Chapter 2: Feature Engineering for Predictive Modelscsv
2.2 Feature Engineering for Classification and Regression Models
Feature engineering for classification and regression models is a critical process that enhances predictive accuracy by creating features that capture underlying patterns in the data. Unlike unsupervised learning techniques such as clustering or exploratory analysis, classification and regression models rely on labeled data to predict a specific target variable. This approach is essential whether the goal is to classify customers by loyalty level, predict house prices, or forecast customer lifetime value.
The process of feature engineering involves several key strategies:
- Feature Creation: Developing new features that encapsulate relevant information from existing data. For example, in a retail context, creating a "purchase frequency" feature from transaction data.
- Feature Transformation: Modifying existing features to better represent the underlying relationships. This might include logarithmic transformations for skewed data or encoding categorical variables.
- Feature Selection: Identifying the most relevant features that contribute significantly to the predictive power of the model, while avoiding overfitting.
These strategies are applicable to both classification models, which predict discrete categories (such as customer churn), and regression models, which predict continuous values (like house prices or customer lifetime value).
To illustrate these concepts, we'll explore a practical example using a retail dataset. Our focus will be on predicting Customer Lifetime Value (CLTV), a key metric in customer relationship management. This example will demonstrate how carefully engineered features can significantly improve the accuracy and interpretability of predictive models in real-world business scenarios.
2.2.1 Step 1: Data Preparation and Understanding
Before diving into feature engineering, it's crucial to thoroughly understand the dataset and evaluate the available variables. This initial step lays the foundation for creating meaningful features that can significantly enhance the predictive power of our models. Let's begin by loading our dataset and examining its structure and contents.
In this case, we're dealing with a retail dataset that contains valuable information about customer transactions. Our primary objectives are twofold:
- Predicting Customer Lifetime Value (CLTV): This is a regression task where we aim to estimate the total value a customer will bring to the business over their entire relationship.
- Predicting Churn: This is a binary classification task where we seek to identify customers who are likely to stop doing business with us.
By carefully analyzing the available variables, we can identify potential predictors that might be particularly useful for these tasks. For instance, transaction history, purchase frequency, and average order value could all provide valuable insights into both CLTV and churn probability.
As we proceed with our analysis, we'll look for patterns and relationships within the data that can inform our feature engineering process. This might involve exploring correlations between variables, identifying outliers or anomalies, and considering domain-specific knowledge about customer behavior in the retail sector.
The goal of this initial exploration is to gain a comprehensive understanding of our data, which will guide us in creating sophisticated, meaningful features that capture the underlying dynamics of customer behavior and value. This foundational work is essential for building robust predictive models that can drive actionable insights and inform strategic decision-making in customer relationship management.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the retail dataset
df = pd.read_csv('retail_cltv_data.csv')
# Display basic information and first few rows
print("Dataset Information:")
print(df.info())
print("\nFirst Few Rows of Data:")
print(df.head())
# Basic statistical summary
print("\nStatistical Summary:")
print(df.describe())
# Check for missing values
print("\nMissing Values:")
print(df.isnull().sum())
# Unique values in categorical columns
categorical_columns = df.select_dtypes(include=['object']).columns
for col in categorical_columns:
print(f"\nUnique values in {col}:")
print(df[col].value_counts())
# Visualize the distribution of a numerical column (e.g., 'Total Spend')
plt.figure(figsize=(10, 6))
sns.histplot(df['Total Spend'], kde=True)
plt.title('Distribution of Total Spend')
plt.xlabel('Total Spend')
plt.ylabel('Count')
plt.show()
# Correlation matrix for numerical columns
numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns
correlation_matrix = df[numerical_columns].corr()
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Numerical Features')
plt.show()Let's break down this code example:
- Import statements:
- We import pandas for data manipulation, matplotlib.pyplot for basic plotting, and seaborn for more advanced statistical visualizations.
- Data Loading:
- The retail dataset is loaded from a CSV file into a pandas DataFrame.
- Basic Information Display:
- df.info() provides an overview of the DataFrame, including column names, data types, and non-null counts.
- df.head() displays the first few rows of the DataFrame.
- Statistical Summary:
- df.describe() generates descriptive statistics for numerical columns, including count, mean, standard deviation, min, max, and quartiles.
- Missing Value Check:
- df.isnull().sum() calculates the number of missing values in each column.
- Categorical Data Analysis:
- We identify categorical columns and display the value counts for each unique category.
- Numerical Data Visualization:
- A histogram is created for the 'Total Spend' column to visualize its distribution.
- The use of seaborn's histplot with kde=True adds a kernel density estimate curve.
- Correlation Analysis:
- A correlation matrix is computed for all numerical columns.
- The matrix is visualized using a heatmap, which helps identify relationships between variables.
This code offers a thorough initial data exploration, examining data types, missing values, numerical data distribution, and feature correlations. Such insights are essential for grasping the dataset's nuances before diving into feature engineering and model development.
2.2.2 Step 2: Creating Predictive Features
Once we have a solid grasp of the dataset, we can embark on the crucial process of feature engineering. This involves creating new features or transforming existing ones to reveal patterns and relationships that align with our target variable, whether it's for a classification or regression task. The goal is to extract meaningful information from the raw data that can enhance the predictive power of our models.
For classification problems, such as predicting customer churn, we might focus on features that capture customer behavior and engagement levels. These could include metrics like the frequency of purchases, the recency of the last interaction, or changes in spending patterns over time.
In regression tasks, like estimating Customer Lifetime Value (CLTV), we might engineer features that reflect long-term customer value. This could involve calculating average order values, identifying seasonal purchasing trends, or developing composite scores that combine multiple aspects of customer behavior.
The art of feature engineering lies in combining domain expertise with data-driven insights to create variables that are not just statistically significant, but also interpretable and actionable from a business perspective. As we proceed, we'll explore specific techniques and examples of how to craft these powerful predictive features.
Feature 1: Recency
Recency measures the time elapsed since a customer's most recent purchase. This metric is a powerful indicator of customer engagement and plays a crucial role in both Customer Lifetime Value (CLTV) prediction and churn classification models. Recent purchases often signal active engagement with a brand, suggesting a higher likelihood of customer loyalty and increased value.
In the context of CLTV prediction, recency can help identify high-value customers who consistently make purchases. These customers are likely to continue their buying behavior, potentially leading to higher lifetime value. Conversely, customers with high recency (i.e., a long time since their last purchase) might be at risk of churning, which could negatively impact their projected CLTV.
For churn classification, recency serves as a key predictor. Customers who have made recent purchases are generally less likely to churn, as their engagement with the brand is still active. On the other hand, those with high recency might be showing signs of disengagement, making them more susceptible to churn.
It's important to note that the interpretation of recency can vary across industries and business models. For instance, in a subscription-based service, high recency might be expected and not necessarily indicative of churn risk. Therefore, recency should always be considered in conjunction with other relevant features and within the specific context of the business to derive the most accurate insights for CLTV prediction and churn classification.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Recency
most_recent_date = df['PurchaseDate'].max()
df['Recency'] = (most_recent_date - df['PurchaseDate']).dt.days
# Calculate the last purchase date per customer
recency_df = df.groupby('CustomerID')['Recency'].min().reset_index()
# Merge Recency back to main dataset
df = df.merge(recency_df, on='CustomerID', suffixes=('', '_Overall'))
# Display the first few rows with the new Recency feature
print("\nData with Recency Feature:")
print(df[['CustomerID', 'PurchaseDate', 'Recency_Overall']].head())
# Visualize the distribution of Recency
plt.figure(figsize=(10, 6))
sns.histplot(df['Recency_Overall'], kde=True)
plt.title('Distribution of Customer Recency')
plt.xlabel('Recency (days)')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_recency = df['Recency_Overall'].mean()
median_recency = df['Recency_Overall'].median()
max_recency = df['Recency_Overall'].max()
print(f"\nAverage Recency: {avg_recency:.2f} days")
print(f"Median Recency: {median_recency:.2f} days")
print(f"Maximum Recency: {max_recency:.2f} days")
# Identify customers with high recency (potential churn risk)
high_recency_threshold = df['Recency_Overall'].quantile(0.75) # 75th percentile
high_recency_customers = df[df['Recency_Overall'] > high_recency_threshold]
print(f"\nNumber of customers with high recency (potential churn risk): {len(high_recency_customers)}")
# Correlation between Recency and other features (if available)
if 'TotalSpend' in df.columns:
correlation = df['Recency_Overall'].corr(df['TotalSpend'])
print(f"\nCorrelation between Recency and Total Spend: {correlation:.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_recency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_recency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code example offers a comprehensive approach to calculating and analyzing the Recency feature. Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We start by importing necessary libraries and loading the dataset.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Recency Calculation:
- Recency is calculated as the number of days between the most recent date in the dataset and each purchase date.
- We then find the minimum recency for each customer, representing their most recent purchase.
- Data Visualization:
- A histogram is created to visualize the distribution of customer recency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum recency values.
- These statistics provide insights into overall customer engagement levels.
- Customer Segmentation:
- Customers with high recency (above the 75th percentile) are identified as potential churn risks.
- This segmentation can be used for targeted retention strategies.
- Feature Correlation:
- If a 'TotalSpend' column is available, we calculate its correlation with Recency.
- This helps understand the relationship between customer spending and engagement.
- Data Persistence:
- The updated dataset with the new Recency feature is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Recency feature but also provides valuable insights into customer behavior, potential churn risks, and the relationship between recency and other important metrics. These insights can be crucial for developing effective customer retention strategies and improving predictive models for both classification (churn prediction) and regression (CLTV estimation) tasks.
Feature 2: Monetary Value
Monetary Value represents the average spending per transaction, serving as a key indicator of customer behavior and potential value. This metric offers valuable insights into customer loyalty, spending capacity, and the risk of churn. For Customer Lifetime Value (CLTV) prediction, higher monetary values often correlate with more profitable customers, as they demonstrate a willingness to invest more in each interaction with the brand.
The significance of Monetary Value extends beyond simple financial metrics. It can reveal customer preferences, price sensitivity, and even the effectiveness of upselling or cross-selling strategies. For instance, customers with consistently high monetary values might be more receptive to premium products or services, presenting opportunities for targeted marketing campaigns.
In the context of churn prediction, fluctuations in Monetary Value over time can be particularly telling. A sudden decrease might signal dissatisfaction or a shift to competitors, while steady or increasing values suggest sustained engagement. By combining Monetary Value with other features like Recency and Frequency, businesses can develop a more nuanced understanding of customer behavior, enabling more accurate predictions and personalized retention strategies.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Calculate Monetary Value as the average purchase value for each customer
monetary_value_df = df.groupby('CustomerID')['Total Spend'].agg(['mean', 'sum', 'count']).reset_index()
monetary_value_df.columns = ['CustomerID', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']
# Merge the monetary value features back to main dataset
df = df.merge(monetary_value_df, on='CustomerID')
# Display the first few rows with the new Monetary Value features
print("\nData with Monetary Value Features:")
print(df[['CustomerID', 'Total Spend', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].head())
# Visualize the distribution of Average Purchase Value
plt.figure(figsize=(10, 6))
sns.histplot(df['AvgPurchaseValue'], kde=True)
plt.title('Distribution of Average Purchase Value')
plt.xlabel('Average Purchase Value')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_purchase_value = df['AvgPurchaseValue'].mean()
median_purchase_value = df['AvgPurchaseValue'].median()
max_purchase_value = df['AvgPurchaseValue'].max()
print(f"\nAverage Purchase Value: ${avg_purchase_value:.2f}")
print(f"Median Purchase Value: ${median_purchase_value:.2f}")
print(f"Maximum Purchase Value: ${max_purchase_value:.2f}")
# Identify high-value customers (top 20%)
high_value_threshold = df['AvgPurchaseValue'].quantile(0.8)
high_value_customers = df[df['AvgPurchaseValue'] > high_value_threshold]
print(f"\nNumber of high-value customers: {len(high_value_customers)}")
# Correlation between Monetary Value and other features
correlation_matrix = df[['AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Monetary Value Features')
plt.show()
# Save the updated dataset
df.to_csv('retail_data_with_monetary_value.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_monetary_value.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code snippet demonstrates a method for calculating and analyzing the Monetary Value feature. Let's examine its key components and their roles:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- Monetary Value Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Three metrics are calculated: mean (AvgPurchaseValue), sum (TotalSpend), and count (PurchaseCount) of 'Total Spend'.
- These features provide a more comprehensive view of customer spending behavior.
- Data Merging:
- The new monetary value features are merged back into the main dataset.
- Data Visualization:
- A histogram is created to visualize the distribution of Average Purchase Value.
- This helps identify patterns in customer spending and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum purchase values.
- These statistics provide insights into overall customer spending patterns.
- Customer Segmentation:
- High-value customers (top 20% based on Average Purchase Value) are identified.
- This segmentation can be used for targeted marketing or loyalty programs.
- Feature Correlation:
- A correlation matrix is computed for the monetary value features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer spending.
- Data Persistence:
- The updated dataset with the new monetary value features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Monetary Value feature but also offers valuable insights into customer spending patterns, identifies high-value clients, and explores relationships between various monetary metrics. These insights are crucial for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 3: Frequency
Frequency is a measure of how often a customer makes purchases within a given timeframe. This metric provides valuable insights into customer behavior and loyalty. Frequent purchases often indicate high engagement, making it a valuable feature for both Customer Lifetime Value (CLTV) prediction and churn classification.
In the context of CLTV prediction, frequency can help identify customers who are likely to generate higher long-term value. Customers with higher purchase frequencies tend to have a stronger relationship with the brand, potentially leading to increased lifetime value. For churn classification, a decline in purchase frequency can be an early warning sign of potential customer disengagement or impending churn.
Moreover, frequency can be analyzed in conjunction with other features to gain deeper insights. For instance, combining frequency with monetary value can help identify high-value, frequent customers who may be prime candidates for loyalty programs or personalized marketing campaigns. Similarly, analyzing the relationship between frequency and recency can reveal patterns in customer behavior, such as seasonal purchasing habits or the effectiveness of retention strategies.
When engineering this feature, it's important to consider the appropriate time frame for calculation, as this can vary depending on the business model and product lifecycle. For some businesses, weekly frequency might be relevant, while for others, monthly or quarterly frequencies could be more insightful. Additionally, tracking changes in frequency over time can provide dynamic insights into evolving customer behavior and market trends.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Frequency by counting transactions per customer
frequency_df = df.groupby('CustomerID').agg({
'PurchaseDate': 'count',
'Total Spend': 'sum'
}).reset_index()
frequency_df.columns = ['CustomerID', 'Frequency', 'TotalSpend']
# Calculate average time between purchases
df_sorted = df.sort_values(['CustomerID', 'PurchaseDate'])
df_sorted['PrevPurchaseDate'] = df_sorted.groupby('CustomerID')['PurchaseDate'].shift(1)
df_sorted['DaysBetweenPurchases'] = (df_sorted['PurchaseDate'] - df_sorted['PrevPurchaseDate']).dt.days
avg_time_between_purchases = df_sorted.groupby('CustomerID')['DaysBetweenPurchases'].mean().reset_index()
avg_time_between_purchases.columns = ['CustomerID', 'AvgDaysBetweenPurchases']
# Merge frequency features back to the main dataset
df = df.merge(frequency_df, on='CustomerID')
df = df.merge(avg_time_between_purchases, on='CustomerID')
# Calculate additional metrics
df['AvgPurchaseValue'] = df['TotalSpend'] / df['Frequency']
print("\nData with Frequency Features:")
print(df[['CustomerID', 'PurchaseDate', 'Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].head())
# Visualize the distribution of Frequency
plt.figure(figsize=(10, 6))
sns.histplot(df['Frequency'], kde=True)
plt.title('Distribution of Purchase Frequency')
plt.xlabel('Number of Purchases')
plt.ylabel('Count of Customers')
plt.show()
# Analyze correlation between Frequency and other metrics
correlation_matrix = df[['Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Frequency-related Features')
plt.show()
# Identify high-frequency customers (top 20%)
high_frequency_threshold = df['Frequency'].quantile(0.8)
high_frequency_customers = df[df['Frequency'] > high_frequency_threshold]
print(f"\nNumber of high-frequency customers: {len(high_frequency_customers)}")
print(f"Average spend of high-frequency customers: ${high_frequency_customers['TotalSpend'].mean():.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_frequency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_frequency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Frequency Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Two metrics are calculated: count of purchases (Frequency) and sum of Total Spend.
