Master Marketing Analytics with Python and Machine Learning

Install Anaconda to set up a Jupyter notebook, select the 64-bit installer, add to path if needed, then download and upload Machine Learning Express code folder to begin hands-on course.

Learn how data science frames business problems into analytical problems, delivering data-driven recommendations and better decisions through machine learning, training and testing, and predictive models for marketing analytics.

Explore the fundamentals of supervised and unsupervised learning, distinguishing regression and classification with target variables, and reveal clustering and association mining with k-means, pca, and support vector machines.

Master the steps to build a supervised learning model: data extraction, target linkage, cleaning, exploratory data analysis, feature engineering, and deployment. Then learn unsupervised clustering with scaling, validation, and deployment.

Explore model fit by contrasting underfitting and overfitting, and explain the bias–variance trade-off to achieve balanced, low-error performance on unseen data.

Analyze classification metrics including confusion matrix, precision, recall, F1, AUC, gain curves, and Gini, then evaluate regression with R-squared and decile accuracy, plus unsupervised measures like silhouette and Davies-Bouldin.

Master marketing analytics with python by mastering data preprocessing: import data and libraries, handle missing values with imputation, scale and encode features, and split data for training and testing.

Identify numeric and categorical features in the data frame, view dtypes to distinguish data types, and drop the ID feature to streamline predictive analytics.

Split the data frame into independent features and the target variable, creating X and Y, then prepare X for processing by checking missing values and separating numeric and character types.

Impute missing values in numeric and object features by mean and most frequent strategies, split the data into numeric and character frames, and apply the simple imputer with fit_transform.

Identify and cap outliers using percentile-based thresholds, preserving variance by clipping values at the 99th percentile and the first percentile, with practical examples on BMI and age.

Encode categorical features with pandas get_dummies and drop_first to produce n minus one indicators, avoiding the dummy variable trap. Use value_counts to identify region, gender, and smoker levels.

Explore feature discretization to model marketing data, using sklearn's k bins discretization to convert continuous features into ordinal bins, aiding exploratory data analysis and feature selection.

Split your data with the train test split to create 70% training and 30% testing sets, using randomization and a fixed random state for reproducibility.

Apply outlier handling with capping and floating, using first and 99th percentile thresholds to cap and floor features.

Explore missing value imputation for numerical and categorical features using mean and mode. Learn to split data into numerical and categorical parts and apply these methods.

Explore feature selection strategies for regression and classification in machine learning, including variance-based removal, bivariate analysis with the dependent variable, and selecting features with select k best in sklearn.

Apply feature selection for regression problems by removing zero-variance features, discretizing numerical features, and one-hot encoding categorical features to predict medical charges.

Learn to convert a continuous target to classification with thresholds, and perform numerical and categorical feature selection using variance threshold and SelectKBest, with one-hot encoding.

Learn to tackle a business problem with clustering analytics in Python, study hierarchical clustering and k-means clustering, and perform hands-on implementation through practical steps.

Explore the bank's credit card customer data to identify distinct customer groups using k-means and hierarchical clustering, leveraging features like education level, card category, and spending behavior.

Master hierarchical clustering, an unsupervised method that groups similar observations into homogeneous clusters using distance measures, with standardization, Euclidean distance, dendrograms, and evaluation via silhouette and Davies-Bouldin in Python.

Learn how to implement hierarchical clustering in Python. Import libraries, clean data, engineer an average spend per transaction, and prepare data with scaling, dendrograms, and cluster profiling.

Apply feature scaling with the Standardscaler, convert to a dataframe, and mark standardized features; use a Seaborn heatmap to remove correlated features, then prepare a dendrogram to determine clusters.

Explore the concept of k-means clustering, its distance-based assignment and centroid updates, convergence, elbow method with wcss, and Python implementation using sklearn.

Explore python-based k-means clustering for credit card spend data, covering data cleaning, feature engineering, clustering evaluation with silhouette and davies-bouldin scores, and profiling with average spend per transaction.

Scale features with python's StandardScaler, analyze correlations via heatmaps, drop redundant variables, and determine the optimal k using the elbow method in k-means.

Applies k-means clustering with four clusters using k-means++ initialization, evaluates with silhouette and Davies-Bouldin scores, builds cluster profiles, and exports results.

Explore dimensionality reduction by framing a business problem, defining data and objectives, and learning how PCA and feature clustering reveal the most relevant features through Python steps and hands-on coding.

Apply sklearn's PCA with four components to build principal component scores, convert them to a data frame, and visualize post-pca correlations to reduce feature interdependence.

Discover how variable clustering reduces dimensionality by grouping correlated features into clusters and selecting representative uncorrelated variables, unlike principal components, with practical Python steps.

