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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 crucial for maintaining a loyal customer base and reducing revenue loss. A leading e-commerce company wants to develop a predictive model to identify customers who are likely to stop making purchases. The goal is to proactively offer personalized promotions and improve customer retention. The company expects a significant Return on Investment (ROI) from this project, with an estimated increase of 15% in customer retention and a revenue gain of $1 million.

Step-by-Step Technical Solution

Step 1: Data Preparation

We will use a sample dataset containing customer information, purchase history, and demographic data. The dataset will be loaded into a pandas DataFrame for preprocessing.

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

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

# Preprocess data
data['churn'] = np.where(data['churn'] == 'yes', 1, 0)
data['gender'] = np.where(data['gender'] == 'male', 1, 0)

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

# Scale data using StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
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Step 2: Analysis Pipeline

We will use a neural network with two hidden layers to predict customer churn. The model will be implemented using the Keras library in Python.

from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping

# Define neural network model
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

# Define early stopping callback
early_stopping = EarlyStopping(patience=5, min_delta=0.001)

# Train model
model.fit(X_train_scaled, y_train, epochs=50, batch_size=128, validation_data=(X_test_scaled, y_test), callbacks=[early_stopping])
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Step 3: Model/Visualization Code

We will use the trained model to make predictions on the test data and evaluate its performance using metrics such as accuracy, precision, and recall.

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import matplotlib.pyplot as plt

# Make predictions on test data
y_pred = model.predict(X_test_scaled)

# Evaluate model performance
accuracy = accuracy_score(y_test, np.where(y_pred > 0.5, 1, 0))
precision = precision_score(y_test, np.where(y_pred > 0.5, 1, 0))
recall = recall_score(y_test, np.where(y_pred > 0.5, 1, 0))
f1 = f1_score(y_test, np.where(y_pred > 0.5, 1, 0))

print('Accuracy:', accuracy)
print('Precision:', precision)
print('Recall:', recall)
print('F1 Score:', f1)

# Plot ROC curve
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, y_pred)
roc_auc = auc(fpr, tpr)
plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()
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Step 4: Performance Evaluation

We will evaluate the model's performance using metrics such as accuracy, precision, and recall. We will also calculate the ROI impact of the model by estimating the number of customers retained and the revenue gain.

# Calculate ROI impact
revenue_gain = 1000000  # estimated revenue gain
customer_retention_rate = 0.15  # estimated increase in customer retention rate
customers_retained = int(customer_retention_rate * len(data))

print('Estimated Revenue Gain: $', revenue_gain)
print('Estimated Customers Retained:', customers_retained)
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Step 5: Production Deployment

We will deploy the model in a production environment using a RESTful API. The API will accept customer data as input and return the predicted churn probability.

from flask import Flask, request, jsonify
from keras.models import load_model

app = Flask(__name__)

# Load trained model
model = load_model('churn_model.h5')

@app.route('/predict', methods=['POST'])
def predict():
    customer_data = request.get_json()
    customer_data = pd.DataFrame([customer_data])
    customer_data = scaler.transform(customer_data)
    prediction = model.predict(customer_data)
    return jsonify({'churn_probability': prediction[0][0]})

if __name__ == '__main__':
    app.run(debug=True)
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SQL Queries

We will use SQL queries to extract data from the database and load it into the pandas DataFrame.

SELECT *
FROM customers
WHERE churn = 'yes';

SELECT *
FROM customers
WHERE gender = 'male';

SELECT *
FROM purchases
WHERE customer_id IN (SELECT customer_id FROM customers WHERE churn = 'yes');
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Metrics/ROI Calculations

We will use the following metrics to evaluate the model's performance:

  • Accuracy
  • Precision
  • Recall
  • F1 Score
  • ROC AUC

We will also calculate the ROI impact of the model by estimating the number of customers retained and the revenue gain.

# Calculate metrics
accuracy = accuracy_score(y_test, np.where(y_pred > 0.5, 1, 0))
precision = precision_score(y_test, np.where(y_pred > 0.5, 1, 0))
recall = recall_score(y_test, np.where(y_pred > 0.5, 1, 0))
f1 = f1_score(y_test, np.where(y_pred > 0.5, 1, 0))
roc_auc = auc(fpr, tpr)

# Calculate ROI impact
revenue_gain = 1000000  # estimated revenue gain
customer_retention_rate = 0.15  # estimated increase in customer retention rate
customers_retained = int(customer_retention_rate * len(data))
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Edge Cases

We will handle edge cases such as missing values, outliers, and class imbalance.

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

# Handle outliers
data = data[(np.abs(data - data.mean()) <= (3 * data.std())).all(axis=1)]

# Handle class imbalance
from sklearn.utils.class_weight import compute_class_weight
class_weights = compute_class_weight('balanced', np.unique(y_train), y_train)
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Scaling Tips

We will use the following scaling tips to improve the model's performance:

  • Use a larger dataset
  • Use data augmentation techniques
  • Use transfer learning
  • Use ensemble methods
  • Use hyperparameter tuning 
# Use a larger dataset
data = pd.concat([data, pd.read_csv('additional_data.csv')])

# Use data augmentation techniques
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
data = scaler.fit_transform(data)

# Use transfer learning
from keras.applications import VGG16
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))

# Use ensemble methods
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=100)

# Use hyperparameter tuning
from sklearn.model_selection import GridSearchCV
param_grid = {'n_estimators': [10, 50, 100], 'max_depth': [5, 10, 15]}
grid_search = GridSearchCV(model, param_grid, cv=5)
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