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Customer Churn Analysis Prediction - Python
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Identifying handwritten digits using Logistic Regression in PyTorch

Last Updated : 10 Sep, 2024
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Logistic Regression is a very commonly used statistical method that allows us to predict a binary output from a set of independent variables. The various properties of logistic regression and its Python implementation have been covered in this article previously. Now, we shall find out how to implement this in PyTorch, a very popular deep learning library that is being developed by Facebook.
Now, we shall see how to classify handwritten digits from the MNIST dataset using Logistic Regression in PyTorch. Firstly, you will need to install PyTorch into your Python environment. The easiest way to do this is to use the pip or conda tool. Visit pytorch.org and install the version of your Python interpreter and the package manager that you would like to use.
With PyTorch installed, let us now have a look at the code. Write the three lines given below to import the required library functions and objects.

Python 
import torch import torch.nn as nn import torchvision.datasets as dsets import torchvision.transforms as transforms from torch.autograd import Variable

Here, the torch.nn module contains the code required for the model, torchvision.datasets contain the MNIST dataset. It contains the dataset of handwritten digits that we shall be using here. The torchvision.transforms module contains various methods to transform objects into others. Here, we shall be using it to transform from images to PyTorch tensors. Also, the torch.autograd module contains the Variable class amongst others, which will be used by us while defining our tensors.
Next, we shall download and load the dataset to memory.

Python 
# Set the batch size batch_size = 64  # MNIST Dataset (Images and Labels) train_dataset = dsets.MNIST(root ='./data',                              train = True,                              transform = transforms.ToTensor(),                             download = True)  test_dataset = dsets.MNIST(root ='./data',                             train = False,                             transform = transforms.ToTensor())  # Dataset Loader (Input Pipeline) train_loader = torch.utils.data.DataLoader(dataset = train_dataset,                                             batch_size = batch_size,                                             shuffle = True)  test_loader = torch.utils.data.DataLoader(dataset = test_dataset,                                            batch_size = batch_size,                                            shuffle = False)  # This code is modified by Susobhan Akhuli

Now, we shall define our hyperparameters.

Python 
# Hyper Parameters  input_size = 784 num_classes = 10 num_epochs = 5 batch_size = 100 learning_rate = 0.001

In our dataset, the image size is 28*28. Thus, our input size is 784. Also, 10 digits are present in this and hence, we can have 10 different outputs. Thus, we set num_classes as 10. Also, we shall train five times on the entire dataset. Finally, we will train in small batches of 100 images each so as to prevent the crashing of the program due to memory overflow.
After this, we shall be defining our model as below. Here, we shall initialize our model as a subclass of torch.nn.Module and then define the forward pass. In the code that we are writing, the softmax is internally calculated during each forward pass and hence we do not need to specify it inside the forward() function.

Python 
class LogisticRegression(nn.Module):     def __init__(self, input_size, num_classes):         super(LogisticRegression, self).__init__()         self.linear = nn.Linear(input_size, num_classes)      def forward(self, x):         out = self.linear(x)         return out

Having defined our class, now we instantiate an object for the same.

Python 
model = LogisticRegression(input_size, num_classes)

Next, we set our loss function and the optimizer. Here, we shall be using the cross-entropy loss and for the optimizer, we shall be using the stochastic gradient descent algorithm with a learning rate of 0.001 as defined in the hyperparameter above.

Python 
criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)

Now, we shall start the training. Here, we shall be performing the following tasks:

  1. Reset all gradients to 0.
  2. Make a forward pass.
  3. Calculate the loss.
  4. Perform backpropagation.
  5. Update all weights.
Python 
# Training the Model  for epoch in range(num_epochs):  	for i, (images, labels) in enumerate(train_loader):  		images = Variable(images.view(-1, 28 * 28))  		labels = Variable(labels)   		# Forward + Backward + Optimize  		optimizer.zero_grad()  		outputs = model(images)  		loss = criterion(outputs, labels)  		loss.backward()  		optimizer.step()   		if (i + 1) % 100 == 0:  			print('Epoch: [% d/% d], Step: [% d/% d], Loss: %.4f' 				% (epoch + 1, num_epochs, i + 1,  					len(train_dataset) // batch_size, loss.item())) # Use loss.item() to get the scalar value  # This code is modified by Susobhan Akhuli

Finally, we shall be testing out the model by using the following code.