Implement variable clustering in Python by preparing data, standardizing features, and computing the covariance matrix. Derive eigenvalues and eigenvectors to identify and select uncorrelated feature clusters for diabetes data.

Apply python-based variable clustering to form up to four feature clusters, selecting representatives via the R squared ratio. The reduced features lower inter-feature correlation and avoid PCA.

Explore simple linear regression, modeling a continuous target with one independent feature, deriving the line y equals a plus b x, and evaluating with r-squared, decile accuracy, and error clustering.

Perform a python-based simple linear regression to predict salary from years of experience, using a discretization step with KBinsDiscretizer, train-test split, and evaluating with R Square.

learn to build a simple linear regression model with python's sklearn, split data 70/30 into train and test, and inspect intercept and coefficient to form the predictive equation.

Learn to evaluate simple linear regression in Python by comparing training and testing R-squared, predicting salaries, and visualizing deciles with Q cut and error-cluster analysis using K-means.

Frame the business problem of predicting medical charges from demographic features to guide pricing decisions, and summarize the data with age, gender, BMI, smoker, region, and charges as the target.

Master multiple linear regression (OLS) to model a continuous target with multiple features, including intercept and slopes. Assess performance with R-squared, deciles, and error cluster analysis, using Python and sklearn.

Evaluate a multiple linear regression model by quantifying training and testing performance, visualizing results, and performing error cluster analysis with r-squared metrics and decile ranking of charges.

Evaluate a multiple linear regression model using r-squared on training and testing data, plus decile analysis and scatter plots to visualize accuracy, then cluster error percentages with k-means for insights.

Explore decision tree regression in supervised learning, outlining binary splits, mean squared error minimization, decile accuracy analysis, and R-squared evaluation, with hands-on Python implementation using sklearn and plot_tree.

Implement a Python decision tree regression pipeline for insurance data, including data import, outlier capping, discretization, feature selection, and encoding of categorical features.

Split the data into a 70/30 training and testing set with sklearn. Build and visualize a decision tree regression model in Python to reveal feature-based rules and insights.

Explore decision tree regression in Python, evaluate with r-squared, compare actual versus predicted values, and use ten-bin k-means clustering to identify where the model is most and least accurate.

Learn how random forest regression uses bootstrap samples and multiple trees with random features, aggregates their predictions to reduce overfitting, and is evaluated with R-squared, decile accuracy, and error clusters.

Implement random forest classification in Python from scratch on a banking churn dataset, covering data import, preprocessing, categorical encoding, train-test split, feature importance, and model evaluation with a confusion matrix.

Explore random forest regression evaluation by comparing R square on training and testing, visualizing predicted versus actual values, and assessing overfitting risk and generalization across data segments.

Learn gradient boosting regression, a boosting ensemble of trees for regression tasks, with iterative reweighting, weighted predictions, and evaluation using R square and error clustering.

Perform gradient boosting regression in Python, covering data import, cleaning, discretization, encoding, model building, and evaluation using r-squared and cluster analysis on the error column for medical charges.

Split the data into training and testing sets (70/30) with seed 20, train a gradient boosting regression model (max depth 4), and reveal feature importances: smoker_yes, BMI, age, and gender.

Assess gradient boosting regression with R-squared on train and test, visualize actual vs predicted charges, and analyze best-performing data via error cluster capture analysis.

Define the business problem, outline data needs, and implement and evaluate classification models in Python, including logistic regression, random forest, support vector machines, and decision trees.

Analyze a banking churn problem using a dataset of customer behavior, demographics, and finances to build models that predict churn probability with classification algorithms in Python.

Learn logistic regression fundamentals, including event definitions and the logit link. See how odds map to probability and evaluate models with decile analysis, confusion matrices, and lift charts.

Implement logistic regression in Python to predict banking churn using a structured classification workflow. Preprocess data, manage features, and evaluate with confusion matrix and KS statistic.

Advance logistic regression in Python by analyzing outliers and missing values, cap with 1st and 99th percentiles via clipping, and impute categorical values using SimpleImputer with most frequent strategy.

Implement logistic regression in Python by selecting features from numerical and categorical data, discretizing numerical features into deciles and ranking them.

Learn how to evaluate a logistic regression model in Python using confusion matrix, accuracy, precision, recall, and F1 score, and analyze probabilities with decile analysis to assess discrimination and lift.

Master decision tree classification using Gini impurity to create prediction boundaries and node structures. Learn evaluation with confusion matrix, decile analysis, lift, and gains charts, using Python sklearn.

Learn Python-driven decision tree classification on banking churn data, including data cleaning, feature encoding, train-test split, model visualization, and evaluation via confusion matrix, decile analysis, and lift charts.

