Classification Model Using Artificial Neural Networks (ANN) with Keras

Building a classification model using Artificial Neural Networks (ANNs) with Keras is an optimized process that utilizes the simplicity and flexibility of Keras to design and train powerful models for various applications. Keras, built on top of TensorFlow, enables you to quickly develop ANN-based models for classification tasks, offering scalability, efficiency, and ease of use.

This blog guides you through creating a classification model using artificial neural networks with Keras. We cover installation, data preprocessing, and practical applications! 

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Building a Classification Model Using Artificial Neural Networks with Keras

Keras, a high-level neural networks API, is perfect for quickly building powerful classification model using Artificial Neural Networks (ANNs). It simplifies the process of creating, training, and evaluating deep learning models. By using Keras, you can use its simplicity and flexibility to develop efficient models for a variety of practical applications, from healthcare to finance, retail, and beyond.

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To get started on building your Classification Model, the first step is installing the necessary libraries, ensuring your environment is ready for the next stages.

Step 1: Install Keras and TensorFlow

The first step is to ensure that Keras and TensorFlow are installed in your environment. TensorFlow, the backbone of Keras, makes the deployment of Classification Model Using Artificial Neural Networks efficient and scalable, enabling applications in diverse fields.

To install Keras and TensorFlow, use the following command:

pip install tensorflow

TensorFlow includes Keras, so no need for a separate installation.

Practical Example: In India's healthcare sector, TensorFlow and Keras are being used to build ANN-based classification models that predict diseases like diabetes and heart conditions by analyzing patient data. These models have helped improve diagnostic accuracy and speed up the process of detection.

Also Read: Keras vs. PyTorch: Difference Between Keras & PyTorch

Step 2: Import Required Libraries

Once Keras and TensorFlow are set up, you can start building your model by importing the necessary libraries. Here’s the basic setup:

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical

• numpy and pandas are essential for handling and manipulating data.
• train_test_split is crucial for splitting your data into training and testing sets.
• StandardScaler ensures that the features are normalized, a vital step for Classification Model Using Artificial Neural Networks to converge more effectively.
• Sequential and Dense are used to define the model architecture.
• to_categorical converts labels into one-hot encoded format for multi-class classification.

Practical Example: In India’s agricultural sector, ANNs are being used to classify crops based on image data (like leaf patterns). By using the right libraries and preprocessing techniques, farmers can identify disease outbreaks early and take corrective measures, leading to better yield prediction and crop management.

Also Read: Clustering vs Classification

Step 3: Data Preprocessing

Data preprocessing is the foundation of building an effective Classification Model Using Artificial Neural Networks. Clean and well-prepared data directly impacts the performance of your model.

1. Load Data: Load your dataset into the environment:

   dataset = pd.read_csv('your_data.csv')
   X = dataset.iloc[:, :-1].values  # Features
   y = dataset.iloc[:, -1].values   # Target labels

2. Splitting the Data: Split your dataset into training and testing sets:

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

3. Normalization: Normalize your features so they all fall within the same range:

   scaler = StandardScaler()
   X_train = scaler.fit_transform(X_train)
   X_test = scaler.transform(X_test)

4. One-Hot Encoding for Labels: For multi-class classification, one-hot encoding is essential:

   y_train = to_categorical(y_train, num_classes=3)
   y_test = to_categorical(y_test, num_classes=3)

Note: The num_classes=3 in this example is just for illustration. In real scenarios, you should replace this with len(np.unique(y)) to dynamically adjust the number of classes based on your dataset.

Practical Example: In the retail industry, ANN-based classification models are widely used to predict customer behavior. For example, a model might classify customers based on purchasing patterns. Preprocessing like one-hot encoding ensures that features like "product category" are transformed into a usable format for the model.

Step 4: Building the Model

With the data prepared, it's time to define the architecture of your Classification Model Using Artificial Neural Networks. In Keras, this is done using a Sequential model, where layers are added one after another.

