you go. There is a lot of ways to improve the performance of the model.
Bewares
Here are things to beware:
- If your model doesn't work out, don't worry. Try another method or tune parameters until it work.
- If the testing loss become very high while training loss is low, you can add dropout to prevent this from happening. This is called overfitting.
Hope I can help you and have fun!