- Time Between Purchases:
- The data is sorted by CustomerID and PurchaseDate.
- We calculate the time difference between consecutive purchases for each customer.
- The average time between purchases is computed for each customer.
- Data Merging:
- The new frequency features are merged back into the main dataset.
- Additional Metrics:
- Average Purchase Value is calculated by dividing Total Spend by Frequency.
- Data Visualization:
- A histogram is created to visualize the distribution of Purchase Frequency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Correlation Analysis:
- A correlation matrix is computed for the frequency-related features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer behavior.
- Customer Segmentation:
- High-frequency customers (top 20% based on Frequency) are identified.
- We calculate and display the number of high-frequency customers and their average spend.
- This segmentation can be used for targeted marketing or loyalty programs.
- Data Persistence:
- The updated dataset with the new frequency features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach calculates the Frequency feature and offers valuable insights into customer behavior. It identifies high-frequency clients and explores relationships between various frequency-related metrics. These insights are essential for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 4: Purchase Trend
For classification or regression models, Purchase Trend is a crucial feature that captures the dynamic nature of customer behavior over time. This feature quantifies how a customer's spending patterns have evolved, providing valuable insights into their engagement and loyalty levels. Positive trends, characterized by increasing purchase frequency or value, often suggest growing customer satisfaction and a strengthening relationship with the brand. These customers may be prime candidates for upselling or cross-selling initiatives.
Conversely, negative trends could signal potential issues such as customer dissatisfaction, increased competition, or changing needs. Such trends might manifest as decreasing purchase frequency, lower transaction values, or longer intervals between purchases. Identifying these negative trends early allows businesses to implement targeted retention strategies, potentially preventing churn before it occurs.
The Purchase Trend feature can be particularly powerful when combined with other metrics like Recency and Frequency. For instance, a customer with high frequency but a negative purchase trend might require different intervention strategies compared to a customer with low frequency but a positive trend. By incorporating this temporal dimension into predictive models, businesses can develop more nuanced and effective customer segmentation strategies, personalized marketing campaigns, and proactive customer service initiatives.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate average spend over time by grouping data by month and CustomerID
df['PurchaseMonth'] = df['PurchaseDate'].dt.to_period('M')
monthly_spend = df.groupby(['CustomerID', 'PurchaseMonth'])['Total Spend'].sum().reset_index()
# Calculate trend as the slope of spending over time for each customer
def calculate_trend(customer_df):
x = np.arange(len(customer_df))
y = customer_df['Total Spend'].values
if len(x) > 1:
return np.polyfit(x, y, 1)[0] # Linear trend slope
return 0
# Apply trend calculation
trend_df = monthly_spend.groupby('CustomerID').apply(calculate_trend).reset_index(name='PurchaseTrend')
# Merge trend feature back to main dataset
df = df.merge(trend_df, on='CustomerID')
print("\nData with Purchase Trend Feature:")
print(df[['CustomerID', 'PurchaseMonth', 'Total Spend', 'PurchaseTrend']].head())
# Visualize Purchase Trend distribution
plt.figure(figsize=(10, 6))
sns.histplot(df['PurchaseTrend'], kde=True)
plt.title('Distribution of Purchase Trends')
plt.xlabel('Purchase Trend (Slope)')
plt.ylabel('Count of Customers')
plt.show()
# Identify customers with positive and negative trends
positive_trend = df[df['PurchaseTrend'] > 0]
negative_trend = df[df['PurchaseTrend'] < 0]
print(f"\nCustomers with positive trend: {len(positive_trend['CustomerID'].unique())}")
print(f"Customers with negative trend: {len(negative_trend['CustomerID'].unique())}")
# Calculate correlation between Purchase Trend and other features
correlation = df[['PurchaseTrend', 'Total Spend', 'Frequency']].corr()
plt.figure(figsize=(8, 6))
sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Correlation between Purchase Trend and Other Features')
plt.show()
# Example: Using Purchase Trend for customer segmentation
df['TrendCategory'] = pd.cut(df['PurchaseTrend'],
bins=[-np.inf, -10, 0, 10, np.inf],
labels=['Strong Negative', 'Slight Negative', 'Slight Positive', 'Strong Positive'])
trend_segment = df.groupby('TrendCategory').agg({
'CustomerID': 'nunique',
'Total Spend': 'mean',
'Frequency': 'mean'
}).reset_index()
print("\nCustomer Segmentation based on Purchase Trend:")
print(trend_segment)
# Save the updated dataset with the new feature
df.to_csv('retail_data_with_trend.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_trend.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down this comprehensive code example:
- Data Loading and Preprocessing:
- We import necessary libraries: pandas for data manipulation, numpy for numerical operations, and matplotlib/seaborn for visualization.
- The dataset is loaded from a CSV file and the 'PurchaseDate' column is converted to datetime format.
- Calculating Purchase Trend:
- We group the data by customer and month to get monthly spending patterns.
- A 'calculate_trend' function is defined to compute the linear trend (slope) of spending over time for each customer.
- This trend is then calculated for each customer and merged back into the main dataset.
- Visualizing Purchase Trend:
- A histogram is created to show the distribution of Purchase Trends across all customers.
- This visualization helps identify the overall trend patterns in the customer base.
- Analyzing Positive and Negative Trends:
- We separate customers with positive and negative trends and count them.
- This provides a quick overview of how many customers are increasing or decreasing their spending over time.
- Correlation Analysis:
- We calculate and visualize the correlation between Purchase Trend and other features like Total Spend and Frequency.
- This helps understand how the trend relates to other important customer metrics.
- Customer Segmentation:
- We categorize customers based on their Purchase Trend into four groups: Strong Negative, Slight Negative, Slight Positive, and Strong Positive.
- For each segment, we calculate the number of customers, average total spend, and average purchase frequency.
- This segmentation can be used for targeted marketing strategies or to identify at-risk customers.
- Data Persistence:
- The updated dataset with the new Purchase Trend feature is saved to a new CSV file.
- This allows for easy access in future analyses or model training.
This code offers a thorough analysis of the Purchase Trend feature, showcasing its distribution, correlations with other features, and application in customer segmentation. These insights prove valuable for both classification tasks—such as churn prediction—and regression tasks like Customer Lifetime Value (CLTV) estimation.
2.2.3 Using Feature Engineering for Model Training
Once these features are engineered, they serve as the foundation for training powerful predictive models. In this section, we'll explore how to leverage these features for both classification and regression tasks, specifically focusing on churn prediction and Customer Lifetime Value (CLTV) estimation.
For churn prediction, a classification task, we'll employ a Logistic Regression model. This model excels at predicting binary outcomes, making it ideal for determining whether a customer is likely to churn or not. The features we've created, such as Recency, Frequency, and Purchase Trend, provide crucial insights into customer behavior that can signal potential churn.
On the other hand, for CLTV prediction, a regression task, we'll utilize a Linear Regression model. This model is well-suited for predicting continuous values, allowing us to estimate the future value a customer may bring to the business. Features like Monetary Value and Purchase Trend are particularly valuable here, as they capture spending patterns and long-term customer behavior.
By incorporating these engineered features into our models, we significantly enhance their predictive power. This allows businesses to make data-driven decisions, implement targeted retention strategies, and optimize customer engagement efforts. Let's dive into the practical implementation of these models using our newly created features.
Example: Training a Logistic Regression Model for Churn Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data_with_features.csv')
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y = df['Churn'] # Target variable for churn
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)
# Train logistic regression model
log_reg = LogisticRegression(random_state=42)
log_reg.fit(X_train, y_train)
# Predictions
y_pred = log_reg.predict(X_test)
y_pred_proba = log_reg.predict_proba(X_test)[:, 1]
# Model evaluation
print("Model Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:")
print(classification_report(y_test, y_pred))
# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(log_reg.coef_[0])})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance')
plt.show()
# Cross-validation
cv_scores = cross_val_score(log_reg, X_scaled, y, cv=5)
print("\nCross-validation scores:", cv_scores)
print("Mean CV score:", np.mean(cv_scores))
# ROC Curve
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)
roc_auc = auc(fpr, tpr)
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')
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 (ROC) Curve')
plt.legend(loc="lower right")
plt.show()This code example demonstrates a comprehensive approach to training and evaluating a Logistic Regression model for churn prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select the relevant features and target variable.
- The features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- We use train_test_split to divide the data into training and testing sets.
- A LogisticRegression model is initialized and trained on the training data.
- Predictions:
- The model makes predictions on the test set.
- We also calculate prediction probabilities, which will be used for the ROC curve.
- Model Evaluation:
- Accuracy score is calculated to give an overall performance metric.
- A detailed classification report is printed, showing precision, recall, and F1-score for each class.
- A confusion matrix is visualized using a heatmap, providing a clear view of true positives, true negatives, false positives, and false negatives.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in the model.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- This helps to ensure that the model's performance is consistent and not overly dependent on a particular train-test split.
- ROC Curve:
- The Receiver Operating Characteristic (ROC) curve is plotted.
- The Area Under the Curve (AUC) is calculated, providing a single score that summarizes the model's performance across all possible classification thresholds.
This comprehensive approach goes beyond merely training the model—it provides a thorough evaluation of its performance. The visualizations (confusion matrix, feature importance, and ROC curve) offer intuitive insights into the model's behavior. Additionally, the cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
Example: Training a Linear Regression Model for CLTV Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y_cltv = df['CLTV']
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split for CLTV
X_train_cltv, X_test_cltv, y_train_cltv, y_test_cltv = train_test_split(X_scaled, y_cltv, test_size=0.3, random_state=42)
# Train linear regression model
lin_reg = LinearRegression()
lin_reg.fit(X_train_cltv, y_train_cltv)
# Predictions and evaluation
y_pred_cltv = lin_reg.predict(X_test_cltv)
mse = mean_squared_error(y_test_cltv, y_pred_cltv)
r2 = r2_score(y_test_cltv, y_pred_cltv)
print("Mean Squared Error:", mse)
print("R-squared Score:", r2)
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(lin_reg.coef_)})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance for CLTV Prediction')
plt.show()
# Residual plot
residuals = y_test_cltv - y_pred_cltv
plt.figure(figsize=(10, 6))
plt.scatter(y_pred_cltv, residuals)
plt.xlabel('Predicted CLTV')
plt.ylabel('Residuals')
plt.title('Residual Plot')
plt.axhline(y=0, color='r', linestyle='--')
plt.show()
# Cross-validation
cv_scores = cross_val_score(lin_reg, X_scaled, y_cltv, cv=5, scoring='neg_mean_squared_error')
cv_rmse = np.sqrt(-cv_scores)
print("\nCross-validation RMSE scores:", cv_rmse)
print("Mean CV RMSE score:", np.mean(cv_rmse))
# Actual vs Predicted plot
plt.figure(figsize=(10, 6))
plt.scatter(y_test_cltv, y_pred_cltv, alpha=0.5)
plt.plot([y_test_cltv.min(), y_test_cltv.max()], [y_test_cltv.min(), y_test_cltv.max()], 'r--', lw=2)
plt.xlabel('Actual CLTV')
plt.ylabel('Predicted CLTV')
plt.title('Actual vs Predicted CLTV')
plt.show()This code example provides a comprehensive approach to training and evaluating a Linear Regression model for Customer Lifetime Value (CLTV) prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select relevant features for CLTV prediction.
- Features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- The data is split into training and testing sets using train_test_split.
- A LinearRegression model is initialized and trained on the training data.
- Predictions and Evaluation:
- The model makes predictions on the test set.
- Mean Squared Error (MSE) is calculated to quantify the model's prediction error.
- R-squared score is computed to measure the proportion of variance in the target variable that is predictable from the features.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in predicting CLTV.
- Residual Analysis:
- A residual plot is created to visualize the difference between actual and predicted values.
- This helps identify any patterns in the model's errors and assess if the linear regression assumptions are met.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- Root Mean Squared Error (RMSE) is used as the evaluation metric for cross-validation.
- Actual vs Predicted Plot:
- A scatter plot is created to compare actual CLTV values against predicted values.
- This visual aid helps in understanding how well the model's predictions align with actual values.
This comprehensive approach not only trains the model but also provides a thorough evaluation of its performance. The visualizations (feature importance, residual plot, and actual vs predicted plot) offer intuitive insights into the model's behavior and performance. The cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
By implementing these additional evaluation techniques and visualizations, we gain a deeper understanding of the model's strengths and limitations in predicting Customer Lifetime Value. This information can be invaluable for refining the model, selecting features, and making data-driven decisions in customer relationship management strategies.
2.2.4 Key Takeaways and Their Implications
- Feature engineering enhances predictive accuracy by creating features that capture underlying patterns and trends. This process involves transforming raw data into meaningful representations that algorithms can better interpret, leading to more robust and accurate models.
- For classification tasks like churn prediction, features such as Recency, Frequency, and Purchase Trend provide crucial insights into customer loyalty and engagement. These metrics help identify at-risk customers, allowing businesses to implement targeted retention strategies.
- In regression tasks like CLTV prediction, features capturing spending habits and behavior over time, such as Monetary Value and Purchase Trend, significantly improve the model's ability to predict lifetime value. This enables businesses to allocate resources more effectively and personalize customer experiences.
- The selection of appropriate features is context-dependent and requires domain expertise. For instance, in healthcare, features like appointment frequency and treatment adherence might be more relevant for predicting patient outcomes.
- Feature importance analysis, as demonstrated in the code examples, provides valuable insights into which factors most significantly influence the target variable. This information can guide business decisions and strategy formulation.
- Cross-validation and residual analysis are crucial steps in evaluating model performance and identifying potential areas for improvement in feature engineering or model selection.
2.2 Feature Engineering for Classification and Regression Models
Feature engineering for classification and regression models is a critical process that enhances predictive accuracy by creating features that capture underlying patterns in the data. Unlike unsupervised learning techniques such as clustering or exploratory analysis, classification and regression models rely on labeled data to predict a specific target variable. This approach is essential whether the goal is to classify customers by loyalty level, predict house prices, or forecast customer lifetime value.
The process of feature engineering involves several key strategies:
- Feature Creation: Developing new features that encapsulate relevant information from existing data. For example, in a retail context, creating a "purchase frequency" feature from transaction data.
- Feature Transformation: Modifying existing features to better represent the underlying relationships. This might include logarithmic transformations for skewed data or encoding categorical variables.
