Hello developers, and welcome back.
In this section we're going to build this amazing app called Car Mileage Predictor App.
This app is a complex app.
It's an advanced application that uses machine learning to predict the mileage based on many parameters.
The first step we're going to get the data from and from an online source.
And by the way, those are real data.
So we're going to work with real data.
I'm going to show you the real data and dealing with real data.
So this would be very exciting dealing with with real data.
So our app will accept in the input layer cylinders.
Displacement.
Horsepower.
Weight.
Acceleration.
Model year origin one, origin two and origin three.
Those are the nine parameters we're going to insert in the input layer.
Then we're going to move to create the hidden layer number one with 64 neurons, then the hidden layer
number two with 64 neurons, and getting the output layer, which is the mileage and predicting the
mileage.
So it's not an easy and simple application.
It's gonna be a little bit complex and advanced.
We're going to use those data in order to train our model with Google Colab.
So the learning objectives is are data loading and exploration, data pre-processing and cleaning,
building a neural network with TensorFlow, Keras model training and evaluation, and making predictions
throughout the whole process.
We're going to use libraries in Python.
We're going to display the data the distribution of mpg in histograms.
Also we're going to learn about heat maps.
We're going to learn about the data parameters and the layers.
Also, we're going to train the model getting the evaluation and much, much more.
Also, we're going to make the difference and a comparison between the actual versus predicted mpg.
Okay.
So it's gonna be a very exciting application.
We're gonna build together using the real data.
And we're going to transform those data into a data frames that our model accept.
Also going to train our model in order to start predicting the data according to this chart or or similar
to this chart.
Also we're going to integrate it with Android Studio.
So this is our application.
For example the user enters four cylinders.
Displacement 150 cc, horsepower 100.
Weight Thousand pounds.
Acceleration zero point the 15.
It's from zero to 60s model year.
Between 70 and 82 and origin would be America.
If I click predict mpg it predicts.
This number if I select European predict mpg and it's 21.39 select Japanese predict mpg.
Which is 22.
So also we're going to handle the errors.
So if the user like exceeded the.
Model year for example like this predict mpg.
We get the error.
Please fix the model year.
Should be between 70 and 82.
So all of those things we're gonna learn in our application.
Our lovely application.
And by the way it's not an easy application.
It's hard application.
Advanced application that we're going to use machine learning AI, and we're going to get real data
based on real data.
We're gonna predict our new data and the MPG.
Okay.
So this is a very exciting application.
This is a very, very interesting and very advanced application that the TensorFlow is used to build
machine learning models.
Also, we're going to use Android Studio and how to integrate machine learning with our Kotlin Jetpack
Compose application.
Again guys, this is a very interesting application we're gonna build together.
So I am very excited to start building this application.
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