1. Initialize the Model:

   model = Sequential()

2. Input Layer: The input layer receives the features. For example, if you are classifying images, the number of neurons would correspond to the number of pixels in each image:

   model.add(Dense(units=64, activation='relu', input_dim=X_train.shape[1]))

3. Hidden Layers: You can add one or more hidden layers with appropriate activation functions:

   model.add(Dense(units=64, activation='relu'))

4. Output Layer: For multi-class classification, you will typically use the softmax activation function, while for binary classification, you would use sigmoid:

   model.add(Dense(units=3, activation='softmax'))  # For a multi-class classification task with 3 classes

Practical Example: In India, ANN-based models are used in fraud detection systems. These systems classify transaction data as either "fraudulent" or "legitimate." The architecture might involve several hidden layers to capture complex patterns in financial transactions, helping banks minimize fraud-related losses.

Step 5: Compile the Model

Once the model structure is ready, compile it by specifying the optimizer, loss function, and evaluation metrics:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

• optimizer='adam': Adam is a popular optimizer that adapts the learning rate during training.
• loss='categorical_crossentropy': For multi-class classification.
• metrics=['accuracy']: To track the model's accuracy during training and evaluation.

Practical Example: In healthcare, models like these are used to classify medical images such as X-rays. The loss function is designed to penalize incorrect classifications, while the optimizer fine-tunes the model to ensure more accurate diagnoses over time.

Step 6: Train the Model

With everything set up, it’s time to train your model:

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

• epochs=50: The model will pass through the entire dataset 50 times.
• batch_size=32: The model will process 32 samples before updating the weights.

Practical Example: In agriculture, training Classification Model Using Artificial Neural Networks helps predict crop diseases. The model learns to classify healthy or diseased crops by analyzing past images of leaves. More epochs ensure that the model generalizes well to new images, improving accuracy.

Step 7: Evaluate the Model

After training, you should evaluate how well your model performs on unseen data:

loss, accuracy = model.evaluate(X_test, y_test)
print(f'Loss: {loss}, Accuracy: {accuracy}')

Practical Example: For a Classification Model Using Artificial Neural Networks in autonomous driving, the evaluation metrics might include not just accuracy but also recall and precision, as false positives (e.g., misclassifying a pedestrian as a non-threat) could be dangerous.

Step 8: Make Predictions

Finally, you can use the trained model to make predictions:

predictions = model.predict(X_test)

For multi-class classification, you can extract the class with the highest probability:

predicted_classes = np.argmax(predictions, axis=1)

Practical Example: In e-commerce, companies use ANNs to predict the next product a customer might buy based on their previous purchase behavior. After training the model, predictions are made for individual users to offer personalized product recommendations.

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Also Read: Deep Learning Models 2025: Types, Uses & Trends

Now that you've built your classification model, it's essential to focus on enhancements and advanced techniques that will help fine-tune its performance and scalability.

Enhancements and Techniques in Classification Model Using Artificial Neural Networks

Once you've built the initial Classification Model Using Artificial Neural Networks, enhancing its performance and generalization capabilities is the next critical step. The challenges of overfitting, high computational costs, and underutilizing available data can all hinder the model's success. 

Let’s explore the key techniques and advanced architectures that can optimize your model for better performance.

1. Regularization

Overfitting occurs when your model is too focused on the training data, making it perform poorly on unseen data. Regularization techniques can help prevent this by balancing data fitting and generalization.

Dropout

Dropout is a powerful regularization technique that randomly disables neurons during training, preventing the model from becoming overly reliant on any single neuron. This encourages the model to learn a broader set of features and helps reduce overfitting in Classification Model using Artificial Neural Networks.

The dropout rate typically ranges from 20-50%, meaning that a random subset of neurons is ignored during each training iteration. This is particularly effective in deep networks, where the sheer number of parameters often leads to overfitting.

