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

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

Business Problem Statement

In the retail industry, predicting customer churn is a critical task. A leading e-commerce company wants to identify customers who are likely to stop making purchases on their platform. The company has a large dataset of customer information, including demographics, purchase history, and browsing behavior. By leveraging deep learning techniques, the company aims to improve its customer retention rate and increase revenue.

The ROI impact of this project is significant. According to a study, a 5% increase in customer retention can lead to a 25-95% increase in profits. By accurately predicting customer churn, the company can proactively target high-risk customers with personalized marketing campaigns, reducing the likelihood of churn and increasing revenue.

Step-by-Step Technical Solution

Step 1: Data Preparation

We will use a combination of pandas and SQL to prepare the data. The dataset consists of the following tables:

  • customers: customer demographics and information
  • orders: order history
  • browsing_history: browsing behavior 
-- Create tables
CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    age INT,
    gender VARCHAR(10),
    location VARCHAR(50)
);

CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    customer_id INT,
    order_date DATE,
    total_amount DECIMAL(10, 2),
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);

CREATE TABLE browsing_history (
    browsing_id INT PRIMARY KEY,
    customer_id INT,
    browsing_date DATE,
    page_visited VARCHAR(100),
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
);

-- Insert sample data
INSERT INTO customers (customer_id, age, gender, location)
VALUES
(1, 25, 'Male', 'New York'),
(2, 30, 'Female', 'Los Angeles'),
(3, 35, 'Male', 'Chicago');

INSERT INTO orders (order_id, customer_id, order_date, total_amount)
VALUES
(1, 1, '2022-01-01', 100.00),
(2, 1, '2022-02-01', 200.00),
(3, 2, '2022-03-01', 50.00);

INSERT INTO browsing_history (browsing_id, customer_id, browsing_date, page_visited)
VALUES
(1, 1, '2022-01-05', 'Home'),
(2, 1, '2022-01-10', 'Product'),
(3, 2, '2022-03-05', 'Home');
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import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Load data from database
import sqlite3
conn = sqlite3.connect('database.db')
cursor = conn.cursor()

cursor.execute('SELECT * FROM customers')
customers = pd.DataFrame(cursor.fetchall(), columns=['customer_id', 'age', 'gender', 'location'])

cursor.execute('SELECT * FROM orders')
orders = pd.DataFrame(cursor.fetchall(), columns=['order_id', 'customer_id', 'order_date', 'total_amount'])

cursor.execute('SELECT * FROM browsing_history')
browsing_history = pd.DataFrame(cursor.fetchall(), columns=['browsing_id', 'customer_id', 'browsing_date', 'page_visited'])

# Merge data
data = pd.merge(customers, orders, on='customer_id', how='left')
data = pd.merge(data, browsing_history, on='customer_id', how='left')

# Handle missing values
data.fillna(0, inplace=True)

# Convert categorical variables
data['gender'] = data['gender'].map({'Male': 0, 'Female': 1})
data['location'] = data['location'].map({'New York': 0, 'Los Angeles': 1, 'Chicago': 2})

# Define features and target
X = data.drop(['customer_id', 'order_id', 'browsing_id'], axis=1)
y = data['customer_id']

# Split data 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)
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Step 2: Analysis Pipeline

We will use a neural network with two hidden layers to predict customer churn.

# Scale data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Define neural network model
model = MLPClassifier(hidden_layer_sizes=(10, 5), max_iter=1000, random_state=42)

# Train model
model.fit(X_train_scaled, y_train)
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Step 3: Model/Visualization Code

We will use the trained model to make predictions on the testing set and evaluate its performance.

# Make predictions
y_pred = model.predict(X_test_scaled)

# Evaluate model performance
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)
print('Classification Report:')
print(classification_report(y_test, y_pred))
print('Confusion Matrix:')
print(confusion_matrix(y_test, y_pred))
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Step 4: Performance Evaluation

We will use metrics such as accuracy, precision, recall, and F1-score to evaluate the performance of the model.

# Calculate metrics
from sklearn.metrics import precision_score, recall_score, f1_score

precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

print('Precision:', precision)
print('Recall:', recall)
print('F1-score:', f1)
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Step 5: Production Deployment

We will deploy the trained model in a production environment using a RESTful API.

# Import required libraries
from flask import Flask, request, jsonify
from sklearn.externals import joblib

# Create Flask app
app = Flask(__name__)

# Load trained model
model = joblib.load('model.pkl')

# Define API endpoint
@app.route('/predict', methods=['POST'])
def predict():
    # Get input data
    data = request.get_json()

    # Make predictions
    prediction = model.predict(data)

    # Return prediction
    return jsonify({'prediction': prediction})

# Run app
if __name__ == '__main__':
    app.run(debug=True)
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Metrics/ROI Calculations

The ROI impact of this project can be calculated as follows:

  • Increase in customer retention rate: 5%
  • Increase in revenue: 25-95%
  • Cost of implementing the project: $100,000
  • ROI: (Increase in revenue - Cost of implementing the project) / Cost of implementing the project
  • ROI: (25-95% of $1,000,000 - $100,000) / $100,000
  • ROI: 150-850%

Edge Cases

  • Handling missing values: We will use imputation techniques such as mean, median, or mode to handle missing values.
  • Handling outliers: We will use techniques such as winsorization or trimming to handle outliers.
  • Handling class imbalance: We will use techniques such as oversampling the minority class, undersampling the majority class, or using class weights to handle class imbalance.

Scaling Tips

  • Use distributed computing frameworks such as Apache Spark or Hadoop to scale the model.
  • Use cloud-based services such as AWS SageMaker or Google Cloud AI Platform to scale the model.
  • Use techniques such as data parallelism or model parallelism to scale the model.
  • Use techniques such as hyperparameter tuning or model selection to improve the performance of the model.

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