Data Science for Marketing Analytics

This lesson covers the techniques to explore and analyze data by means of solving some problems critical for businesses, such as identifying attributes useful for marketing, analyzing key performance indicators, performing comparative analyses, and generating insights and visualizations.

We will use the pandas, Matplotlib, and seaborn libraries in Python to solve these problems. It will cover the objectives and topics that will be explained.

Before we start with the model creation, we should summarize the attributes in our data and objectively compare them with our business expectations. To quantify business expectations, we generally have target metrics whose relationships we want to analyze with the attributes in our data. These metrics may depend on domain knowledge and business acumen and are known as Key Performance Indicators (KPIs).

In this section you will learn the following:

• Traditional Marketing Analytics Model

• Exploring the Attributes in Sales Data

Once we have identified the KPIs for our analysis, we can proceed to make insights with respect to only those variables that affect the bottom line of the KPIs.

In this section, you will learn the following:

• Selecting and Renaming Attributes

• Transforming Values

• Targeting Insights for Specific Use Cases

• Reshaping the Data

• Understanding and Stacking and Unstacking

• Pivot Tables

An important aspect of exploring data is to be able to represent the data visually.

When data is represented visually, the underlying numbers and distribution become very easy to understand and differences become easy to spot.

In this section, you will learn the following:

• Plots

• Visualizing Data with Pandas

• Visualizing with Seaborn

• Visualizing through Seaborn

• Visualization with Matplotlib

This section will summarize the topics covered in this lesson.

This lesson covers various customer segmentation methods, deals with the concepts of similarity and data standardization, and explains k-means clustering.

Customer segmentation is the act of separating your target customers into different groups based on demographic or behavioral data so that marketing strategies can be tailored more specifically to each group.

In this section you will learn to do the following:

• Customer Segmentation Methods

• Traditional Segmentation Methods

• Unsupervised Learning (Clustering) for Customer Segmentation

For a clustering algorithm to try to find groups of customers, they need some measure of what it means for a customer to be similar or different.

In this section, we will learn how to think about how similar two data points are and how to standardize data to prepare it for clustering.

In this section you will learn to do the following:

• Determining Similarity

• Standardizing Data

• Standardizing Age and Income Data for Costumers

• Calculating Distance

• Calculating Distance Between Three Customers

k-means clustering is a very common unsupervised learning technique with a very wide range of applications. It is powerful because it is conceptually relatively simple, scales to very large datasets, and tends to work quite well in practice.

You will learn the conceptual foundations of k-means clustering, how to apply k-means clustering to data, and how to deal with high-dimensional data (that is, data with many different variables) in the context of clustering.

In this section you will learn to do the following:

• Understanding k-means Clustering

• K-means Clustering on Income/Age Data

• High-Dimensional Data

• Visualizing through Seaborn

This section will summarize the topics covered in this lesson.

This lesson covers various clustering algorithms (apart from k-means) and explains how they can be evaluated. It will cover the objectives and topics that will be explained.

In the previous lesson, we just used a predefined number of clusters, but in the real world, we don’t always know what number of clusters to expect. There are different ways of trying to come up with the correct number of clusters.

In this section you will learn the following:

• Choosing the Number of Clusters

• Simple Visual Inspection

• Choosing the Number of Clusters Based on Visual Inspection

• Elbow Method with Sum of Squared Errors

• Determining the Number of Clusters Using the Elbow Method

k-means is a useful clustering algorithm because it is simple, widely applicable, and scales very well to large datasets. However, it is not the only clustering algorithm available. Each clustering algorithm has its own strengths and weaknesses, so it’s often worth having more than one in your toolkit. We will look at some of the other popular clustering algorithms in this section.

In this section, you will learn the following:

• Mean-Shift Clustering

• Performing Mean-Shift Clustering to Cluster Data

• K-Prototypes and k-Modes Clustering

• Clustering Data Using the k-prototypes Method

Being able to perform clustering in different ways is only useful if you know how to evaluate different clustering methods and compare them in an objective way.