Implement data cleaning for decision tree classification by analyzing feature distributions. Cap outliers at 1st and 99th percentile, impute missing values, encode categoricals with get_dummies drop_first, and build X_all.

This lecture covers train-test split, building a Gini decision tree with depth four, and visualizing feature importances and rules that show churn drivers like current balance and current month debit.

Evaluate a Python decision tree classifier using confusion matrix and metrics (accuracy, precision, recall, F1), then apply probability decile analysis with Gaines chart and Lorenz curve to gauge discrimination.

Understand random forest classification as an ensemble method using bagging and sampling with replacement, with majority voting for predictions and evaluation via confusion matrices, probability decile analysis, and sklearn.

Implement random forest classification in Python on a banking churn dataset. Explore data import, type handling, encoding, train-test split, model building, and evaluation with feature importance and confusion matrix.

Clean and prepare data for random forest classification by performing outlier capping, missing value imputation, and one-hot encoding of categorical features, then assemble a master dataset for model building.

Split the data into 70% training and 30% testing, then build a random forest classifier in Python. Evaluate feature importances and visualize the top ten features.

Explore random forest classification in Python, evaluate with the confusion matrix, precision, recall, and F1, then analyze probability deciles, KS statistic, and lift for churn prediction.

Master gradient boosting classification, an ensemble method that iteratively improves weak learners and aggregates predictions. Evaluate discrimination with deciles, rank ordering, lift, gains charts, for churn prediction using sklearn.

Clean data for gradient boosting classification in Python by outlier capping, missing value analysis and imputation, one-hot encoding with drop-first, and building a master dataframe.

Split the data into 70% training and 30% testing, then train a gradient boosting classifier with max_depth 4 and random_state 42. Retrieve and visualize the top ten feature importances.

Explore gradient boosting classification in Python and learn model evaluation with confusion matrices, probability deciles, gains charts, Lorenz curve, and KS statistics to optimize churn prediction.

Explore stacking classifiers, an ensemble method that combines base learners like random forest and GBM with a logistic regression meta learner, and evaluate with confusion matrices and probability decile charts.

Implement stacking classification in Python using a banking churn dataset. Build base learners, a meta learner, and evaluate the model with probability analysis and visualizations.

Explore stacking classifier workflow in Python: cap and float outliers using 1st and 99th percentiles, impute missing values, one-hot encode categoricals with drop_first, and build the master dataset for modeling.

Define the stacking classifier workflow in Python using a 70-30 train-test split and base learners—random forest and gradient boosting. Use logistic regression as the meta learner and fit the stack.

Apply kNN classification in Python to predict bank customer churn, preprocessing data, encoding categorical features, scaling, splitting, and evaluating with confusion matrix, accuracy, precision, and recall, plus probability decile analysis.

Using Python, this lecture cleans data for knn classification by outlier capping at the 1st and 99th percentiles, imputes missing values, encodes categoricals, and merges features.

Learn to implement and evaluate a k-nn classifier in Python, using dot predict, confusion matrix, and key metrics, then analyze predicted probabilities and decile-based gains.

Explore the naive Bayes classifier and Bayes theorem, including priors, likelihoods, and posteriors, with a weather example and evaluation via confusion matrices and precision, recall, and F1.

Naive Bayes classifier in Python by cleaning data: use pandas describe to examine first and 99th percentile outliers, apply outlier cap, and evaluate standard deviation impact.

Evaluate a naive Bayes model on test data with predict and predict_proba, and perform probability decile analysis to measure lift over the baseline churn rate of 18.5%.

Explore the concept of support vector machines, including maximum margin, margin, and support vectors; learn kernel tricks, kernel choices, and SVM evaluation with decile analysis, lift, and gains charts.

Apply SVM classification in Python to predict bank churn, covering data import, preprocessing, encoding categorical features, train-test split, and evaluation with accuracy, precision, recall, and F1 score.

Analyze feature distributions, cap extremes with 1st and 99th percentiles via clipping, impute categorical values, one-hot encode with drop_first, and build the master feature set X_all.

Scale all features with StandardScaler and create a standardized feature set. Split data into 70% training and 30% testing, then train an SVM classifier with SVC and probability=True in Python.

Apply SVM classification in Python and evaluate with confusion matrix, accuracy, precision, recall, and F1. Analyze predicted churn probabilities through deciles, KS, lift, and event capture for targeted churn mitigation.

Explore model optimization by examining cross validation, learn how grid search selects optimal model parameters, and implement grid search in Python to compare decision tree models for churn prediction.

Explore ten folds cross validation and grid search to tune model parameters with GridSearchCV, compare models with a confusion matrix, and select depth and min samples split for decision tree.

Explore grid search and hyperparameter tuning in Python by optimizing a decision tree on a banking churn dataset, using 10-fold cross-validation to improve accuracy.