- Feature Selection: Identifying the most relevant features that contribute significantly to the predictive power of the model, while avoiding overfitting.
These strategies are applicable to both classification models, which predict discrete categories (such as customer churn), and regression models, which predict continuous values (like house prices or customer lifetime value).
To illustrate these concepts, we'll explore a practical example using a retail dataset. Our focus will be on predicting Customer Lifetime Value (CLTV), a key metric in customer relationship management. This example will demonstrate how carefully engineered features can significantly improve the accuracy and interpretability of predictive models in real-world business scenarios.
2.2.1 Step 1: Data Preparation and Understanding
Before diving into feature engineering, it's crucial to thoroughly understand the dataset and evaluate the available variables. This initial step lays the foundation for creating meaningful features that can significantly enhance the predictive power of our models. Let's begin by loading our dataset and examining its structure and contents.
In this case, we're dealing with a retail dataset that contains valuable information about customer transactions. Our primary objectives are twofold:
- Predicting Customer Lifetime Value (CLTV): This is a regression task where we aim to estimate the total value a customer will bring to the business over their entire relationship.
- Predicting Churn: This is a binary classification task where we seek to identify customers who are likely to stop doing business with us.
By carefully analyzing the available variables, we can identify potential predictors that might be particularly useful for these tasks. For instance, transaction history, purchase frequency, and average order value could all provide valuable insights into both CLTV and churn probability.
As we proceed with our analysis, we'll look for patterns and relationships within the data that can inform our feature engineering process. This might involve exploring correlations between variables, identifying outliers or anomalies, and considering domain-specific knowledge about customer behavior in the retail sector.
The goal of this initial exploration is to gain a comprehensive understanding of our data, which will guide us in creating sophisticated, meaningful features that capture the underlying dynamics of customer behavior and value. This foundational work is essential for building robust predictive models that can drive actionable insights and inform strategic decision-making in customer relationship management.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the retail dataset
df = pd.read_csv('retail_cltv_data.csv')
# Display basic information and first few rows
print("Dataset Information:")
print(df.info())
print("\nFirst Few Rows of Data:")
print(df.head())
# Basic statistical summary
print("\nStatistical Summary:")
print(df.describe())
# Check for missing values
print("\nMissing Values:")
print(df.isnull().sum())
# Unique values in categorical columns
categorical_columns = df.select_dtypes(include=['object']).columns
for col in categorical_columns:
print(f"\nUnique values in {col}:")
print(df[col].value_counts())
# Visualize the distribution of a numerical column (e.g., 'Total Spend')
plt.figure(figsize=(10, 6))
sns.histplot(df['Total Spend'], kde=True)
plt.title('Distribution of Total Spend')
plt.xlabel('Total Spend')
plt.ylabel('Count')
plt.show()
# Correlation matrix for numerical columns
numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns
correlation_matrix = df[numerical_columns].corr()
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Numerical Features')
plt.show()Let's break down this code example:
- Import statements:
- We import pandas for data manipulation, matplotlib.pyplot for basic plotting, and seaborn for more advanced statistical visualizations.
- Data Loading:
- The retail dataset is loaded from a CSV file into a pandas DataFrame.
- Basic Information Display:
- df.info() provides an overview of the DataFrame, including column names, data types, and non-null counts.
- df.head() displays the first few rows of the DataFrame.
- Statistical Summary:
- df.describe() generates descriptive statistics for numerical columns, including count, mean, standard deviation, min, max, and quartiles.
- Missing Value Check:
- df.isnull().sum() calculates the number of missing values in each column.
- Categorical Data Analysis:
- We identify categorical columns and display the value counts for each unique category.
- Numerical Data Visualization:
- A histogram is created for the 'Total Spend' column to visualize its distribution.
- The use of seaborn's histplot with kde=True adds a kernel density estimate curve.
- Correlation Analysis:
- A correlation matrix is computed for all numerical columns.
- The matrix is visualized using a heatmap, which helps identify relationships between variables.
This code offers a thorough initial data exploration, examining data types, missing values, numerical data distribution, and feature correlations. Such insights are essential for grasping the dataset's nuances before diving into feature engineering and model development.
2.2.2 Step 2: Creating Predictive Features
Once we have a solid grasp of the dataset, we can embark on the crucial process of feature engineering. This involves creating new features or transforming existing ones to reveal patterns and relationships that align with our target variable, whether it's for a classification or regression task. The goal is to extract meaningful information from the raw data that can enhance the predictive power of our models.
For classification problems, such as predicting customer churn, we might focus on features that capture customer behavior and engagement levels. These could include metrics like the frequency of purchases, the recency of the last interaction, or changes in spending patterns over time.
In regression tasks, like estimating Customer Lifetime Value (CLTV), we might engineer features that reflect long-term customer value. This could involve calculating average order values, identifying seasonal purchasing trends, or developing composite scores that combine multiple aspects of customer behavior.
The art of feature engineering lies in combining domain expertise with data-driven insights to create variables that are not just statistically significant, but also interpretable and actionable from a business perspective. As we proceed, we'll explore specific techniques and examples of how to craft these powerful predictive features.
Feature 1: Recency
Recency measures the time elapsed since a customer's most recent purchase. This metric is a powerful indicator of customer engagement and plays a crucial role in both Customer Lifetime Value (CLTV) prediction and churn classification models. Recent purchases often signal active engagement with a brand, suggesting a higher likelihood of customer loyalty and increased value.
In the context of CLTV prediction, recency can help identify high-value customers who consistently make purchases. These customers are likely to continue their buying behavior, potentially leading to higher lifetime value. Conversely, customers with high recency (i.e., a long time since their last purchase) might be at risk of churning, which could negatively impact their projected CLTV.
For churn classification, recency serves as a key predictor. Customers who have made recent purchases are generally less likely to churn, as their engagement with the brand is still active. On the other hand, those with high recency might be showing signs of disengagement, making them more susceptible to churn.
It's important to note that the interpretation of recency can vary across industries and business models. For instance, in a subscription-based service, high recency might be expected and not necessarily indicative of churn risk. Therefore, recency should always be considered in conjunction with other relevant features and within the specific context of the business to derive the most accurate insights for CLTV prediction and churn classification.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Recency
most_recent_date = df['PurchaseDate'].max()
df['Recency'] = (most_recent_date - df['PurchaseDate']).dt.days
# Calculate the last purchase date per customer
recency_df = df.groupby('CustomerID')['Recency'].min().reset_index()
# Merge Recency back to main dataset
df = df.merge(recency_df, on='CustomerID', suffixes=('', '_Overall'))
# Display the first few rows with the new Recency feature
print("\nData with Recency Feature:")
print(df[['CustomerID', 'PurchaseDate', 'Recency_Overall']].head())
# Visualize the distribution of Recency
plt.figure(figsize=(10, 6))
sns.histplot(df['Recency_Overall'], kde=True)
plt.title('Distribution of Customer Recency')
plt.xlabel('Recency (days)')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_recency = df['Recency_Overall'].mean()
median_recency = df['Recency_Overall'].median()
max_recency = df['Recency_Overall'].max()
print(f"\nAverage Recency: {avg_recency:.2f} days")
print(f"Median Recency: {median_recency:.2f} days")
print(f"Maximum Recency: {max_recency:.2f} days")
# Identify customers with high recency (potential churn risk)
high_recency_threshold = df['Recency_Overall'].quantile(0.75) # 75th percentile
high_recency_customers = df[df['Recency_Overall'] > high_recency_threshold]
print(f"\nNumber of customers with high recency (potential churn risk): {len(high_recency_customers)}")
# Correlation between Recency and other features (if available)
if 'TotalSpend' in df.columns:
correlation = df['Recency_Overall'].corr(df['TotalSpend'])
print(f"\nCorrelation between Recency and Total Spend: {correlation:.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_recency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_recency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code example offers a comprehensive approach to calculating and analyzing the Recency feature. Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We start by importing necessary libraries and loading the dataset.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Recency Calculation:
- Recency is calculated as the number of days between the most recent date in the dataset and each purchase date.
- We then find the minimum recency for each customer, representing their most recent purchase.
- Data Visualization:
- A histogram is created to visualize the distribution of customer recency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum recency values.
- These statistics provide insights into overall customer engagement levels.
- Customer Segmentation:
- Customers with high recency (above the 75th percentile) are identified as potential churn risks.
- This segmentation can be used for targeted retention strategies.
- Feature Correlation:
- If a 'TotalSpend' column is available, we calculate its correlation with Recency.
- This helps understand the relationship between customer spending and engagement.
- Data Persistence:
- The updated dataset with the new Recency feature is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Recency feature but also provides valuable insights into customer behavior, potential churn risks, and the relationship between recency and other important metrics. These insights can be crucial for developing effective customer retention strategies and improving predictive models for both classification (churn prediction) and regression (CLTV estimation) tasks.
Feature 2: Monetary Value
Monetary Value represents the average spending per transaction, serving as a key indicator of customer behavior and potential value. This metric offers valuable insights into customer loyalty, spending capacity, and the risk of churn. For Customer Lifetime Value (CLTV) prediction, higher monetary values often correlate with more profitable customers, as they demonstrate a willingness to invest more in each interaction with the brand.
The significance of Monetary Value extends beyond simple financial metrics. It can reveal customer preferences, price sensitivity, and even the effectiveness of upselling or cross-selling strategies. For instance, customers with consistently high monetary values might be more receptive to premium products or services, presenting opportunities for targeted marketing campaigns.
In the context of churn prediction, fluctuations in Monetary Value over time can be particularly telling. A sudden decrease might signal dissatisfaction or a shift to competitors, while steady or increasing values suggest sustained engagement. By combining Monetary Value with other features like Recency and Frequency, businesses can develop a more nuanced understanding of customer behavior, enabling more accurate predictions and personalized retention strategies.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Calculate Monetary Value as the average purchase value for each customer
monetary_value_df = df.groupby('CustomerID')['Total Spend'].agg(['mean', 'sum', 'count']).reset_index()
monetary_value_df.columns = ['CustomerID', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']
# Merge the monetary value features back to main dataset
df = df.merge(monetary_value_df, on='CustomerID')
# Display the first few rows with the new Monetary Value features
print("\nData with Monetary Value Features:")
print(df[['CustomerID', 'Total Spend', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].head())
# Visualize the distribution of Average Purchase Value
plt.figure(figsize=(10, 6))
sns.histplot(df['AvgPurchaseValue'], kde=True)
plt.title('Distribution of Average Purchase Value')
plt.xlabel('Average Purchase Value')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_purchase_value = df['AvgPurchaseValue'].mean()
median_purchase_value = df['AvgPurchaseValue'].median()
max_purchase_value = df['AvgPurchaseValue'].max()
print(f"\nAverage Purchase Value: ${avg_purchase_value:.2f}")
print(f"Median Purchase Value: ${median_purchase_value:.2f}")
print(f"Maximum Purchase Value: ${max_purchase_value:.2f}")
# Identify high-value customers (top 20%)
high_value_threshold = df['AvgPurchaseValue'].quantile(0.8)
high_value_customers = df[df['AvgPurchaseValue'] > high_value_threshold]
print(f"\nNumber of high-value customers: {len(high_value_customers)}")
# Correlation between Monetary Value and other features
correlation_matrix = df[['AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Monetary Value Features')
plt.show()
# Save the updated dataset
df.to_csv('retail_data_with_monetary_value.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_monetary_value.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code snippet demonstrates a method for calculating and analyzing the Monetary Value feature. Let's examine its key components and their roles:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- Monetary Value Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Three metrics are calculated: mean (AvgPurchaseValue), sum (TotalSpend), and count (PurchaseCount) of 'Total Spend'.
- These features provide a more comprehensive view of customer spending behavior.
- Data Merging:
- The new monetary value features are merged back into the main dataset.
- Data Visualization:
- A histogram is created to visualize the distribution of Average Purchase Value.
- This helps identify patterns in customer spending and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum purchase values.
- These statistics provide insights into overall customer spending patterns.
- Customer Segmentation:
- High-value customers (top 20% based on Average Purchase Value) are identified.
- This segmentation can be used for targeted marketing or loyalty programs.
- Feature Correlation:
- A correlation matrix is computed for the monetary value features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer spending.
- Data Persistence:
- The updated dataset with the new monetary value features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Monetary Value feature but also offers valuable insights into customer spending patterns, identifies high-value clients, and explores relationships between various monetary metrics. These insights are crucial for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 3: Frequency
Frequency is a measure of how often a customer makes purchases within a given timeframe. This metric provides valuable insights into customer behavior and loyalty. Frequent purchases often indicate high engagement, making it a valuable feature for both Customer Lifetime Value (CLTV) prediction and churn classification.
In the context of CLTV prediction, frequency can help identify customers who are likely to generate higher long-term value. Customers with higher purchase frequencies tend to have a stronger relationship with the brand, potentially leading to increased lifetime value. For churn classification, a decline in purchase frequency can be an early warning sign of potential customer disengagement or impending churn.
Moreover, frequency can be analyzed in conjunction with other features to gain deeper insights. For instance, combining frequency with monetary value can help identify high-value, frequent customers who may be prime candidates for loyalty programs or personalized marketing campaigns. Similarly, analyzing the relationship between frequency and recency can reveal patterns in customer behavior, such as seasonal purchasing habits or the effectiveness of retention strategies.
When engineering this feature, it's important to consider the appropriate time frame for calculation, as this can vary depending on the business model and product lifecycle. For some businesses, weekly frequency might be relevant, while for others, monthly or quarterly frequencies could be more insightful. Additionally, tracking changes in frequency over time can provide dynamic insights into evolving customer behavior and market trends.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Frequency by counting transactions per customer
frequency_df = df.groupby('CustomerID').agg({
'PurchaseDate': 'count',
'Total Spend': 'sum'
}).reset_index()
frequency_df.columns = ['CustomerID', 'Frequency', 'TotalSpend']
# Calculate average time between purchases
df_sorted = df.sort_values(['CustomerID', 'PurchaseDate'])
df_sorted['PrevPurchaseDate'] = df_sorted.groupby('CustomerID')['PurchaseDate'].shift(1)
df_sorted['DaysBetweenPurchases'] = (df_sorted['PurchaseDate'] - df_sorted['PrevPurchaseDate']).dt.days
avg_time_between_purchases = df_sorted.groupby('CustomerID')['DaysBetweenPurchases'].mean().reset_index()
avg_time_between_purchases.columns = ['CustomerID', 'AvgDaysBetweenPurchases']
# Merge frequency features back to the main dataset
df = df.merge(frequency_df, on='CustomerID')
df = df.merge(avg_time_between_purchases, on='CustomerID')
# Calculate additional metrics
df['AvgPurchaseValue'] = df['TotalSpend'] / df['Frequency']
print("\nData with Frequency Features:")
print(df[['CustomerID', 'PurchaseDate', 'Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].head())
# Visualize the distribution of Frequency
plt.figure(figsize=(10, 6))
sns.histplot(df['Frequency'], kde=True)
plt.title('Distribution of Purchase Frequency')
plt.xlabel('Number of Purchases')
plt.ylabel('Count of Customers')
plt.show()
# Analyze correlation between Frequency and other metrics
correlation_matrix = df[['Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Frequency-related Features')
plt.show()
# Identify high-frequency customers (top 20%)
high_frequency_threshold = df['Frequency'].quantile(0.8)
high_frequency_customers = df[df['Frequency'] > high_frequency_threshold]
print(f"\nNumber of high-frequency customers: {len(high_frequency_customers)}")
print(f"Average spend of high-frequency customers: ${high_frequency_customers['TotalSpend'].mean():.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_frequency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_frequency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Frequency Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Two metrics are calculated: count of purchases (Frequency) and sum of Total Spend.