Subjective methods, such as visual inspection, can always be used, but the silhouette score is a powerful objective method that can be used with data that is more difficult to visualize..

In this section, you will learn the following:

• Silhouette Score

• Calculating Silhouette Score and Comparing to the Mean-Shift Algorithm

• Train and Test Split

• Using a Train-Test Split to Evaluate Clustering Performance

This section will summarize the topics covered in this lesson.

In this lesson, you will learn about regression, a supervised learning technique used to predict continuous outcomes. We will begin with an explanation of regression. Then, we will discuss feature engineering and data cleaning for regression. Finally, we will learn how to perform regression and interpret the results.

Feature engineering is the process of taking data and transforming it for use in predictions. The idea is to create features that capture aspects of what's important to the outcome of interest. This process requires both data expertise and domain knowledge—you need to know what can be done with the data that you have, as well as knowledge of what might be predictive of the outcome, you're interested in. We will first look at how to transform data to create features, and then how to clean the data of the resulting features to ensure models are trained on high-quality data. 

In this section you will learn the following:

• Feature Engineering for Regression

• Feature Creation

• Data Cleaning

• Creating Features for Transaction Data

• Assessing Features Using Visualization and Corelations

• Assessing Corelations

• Examining Relationships between Predictors and Outcome

Linear regression is a type of regression model that uses linear relationships between predictors and the outcome to predict the outcome.

In this section, you will learn the following:

• Regression Hierarchy

• Linear

• Building a Linear Model Predicting Customer Spend

This section will summarize the topics covered in this lesson.

This lesson covers other regression techniques such as lasso regression and explains how to evaluate various regression models using common measures of accuracy.

In order to evaluate regression models, we first need to define some metrics.

The common metrics used to evaluate regression models rely on the concepts of residuals and errors, which are quantifications of how much a model mispredicts a particular data point.

In this section you will learn the following:

• Residuals and Errors

• Mean Absolute Error

• Root Mean Squared Error

• Evaluating Regression Models of Location Revenue MAE and RMSE

In the previous section, we saw how an evaluation metric such as the RMSE can be used to decide whether a variable should be included in a model or not. However, this method can be cumbersome when there are many variables involved. One common way to select which features will be used by a model is to use regularization.

In this section, you will learn the following Pain Points:

• Using Regularization for Feature Selection

• Using Lasso Regression for Feature Selection

Linear models are not the only type of regression models. Another powerful technique is to use regression trees.

In this section you will learn the following:

• Tree Based Regression Models

• Random Forests

• Using Tree Base Regression Models to Capture Non-Linear Trends

This section will summarize the topics covered in this lesson.

Churn prediction is one of the most common use cases of machine learning.Predicting customer churn is important for an organization because acquiring new customers is easy, but retaining them is more difficult.
Similarly, high employee churn can also affect a company, since it spends a huge sum of money on grooming talent. Also, organizations that have high retention rates benefit from consistent growth, which can also lead to high referrals from existing customers.

Logistic regression is one of the most widely used classification methods, and it works well when data is linearly separable. The objective of logistic regression is to squash the output of linear regression to classes 0 and 1.

In this section you will learn the following:

• Classification Problems

• Workflow of a Supervised Classification Task

• Types of Classification Problems

• Revisiting Linear Regression

• Logistic Regression

• Plotting the Sigmoid Function

• Cost Function for Logistic Regression

• Loading, Splitting, and Applying Linear and Logistic Regression

Logistic OSEMN is one of the most common data science pipelines used for approaching any kind of data science problem. It's pronounced awesome.