Python 
# Test the Model correct = 0 total = 0 for images, labels in test_loader:     images = Variable(images.view(-1, 28 * 28))     outputs = model(images)     _, predicted = torch.max(outputs.data, 1)     total += labels.size(0)     correct += (predicted == labels).sum()  print('Accuracy of the model on the 10000 test images: % d %%' % (             100 * correct / total)) 
Accuracy of the model on the 10000 test images:  84 %

Assuming that you performed all steps correctly, you will get an accuracy of 82%, which is far off from today's state-of-the-art model, which uses a special type of neural network architecture. For your reference, you can find the entire code for this article below:

Python 
import torch  import torch.nn as nn  import torchvision.datasets as dsets  import torchvision.transforms as transforms  from torch.autograd import Variable    # MNIST Dataset (Images and Labels)  train_dataset = dsets.MNIST(root ='./data',  							train = True,  							transform = transforms.ToTensor(),  							download = True)   test_dataset = dsets.MNIST(root ='./data',  						train = False,  						transform = transforms.ToTensor())   # Dataset Loader (Input Pipeline)  train_loader = torch.utils.data.DataLoader(dataset = train_dataset,  										batch_size = batch_size,  										shuffle = True)   test_loader = torch.utils.data.DataLoader(dataset = test_dataset,  										batch_size = batch_size,  										shuffle = False)   # Hyper Parameters  input_size = 784 num_classes = 10 num_epochs = 5 batch_size = 100 learning_rate = 0.001  # Model  class LogisticRegression(nn.Module):  	def __init__(self, input_size, num_classes):  		super(LogisticRegression, self).__init__()  		self.linear = nn.Linear(input_size, num_classes)   	def forward(self, x):  		out = self.linear(x)  		return out    model = LogisticRegression(input_size, num_classes)   # Loss and Optimizer  # Softmax is internally computed.  # Set parameters to be updated.  criterion = nn.CrossEntropyLoss()  optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)   # Training the Model  for epoch in range(num_epochs):  	for i, (images, labels) in enumerate(train_loader):  		images = Variable(images.view(-1, 28 * 28))  		labels = Variable(labels)   		# Forward + Backward + Optimize  		optimizer.zero_grad()  		outputs = model(images)  		loss = criterion(outputs, labels)  		loss.backward()  		optimizer.step()   		if (i + 1) % 100 == 0:  			print('Epoch: [% d/% d], Step: [% d/% d], Loss: %.4f' 				% (epoch + 1, num_epochs, i + 1,  					len(train_dataset) // batch_size, loss.item()))   # Test the Model  correct = 0 total = 0 for images, labels in test_loader:  	images = Variable(images.view(-1, 28 * 28))  	outputs = model(images)  	_, predicted = torch.max(outputs.data, 1)  	total += labels.size(0)  	correct += (predicted == labels).sum()   print('Accuracy of the model on the 10000 test images: % d %%' % (  			100 * correct / total))   # This code is modified by Susobhan Akhuli 
Epoch: [ 1/ 5], Step: [ 100/ 600], Loss: 2.2127
Epoch: [ 1/ 5], Step: [ 200/ 600], Loss: 2.1009
Epoch: [ 1/ 5], Step: [ 300/ 600], Loss: 2.0020
Epoch: [ 1/ 5], Step: [ 400/ 600], Loss: 1.9426
Epoch: [ 1/ 5], Step: [ 500/ 600], Loss: 1.9161
Epoch: [ 1/ 5], Step: [ 600/ 600], Loss: 1.7942
Epoch: [ 2/ 5], Step: [ 100/ 600], Loss: 1.7794
Epoch: [ 2/ 5], Step: [ 200/ 600], Loss: 1.6350
Epoch: [ 2/ 5], Step: [ 300/ 600], Loss: 1.6210
Epoch: [ 2/ 5], Step: [ 400/ 600], Loss: 1.6226
Epoch: [ 2/ 5], Step: [ 500/ 600], Loss: 1.5117
Epoch: [ 2/ 5], Step: [ 600/ 600], Loss: 1.3767
Epoch: [ 3/ 5], Step: [ 100/ 600], Loss: 1.4091
Epoch: [ 3/ 5], Step: [ 200/ 600], Loss: 1.3804
Epoch: [ 3/ 5], Step: [ 300/ 600], Loss: 1.3727
Epoch: [ 3/ 5], Step: [ 400/ 600], Loss: 1.2697
Epoch: [ 3/ 5], Step: [ 500/ 600], Loss: 1.2719
Epoch: [ 3/ 5], Step: [ 600/ 600], Loss: 1.2932
Epoch: [ 4/ 5], Step: [ 100/ 600], Loss: 1.3018
Epoch: [ 4/ 5], Step: [ 200/ 600], Loss: 1.2399
Epoch: [ 4/ 5], Step: [ 300/ 600], Loss: 1.1748
Epoch: [ 4/ 5], Step: [ 400/ 600], Loss: 1.0913
Epoch: [ 4/ 5], Step: [ 500/ 600], Loss: 1.1653
Epoch: [ 4/ 5], Step: [ 600/ 600], Loss: 1.0840
Epoch: [ 5/ 5], Step: [ 100/ 600], Loss: 1.0235
Epoch: [ 5/ 5], Step: [ 200/ 600], Loss: 1.0301
Epoch: [ 5/ 5], Step: [ 300/ 600], Loss: 0.9974
Epoch: [ 5/ 5], Step: [ 400/ 600], Loss: 0.9303
Epoch: [ 5/ 5], Step: [ 500/ 600], Loss: 1.0271
Epoch: [ 5/ 5], Step: [ 600/ 600], Loss: 0.9955