- Time Between Purchases:
- The data is sorted by CustomerID and PurchaseDate.
- We calculate the time difference between consecutive purchases for each customer.
- The average time between purchases is computed for each customer.
- Data Merging:
- The new frequency features are merged back into the main dataset.
- Additional Metrics:
- Average Purchase Value is calculated by dividing Total Spend by Frequency.
- Data Visualization:
- A histogram is created to visualize the distribution of Purchase Frequency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Correlation Analysis:
- A correlation matrix is computed for the frequency-related features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer behavior.
- Customer Segmentation:
- High-frequency customers (top 20% based on Frequency) are identified.
- We calculate and display the number of high-frequency customers and their average spend.
- This segmentation can be used for targeted marketing or loyalty programs.
- Data Persistence:
- The updated dataset with the new frequency features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach calculates the Frequency feature and offers valuable insights into customer behavior. It identifies high-frequency clients and explores relationships between various frequency-related metrics. These insights are essential for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 4: Purchase Trend
For classification or regression models, Purchase Trend is a crucial feature that captures the dynamic nature of customer behavior over time. This feature quantifies how a customer's spending patterns have evolved, providing valuable insights into their engagement and loyalty levels. Positive trends, characterized by increasing purchase frequency or value, often suggest growing customer satisfaction and a strengthening relationship with the brand. These customers may be prime candidates for upselling or cross-selling initiatives.
Conversely, negative trends could signal potential issues such as customer dissatisfaction, increased competition, or changing needs. Such trends might manifest as decreasing purchase frequency, lower transaction values, or longer intervals between purchases. Identifying these negative trends early allows businesses to implement targeted retention strategies, potentially preventing churn before it occurs.
The Purchase Trend feature can be particularly powerful when combined with other metrics like Recency and Frequency. For instance, a customer with high frequency but a negative purchase trend might require different intervention strategies compared to a customer with low frequency but a positive trend. By incorporating this temporal dimension into predictive models, businesses can develop more nuanced and effective customer segmentation strategies, personalized marketing campaigns, and proactive customer service initiatives.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate average spend over time by grouping data by month and CustomerID
df['PurchaseMonth'] = df['PurchaseDate'].dt.to_period('M')
monthly_spend = df.groupby(['CustomerID', 'PurchaseMonth'])['Total Spend'].sum().reset_index()
# Calculate trend as the slope of spending over time for each customer
def calculate_trend(customer_df):
x = np.arange(len(customer_df))
y = customer_df['Total Spend'].values
if len(x) > 1:
return np.polyfit(x, y, 1)[0] # Linear trend slope
return 0
# Apply trend calculation
trend_df = monthly_spend.groupby('CustomerID').apply(calculate_trend).reset_index(name='PurchaseTrend')
# Merge trend feature back to main dataset
df = df.merge(trend_df, on='CustomerID')
print("\nData with Purchase Trend Feature:")
print(df[['CustomerID', 'PurchaseMonth', 'Total Spend', 'PurchaseTrend']].head())
# Visualize Purchase Trend distribution
plt.figure(figsize=(10, 6))
sns.histplot(df['PurchaseTrend'], kde=True)
plt.title('Distribution of Purchase Trends')
plt.xlabel('Purchase Trend (Slope)')
plt.ylabel('Count of Customers')
plt.show()
# Identify customers with positive and negative trends
positive_trend = df[df['PurchaseTrend'] > 0]
negative_trend = df[df['PurchaseTrend'] < 0]
print(f"\nCustomers with positive trend: {len(positive_trend['CustomerID'].unique())}")
print(f"Customers with negative trend: {len(negative_trend['CustomerID'].unique())}")
# Calculate correlation between Purchase Trend and other features
correlation = df[['PurchaseTrend', 'Total Spend', 'Frequency']].corr()
plt.figure(figsize=(8, 6))
sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Correlation between Purchase Trend and Other Features')
plt.show()
# Example: Using Purchase Trend for customer segmentation
df['TrendCategory'] = pd.cut(df['PurchaseTrend'],
bins=[-np.inf, -10, 0, 10, np.inf],
labels=['Strong Negative', 'Slight Negative', 'Slight Positive', 'Strong Positive'])
trend_segment = df.groupby('TrendCategory').agg({
'CustomerID': 'nunique',
'Total Spend': 'mean',
'Frequency': 'mean'
}).reset_index()
print("\nCustomer Segmentation based on Purchase Trend:")
print(trend_segment)
# Save the updated dataset with the new feature
df.to_csv('retail_data_with_trend.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_trend.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down this comprehensive code example:
- Data Loading and Preprocessing:
- We import necessary libraries: pandas for data manipulation, numpy for numerical operations, and matplotlib/seaborn for visualization.
- The dataset is loaded from a CSV file and the 'PurchaseDate' column is converted to datetime format.
- Calculating Purchase Trend:
- We group the data by customer and month to get monthly spending patterns.
- A 'calculate_trend' function is defined to compute the linear trend (slope) of spending over time for each customer.
- This trend is then calculated for each customer and merged back into the main dataset.
- Visualizing Purchase Trend:
- A histogram is created to show the distribution of Purchase Trends across all customers.
- This visualization helps identify the overall trend patterns in the customer base.
- Analyzing Positive and Negative Trends:
- We separate customers with positive and negative trends and count them.
- This provides a quick overview of how many customers are increasing or decreasing their spending over time.
- Correlation Analysis:
- We calculate and visualize the correlation between Purchase Trend and other features like Total Spend and Frequency.
- This helps understand how the trend relates to other important customer metrics.
- Customer Segmentation:
- We categorize customers based on their Purchase Trend into four groups: Strong Negative, Slight Negative, Slight Positive, and Strong Positive.
- For each segment, we calculate the number of customers, average total spend, and average purchase frequency.
- This segmentation can be used for targeted marketing strategies or to identify at-risk customers.
- Data Persistence:
- The updated dataset with the new Purchase Trend feature is saved to a new CSV file.
- This allows for easy access in future analyses or model training.
This code offers a thorough analysis of the Purchase Trend feature, showcasing its distribution, correlations with other features, and application in customer segmentation. These insights prove valuable for both classification tasks—such as churn prediction—and regression tasks like Customer Lifetime Value (CLTV) estimation.
2.2.3 Using Feature Engineering for Model Training
Once these features are engineered, they serve as the foundation for training powerful predictive models. In this section, we'll explore how to leverage these features for both classification and regression tasks, specifically focusing on churn prediction and Customer Lifetime Value (CLTV) estimation.
For churn prediction, a classification task, we'll employ a Logistic Regression model. This model excels at predicting binary outcomes, making it ideal for determining whether a customer is likely to churn or not. The features we've created, such as Recency, Frequency, and Purchase Trend, provide crucial insights into customer behavior that can signal potential churn.
On the other hand, for CLTV prediction, a regression task, we'll utilize a Linear Regression model. This model is well-suited for predicting continuous values, allowing us to estimate the future value a customer may bring to the business. Features like Monetary Value and Purchase Trend are particularly valuable here, as they capture spending patterns and long-term customer behavior.
By incorporating these engineered features into our models, we significantly enhance their predictive power. This allows businesses to make data-driven decisions, implement targeted retention strategies, and optimize customer engagement efforts. Let's dive into the practical implementation of these models using our newly created features.
Example: Training a Logistic Regression Model for Churn Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data_with_features.csv')
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y = df['Churn'] # Target variable for churn
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)
# Train logistic regression model
log_reg = LogisticRegression(random_state=42)
log_reg.fit(X_train, y_train)
# Predictions
y_pred = log_reg.predict(X_test)
y_pred_proba = log_reg.predict_proba(X_test)[:, 1]
# Model evaluation
print("Model Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:")
print(classification_report(y_test, y_pred))
# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(log_reg.coef_[0])})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance')
plt.show()
# Cross-validation
cv_scores = cross_val_score(log_reg, X_scaled, y, cv=5)
print("\nCross-validation scores:", cv_scores)
print("Mean CV score:", np.mean(cv_scores))
# ROC Curve
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)
roc_auc = auc(fpr, tpr)
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')
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 (ROC) Curve')
plt.legend(loc="lower right")
plt.show()This code example demonstrates a comprehensive approach to training and evaluating a Logistic Regression model for churn prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select the relevant features and target variable.
- The features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- We use train_test_split to divide the data into training and testing sets.
- A LogisticRegression model is initialized and trained on the training data.
- Predictions:
- The model makes predictions on the test set.
- We also calculate prediction probabilities, which will be used for the ROC curve.
- Model Evaluation:
- Accuracy score is calculated to give an overall performance metric.
- A detailed classification report is printed, showing precision, recall, and F1-score for each class.
- A confusion matrix is visualized using a heatmap, providing a clear view of true positives, true negatives, false positives, and false negatives.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in the model.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- This helps to ensure that the model's performance is consistent and not overly dependent on a particular train-test split.
- ROC Curve:
- The Receiver Operating Characteristic (ROC) curve is plotted.
- The Area Under the Curve (AUC) is calculated, providing a single score that summarizes the model's performance across all possible classification thresholds.
This comprehensive approach goes beyond merely training the model—it provides a thorough evaluation of its performance. The visualizations (confusion matrix, feature importance, and ROC curve) offer intuitive insights into the model's behavior. Additionally, the cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
Example: Training a Linear Regression Model for CLTV Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y_cltv = df['CLTV']
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split for CLTV
X_train_cltv, X_test_cltv, y_train_cltv, y_test_cltv = train_test_split(X_scaled, y_cltv, test_size=0.3, random_state=42)
# Train linear regression model
lin_reg = LinearRegression()
lin_reg.fit(X_train_cltv, y_train_cltv)
# Predictions and evaluation
y_pred_cltv = lin_reg.predict(X_test_cltv)
mse = mean_squared_error(y_test_cltv, y_pred_cltv)
r2 = r2_score(y_test_cltv, y_pred_cltv)
print("Mean Squared Error:", mse)
print("R-squared Score:", r2)
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(lin_reg.coef_)})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance for CLTV Prediction')
plt.show()
# Residual plot
residuals = y_test_cltv - y_pred_cltv
plt.figure(figsize=(10, 6))
plt.scatter(y_pred_cltv, residuals)
plt.xlabel('Predicted CLTV')
plt.ylabel('Residuals')
plt.title('Residual Plot')
plt.axhline(y=0, color='r', linestyle='--')
plt.show()
# Cross-validation
cv_scores = cross_val_score(lin_reg, X_scaled, y_cltv, cv=5, scoring='neg_mean_squared_error')
cv_rmse = np.sqrt(-cv_scores)
print("\nCross-validation RMSE scores:", cv_rmse)
print("Mean CV RMSE score:", np.mean(cv_rmse))
# Actual vs Predicted plot
plt.figure(figsize=(10, 6))
plt.scatter(y_test_cltv, y_pred_cltv, alpha=0.5)
plt.plot([y_test_cltv.min(), y_test_cltv.max()], [y_test_cltv.min(), y_test_cltv.max()], 'r--', lw=2)
plt.xlabel('Actual CLTV')
plt.ylabel('Predicted CLTV')
plt.title('Actual vs Predicted CLTV')
plt.show()This code example provides a comprehensive approach to training and evaluating a Linear Regression model for Customer Lifetime Value (CLTV) prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select relevant features for CLTV prediction.
- Features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- The data is split into training and testing sets using train_test_split.
- A LinearRegression model is initialized and trained on the training data.
- Predictions and Evaluation:
- The model makes predictions on the test set.
- Mean Squared Error (MSE) is calculated to quantify the model's prediction error.
- R-squared score is computed to measure the proportion of variance in the target variable that is predictable from the features.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in predicting CLTV.
- Residual Analysis:
- A residual plot is created to visualize the difference between actual and predicted values.
- This helps identify any patterns in the model's errors and assess if the linear regression assumptions are met.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- Root Mean Squared Error (RMSE) is used as the evaluation metric for cross-validation.
- Actual vs Predicted Plot:
- A scatter plot is created to compare actual CLTV values against predicted values.
- This visual aid helps in understanding how well the model's predictions align with actual values.
This comprehensive approach not only trains the model but also provides a thorough evaluation of its performance. The visualizations (feature importance, residual plot, and actual vs predicted plot) offer intuitive insights into the model's behavior and performance. The cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
By implementing these additional evaluation techniques and visualizations, we gain a deeper understanding of the model's strengths and limitations in predicting Customer Lifetime Value. This information can be invaluable for refining the model, selecting features, and making data-driven decisions in customer relationship management strategies.
2.2.4 Key Takeaways and Their Implications
- Feature engineering enhances predictive accuracy by creating features that capture underlying patterns and trends. This process involves transforming raw data into meaningful representations that algorithms can better interpret, leading to more robust and accurate models.
- For classification tasks like churn prediction, features such as Recency, Frequency, and Purchase Trend provide crucial insights into customer loyalty and engagement. These metrics help identify at-risk customers, allowing businesses to implement targeted retention strategies.
- In regression tasks like CLTV prediction, features capturing spending habits and behavior over time, such as Monetary Value and Purchase Trend, significantly improve the model's ability to predict lifetime value. This enables businesses to allocate resources more effectively and personalize customer experiences.
- The selection of appropriate features is context-dependent and requires domain expertise. For instance, in healthcare, features like appointment frequency and treatment adherence might be more relevant for predicting patient outcomes.
- Feature importance analysis, as demonstrated in the code examples, provides valuable insights into which factors most significantly influence the target variable. This information can guide business decisions and strategy formulation.
- Cross-validation and residual analysis are crucial steps in evaluating model performance and identifying potential areas for improvement in feature engineering or model selection.
2.2 Feature Engineering for Classification and Regression Models
Feature engineering for classification and regression models is a critical process that enhances predictive accuracy by creating features that capture underlying patterns in the data. Unlike unsupervised learning techniques such as clustering or exploratory analysis, classification and regression models rely on labeled data to predict a specific target variable. This approach is essential whether the goal is to classify customers by loyalty level, predict house prices, or forecast customer lifetime value.
The process of feature engineering involves several key strategies:
- Feature Creation: Developing new features that encapsulate relevant information from existing data. For example, in a retail context, creating a "purchase frequency" feature from transaction data.
- Feature Transformation: Modifying existing features to better represent the underlying relationships. This might include logarithmic transformations for skewed data or encoding categorical variables.
- Feature Selection: Identifying the most relevant features that contribute significantly to the predictive power of the model, while avoiding overfitting.
These strategies are applicable to both classification models, which predict discrete categories (such as customer churn), and regression models, which predict continuous values (like house prices or customer lifetime value).
To illustrate these concepts, we'll explore a practical example using a retail dataset. Our focus will be on predicting Customer Lifetime Value (CLTV), a key metric in customer relationship management. This example will demonstrate how carefully engineered features can significantly improve the accuracy and interpretability of predictive models in real-world business scenarios.
