Predicting House Prices with TensorFlow

In this course, you will train a model to tackle a regression problem and predict house prices using Tensorflow.

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Predicting House Prices with TensorFlow

Task List


We will cover the following tasks in 49 minutes:


Introduction

  • A look at what we will create.
  • A look at the development environment.

Introduction

Importing TensorFlow and the Dataset

  • Importing Tensorflow and Keras.
  • Importing the Boston Housing dataset.
  • Splitting the data in training and test sets.
  • A look at the shape of the data.
  • Shuffling the training data randomly.

Importing TensorFlow and the Dataset

Features

  • A quick description of some of the features.
  • Creating a dataframe from the training set using pandas.
  • A look at the training labels.

Features

Feature Scaling

  • Normalisation of data to scale various features.
  • A look at the normalised data.

Feature Scaling

Creating the Model

  • Creating a Sequential model with Keras.
  • Adding layers to our neural network model.
  • Compiling the model.
  • Looking at the model summary.

Creating the Model

Training the Model

  • Training by fitting the training set to the model.
  • Model evaluation on the test set.
  • Plotting Mean Absolute Error and Validation Mean Absolute Error values from the training process.
  • Training a new model with the “Early Stop” callback from Keras.
  • Plotting Mean Absolute Error and Validation Mean Absolute Error values from the training process on the new model.
  • Evaluating the new model on the test set.

Training the Model

Predictions

  • Getting predictions from the model on the test set.
  • Creating a scatter plot of actual labels vs predictions.
  • Creating a histogram of the prediction error for the test set.

Predictions

Watch Preview

Note: The actual session is completely hands-on where you interact with the software while watching the host's (Amit) instructions. You can preview the instructions here

Host: Amit


Amit

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Frequently Asked Questions


In Rhyme, all sessions are completely hands-on. You don't just passively watch someone else use the software. You use the software directly. While following the host's (Amit) instructions. Using the software is the only way to understand a software!
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