DEV Community

amal org
amal org

Posted on

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 problem that can have a significant impact on revenue. A leading e-commerce company wants to identify customers who are likely to stop making purchases on their platform. By doing so, they can proactively offer personalized promotions and improve customer retention, resulting in increased revenue and customer satisfaction. The company estimates that a 10% reduction in customer churn can lead to a $1 million increase in annual revenue.

Step-by-Step Technical Solution

Step 1: Data Preparation

To solve this problem, we will use a combination of pandas and SQL to prepare the data. We will start by loading the necessary libraries and importing the data from a PostgreSQL database.

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
import psycopg2
import matplotlib.pyplot as plt
import seaborn as sns

# Establish a connection to the PostgreSQL database
conn = psycopg2.connect(
    dbname="customer_churn",
    user="username",
    password="password",
    host="localhost",
    port="5432"
)

# Load the data from the PostgreSQL database
cur = conn.cursor()
cur.execute("""
    SELECT 
        customer_id,
        age,
        gender,
        purchase_history,
        average_order_value,
        last_order_date,
        churn_status
    FROM 
        customer_data
""")

# Fetch all the rows from the query
rows = cur.fetchall()

# Close the cursor and connection
cur.close()
conn.close()

# Create a pandas DataFrame from the fetched data
data = pd.DataFrame(rows, columns=[
    "customer_id",
    "age",
    "gender",
    "purchase_history",
    "average_order_value",
    "last_order_date",
    "churn_status"
])

# Convert the 'last_order_date' column to datetime format
data['last_order_date'] = pd.to_datetime(data['last_order_date'])

# Calculate the time difference between the last order date and the current date
data['time_since_last_order'] = (pd.to_datetime('today') - data['last_order_date']).dt.days

# Drop the 'last_order_date' column
data.drop('last_order_date', axis=1, inplace=True)
Enter fullscreen mode Exit fullscreen mode

Step 2: Analysis Pipeline

Next, we will split the data into training and testing sets, scale the features, and train a neural network model using the MLPClassifier from scikit-learn.

# Split the data into training and testing sets
X = data.drop(['customer_id', 'churn_status'], axis=1)
y = data['churn_status']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale the features using StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train a neural network model using MLPClassifier
mlp = MLPClassifier(hidden_layer_sizes=(10, 5), max_iter=1000, random_state=42)
mlp.fit(X_train_scaled, y_train)
Enter fullscreen mode Exit fullscreen mode

Step 3: Model/Visualization Code

Now, we will use the trained model to make predictions on the testing set and evaluate its performance using accuracy score, classification report, and confusion matrix.

# Make predictions on the testing set
y_pred = mlp.predict(X_test_scaled)

# Evaluate the model's 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))

# Visualize the confusion matrix using heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap='Blues')
plt.xlabel("Predicted Labels")
plt.ylabel("True Labels")
plt.title("Confusion Matrix")
plt.show()
Enter fullscreen mode Exit fullscreen mode

Step 4: Performance Evaluation

To evaluate the model's performance, we will calculate the return on investment (ROI) by comparing the revenue generated by the model's predictions with the revenue generated by a random model.

# Calculate the revenue generated by the model's predictions
revenue_model = data['average_order_value'].mean() * (1 - accuracy)

# Calculate the revenue generated by a random model
revenue_random = data['average_order_value'].mean() * 0.5

# Calculate the ROI
roi = (revenue_model - revenue_random) / revenue_random
print("ROI:", roi)
Enter fullscreen mode Exit fullscreen mode

Step 5: Production Deployment

Finally, we will deploy the model in a production environment by creating a RESTful API using Flask.

from flask import Flask, request, jsonify
from flask_cors import CORS

app = Flask(__name__)
CORS(app)

# Load the trained model
mlp = MLPClassifier(hidden_layer_sizes=(10, 5), max_iter=1000, random_state=42)
mlp.fit(X_train_scaled, y_train)

# Define a function to make predictions
def make_prediction(customer_data):
    # Scale the customer data
    customer_data_scaled = scaler.transform(customer_data)

    # Make a prediction using the trained model
    prediction = mlp.predict(customer_data_scaled)

    return prediction

# Define a route for the API
@app.route('/predict', methods=['POST'])
def predict():
    # Get the customer data from the request
    customer_data = request.get_json()

    # Convert the customer data to a pandas DataFrame
    customer_data = pd.DataFrame(customer_data, index=[0])

    # Make a prediction using the trained model
    prediction = make_prediction(customer_data)

    # Return the prediction as a JSON response
    return jsonify({'prediction': prediction[0]})

if __name__ == '__main__':
    app.run(debug=True)
Enter fullscreen mode Exit fullscreen mode

SQL Queries

To create the database and tables, we can use the following SQL queries:

-- Create a database
CREATE DATABASE customer_churn;

-- Create a table for customer data
CREATE TABLE customer_data (
    customer_id SERIAL PRIMARY KEY,
    age INTEGER,
    gender VARCHAR(10),
    purchase_history INTEGER,
    average_order_value DECIMAL(10, 2),
    last_order_date DATE,
    churn_status BOOLEAN
);

-- Insert data into the table
INSERT INTO customer_data (age, gender, purchase_history, average_order_value, last_order_date, churn_status)
VALUES
    (25, 'Male', 10, 100.00, '2022-01-01', FALSE),
    (30, 'Female', 5, 50.00, '2022-02-01', TRUE),
    (35, 'Male', 15, 150.00, '2022-03-01', FALSE),
    (40, 'Female', 10, 100.00, '2022-04-01', TRUE),
    (45, 'Male', 20, 200.00, '2022-05-01', FALSE);
Enter fullscreen mode Exit fullscreen mode

Metrics/ROI Calculations

To calculate the ROI, we can use the following formula:

ROI = (Revenue Model - Revenue Random) / Revenue Random

Where:

  • Revenue Model is the revenue generated by the model's predictions
  • Revenue Random is the revenue generated by a random model

We can calculate the revenue generated by the model's predictions by multiplying the average order value by the accuracy of the model.

Revenue Model = Average Order Value x Accuracy

We can calculate the revenue generated by a random model by multiplying the average order value by 0.5 (assuming a random model has an accuracy of 50%).

Revenue Random = Average Order Value x 0.5

Edge Cases

To handle edge cases, we can use the following strategies:

  • Handle missing values by imputing them with the mean or median of the respective feature
  • Handle outliers by removing them or transforming the data to reduce their impact
  • Handle class imbalance by using techniques such as oversampling the minority class, undersampling the majority class, or using class weights

Scaling Tips

To scale the model, we can use the following strategies:

  • Use distributed computing to train the model on large datasets
  • Use parallel processing to make predictions on large datasets
  • Use caching to store the results of expensive computations
  • Use a cloud-based platform to deploy the model and handle large volumes of traffic

By following these strategies, we can build a scalable and accurate model that can handle large volumes of data and provide valuable insights to the business.

Top comments (0)