2.2.1 Step 1: Data Preparation and Understanding
Before diving into feature engineering, it's crucial to thoroughly understand the dataset and evaluate the available variables. This initial step lays the foundation for creating meaningful features that can significantly enhance the predictive power of our models. Let's begin by loading our dataset and examining its structure and contents.
In this case, we're dealing with a retail dataset that contains valuable information about customer transactions. Our primary objectives are twofold:
- Predicting Customer Lifetime Value (CLTV): This is a regression task where we aim to estimate the total value a customer will bring to the business over their entire relationship.
- Predicting Churn: This is a binary classification task where we seek to identify customers who are likely to stop doing business with us.
By carefully analyzing the available variables, we can identify potential predictors that might be particularly useful for these tasks. For instance, transaction history, purchase frequency, and average order value could all provide valuable insights into both CLTV and churn probability.
As we proceed with our analysis, we'll look for patterns and relationships within the data that can inform our feature engineering process. This might involve exploring correlations between variables, identifying outliers or anomalies, and considering domain-specific knowledge about customer behavior in the retail sector.
The goal of this initial exploration is to gain a comprehensive understanding of our data, which will guide us in creating sophisticated, meaningful features that capture the underlying dynamics of customer behavior and value. This foundational work is essential for building robust predictive models that can drive actionable insights and inform strategic decision-making in customer relationship management.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the retail dataset
df = pd.read_csv('retail_cltv_data.csv')
# Display basic information and first few rows
print("Dataset Information:")
print(df.info())
print("\nFirst Few Rows of Data:")
print(df.head())
# Basic statistical summary
print("\nStatistical Summary:")
print(df.describe())
# Check for missing values
print("\nMissing Values:")
print(df.isnull().sum())
# Unique values in categorical columns
categorical_columns = df.select_dtypes(include=['object']).columns
for col in categorical_columns:
print(f"\nUnique values in {col}:")
print(df[col].value_counts())
# Visualize the distribution of a numerical column (e.g., 'Total Spend')
plt.figure(figsize=(10, 6))
sns.histplot(df['Total Spend'], kde=True)
plt.title('Distribution of Total Spend')
plt.xlabel('Total Spend')
plt.ylabel('Count')
plt.show()
# Correlation matrix for numerical columns
numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns
correlation_matrix = df[numerical_columns].corr()
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Numerical Features')
plt.show()Let's break down this code example:
- Import statements:
- We import pandas for data manipulation, matplotlib.pyplot for basic plotting, and seaborn for more advanced statistical visualizations.
- Data Loading:
- The retail dataset is loaded from a CSV file into a pandas DataFrame.
- Basic Information Display:
- df.info() provides an overview of the DataFrame, including column names, data types, and non-null counts.
- df.head() displays the first few rows of the DataFrame.
- Statistical Summary:
- df.describe() generates descriptive statistics for numerical columns, including count, mean, standard deviation, min, max, and quartiles.
- Missing Value Check:
- df.isnull().sum() calculates the number of missing values in each column.
- Categorical Data Analysis:
- We identify categorical columns and display the value counts for each unique category.
- Numerical Data Visualization:
- A histogram is created for the 'Total Spend' column to visualize its distribution.
- The use of seaborn's histplot with kde=True adds a kernel density estimate curve.
- Correlation Analysis:
- A correlation matrix is computed for all numerical columns.
- The matrix is visualized using a heatmap, which helps identify relationships between variables.
This code offers a thorough initial data exploration, examining data types, missing values, numerical data distribution, and feature correlations. Such insights are essential for grasping the dataset's nuances before diving into feature engineering and model development.
2.2.2 Step 2: Creating Predictive Features
Once we have a solid grasp of the dataset, we can embark on the crucial process of feature engineering. This involves creating new features or transforming existing ones to reveal patterns and relationships that align with our target variable, whether it's for a classification or regression task. The goal is to extract meaningful information from the raw data that can enhance the predictive power of our models.
For classification problems, such as predicting customer churn, we might focus on features that capture customer behavior and engagement levels. These could include metrics like the frequency of purchases, the recency of the last interaction, or changes in spending patterns over time.
In regression tasks, like estimating Customer Lifetime Value (CLTV), we might engineer features that reflect long-term customer value. This could involve calculating average order values, identifying seasonal purchasing trends, or developing composite scores that combine multiple aspects of customer behavior.
The art of feature engineering lies in combining domain expertise with data-driven insights to create variables that are not just statistically significant, but also interpretable and actionable from a business perspective. As we proceed, we'll explore specific techniques and examples of how to craft these powerful predictive features.
Feature 1: Recency
Recency measures the time elapsed since a customer's most recent purchase. This metric is a powerful indicator of customer engagement and plays a crucial role in both Customer Lifetime Value (CLTV) prediction and churn classification models. Recent purchases often signal active engagement with a brand, suggesting a higher likelihood of customer loyalty and increased value.
In the context of CLTV prediction, recency can help identify high-value customers who consistently make purchases. These customers are likely to continue their buying behavior, potentially leading to higher lifetime value. Conversely, customers with high recency (i.e., a long time since their last purchase) might be at risk of churning, which could negatively impact their projected CLTV.
For churn classification, recency serves as a key predictor. Customers who have made recent purchases are generally less likely to churn, as their engagement with the brand is still active. On the other hand, those with high recency might be showing signs of disengagement, making them more susceptible to churn.
It's important to note that the interpretation of recency can vary across industries and business models. For instance, in a subscription-based service, high recency might be expected and not necessarily indicative of churn risk. Therefore, recency should always be considered in conjunction with other relevant features and within the specific context of the business to derive the most accurate insights for CLTV prediction and churn classification.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Recency
most_recent_date = df['PurchaseDate'].max()
df['Recency'] = (most_recent_date - df['PurchaseDate']).dt.days
# Calculate the last purchase date per customer
recency_df = df.groupby('CustomerID')['Recency'].min().reset_index()
# Merge Recency back to main dataset
df = df.merge(recency_df, on='CustomerID', suffixes=('', '_Overall'))
# Display the first few rows with the new Recency feature
print("\nData with Recency Feature:")
print(df[['CustomerID', 'PurchaseDate', 'Recency_Overall']].head())
# Visualize the distribution of Recency
plt.figure(figsize=(10, 6))
sns.histplot(df['Recency_Overall'], kde=True)
plt.title('Distribution of Customer Recency')
plt.xlabel('Recency (days)')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_recency = df['Recency_Overall'].mean()
median_recency = df['Recency_Overall'].median()
max_recency = df['Recency_Overall'].max()
print(f"\nAverage Recency: {avg_recency:.2f} days")
print(f"Median Recency: {median_recency:.2f} days")
print(f"Maximum Recency: {max_recency:.2f} days")
# Identify customers with high recency (potential churn risk)
high_recency_threshold = df['Recency_Overall'].quantile(0.75) # 75th percentile
high_recency_customers = df[df['Recency_Overall'] > high_recency_threshold]
print(f"\nNumber of customers with high recency (potential churn risk): {len(high_recency_customers)}")
# Correlation between Recency and other features (if available)
if 'TotalSpend' in df.columns:
correlation = df['Recency_Overall'].corr(df['TotalSpend'])
print(f"\nCorrelation between Recency and Total Spend: {correlation:.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_recency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_recency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code example offers a comprehensive approach to calculating and analyzing the Recency feature. Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We start by importing necessary libraries and loading the dataset.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Recency Calculation:
- Recency is calculated as the number of days between the most recent date in the dataset and each purchase date.
- We then find the minimum recency for each customer, representing their most recent purchase.
- Data Visualization:
- A histogram is created to visualize the distribution of customer recency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum recency values.
- These statistics provide insights into overall customer engagement levels.
- Customer Segmentation:
- Customers with high recency (above the 75th percentile) are identified as potential churn risks.
- This segmentation can be used for targeted retention strategies.
- Feature Correlation:
- If a 'TotalSpend' column is available, we calculate its correlation with Recency.
- This helps understand the relationship between customer spending and engagement.
- Data Persistence:
- The updated dataset with the new Recency feature is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Recency feature but also provides valuable insights into customer behavior, potential churn risks, and the relationship between recency and other important metrics. These insights can be crucial for developing effective customer retention strategies and improving predictive models for both classification (churn prediction) and regression (CLTV estimation) tasks.
Feature 2: Monetary Value
Monetary Value represents the average spending per transaction, serving as a key indicator of customer behavior and potential value. This metric offers valuable insights into customer loyalty, spending capacity, and the risk of churn. For Customer Lifetime Value (CLTV) prediction, higher monetary values often correlate with more profitable customers, as they demonstrate a willingness to invest more in each interaction with the brand.
The significance of Monetary Value extends beyond simple financial metrics. It can reveal customer preferences, price sensitivity, and even the effectiveness of upselling or cross-selling strategies. For instance, customers with consistently high monetary values might be more receptive to premium products or services, presenting opportunities for targeted marketing campaigns.
In the context of churn prediction, fluctuations in Monetary Value over time can be particularly telling. A sudden decrease might signal dissatisfaction or a shift to competitors, while steady or increasing values suggest sustained engagement. By combining Monetary Value with other features like Recency and Frequency, businesses can develop a more nuanced understanding of customer behavior, enabling more accurate predictions and personalized retention strategies.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Calculate Monetary Value as the average purchase value for each customer
monetary_value_df = df.groupby('CustomerID')['Total Spend'].agg(['mean', 'sum', 'count']).reset_index()
monetary_value_df.columns = ['CustomerID', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']
# Merge the monetary value features back to main dataset
df = df.merge(monetary_value_df, on='CustomerID')
# Display the first few rows with the new Monetary Value features
print("\nData with Monetary Value Features:")
print(df[['CustomerID', 'Total Spend', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].head())
# Visualize the distribution of Average Purchase Value
plt.figure(figsize=(10, 6))
sns.histplot(df['AvgPurchaseValue'], kde=True)
plt.title('Distribution of Average Purchase Value')
plt.xlabel('Average Purchase Value')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_purchase_value = df['AvgPurchaseValue'].mean()
median_purchase_value = df['AvgPurchaseValue'].median()
max_purchase_value = df['AvgPurchaseValue'].max()
print(f"\nAverage Purchase Value: ${avg_purchase_value:.2f}")
print(f"Median Purchase Value: ${median_purchase_value:.2f}")
print(f"Maximum Purchase Value: ${max_purchase_value:.2f}")
# Identify high-value customers (top 20%)
high_value_threshold = df['AvgPurchaseValue'].quantile(0.8)
high_value_customers = df[df['AvgPurchaseValue'] > high_value_threshold]
print(f"\nNumber of high-value customers: {len(high_value_customers)}")
# Correlation between Monetary Value and other features
correlation_matrix = df[['AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Monetary Value Features')
plt.show()
# Save the updated dataset
df.to_csv('retail_data_with_monetary_value.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_monetary_value.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code snippet demonstrates a method for calculating and analyzing the Monetary Value feature. Let's examine its key components and their roles:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- Monetary Value Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Three metrics are calculated: mean (AvgPurchaseValue), sum (TotalSpend), and count (PurchaseCount) of 'Total Spend'.
- These features provide a more comprehensive view of customer spending behavior.
- Data Merging:
- The new monetary value features are merged back into the main dataset.
- Data Visualization:
- A histogram is created to visualize the distribution of Average Purchase Value.
- This helps identify patterns in customer spending and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum purchase values.
- These statistics provide insights into overall customer spending patterns.
- Customer Segmentation:
- High-value customers (top 20% based on Average Purchase Value) are identified.
- This segmentation can be used for targeted marketing or loyalty programs.
- Feature Correlation:
- A correlation matrix is computed for the monetary value features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer spending.
- Data Persistence:
- The updated dataset with the new monetary value features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Monetary Value feature but also offers valuable insights into customer spending patterns, identifies high-value clients, and explores relationships between various monetary metrics. These insights are crucial for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 3: Frequency
Frequency is a measure of how often a customer makes purchases within a given timeframe. This metric provides valuable insights into customer behavior and loyalty. Frequent purchases often indicate high engagement, making it a valuable feature for both Customer Lifetime Value (CLTV) prediction and churn classification.
In the context of CLTV prediction, frequency can help identify customers who are likely to generate higher long-term value. Customers with higher purchase frequencies tend to have a stronger relationship with the brand, potentially leading to increased lifetime value. For churn classification, a decline in purchase frequency can be an early warning sign of potential customer disengagement or impending churn.
Moreover, frequency can be analyzed in conjunction with other features to gain deeper insights. For instance, combining frequency with monetary value can help identify high-value, frequent customers who may be prime candidates for loyalty programs or personalized marketing campaigns. Similarly, analyzing the relationship between frequency and recency can reveal patterns in customer behavior, such as seasonal purchasing habits or the effectiveness of retention strategies.
When engineering this feature, it's important to consider the appropriate time frame for calculation, as this can vary depending on the business model and product lifecycle. For some businesses, weekly frequency might be relevant, while for others, monthly or quarterly frequencies could be more insightful. Additionally, tracking changes in frequency over time can provide dynamic insights into evolving customer behavior and market trends.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Frequency by counting transactions per customer
frequency_df = df.groupby('CustomerID').agg({
'PurchaseDate': 'count',
'Total Spend': 'sum'
}).reset_index()
frequency_df.columns = ['CustomerID', 'Frequency', 'TotalSpend']
# Calculate average time between purchases
df_sorted = df.sort_values(['CustomerID', 'PurchaseDate'])
df_sorted['PrevPurchaseDate'] = df_sorted.groupby('CustomerID')['PurchaseDate'].shift(1)
df_sorted['DaysBetweenPurchases'] = (df_sorted['PurchaseDate'] - df_sorted['PrevPurchaseDate']).dt.days
avg_time_between_purchases = df_sorted.groupby('CustomerID')['DaysBetweenPurchases'].mean().reset_index()
avg_time_between_purchases.columns = ['CustomerID', 'AvgDaysBetweenPurchases']
# Merge frequency features back to the main dataset
df = df.merge(frequency_df, on='CustomerID')
df = df.merge(avg_time_between_purchases, on='CustomerID')
# Calculate additional metrics
df['AvgPurchaseValue'] = df['TotalSpend'] / df['Frequency']
print("\nData with Frequency Features:")
print(df[['CustomerID', 'PurchaseDate', 'Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].head())
# Visualize the distribution of Frequency
plt.figure(figsize=(10, 6))
sns.histplot(df['Frequency'], kde=True)
plt.title('Distribution of Purchase Frequency')
plt.xlabel('Number of Purchases')
plt.ylabel('Count of Customers')
plt.show()
# Analyze correlation between Frequency and other metrics
correlation_matrix = df[['Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Frequency-related Features')
plt.show()
# Identify high-frequency customers (top 20%)
high_frequency_threshold = df['Frequency'].quantile(0.8)
high_frequency_customers = df[df['Frequency'] > high_frequency_threshold]
print(f"\nNumber of high-frequency customers: {len(high_frequency_customers)}")
print(f"Average spend of high-frequency customers: ${high_frequency_customers['TotalSpend'].mean():.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_frequency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_frequency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Frequency Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Two metrics are calculated: count of purchases (Frequency) and sum of Total Spend.
- Time Between Purchases:
- The data is sorted by CustomerID and PurchaseDate.
- We calculate the time difference between consecutive purchases for each customer.
- The average time between purchases is computed for each customer.
- Data Merging:
- The new frequency features are merged back into the main dataset.
- Additional Metrics:
- Average Purchase Value is calculated by dividing Total Spend by Frequency.