In this section you will learn the following:

• Using Regularization for Feature Selection

• Obtaining the Data

• Imputing Missing Values

• Remaining Columns and Changing the Data Type

• Exploring the Data

• Statistical Overview

• Corelation

• Obtaining the Statistical Overview and Corelation Plot

• Visualizing the Data

• Performing Exploratory Data Analysis (EDA)

Modeling the data not only includes building your machine learning model but also selecting important features/columns that will go into your model. This section will be divided into two parts: Feature Selection and Model building.

In this section you will learn the following:

• Feature Selection

• Methods of Feature Selection

• Performing Feature Selection

• Model Building

• Building a Logistic Regression Model

• Interpreting the Data

This section will summarize the topics covered in this lesson.

This lesson will cover how to evaluate the performance of the various models and choose the most appropriate one. Choosing an appropriate machine learning model is an art that requires experience, and each algorithm has its own advantages and disadvantages.

When dealing with data that is linearly separable, the goal of the Support Vector Machine (SVM) learning algorithm is to find the boundary between classes so that there are fewer misclassification errors.

In this section you will learn the following:

• Support Vector Machines

• Advantages and Disadvantages of SVM

• Intuition Behind Maximum Margin

• Linearly Inseparable Cases

• Linearly Inseparable Cases Using Kernel

• Training an SVM Algorithm over a Dataset

There Decision trees are mostly used for classification tasks. They are a non-parametric form of supervised learning method. Decision trees work on the concept of finding out the target variable by learning simple decision rules from data. They can be used for both classification and regression tasks.

In this section, you will learn the following:

• Decision Trees

• Advantages and Disadvantages of Decision Trees

• Implementing a Decision Tree Algorithm Over a Dataset

• Important Terminology of Decision Trees

• Decision Tree Algorithm Formulation

• Information Gain

• Gini Impurity

• Entropy

• Misclassification Error

• Random Forest

• Random Forest Algorithm

• Advantages and Disadvantages of Random Forest

• Implementing a Random Forest Model Over a Dataset

Preprocessing data before applying any machine learning model can improve the accuracy of the model to a large extent. Therefore, it is important to preprocess data before applying a machine learning algorithm.

In this section you will learn the following:

• Pre-processing Data

• Standardization

• Standardizing Data

• Scaling

• Scaling Data after Feature Selection

• Normalization

• Performing Normalization on Data

• Model Evaluation

• Implementing Stratified k-fold

• Fine Tuning of the Model

In the case of classification algorithms, we use a confusion matrix, which gives us the performance of the learning algorithm. It is a square matrix that counts the number of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) outcomes.

In this section you will learn the following:

• Confusion Matrix

• Evaluating the Performance Metrics for a Model

• ROC Curve

• Plotting the ROC Curve

This section will summarize the topics covered in this lesson.

In this lesson, you will start by exploring multiclass classification. Then, you will deep dive into the intuition behind multiclass classification problems and see how to tackle class-imbalanced data. Finally, you will create a multiclass classification classifier.

The classification algorithms that we discussed earlier were mostly binary classifiers, where the target variable can have only two categorical values or classes.

However, there can be scenarios where we have more than two classes to classify samples into. For instance, given data on customer transactions, the marketing team may be tasked with identifying the credit card product most suitable for a customer, such as cashback, air miles, gas station, or shopping.

In this section you will learn the following:

• Multiclass Classification

• Classifiers in Multiclass Classification

• Implementing a Multiclass Classification Algorithm on a Dataset

• Performance Metrics – Micro Average

• Evaluating Performance Using Multiclass Performance Metrics

Class imbalance is the most common problem that a data scientist can encounter.

Most real-world classification tasks involve classifying data, where one class or multiple classes are over-represented. This is called class imbalance. Common examples where class-imbalanced data is encountered is in fraud detection, anti money laundering, spam detection, and cancer detection.

In this section, you will learn the following:

• Performing Classification on Imbalanced Data

• Dealing with Class Imbalanced Data

• Visualizing Sampling Techniques

• Fitting a Random Forest Classifier Using SMOTE and Building the Confusion Matrix

This section will summarize the topics covered in this lesson.