L1/L2 Regularization

L1 and L2 regularization are two techniques that add penalty terms to the loss function. These penalties constrain the complexity of the model by encouraging smaller weights.

• L1 Regularization adds the absolute value of the weights to the loss function. This can lead to sparse models by pushing some weights to exactly zero, effectively selecting features. In a Classification Model Using Artificial Neural Networks, this helps in feature selection, especially when you have many irrelevant features.
• L2 Regularization adds the squared value of the weights to the loss function, which discourages large weights. Unlike L1, L2 regularization doesn’t push weights to zero but keeps them small. This is helpful when you want to prevent large, over-complicated models that might overfit.

Regularization via L1/L2 helps in training a Classification Model Using Artificial Neural Networks that generalizes well, ensuring the model’s accuracy and robustness.

2. Advanced Architectures

Once you've understood basic ANN architectures, you can enhance your Classification Model Using Artificial Neural Networks by exploring advanced neural network architectures. These architectures are designed to tackle more complex data structures and problems.

Let’s explore some of the most effective architectures that elevate the performance of neural networks.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are designed specifically for image and video recognition tasks, excelling at processing high-dimensional data like images. By applying convolutional filters, CNNs detect patterns such as edges, textures, and shapes, making them ideal for object detection, facial recognition, and image segmentation.

In medical imaging, CNNs are used to identify anomalies like tumors in X-rays and MRIs, enhancing diagnostic accuracy and reducing human error. Their power lies in capturing features at multiple levels, from basic edges to complex shapes, all while reducing computational costs through shared weights.

Recurrent Neural Networks (RNNs)

Recurrent Neural Networks (RNNs) are ideal for sequential data like time series, text, or speech, as they capture dependencies between elements in a sequence by maintaining "memory" of previous inputs.

In Classification Models Using Artificial Neural Networks, RNNs are key for tasks like speech recognition, language modeling, and time series forecasting. In NLP, they handle tasks such as sentiment analysis, next-word prediction, and named entity recognition. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) excel at capturing long-range dependencies in data.

3. Transfer Learning

Training a neural network from scratch can be time-consuming and resource-intensive, but transfer learning offers an efficient solution. By using pre-trained models, you can utilize knowledge from large datasets and apply it to new, related tasks.

When building a Classification Model Using Artificial Neural Networks, models like VGG, ResNet, or BERT can be fine-tuned to suit your specific problem. This is particularly valuable in areas with limited labeled data or computing resources. 

Transfer learning has proven especially useful in image recognition and NLP, enabling AI applications in sectors like retail, healthcare, and finance in India.

4. Ensemble Methods

Ensemble Methods are a powerful technique for improving the performance of Classification Models Using Artificial Neural Networks by combining multiple models. The idea is simple: instead of relying on a single model, you combine the predictions of several models to improve accuracy, robustness, and generalization.

The most common ensemble methods include:

• Bagging: This involves training multiple models independently and combining their predictions, typically using a method like majority voting. Random Forests, a popular algorithm based on bagging, are often used in classification tasks.
• Boosting: In boosting, models are trained sequentially, with each new model attempting to correct the mistakes of the previous one. Algorithms like AdaBoost and XGBoost are widely used in boosting for classification tasks, providing better performance than individual models.

Also Read: 9 Key Types of Artificial Neural Networks for ML Engineers

Having explored key techniques to enhance your model's performance, it's now time to look ahead at the emerging trends that will shape the future of Classification Models using Artificial Neural Networks.

Future Trends in Classification Model Using Artificial Neural Networks

As the field of machine learning continues to advance, the evolution of Classification Models Using Artificial Neural Networks (ANNs) brings with it promising new technologies and methodologies. These developments not only improve the efficiency and effectiveness of models but also expand their applications across diverse industries. 

Let’s explore the future trends that will shape the development and deployment of Classification Models Using Artificial Neural Networks.

1. Explainable AI (XAI)

ANNs are often seen as "black boxes," making it difficult to understand how decisions are made, especially important in sectors like healthcare, finance, and law.