- Data Visualization:
- A histogram is created to visualize the distribution of Purchase Frequency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Correlation Analysis:
- A correlation matrix is computed for the frequency-related features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer behavior.
- Customer Segmentation:
- High-frequency customers (top 20% based on Frequency) are identified.
- We calculate and display the number of high-frequency customers and their average spend.
- This segmentation can be used for targeted marketing or loyalty programs.
- Data Persistence:
- The updated dataset with the new frequency features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach calculates the Frequency feature and offers valuable insights into customer behavior. It identifies high-frequency clients and explores relationships between various frequency-related metrics. These insights are essential for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 4: Purchase Trend
For classification or regression models, Purchase Trend is a crucial feature that captures the dynamic nature of customer behavior over time. This feature quantifies how a customer's spending patterns have evolved, providing valuable insights into their engagement and loyalty levels. Positive trends, characterized by increasing purchase frequency or value, often suggest growing customer satisfaction and a strengthening relationship with the brand. These customers may be prime candidates for upselling or cross-selling initiatives.
Conversely, negative trends could signal potential issues such as customer dissatisfaction, increased competition, or changing needs. Such trends might manifest as decreasing purchase frequency, lower transaction values, or longer intervals between purchases. Identifying these negative trends early allows businesses to implement targeted retention strategies, potentially preventing churn before it occurs.
The Purchase Trend feature can be particularly powerful when combined with other metrics like Recency and Frequency. For instance, a customer with high frequency but a negative purchase trend might require different intervention strategies compared to a customer with low frequency but a positive trend. By incorporating this temporal dimension into predictive models, businesses can develop more nuanced and effective customer segmentation strategies, personalized marketing campaigns, and proactive customer service initiatives.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate average spend over time by grouping data by month and CustomerID
df['PurchaseMonth'] = df['PurchaseDate'].dt.to_period('M')
monthly_spend = df.groupby(['CustomerID', 'PurchaseMonth'])['Total Spend'].sum().reset_index()
# Calculate trend as the slope of spending over time for each customer
def calculate_trend(customer_df):
x = np.arange(len(customer_df))
y = customer_df['Total Spend'].values
if len(x) > 1:
return np.polyfit(x, y, 1)[0] # Linear trend slope
return 0
# Apply trend calculation
trend_df = monthly_spend.groupby('CustomerID').apply(calculate_trend).reset_index(name='PurchaseTrend')
# Merge trend feature back to main dataset
df = df.merge(trend_df, on='CustomerID')
print("\nData with Purchase Trend Feature:")
print(df[['CustomerID', 'PurchaseMonth', 'Total Spend', 'PurchaseTrend']].head())
# Visualize Purchase Trend distribution
plt.figure(figsize=(10, 6))
sns.histplot(df['PurchaseTrend'], kde=True)
plt.title('Distribution of Purchase Trends')
plt.xlabel('Purchase Trend (Slope)')
plt.ylabel('Count of Customers')
plt.show()
# Identify customers with positive and negative trends
positive_trend = df[df['PurchaseTrend'] > 0]
negative_trend = df[df['PurchaseTrend'] < 0]
print(f"\nCustomers with positive trend: {len(positive_trend['CustomerID'].unique())}")
print(f"Customers with negative trend: {len(negative_trend['CustomerID'].unique())}")
# Calculate correlation between Purchase Trend and other features
correlation = df[['PurchaseTrend', 'Total Spend', 'Frequency']].corr()
plt.figure(figsize=(8, 6))
sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Correlation between Purchase Trend and Other Features')
plt.show()
# Example: Using Purchase Trend for customer segmentation
df['TrendCategory'] = pd.cut(df['PurchaseTrend'],
bins=[-np.inf, -10, 0, 10, np.inf],
labels=['Strong Negative', 'Slight Negative', 'Slight Positive', 'Strong Positive'])
trend_segment = df.groupby('TrendCategory').agg({
'CustomerID': 'nunique',
'Total Spend': 'mean',
'Frequency': 'mean'
}).reset_index()
print("\nCustomer Segmentation based on Purchase Trend:")
print(trend_segment)
# Save the updated dataset with the new feature
df.to_csv('retail_data_with_trend.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_trend.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down this comprehensive code example:
- Data Loading and Preprocessing:
- We import necessary libraries: pandas for data manipulation, numpy for numerical operations, and matplotlib/seaborn for visualization.
- The dataset is loaded from a CSV file and the 'PurchaseDate' column is converted to datetime format.
- Calculating Purchase Trend:
- We group the data by customer and month to get monthly spending patterns.
- A 'calculate_trend' function is defined to compute the linear trend (slope) of spending over time for each customer.
- This trend is then calculated for each customer and merged back into the main dataset.
- Visualizing Purchase Trend:
- A histogram is created to show the distribution of Purchase Trends across all customers.
- This visualization helps identify the overall trend patterns in the customer base.
- Analyzing Positive and Negative Trends:
- We separate customers with positive and negative trends and count them.
- This provides a quick overview of how many customers are increasing or decreasing their spending over time.
- Correlation Analysis:
- We calculate and visualize the correlation between Purchase Trend and other features like Total Spend and Frequency.
- This helps understand how the trend relates to other important customer metrics.
- Customer Segmentation:
- We categorize customers based on their Purchase Trend into four groups: Strong Negative, Slight Negative, Slight Positive, and Strong Positive.
- For each segment, we calculate the number of customers, average total spend, and average purchase frequency.
- This segmentation can be used for targeted marketing strategies or to identify at-risk customers.
- Data Persistence:
- The updated dataset with the new Purchase Trend feature is saved to a new CSV file.
- This allows for easy access in future analyses or model training.
This code offers a thorough analysis of the Purchase Trend feature, showcasing its distribution, correlations with other features, and application in customer segmentation. These insights prove valuable for both classification tasks—such as churn prediction—and regression tasks like Customer Lifetime Value (CLTV) estimation.
2.2.3 Using Feature Engineering for Model Training
Once these features are engineered, they serve as the foundation for training powerful predictive models. In this section, we'll explore how to leverage these features for both classification and regression tasks, specifically focusing on churn prediction and Customer Lifetime Value (CLTV) estimation.
For churn prediction, a classification task, we'll employ a Logistic Regression model. This model excels at predicting binary outcomes, making it ideal for determining whether a customer is likely to churn or not. The features we've created, such as Recency, Frequency, and Purchase Trend, provide crucial insights into customer behavior that can signal potential churn.
On the other hand, for CLTV prediction, a regression task, we'll utilize a Linear Regression model. This model is well-suited for predicting continuous values, allowing us to estimate the future value a customer may bring to the business. Features like Monetary Value and Purchase Trend are particularly valuable here, as they capture spending patterns and long-term customer behavior.
By incorporating these engineered features into our models, we significantly enhance their predictive power. This allows businesses to make data-driven decisions, implement targeted retention strategies, and optimize customer engagement efforts. Let's dive into the practical implementation of these models using our newly created features.
Example: Training a Logistic Regression Model for Churn Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data_with_features.csv')
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y = df['Churn'] # Target variable for churn
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)
# Train logistic regression model
log_reg = LogisticRegression(random_state=42)
log_reg.fit(X_train, y_train)
# Predictions
y_pred = log_reg.predict(X_test)
y_pred_proba = log_reg.predict_proba(X_test)[:, 1]
# Model evaluation
print("Model Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:")
print(classification_report(y_test, y_pred))
# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(log_reg.coef_[0])})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance')
plt.show()
# Cross-validation
cv_scores = cross_val_score(log_reg, X_scaled, y, cv=5)
print("\nCross-validation scores:", cv_scores)
print("Mean CV score:", np.mean(cv_scores))
# ROC Curve
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)
roc_auc = auc(fpr, tpr)
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')
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 (ROC) Curve')
plt.legend(loc="lower right")
plt.show()This code example demonstrates a comprehensive approach to training and evaluating a Logistic Regression model for churn prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select the relevant features and target variable.
- The features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- We use train_test_split to divide the data into training and testing sets.
- A LogisticRegression model is initialized and trained on the training data.
- Predictions:
- The model makes predictions on the test set.
- We also calculate prediction probabilities, which will be used for the ROC curve.
- Model Evaluation:
- Accuracy score is calculated to give an overall performance metric.
- A detailed classification report is printed, showing precision, recall, and F1-score for each class.
- A confusion matrix is visualized using a heatmap, providing a clear view of true positives, true negatives, false positives, and false negatives.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in the model.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- This helps to ensure that the model's performance is consistent and not overly dependent on a particular train-test split.
- ROC Curve:
- The Receiver Operating Characteristic (ROC) curve is plotted.
- The Area Under the Curve (AUC) is calculated, providing a single score that summarizes the model's performance across all possible classification thresholds.
This comprehensive approach goes beyond merely training the model—it provides a thorough evaluation of its performance. The visualizations (confusion matrix, feature importance, and ROC curve) offer intuitive insights into the model's behavior. Additionally, the cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
Example: Training a Linear Regression Model for CLTV Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y_cltv = df['CLTV']
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split for CLTV
X_train_cltv, X_test_cltv, y_train_cltv, y_test_cltv = train_test_split(X_scaled, y_cltv, test_size=0.3, random_state=42)
# Train linear regression model
lin_reg = LinearRegression()
lin_reg.fit(X_train_cltv, y_train_cltv)
# Predictions and evaluation
y_pred_cltv = lin_reg.predict(X_test_cltv)
mse = mean_squared_error(y_test_cltv, y_pred_cltv)
r2 = r2_score(y_test_cltv, y_pred_cltv)
print("Mean Squared Error:", mse)
print("R-squared Score:", r2)
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(lin_reg.coef_)})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance for CLTV Prediction')
plt.show()
# Residual plot
residuals = y_test_cltv - y_pred_cltv
plt.figure(figsize=(10, 6))
plt.scatter(y_pred_cltv, residuals)
plt.xlabel('Predicted CLTV')
plt.ylabel('Residuals')
plt.title('Residual Plot')
plt.axhline(y=0, color='r', linestyle='--')
plt.show()
# Cross-validation
cv_scores = cross_val_score(lin_reg, X_scaled, y_cltv, cv=5, scoring='neg_mean_squared_error')
cv_rmse = np.sqrt(-cv_scores)
print("\nCross-validation RMSE scores:", cv_rmse)
print("Mean CV RMSE score:", np.mean(cv_rmse))
# Actual vs Predicted plot
plt.figure(figsize=(10, 6))
plt.scatter(y_test_cltv, y_pred_cltv, alpha=0.5)
plt.plot([y_test_cltv.min(), y_test_cltv.max()], [y_test_cltv.min(), y_test_cltv.max()], 'r--', lw=2)
plt.xlabel('Actual CLTV')
plt.ylabel('Predicted CLTV')
plt.title('Actual vs Predicted CLTV')
plt.show()This code example provides a comprehensive approach to training and evaluating a Linear Regression model for Customer Lifetime Value (CLTV) prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select relevant features for CLTV prediction.
- Features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- The data is split into training and testing sets using train_test_split.
- A LinearRegression model is initialized and trained on the training data.
- Predictions and Evaluation:
- The model makes predictions on the test set.
- Mean Squared Error (MSE) is calculated to quantify the model's prediction error.
- R-squared score is computed to measure the proportion of variance in the target variable that is predictable from the features.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in predicting CLTV.
- Residual Analysis:
- A residual plot is created to visualize the difference between actual and predicted values.
- This helps identify any patterns in the model's errors and assess if the linear regression assumptions are met.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- Root Mean Squared Error (RMSE) is used as the evaluation metric for cross-validation.
- Actual vs Predicted Plot:
- A scatter plot is created to compare actual CLTV values against predicted values.
- This visual aid helps in understanding how well the model's predictions align with actual values.
This comprehensive approach not only trains the model but also provides a thorough evaluation of its performance. The visualizations (feature importance, residual plot, and actual vs predicted plot) offer intuitive insights into the model's behavior and performance. The cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
By implementing these additional evaluation techniques and visualizations, we gain a deeper understanding of the model's strengths and limitations in predicting Customer Lifetime Value. This information can be invaluable for refining the model, selecting features, and making data-driven decisions in customer relationship management strategies.
2.2.4 Key Takeaways and Their Implications
- Feature engineering enhances predictive accuracy by creating features that capture underlying patterns and trends. This process involves transforming raw data into meaningful representations that algorithms can better interpret, leading to more robust and accurate models.
- For classification tasks like churn prediction, features such as Recency, Frequency, and Purchase Trend provide crucial insights into customer loyalty and engagement. These metrics help identify at-risk customers, allowing businesses to implement targeted retention strategies.
- In regression tasks like CLTV prediction, features capturing spending habits and behavior over time, such as Monetary Value and Purchase Trend, significantly improve the model's ability to predict lifetime value. This enables businesses to allocate resources more effectively and personalize customer experiences.
- The selection of appropriate features is context-dependent and requires domain expertise. For instance, in healthcare, features like appointment frequency and treatment adherence might be more relevant for predicting patient outcomes.
- Feature importance analysis, as demonstrated in the code examples, provides valuable insights into which factors most significantly influence the target variable. This information can guide business decisions and strategy formulation.
- Cross-validation and residual analysis are crucial steps in evaluating model performance and identifying potential areas for improvement in feature engineering or model selection.
2.2 Feature Engineering for Classification and Regression Models
Feature engineering for classification and regression models is a critical process that enhances predictive accuracy by creating features that capture underlying patterns in the data. Unlike unsupervised learning techniques such as clustering or exploratory analysis, classification and regression models rely on labeled data to predict a specific target variable. This approach is essential whether the goal is to classify customers by loyalty level, predict house prices, or forecast customer lifetime value.
The process of feature engineering involves several key strategies:
- Feature Creation: Developing new features that encapsulate relevant information from existing data. For example, in a retail context, creating a "purchase frequency" feature from transaction data.
- Feature Transformation: Modifying existing features to better represent the underlying relationships. This might include logarithmic transformations for skewed data or encoding categorical variables.
- Feature Selection: Identifying the most relevant features that contribute significantly to the predictive power of the model, while avoiding overfitting.
These strategies are applicable to both classification models, which predict discrete categories (such as customer churn), and regression models, which predict continuous values (like house prices or customer lifetime value).
To illustrate these concepts, we'll explore a practical example using a retail dataset. Our focus will be on predicting Customer Lifetime Value (CLTV), a key metric in customer relationship management. This example will demonstrate how carefully engineered features can significantly improve the accuracy and interpretability of predictive models in real-world business scenarios.
2.2.1 Step 1: Data Preparation and Understanding
Before diving into feature engineering, it's crucial to thoroughly understand the dataset and evaluate the available variables. This initial step lays the foundation for creating meaningful features that can significantly enhance the predictive power of our models. Let's begin by loading our dataset and examining its structure and contents.
In this case, we're dealing with a retail dataset that contains valuable information about customer transactions. Our primary objectives are twofold:
- Predicting Customer Lifetime Value (CLTV): This is a regression task where we aim to estimate the total value a customer will bring to the business over their entire relationship.
- Predicting Churn: This is a binary classification task where we seek to identify customers who are likely to stop doing business with us.
By carefully analyzing the available variables, we can identify potential predictors that might be particularly useful for these tasks. For instance, transaction history, purchase frequency, and average order value could all provide valuable insights into both CLTV and churn probability.