Accuracy of the model on the 10000 test images:  82 %

References:

  • PyTorchZeroToAll
  • yunjey on Github

Get the complete notebook link here:

Colab Link : click here.


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Customer Churn Analysis Prediction - Python

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  • Machine Learning
  • python
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    Convolutional Neural Network (CNN) are a type of deep learning model specifically designed for processing structured grid data such as images. In this article we will build a CNN model to classify different types of flowers from a dataset containing images of various flowers like roses, daisies, dan
    6 min read
    Emojify using Face Recognition with Machine Learning
    In this article, we will learn how to implement a modification app that will show an emoji of expression which resembles the expression on your face. This is a fun project based on computer vision in which we use an image classification model in reality to classify different expressions of a person.
    7 min read
    Cat & Dog Classification using Convolutional Neural Network in Python
    Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi
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    Traffic Signs Recognition using CNN and Keras in Python
    We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us
    5 min read
    Lung Cancer Detection using Convolutional Neural Network (CNN)
    Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo
    7 min read
    Lung Cancer Detection Using Transfer Learning
    Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.In this article, we will learn how to build a classifier using the Transfer Learning technique
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    Pneumonia Detection using Deep Learning
    In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta
    7 min read
    Detecting Covid-19 with Chest X-ray
    COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic
    9 min read
    Skin Cancer Detection using TensorFlow
    In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us
    5 min read
    Age Detection using Deep Learning in OpenCV
    The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'
    5 min read
    Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
    In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff
    4 min read
    Detecting COVID-19 From Chest X-Ray Images using CNN
    A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T
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    Image Segmentation Using TensorFlow
    Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,
    7 min read
    License Plate Recognition with OpenCV and Tesseract OCR
    License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar
    5 min read
    Detect and Recognize Car License Plate from a video in real time
    Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
    11 min read
    Residual Networks (ResNet) - Deep Learning
    After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble
    9 min read

    Natural Language Processing Projects

    Twitter Sentiment Analysis using Python
    This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
    10 min read
    Facebook Sentiment Analysis using python
    This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
    6 min read
    Next Sentence Prediction using BERT
    Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
    4 min read
    Hate Speech Detection using Deep Learning
    There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
    5 min read
    Image Caption Generator using Deep Learning on Flickr8K dataset
    Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
    12 min read
    Movie recommendation based on emotion in Python
    Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
    4 min read
    Speech Recognition in Python using Google Speech API
    Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
    2 min read
    Voice Assistant using python
    Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
    3 min read
    Human Activity Recognition - Using Deep Learning Model
    Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
    6 min read
    Fine-tuning BERT model for Sentiment Analysis
    Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
    6 min read
    Sentiment Classification Using BERT
    BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
    12 min read
    Sentiment Analysis with an Recurrent Neural Networks (RNN)
    Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
    5 min read
    Building an Autocorrector Using NLP in Python
    Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
    4 min read
    Python | NLP analysis of Restaurant reviews
    Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach
    7 min read
    Restaurant Review Analysis Using NLP and SQLite
    Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
    9 min read
    Twitter Sentiment Analysis using Python
    This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
    10 min read

    Clustering Projects

    Customer Segmentation using Unsupervised Machine Learning in Python
    Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning
    5 min read
    Music Recommendation System Using Machine Learning
    When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona
    4 min read
    K means Clustering – Introduction
    K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ
    4 min read
    Image Segmentation using K Means Clustering
    Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.  Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im
    2 min read

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