What Is XAI?

• LIME (Local Interpretable Model-Agnostic Explanations): Explains individual predictions by approximating complex models with simpler, interpretable ones.
• SHAP (Shapley Additive Explanations): Helps identify which features contribute most to a model’s prediction, improving trust and transparency.

Why It Matters:

• Helps industries like finance provide clear explanations for automated decisions (e.g., credit scoring).
• Enhances accountability and trust in AI-driven processes.

2. Edge Computing

Running machine learning models locally on devices instead of relying on cloud systems.

Benefits of Edge Computing:

• Reduced Latency: Faster response times for real-time applications like autonomous vehicles and smart devices.
• Improved Privacy: Data stays on the device, ensuring privacy and reducing the need for internet connectivity.
• Example: In India's agriculture sector, drones equipped with ANN models analyze crop health locally and give immediate feedback to farmers.

Hardware Advances:

• Google’s Edge TPU and Nvidia’s Jetson chips are making it easier to run deep learning models efficiently at the edge.

3. Automated Machine Learning (AutoML)

Training and tuning complex models is resource-intensive, requiring significant expertise.

What Is AutoML?

• Automates tasks like model selection, hyperparameter tuning, and optimization.
• Platform Examples: Google AutoML, Microsoft Azure AutoML, making machine learning accessible to non-experts.

Why It’s Important:

• AI for Everyone: Allows businesses and industries in India (e.g., retail, healthcare, finance) to deploy machine learning without needing a team of experts.
• Faster Deployment: Speeds up AI adoption and decision-making at lower costs.

4. Integration with Other Technologies

The future of Classification Models Using Artificial Neural Networks lies in their integration with emerging technologies like IoT, blockchain, and quantum computing. This convergence will enable more powerful, intelligent, and secure systems.

IoT & ANN Integration:

• Smart Devices: IoT devices generate massive amounts of data. ANNs process this data in real-time for applications like smart homes (e.g., controlling lights, temperature, and security systems).
• India Example: Smart cities are using IoT devices with ANNs to optimize traffic flow, waste management, and energy consumption.

Blockchain & ANN Integration:

• Enhanced Security: Blockchain ensures the integrity and transparency of data used in training ANN models, especially in industries like supply chain management.
• Tamper-Proof Data: Blockchain safeguards the classification process in ANN-based applications.

Quantum Computing & ANN Integration:

• The Potential: Quantum computing promises faster computations and more efficient training for large datasets, advancing deep learning.
• Future Outlook: Although still in early stages, researchers are exploring how quantum algorithms can enhance ANN capabilities.

Also Read: 16 Neural Network Project Ideas For Beginners [2025]

As new trends emerge in Classification Models, upGrad’s specialized courses will equip you with the skills to become an expert and drive innovation in the field.

Become An Expert in Classification Models and ANN with upGrad!

Building Classification Models with Artificial Neural Networks and Keras allows you to solve practical problems across diverse industries. For optimal results, focus on data preprocessing, normalize features and one-hot encode labels. 

Start with a simple architecture, then experiment with adding layers, applying dropout for regularization, and tuning hyperparameters to enhance model performance.

Classification models can be challenging. However, with hands-on projects and expert insights, upGrad helps you refine your skills. We also help bridge knowledge gaps, accelerating your career in machine learning.

Here are some additional free courses to help you get started.

• Fundamentals of Deep Learning and Neural Networks
• Artificial Intelligence in Practical
• Advanced SQL: Programming Constructs and Stored Functions
• Case Study using Tableau, Python and SQL
• Analyzing Patterns in Data and Storytelling

If you're unsure where to begin or which area to focus on, upGrad’s expert career counselors can guide you based on your goals. You can also visit a nearby upGrad offline center to explore course options, get hands-on experience, and speak directly with mentors! 

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Reference:
https://arxiv.org/abs/2501.08455