As we proceed with our analysis, we'll look for patterns and relationships within the data that can inform our feature engineering process. This might involve exploring correlations between variables, identifying outliers or anomalies, and considering domain-specific knowledge about customer behavior in the retail sector.
The goal of this initial exploration is to gain a comprehensive understanding of our data, which will guide us in creating sophisticated, meaningful features that capture the underlying dynamics of customer behavior and value. This foundational work is essential for building robust predictive models that can drive actionable insights and inform strategic decision-making in customer relationship management.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the retail dataset
df = pd.read_csv('retail_cltv_data.csv')
# Display basic information and first few rows
print("Dataset Information:")
print(df.info())
print("\nFirst Few Rows of Data:")
print(df.head())
# Basic statistical summary
print("\nStatistical Summary:")
print(df.describe())
# Check for missing values
print("\nMissing Values:")
print(df.isnull().sum())
# Unique values in categorical columns
categorical_columns = df.select_dtypes(include=['object']).columns
for col in categorical_columns:
print(f"\nUnique values in {col}:")
print(df[col].value_counts())
# Visualize the distribution of a numerical column (e.g., 'Total Spend')
plt.figure(figsize=(10, 6))
sns.histplot(df['Total Spend'], kde=True)
plt.title('Distribution of Total Spend')
plt.xlabel('Total Spend')
plt.ylabel('Count')
plt.show()
# Correlation matrix for numerical columns
numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns
correlation_matrix = df[numerical_columns].corr()
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Numerical Features')
plt.show()Let's break down this code example:
- Import statements:
- We import pandas for data manipulation, matplotlib.pyplot for basic plotting, and seaborn for more advanced statistical visualizations.
- Data Loading:
- The retail dataset is loaded from a CSV file into a pandas DataFrame.
- Basic Information Display:
- df.info() provides an overview of the DataFrame, including column names, data types, and non-null counts.
- df.head() displays the first few rows of the DataFrame.
- Statistical Summary:
- df.describe() generates descriptive statistics for numerical columns, including count, mean, standard deviation, min, max, and quartiles.
- Missing Value Check:
- df.isnull().sum() calculates the number of missing values in each column.
- Categorical Data Analysis:
- We identify categorical columns and display the value counts for each unique category.
- Numerical Data Visualization:
- A histogram is created for the 'Total Spend' column to visualize its distribution.
- The use of seaborn's histplot with kde=True adds a kernel density estimate curve.
- Correlation Analysis:
- A correlation matrix is computed for all numerical columns.
- The matrix is visualized using a heatmap, which helps identify relationships between variables.
This code offers a thorough initial data exploration, examining data types, missing values, numerical data distribution, and feature correlations. Such insights are essential for grasping the dataset's nuances before diving into feature engineering and model development.
2.2.2 Step 2: Creating Predictive Features
Once we have a solid grasp of the dataset, we can embark on the crucial process of feature engineering. This involves creating new features or transforming existing ones to reveal patterns and relationships that align with our target variable, whether it's for a classification or regression task. The goal is to extract meaningful information from the raw data that can enhance the predictive power of our models.
For classification problems, such as predicting customer churn, we might focus on features that capture customer behavior and engagement levels. These could include metrics like the frequency of purchases, the recency of the last interaction, or changes in spending patterns over time.
In regression tasks, like estimating Customer Lifetime Value (CLTV), we might engineer features that reflect long-term customer value. This could involve calculating average order values, identifying seasonal purchasing trends, or developing composite scores that combine multiple aspects of customer behavior.
The art of feature engineering lies in combining domain expertise with data-driven insights to create variables that are not just statistically significant, but also interpretable and actionable from a business perspective. As we proceed, we'll explore specific techniques and examples of how to craft these powerful predictive features.
Feature 1: Recency
Recency measures the time elapsed since a customer's most recent purchase. This metric is a powerful indicator of customer engagement and plays a crucial role in both Customer Lifetime Value (CLTV) prediction and churn classification models. Recent purchases often signal active engagement with a brand, suggesting a higher likelihood of customer loyalty and increased value.
In the context of CLTV prediction, recency can help identify high-value customers who consistently make purchases. These customers are likely to continue their buying behavior, potentially leading to higher lifetime value. Conversely, customers with high recency (i.e., a long time since their last purchase) might be at risk of churning, which could negatively impact their projected CLTV.
For churn classification, recency serves as a key predictor. Customers who have made recent purchases are generally less likely to churn, as their engagement with the brand is still active. On the other hand, those with high recency might be showing signs of disengagement, making them more susceptible to churn.
It's important to note that the interpretation of recency can vary across industries and business models. For instance, in a subscription-based service, high recency might be expected and not necessarily indicative of churn risk. Therefore, recency should always be considered in conjunction with other relevant features and within the specific context of the business to derive the most accurate insights for CLTV prediction and churn classification.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Recency
most_recent_date = df['PurchaseDate'].max()
df['Recency'] = (most_recent_date - df['PurchaseDate']).dt.days
# Calculate the last purchase date per customer
recency_df = df.groupby('CustomerID')['Recency'].min().reset_index()
# Merge Recency back to main dataset
df = df.merge(recency_df, on='CustomerID', suffixes=('', '_Overall'))
# Display the first few rows with the new Recency feature
print("\nData with Recency Feature:")
print(df[['CustomerID', 'PurchaseDate', 'Recency_Overall']].head())
# Visualize the distribution of Recency
plt.figure(figsize=(10, 6))
sns.histplot(df['Recency_Overall'], kde=True)
plt.title('Distribution of Customer Recency')
plt.xlabel('Recency (days)')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_recency = df['Recency_Overall'].mean()
median_recency = df['Recency_Overall'].median()
max_recency = df['Recency_Overall'].max()
print(f"\nAverage Recency: {avg_recency:.2f} days")
print(f"Median Recency: {median_recency:.2f} days")
print(f"Maximum Recency: {max_recency:.2f} days")
# Identify customers with high recency (potential churn risk)
high_recency_threshold = df['Recency_Overall'].quantile(0.75) # 75th percentile
high_recency_customers = df[df['Recency_Overall'] > high_recency_threshold]
print(f"\nNumber of customers with high recency (potential churn risk): {len(high_recency_customers)}")
# Correlation between Recency and other features (if available)
if 'TotalSpend' in df.columns:
correlation = df['Recency_Overall'].corr(df['TotalSpend'])
print(f"\nCorrelation between Recency and Total Spend: {correlation:.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_recency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_recency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code example offers a comprehensive approach to calculating and analyzing the Recency feature. Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We start by importing necessary libraries and loading the dataset.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Recency Calculation:
- Recency is calculated as the number of days between the most recent date in the dataset and each purchase date.
- We then find the minimum recency for each customer, representing their most recent purchase.
- Data Visualization:
- A histogram is created to visualize the distribution of customer recency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum recency values.
- These statistics provide insights into overall customer engagement levels.
- Customer Segmentation:
- Customers with high recency (above the 75th percentile) are identified as potential churn risks.
- This segmentation can be used for targeted retention strategies.
- Feature Correlation:
- If a 'TotalSpend' column is available, we calculate its correlation with Recency.
- This helps understand the relationship between customer spending and engagement.
- Data Persistence:
- The updated dataset with the new Recency feature is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Recency feature but also provides valuable insights into customer behavior, potential churn risks, and the relationship between recency and other important metrics. These insights can be crucial for developing effective customer retention strategies and improving predictive models for both classification (churn prediction) and regression (CLTV estimation) tasks.
Feature 2: Monetary Value
Monetary Value represents the average spending per transaction, serving as a key indicator of customer behavior and potential value. This metric offers valuable insights into customer loyalty, spending capacity, and the risk of churn. For Customer Lifetime Value (CLTV) prediction, higher monetary values often correlate with more profitable customers, as they demonstrate a willingness to invest more in each interaction with the brand.
The significance of Monetary Value extends beyond simple financial metrics. It can reveal customer preferences, price sensitivity, and even the effectiveness of upselling or cross-selling strategies. For instance, customers with consistently high monetary values might be more receptive to premium products or services, presenting opportunities for targeted marketing campaigns.
In the context of churn prediction, fluctuations in Monetary Value over time can be particularly telling. A sudden decrease might signal dissatisfaction or a shift to competitors, while steady or increasing values suggest sustained engagement. By combining Monetary Value with other features like Recency and Frequency, businesses can develop a more nuanced understanding of customer behavior, enabling more accurate predictions and personalized retention strategies.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Calculate Monetary Value as the average purchase value for each customer
monetary_value_df = df.groupby('CustomerID')['Total Spend'].agg(['mean', 'sum', 'count']).reset_index()
monetary_value_df.columns = ['CustomerID', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']
# Merge the monetary value features back to main dataset
df = df.merge(monetary_value_df, on='CustomerID')
# Display the first few rows with the new Monetary Value features
print("\nData with Monetary Value Features:")
print(df[['CustomerID', 'Total Spend', 'AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].head())
# Visualize the distribution of Average Purchase Value
plt.figure(figsize=(10, 6))
sns.histplot(df['AvgPurchaseValue'], kde=True)
plt.title('Distribution of Average Purchase Value')
plt.xlabel('Average Purchase Value')
plt.ylabel('Count')
plt.show()
# Calculate additional statistics
avg_purchase_value = df['AvgPurchaseValue'].mean()
median_purchase_value = df['AvgPurchaseValue'].median()
max_purchase_value = df['AvgPurchaseValue'].max()
print(f"\nAverage Purchase Value: ${avg_purchase_value:.2f}")
print(f"Median Purchase Value: ${median_purchase_value:.2f}")
print(f"Maximum Purchase Value: ${max_purchase_value:.2f}")
# Identify high-value customers (top 20%)
high_value_threshold = df['AvgPurchaseValue'].quantile(0.8)
high_value_customers = df[df['AvgPurchaseValue'] > high_value_threshold]
print(f"\nNumber of high-value customers: {len(high_value_customers)}")
# Correlation between Monetary Value and other features
correlation_matrix = df[['AvgPurchaseValue', 'TotalSpend', 'PurchaseCount']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Monetary Value Features')
plt.show()
# Save the updated dataset
df.to_csv('retail_data_with_monetary_value.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_monetary_value.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
This code snippet demonstrates a method for calculating and analyzing the Monetary Value feature. Let's examine its key components and their roles:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- Monetary Value Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Three metrics are calculated: mean (AvgPurchaseValue), sum (TotalSpend), and count (PurchaseCount) of 'Total Spend'.
- These features provide a more comprehensive view of customer spending behavior.
- Data Merging:
- The new monetary value features are merged back into the main dataset.
- Data Visualization:
- A histogram is created to visualize the distribution of Average Purchase Value.
- This helps identify patterns in customer spending and potential segmentation opportunities.
- Statistical Analysis:
- We calculate and display average, median, and maximum purchase values.
- These statistics provide insights into overall customer spending patterns.
- Customer Segmentation:
- High-value customers (top 20% based on Average Purchase Value) are identified.
- This segmentation can be used for targeted marketing or loyalty programs.
- Feature Correlation:
- A correlation matrix is computed for the monetary value features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer spending.
- Data Persistence:
- The updated dataset with the new monetary value features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach not only calculates the Monetary Value feature but also offers valuable insights into customer spending patterns, identifies high-value clients, and explores relationships between various monetary metrics. These insights are crucial for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 3: Frequency
Frequency is a measure of how often a customer makes purchases within a given timeframe. This metric provides valuable insights into customer behavior and loyalty. Frequent purchases often indicate high engagement, making it a valuable feature for both Customer Lifetime Value (CLTV) prediction and churn classification.
In the context of CLTV prediction, frequency can help identify customers who are likely to generate higher long-term value. Customers with higher purchase frequencies tend to have a stronger relationship with the brand, potentially leading to increased lifetime value. For churn classification, a decline in purchase frequency can be an early warning sign of potential customer disengagement or impending churn.
Moreover, frequency can be analyzed in conjunction with other features to gain deeper insights. For instance, combining frequency with monetary value can help identify high-value, frequent customers who may be prime candidates for loyalty programs or personalized marketing campaigns. Similarly, analyzing the relationship between frequency and recency can reveal patterns in customer behavior, such as seasonal purchasing habits or the effectiveness of retention strategies.
When engineering this feature, it's important to consider the appropriate time frame for calculation, as this can vary depending on the business model and product lifecycle. For some businesses, weekly frequency might be relevant, while for others, monthly or quarterly frequencies could be more insightful. Additionally, tracking changes in frequency over time can provide dynamic insights into evolving customer behavior and market trends.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate Frequency by counting transactions per customer
frequency_df = df.groupby('CustomerID').agg({
'PurchaseDate': 'count',
'Total Spend': 'sum'
}).reset_index()
frequency_df.columns = ['CustomerID', 'Frequency', 'TotalSpend']
# Calculate average time between purchases
df_sorted = df.sort_values(['CustomerID', 'PurchaseDate'])
df_sorted['PrevPurchaseDate'] = df_sorted.groupby('CustomerID')['PurchaseDate'].shift(1)
df_sorted['DaysBetweenPurchases'] = (df_sorted['PurchaseDate'] - df_sorted['PrevPurchaseDate']).dt.days
avg_time_between_purchases = df_sorted.groupby('CustomerID')['DaysBetweenPurchases'].mean().reset_index()
avg_time_between_purchases.columns = ['CustomerID', 'AvgDaysBetweenPurchases']
# Merge frequency features back to the main dataset
df = df.merge(frequency_df, on='CustomerID')
df = df.merge(avg_time_between_purchases, on='CustomerID')
# Calculate additional metrics
df['AvgPurchaseValue'] = df['TotalSpend'] / df['Frequency']
print("\nData with Frequency Features:")
print(df[['CustomerID', 'PurchaseDate', 'Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].head())
# Visualize the distribution of Frequency
plt.figure(figsize=(10, 6))
sns.histplot(df['Frequency'], kde=True)
plt.title('Distribution of Purchase Frequency')
plt.xlabel('Number of Purchases')
plt.ylabel('Count of Customers')
plt.show()
# Analyze correlation between Frequency and other metrics
correlation_matrix = df[['Frequency', 'TotalSpend', 'AvgDaysBetweenPurchases', 'AvgPurchaseValue']].corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Frequency-related Features')
plt.show()
# Identify high-frequency customers (top 20%)
high_frequency_threshold = df['Frequency'].quantile(0.8)
high_frequency_customers = df[df['Frequency'] > high_frequency_threshold]
print(f"\nNumber of high-frequency customers: {len(high_frequency_customers)}")
print(f"Average spend of high-frequency customers: ${high_frequency_customers['TotalSpend'].mean():.2f}")
# Save the updated dataset
df.to_csv('retail_data_with_frequency.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_frequency.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down the key components and their functions:
- Data Loading and Initial Processing:
- We import necessary libraries (pandas for data manipulation, matplotlib and seaborn for visualization).
- The dataset is loaded from a CSV file into a pandas DataFrame.
- The 'PurchaseDate' column is converted to datetime format for accurate calculations.
- Frequency Calculation:
- We use the groupby function to aggregate data by CustomerID.
- Two metrics are calculated: count of purchases (Frequency) and sum of Total Spend.
- Time Between Purchases:
- The data is sorted by CustomerID and PurchaseDate.
- We calculate the time difference between consecutive purchases for each customer.
- The average time between purchases is computed for each customer.
- Data Merging:
- The new frequency features are merged back into the main dataset.
