Data Analyst Guide: Mastering Neural Networks: When Analysts Should Use Deep Learning

Source: Dev.to

The Question Every Data Analyst Asks

What problems can neural networks solve, and when should I use them?

The answer lies in complex, non‑linear relationships between variables. Neural networks excel at identifying patterns in large datasets, making them ideal for tasks such as:

• Image classification
• Natural language processing
• Predictive modeling

For instance, a McKinsey study found that companies using deep learning saw a 10‑20 % increase in revenue and a 5‑10 % reduction in costs.

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Real‑World Story

Retail example – Walmart

Walmart collects massive amounts of customer data (purchase history, browsing behavior, demographics). By applying neural networks, Walmart can build a predictive model that recommends products tailored to each shopper. Reported outcomes include:

• +15 % sales
• +20 % customer‑satisfaction

When data are limited or relationships are simple, traditional methods (linear regression, decision trees) may be more effective.

Sample Code (Python + scikit‑learn)

import pandas as pd
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split

# Load the dataset
data = pd.read_csv('customer_purchases.csv')

# Split the data into features and target
X = data.drop('target', axis=1)
y = data['target']

# Train‑test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Create a neural network classifier
clf = MLPClassifier(hidden_layer_sizes=(10, 10), max_iter=1000)

# Train the model
clf.fit(X_train, y_train)

# Evaluate the model
accuracy = clf.score(X_test, y_test)
print(f'Accuracy: {accuracy:.3f}')

The hidden_layer_sizes argument defines the number of neurons per hidden layer, while max_iter caps the training iterations.

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Step‑by‑Step Solution

1. Problem Definition

Identify a complex problem with non‑linear relationships (e.g., predicting customer churn from usage patterns and demographics).

2. Data Preparation

Collect, clean, and transform the data. Example using pandas and scikit‑learn:

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler

# Load the dataset
data = pd.read_csv('customer_data.csv')

# Handle missing values
data.fillna(data.mean(), inplace=True)

# Scale selected features
scaler = StandardScaler()
data[['feature1', 'feature2']] = scaler.fit_transform(
    data[['feature1', 'feature2']]
)

3. Analysis & Visualization

Explore the data with visualizations to understand variable relationships.

import matplotlib.pyplot as plt
import seaborn as sns

# Histogram of the target variable
sns.histplot(data['target'])
plt.show()

# Correlation matrix heatmap
corr_matrix = data.corr()
sns.heatmap(
    corr_matrix,
    annot=True,
    cmap='coolwarm',
    square=True
)
plt.show()

4. Implementation

Build a neural network using TensorFlow/Keras (or PyTorch, scikit‑learn, etc.):

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Define the model architecture
model = Sequential([
    Dense(64, activation='relu', input_shape=(10,)),  # adjust input_shape to your features
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(
    loss='binary_crossentropy',
    optimizer='adam',
    metrics=['accuracy']
)

5. Performance Metrics

Evaluate the trained model with appropriate metrics.

# Assuming X_test and y_test are already defined
loss, accuracy = model.evaluate(X_test, y_test)
print(f'Loss: {loss:.3f}, Accuracy: {accuracy:.3f}')

Typical metrics to report: accuracy, precision, recall, F1‑score, ROC‑AUC, etc.

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Expected Results & Impact

Applying neural networks to a complex problem usually yields significant improvements in predictive accuracy and downstream business outcomes.

• Netflix – recommendation engine → +75 % user engagement
• Uber – demand‑prediction model → ‑10 % average wait time

A Boston Consulting Group study reported that firms leveraging AI/ML experience a 10‑20 % increase in key performance indicators such as revenue, cost efficiency, and customer satisfaction.

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Takeaway

Use deep learning when:

1. The problem involves large, high‑dimensional datasets.
2. Relationships between variables are highly non‑linear.
3. You need state‑of‑the‑art predictive performance.

Otherwise, start with simpler models (linear regression, tree‑based methods) to establish baselines and ensure interpretability.

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Advanced Implementation

To take your neural network implementation to the next level, consider the following advanced techniques:

• Transfer learning – Use pre‑trained models as a starting point for your own model, fine‑tuning the weights to fit your specific problem.
• Ensemble methods – Combine the predictions of multiple models to improve overall performance.
• Hyperparameter tuning – Use techniques like grid search or random search to optimize the hyperparameters of your model.
• Regularization – Apply dropout or L1/L2 regularization to prevent overfitting.

Transfer Learning Example (Keras)

from tensorflow.keras.applications import VGG16
import tensorflow as tf

# Load the pre‑trained VGG16 model
base_model = VGG16(
    weights='imagenet',
    include_top=False,
    input_shape=(224, 224, 3)
)

# Freeze the base model layers
base_model.trainable = False

# Add a new classification head
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
predictions = tf.keras.layers.Dense(1, activation='sigmoid')(x)

# Create the final model
model = tf.keras.Model(inputs=base_model.input, outputs=predictions)

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Conclusion & Next Steps

Neural networks are a powerful tool for data analysts, offering a range of benefits and applications. By following the steps outlined in this article, you can apply neural networks to your own problems and achieve significant improvements in predictive accuracy and business outcomes.

Actionable Checklist

1. Identify a complex problem – Look for problems with non‑linear relationships between variables.
2. Collect and preprocess data – Handle missing values, scale the data, and explore variable relationships.
3. Implement a neural network – Use a library like TensorFlow or PyTorch, or a high‑level API such as scikit‑learn.
4. Evaluate the model – Use metrics like accuracy, precision, recall, and F1‑score to assess performance.
5. Refine and iterate – Apply transfer learning, ensemble methods, and hyperparameter tuning to boost performance.

By staying up‑to‑date with the latest developments in neural networks and deep learning, you can unlock the full potential of these tools and drive business success.