How to perform basic regression using keras model?

How to perform basic regression using keras model?

How to perform basic regression using keras model?

This recipe helps you perform basic regression using keras model


Recipe Objective

In machine learning, our main motive is to create a model that can relate the dependent variable(i.e target) with the independent variable(i.e. data). The most common model to do this is regression analysis. Regression fits the best possible curve on the training data set so that it can predict the target using the same curve.

So this recipe is a short example of How to perform basic regression using keras model?

Step 1 - Import the library

import pandas as pd import numpy as np from keras.datasets import mnist from sklearn.model_selection import train_test_split from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout

We have imported pandas, numpy, mnist(which is the dataset), train_test_split, Sequential, Dense and Dropout. We will use these later in the recipe.

Step 2 - Loading the Dataset

Here we have used the inbuilt mnist dataset and stored the train data in X_train and y_train. We have used X_test and y_test to store the test data. (X_train, y_train), (X_test, y_test) = mnist.load_data()

Step 3 - Creating Regression Model

We have created an object model for sequential model. We can use two args i.e layers and name. model = Sequential() Now, We are adding the layers by using 'add'. We can specify the type of layer, activation function to be used and many other things while adding the layer.
Here we are making regression model so we are making the linear stack of layers. We are using the activation function as 'relu' that is rectified linear unit, it has a advantage of being non linear also. model.add(Dense(512, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(256, activation='relu')) model.add(Dropout(0.25)) model.add(Dense(10))

Step 4 - Compiling the model

We can compile a model by using compile attribute. Let us first look at its parameters before using it.

  • optimizer : In this, we can pass the optimizer we want to use. There is various optimizer like SGD, Adam etc.
  • loss : In this, we can pass a loss function which we want for the model
  • metrics : In this, we can pass the metric on which we want the model to be scored
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])

Step 5 - Fitting the model

We can fit a model on the data we have and can use the model after that. Here we are using the data which we have splitted i.e the training data for fitting the model.
While fitting we can pass various parameters like batch_size, epochs, verbose, validation_data and so on., y_train, batch_size=128, epochs=2, verbose=1, validation_data=(X_test, y_test) model.summary()

Step 6 - Evaluating the model

After fitting a model we want to evaluate the model. Here we are using model.evaluate to evaluate the model and it will give us the loss and the accuracy. Here we have also printed the score. score = model.evaluate(X_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1])

Step 7 - Predicting the output

Finally we are predicting the output for this we are using another part of the data that we get from test_train_split i.e. test data. We will use it and predict the output. y_pred = model.predict(X_test) print(y_pred) As an output we get:

Epoch 1/2
469/469 [==============================] - 7s 14ms/step - loss: 0.3174 - accuracy: 0.9033 - val_loss: 0.1212 - val_accuracy: 0.9630
Epoch 2/2
469/469 [==============================] - 6s 14ms/step - loss: 0.1560 - accuracy: 0.9534 - val_loss: 0.0918 - val_accuracy: 0.9720
Test loss: 0.09184003621339798
Test accuracy: 0.972000002861023

[[8.92436292e-10 1.32853462e-09 6.39653945e-06 ... 9.99989152e-01
  1.79315840e-09 2.44941958e-07]
 [9.11153306e-11 1.03196271e-05 9.99982357e-01 ... 1.89035987e-09
  9.82423032e-09 8.40081246e-14]
 [1.10766098e-06 9.99514341e-01 1.26151179e-04 ... 1.44331687e-04
  4.99823145e-05 6.05678633e-06]
 [2.03985762e-09 1.29704825e-08 2.95020914e-08 ... 1.23884201e-05
  6.87194824e-06 1.75449488e-04]
 [5.91818647e-08 1.97798578e-08 7.46679774e-10 ... 5.06311437e-09
  1.96506153e-04 1.14137793e-08]
 [1.13083731e-09 5.45665553e-12 2.54836174e-09 ... 3.70580059e-13
  6.02386641e-10 3.15489106e-12]]

Relevant Projects

Deep Learning with Keras in R to Predict Customer Churn
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

Predict Macro Economic Trends using Kaggle Financial Dataset
In this machine learning project, you will uncover the predictive value in an uncertain world by using various artificial intelligence, machine learning, advanced regression and feature transformation techniques.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Human Activity Recognition Using Multiclass Classification in Python
In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.