- Additional Metrics:
- Average Purchase Value is calculated by dividing Total Spend by Frequency.
- Data Visualization:
- A histogram is created to visualize the distribution of Purchase Frequency.
- This helps identify patterns in customer behavior and potential segmentation opportunities.
- Correlation Analysis:
- A correlation matrix is computed for the frequency-related features.
- This is visualized using a heatmap, helping to understand relationships between different aspects of customer behavior.
- Customer Segmentation:
- High-frequency customers (top 20% based on Frequency) are identified.
- We calculate and display the number of high-frequency customers and their average spend.
- This segmentation can be used for targeted marketing or loyalty programs.
- Data Persistence:
- The updated dataset with the new frequency features is saved to a CSV file.
- This allows for easy access in future analyses or model training.
This comprehensive approach calculates the Frequency feature and offers valuable insights into customer behavior. It identifies high-frequency clients and explores relationships between various frequency-related metrics. These insights are essential for developing effective marketing strategies, refining customer segmentation, and enhancing predictive models for both classification (such as churn prediction) and regression (like CLTV estimation) tasks.
Feature 4: Purchase Trend
For classification or regression models, Purchase Trend is a crucial feature that captures the dynamic nature of customer behavior over time. This feature quantifies how a customer's spending patterns have evolved, providing valuable insights into their engagement and loyalty levels. Positive trends, characterized by increasing purchase frequency or value, often suggest growing customer satisfaction and a strengthening relationship with the brand. These customers may be prime candidates for upselling or cross-selling initiatives.
Conversely, negative trends could signal potential issues such as customer dissatisfaction, increased competition, or changing needs. Such trends might manifest as decreasing purchase frequency, lower transaction values, or longer intervals between purchases. Identifying these negative trends early allows businesses to implement targeted retention strategies, potentially preventing churn before it occurs.
The Purchase Trend feature can be particularly powerful when combined with other metrics like Recency and Frequency. For instance, a customer with high frequency but a negative purchase trend might require different intervention strategies compared to a customer with low frequency but a positive trend. By incorporating this temporal dimension into predictive models, businesses can develop more nuanced and effective customer segmentation strategies, personalized marketing campaigns, and proactive customer service initiatives.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data.csv')
df = pd.read_csv('retail_data.csv')
# Convert 'PurchaseDate' to datetime
df['PurchaseDate'] = pd.to_datetime(df['PurchaseDate'])
# Calculate average spend over time by grouping data by month and CustomerID
df['PurchaseMonth'] = df['PurchaseDate'].dt.to_period('M')
monthly_spend = df.groupby(['CustomerID', 'PurchaseMonth'])['Total Spend'].sum().reset_index()
# Calculate trend as the slope of spending over time for each customer
def calculate_trend(customer_df):
x = np.arange(len(customer_df))
y = customer_df['Total Spend'].values
if len(x) > 1:
return np.polyfit(x, y, 1)[0] # Linear trend slope
return 0
# Apply trend calculation
trend_df = monthly_spend.groupby('CustomerID').apply(calculate_trend).reset_index(name='PurchaseTrend')
# Merge trend feature back to main dataset
df = df.merge(trend_df, on='CustomerID')
print("\nData with Purchase Trend Feature:")
print(df[['CustomerID', 'PurchaseMonth', 'Total Spend', 'PurchaseTrend']].head())
# Visualize Purchase Trend distribution
plt.figure(figsize=(10, 6))
sns.histplot(df['PurchaseTrend'], kde=True)
plt.title('Distribution of Purchase Trends')
plt.xlabel('Purchase Trend (Slope)')
plt.ylabel('Count of Customers')
plt.show()
# Identify customers with positive and negative trends
positive_trend = df[df['PurchaseTrend'] > 0]
negative_trend = df[df['PurchaseTrend'] < 0]
print(f"\nCustomers with positive trend: {len(positive_trend['CustomerID'].unique())}")
print(f"Customers with negative trend: {len(negative_trend['CustomerID'].unique())}")
# Calculate correlation between Purchase Trend and other features
correlation = df[['PurchaseTrend', 'Total Spend', 'Frequency']].corr()
plt.figure(figsize=(8, 6))
sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Correlation between Purchase Trend and Other Features')
plt.show()
# Example: Using Purchase Trend for customer segmentation
df['TrendCategory'] = pd.cut(df['PurchaseTrend'],
bins=[-np.inf, -10, 0, 10, np.inf],
labels=['Strong Negative', 'Slight Negative', 'Slight Positive', 'Strong Positive'])
trend_segment = df.groupby('TrendCategory').agg({
'CustomerID': 'nunique',
'Total Spend': 'mean',
'Frequency': 'mean'
}).reset_index()
print("\nCustomer Segmentation based on Purchase Trend:")
print(trend_segment)
# Save the updated dataset with the new feature
df.to_csv('retail_data_with_trend.csv', index=False)
print("\nUpdated dataset saved as 'retail_data_with_trend.csv'")retail_data.csv: https://cdn.prod.website-files.com/661b9e736a74273c4f628d5f/67d1a0c21f2a5e17fcd69098_retail_data.csv
Let's break down this comprehensive code example:
- Data Loading and Preprocessing:
- We import necessary libraries: pandas for data manipulation, numpy for numerical operations, and matplotlib/seaborn for visualization.
- The dataset is loaded from a CSV file and the 'PurchaseDate' column is converted to datetime format.
- Calculating Purchase Trend:
- We group the data by customer and month to get monthly spending patterns.
- A 'calculate_trend' function is defined to compute the linear trend (slope) of spending over time for each customer.
- This trend is then calculated for each customer and merged back into the main dataset.
- Visualizing Purchase Trend:
- A histogram is created to show the distribution of Purchase Trends across all customers.
- This visualization helps identify the overall trend patterns in the customer base.
- Analyzing Positive and Negative Trends:
- We separate customers with positive and negative trends and count them.
- This provides a quick overview of how many customers are increasing or decreasing their spending over time.
- Correlation Analysis:
- We calculate and visualize the correlation between Purchase Trend and other features like Total Spend and Frequency.
- This helps understand how the trend relates to other important customer metrics.
- Customer Segmentation:
- We categorize customers based on their Purchase Trend into four groups: Strong Negative, Slight Negative, Slight Positive, and Strong Positive.
- For each segment, we calculate the number of customers, average total spend, and average purchase frequency.
- This segmentation can be used for targeted marketing strategies or to identify at-risk customers.
- Data Persistence:
- The updated dataset with the new Purchase Trend feature is saved to a new CSV file.
- This allows for easy access in future analyses or model training.
This code offers a thorough analysis of the Purchase Trend feature, showcasing its distribution, correlations with other features, and application in customer segmentation. These insights prove valuable for both classification tasks—such as churn prediction—and regression tasks like Customer Lifetime Value (CLTV) estimation.
2.2.3 Using Feature Engineering for Model Training
Once these features are engineered, they serve as the foundation for training powerful predictive models. In this section, we'll explore how to leverage these features for both classification and regression tasks, specifically focusing on churn prediction and Customer Lifetime Value (CLTV) estimation.
For churn prediction, a classification task, we'll employ a Logistic Regression model. This model excels at predicting binary outcomes, making it ideal for determining whether a customer is likely to churn or not. The features we've created, such as Recency, Frequency, and Purchase Trend, provide crucial insights into customer behavior that can signal potential churn.
On the other hand, for CLTV prediction, a regression task, we'll utilize a Linear Regression model. This model is well-suited for predicting continuous values, allowing us to estimate the future value a customer may bring to the business. Features like Monetary Value and Purchase Trend are particularly valuable here, as they capture spending patterns and long-term customer behavior.
By incorporating these engineered features into our models, we significantly enhance their predictive power. This allows businesses to make data-driven decisions, implement targeted retention strategies, and optimize customer engagement efforts. Let's dive into the practical implementation of these models using our newly created features.
Example: Training a Logistic Regression Model for Churn Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset (assuming we have a CSV file named 'retail_data_with_features.csv')
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y = df['Churn'] # Target variable for churn
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)
# Train logistic regression model
log_reg = LogisticRegression(random_state=42)
log_reg.fit(X_train, y_train)
# Predictions
y_pred = log_reg.predict(X_test)
y_pred_proba = log_reg.predict_proba(X_test)[:, 1]
# Model evaluation
print("Model Accuracy:", accuracy_score(y_test, y_pred))
print("\nClassification Report:")
print(classification_report(y_test, y_pred))
# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.show()
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(log_reg.coef_[0])})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance')
plt.show()
# Cross-validation
cv_scores = cross_val_score(log_reg, X_scaled, y, cv=5)
print("\nCross-validation scores:", cv_scores)
print("Mean CV score:", np.mean(cv_scores))
# ROC Curve
from sklearn.metrics import roc_curve, auc
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)
roc_auc = auc(fpr, tpr)
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc:.2f})')
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 (ROC) Curve')
plt.legend(loc="lower right")
plt.show()This code example demonstrates a comprehensive approach to training and evaluating a Logistic Regression model for churn prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select the relevant features and target variable.
- The features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- We use train_test_split to divide the data into training and testing sets.
- A LogisticRegression model is initialized and trained on the training data.
- Predictions:
- The model makes predictions on the test set.
- We also calculate prediction probabilities, which will be used for the ROC curve.
- Model Evaluation:
- Accuracy score is calculated to give an overall performance metric.
- A detailed classification report is printed, showing precision, recall, and F1-score for each class.
- A confusion matrix is visualized using a heatmap, providing a clear view of true positives, true negatives, false positives, and false negatives.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in the model.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- This helps to ensure that the model's performance is consistent and not overly dependent on a particular train-test split.
- ROC Curve:
- The Receiver Operating Characteristic (ROC) curve is plotted.
- The Area Under the Curve (AUC) is calculated, providing a single score that summarizes the model's performance across all possible classification thresholds.
This comprehensive approach goes beyond merely training the model—it provides a thorough evaluation of its performance. The visualizations (confusion matrix, feature importance, and ROC curve) offer intuitive insights into the model's behavior. Additionally, the cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
Example: Training a Linear Regression Model for CLTV Prediction
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
# Load the dataset
df = pd.read_csv('retail_data_with_features.csv')
# Select features and target
features = ['Recency_Overall', 'AvgPurchaseValue', 'Frequency', 'PurchaseTrend']
X = df[features]
y_cltv = df['CLTV']
# Standardize the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Train-test split for CLTV
X_train_cltv, X_test_cltv, y_train_cltv, y_test_cltv = train_test_split(X_scaled, y_cltv, test_size=0.3, random_state=42)
# Train linear regression model
lin_reg = LinearRegression()
lin_reg.fit(X_train_cltv, y_train_cltv)
# Predictions and evaluation
y_pred_cltv = lin_reg.predict(X_test_cltv)
mse = mean_squared_error(y_test_cltv, y_pred_cltv)
r2 = r2_score(y_test_cltv, y_pred_cltv)
print("Mean Squared Error:", mse)
print("R-squared Score:", r2)
# Feature importance
feature_importance = pd.DataFrame({'feature': features, 'importance': abs(lin_reg.coef_)})
feature_importance = feature_importance.sort_values('importance', ascending=False)
plt.figure(figsize=(10, 6))
sns.barplot(x='importance', y='feature', data=feature_importance)
plt.title('Feature Importance for CLTV Prediction')
plt.show()
# Residual plot
residuals = y_test_cltv - y_pred_cltv
plt.figure(figsize=(10, 6))
plt.scatter(y_pred_cltv, residuals)
plt.xlabel('Predicted CLTV')
plt.ylabel('Residuals')
plt.title('Residual Plot')
plt.axhline(y=0, color='r', linestyle='--')
plt.show()
# Cross-validation
cv_scores = cross_val_score(lin_reg, X_scaled, y_cltv, cv=5, scoring='neg_mean_squared_error')
cv_rmse = np.sqrt(-cv_scores)
print("\nCross-validation RMSE scores:", cv_rmse)
print("Mean CV RMSE score:", np.mean(cv_rmse))
# Actual vs Predicted plot
plt.figure(figsize=(10, 6))
plt.scatter(y_test_cltv, y_pred_cltv, alpha=0.5)
plt.plot([y_test_cltv.min(), y_test_cltv.max()], [y_test_cltv.min(), y_test_cltv.max()], 'r--', lw=2)
plt.xlabel('Actual CLTV')
plt.ylabel('Predicted CLTV')
plt.title('Actual vs Predicted CLTV')
plt.show()This code example provides a comprehensive approach to training and evaluating a Linear Regression model for Customer Lifetime Value (CLTV) prediction. Let's break down its key components:
- Data Preparation:
- We load the dataset and select relevant features for CLTV prediction.
- Features are standardized using StandardScaler to ensure all features are on the same scale.
- Model Training:
- The data is split into training and testing sets using train_test_split.
- A LinearRegression model is initialized and trained on the training data.
- Predictions and Evaluation:
- The model makes predictions on the test set.
- Mean Squared Error (MSE) is calculated to quantify the model's prediction error.
- R-squared score is computed to measure the proportion of variance in the target variable that is predictable from the features.
- Feature Importance:
- The absolute values of the model coefficients are used to rank feature importance.
- A bar plot visualizes the importance of each feature in predicting CLTV.
- Residual Analysis:
- A residual plot is created to visualize the difference between actual and predicted values.
- This helps identify any patterns in the model's errors and assess if the linear regression assumptions are met.
- Cross-validation:
- Cross-validation is performed to assess the model's performance across different subsets of the data.
- Root Mean Squared Error (RMSE) is used as the evaluation metric for cross-validation.
- Actual vs Predicted Plot:
- A scatter plot is created to compare actual CLTV values against predicted values.
- This visual aid helps in understanding how well the model's predictions align with actual values.
This comprehensive approach not only trains the model but also provides a thorough evaluation of its performance. The visualizations (feature importance, residual plot, and actual vs predicted plot) offer intuitive insights into the model's behavior and performance. The cross-validation step enhances the evaluation's robustness, ensuring the model's performance remains consistent across various data subsets.
By implementing these additional evaluation techniques and visualizations, we gain a deeper understanding of the model's strengths and limitations in predicting Customer Lifetime Value. This information can be invaluable for refining the model, selecting features, and making data-driven decisions in customer relationship management strategies.
2.2.4 Key Takeaways and Their Implications
- Feature engineering enhances predictive accuracy by creating features that capture underlying patterns and trends. This process involves transforming raw data into meaningful representations that algorithms can better interpret, leading to more robust and accurate models.
- For classification tasks like churn prediction, features such as Recency, Frequency, and Purchase Trend provide crucial insights into customer loyalty and engagement. These metrics help identify at-risk customers, allowing businesses to implement targeted retention strategies.
- In regression tasks like CLTV prediction, features capturing spending habits and behavior over time, such as Monetary Value and Purchase Trend, significantly improve the model's ability to predict lifetime value. This enables businesses to allocate resources more effectively and personalize customer experiences.
- The selection of appropriate features is context-dependent and requires domain expertise. For instance, in healthcare, features like appointment frequency and treatment adherence might be more relevant for predicting patient outcomes.
- Feature importance analysis, as demonstrated in the code examples, provides valuable insights into which factors most significantly influence the target variable. This information can guide business decisions and strategy formulation.
- Cross-validation and residual analysis are crucial steps in evaluating model performance and identifying potential areas for improvement in feature engineering or model selection.