Machine Learning for Campaign Management

Analyze total ad spends and clicks to identify segments like higher developers and Node.js with the most investment. Use campaign prediction to segment by competition and prioritize highest ROI campaigns.

Analyze and compare campaigns to identify the top performers by revenue and ad spend, and validate ROI with keyword insights across merged campaign data.

Visualize ad spend and monthly clicks, identifying top keywords like hire developers and outsourcing software. Leverage these insights to align campaigns for higher conversions and optimize Google ad displays.

Build a machine learning model for campaign conversion prediction, identify failed campaigns, and optimize roi on the Google network by data economics, reprioritizing campaigns and uncovering keywords from competitive data.

Predict campaign conversions using Google Ads data, enrich campaigns, and build a pipeline with one-hot encoding, numerical encoding, and scaling. Compare logistic regression with ensemble models to optimize ROI.

Turn a digital campaign into an ai-enabled system by cleaning data, concatenating six company datasets, and building a pipeline to predict conversions and optimize ad spend.

Import and merge multiple datasets, compute conversions from clicks per month, and clean features to prepare a production-ready dataset for campaign optimization and predictions.

Clean and sanitize campaign data by converting conversions to binary, filter by monthly clicks and threshold cost per click, then apply one-hot encoding and prepare data pipeline for model training.

Build ensemble model prediction for campaign conversions by training different classifiers (decision tree, kNN, Bernoulli NB, random forest, AdaBoost, MLP, SVC) with cross-validation to select the best model.

Builds and executes a campaign model pipeline with one-hot encoding, thresholds for null columns removal, splitting, and scaling to generate predictions for profitability and CRO optimization on Google display ads.

Build an end-to-end AI model pipeline to forecast ad campaign conversions, comparing MLP, GBM, random forest, and decision tree for optimal ROI.

Create and map cost per click to an seo bucket, calculate profitability with conversions and billing rate, and derive the total revenue and PNL using lambda-based calculations for campaign ROI.

Learn to compute ROI from net P&O over total expense, including employee cost and total add cost, then save to CSV, filter by ROI and prediction, and sort by ROI.

Analyze campaign trends across multiple companies by visualizing stacked bar charts of monthly clicks for five top terms, using iplots and offline plotly in Python.

Visualize campaign trends by comparing monthly clicks and spending across five companies, highlighting which campaigns follow investments and how conversions relate to ROI.

Compare campaigns against competitor benchmarks to allocate budget wisely and identify market winners and niche keywords, using conversion predictions and marketplace benchmarks to optimize revenue.

Apply budget versus conversion forecasting to evaluate the target company by computing exp clicks, conversions, ad spend, revenue, and return on investment from campaign data.

Compare benchmark and target campaigns by modeling conversions, budgets, and ROI using keyword-based terms and data frames. Visualize results with bar charts to compare campaigns for informed decision-making.

Visualize benchmark vs campaigns by comparing top keywords such as web developer and website developer, analyzing conversions to optimize a thousand-dollar budget and maximize ROI.

Explore how increasing budgets across emails, social media, PPC, and SEO affect ROI for marketing campaigns and learn to optimize returns.

Learn to predict campaign roi by selecting key features such as ad spend, campaign channel, click-through rate, conversion rate, engagement, social shares, and email opens, then train a random forest.

Build a data pipeline by dropping columns with low population from a dataset using a threshold such as 0.5, cleansing the data, and improving the target variable.

Learn to apply one-hot encoding to categorical variables, converting them into binary vectors with pd.getDummies and preparing the data pipeline.

Build a linear regression model for impression prediction using scikit-learn packages, scale features, split data, and evaluate with cross-validation, preparing for a random forest in the next lecture.

Save the fitted linear regression model to a pickle file using joblet, saving the model and its columns for fast predictions without retraining.

Compare linear regression and random forest models to optimize campaign predictions with train-test split, scaling, and hyperparameter tuning. Random forest outperforms linear regression (74.31% vs 66.9%), guiding model selection.

Implement a gradient boosting regressor for impression prediction by building a data pipeline with preprocessing, splitting, scaling, and one-hot encoding; tune with grid search and save the model for predictions.

Learn how to implement the gradient boosting regressor with sklearn pipeline versus a user-defined pipeline, achieve high R2 scores around 97.9% through grid search, and streamline preprocessing.

Create and compare channel-specific predictions for Instagram, Google, and Facebook ads under a given budget, using data frames and pipelines to identify top impressions by region and platform.

Analyze a multi-channel digital marketing dataset to quantify channel distribution and conversion patterns across campaigns, with insights into gender, age, visits, and email clicks, using a Streamlit analytics workflow.

Create a transition matrix from multi-channel touchpoints to map the customer journey, normalize to obtain transition probabilities, compute conversion properties for Markov chain attribution, and visualize conversion distribution by channel.

Analyze campaign performance by building bar charts of AddSpin versus Conversion and conversion by channel, gender, age, website visits, time on site, and email clicks using Plotly and Streamlit.

Develop a click prediction data pipeline by importing and cleaning a dataset, converting CTR to numeric, applying threshold-based column drops, and performing one-hot encoding for categoricals.

Scale the dataset for machine learning campaign management by applying standard scaling to features and targets using two standard scaler instances, transforming train and test sets, and returning scaled data.

Continue scaling the dataset by flattening into one column with reshape, then build a model pipeline using x-train and y-train features like date, cpc, ctr, and impressions.

Build a model pipeline to read csv data, sort by date, convert date to datetime, cast category, clean ctr to float, define clicks, and drop date and keyword.

Build a model pipeline by preparing data (typecasting, dropping date and keyword columns), splitting into train and test sets, applying scaling, and returning scaled features and targets.

Execute the model pipeline, train a random forest regression model on scaled and unscaled features, and compare actual versus predicted clicks to optimize campaigns by ctr and acquisition cost.

Build and evaluate a random forest regressor model, training with scaled features and assessing performance using r2 score, mean squared error, and mean absolute error.

Run the execute model, verify the pipeline and data shapes, and evaluate predictions with a random forest regressor achieving an R2 of 99.91% and MAE near 0.

Explore cohort analysis with RFM scoring, data cleaning, and filtering to build retention insights; visualize country trends, Pareto charts, and revenue by customer.

Import and prep data for marketing cohort analysis by loading the online retail final.csv dataset, configuring metrics, and building a null value checker for data quality.

Visualize customer transaction data with KDE and box plots to reveal skewness, then remove outliers by filtering quantity between 0 and 20 and compare before and after distributions.

Remove outliers in campaign data by filtering quantity to between 0 and 20. Analyze distributions with kd plots and box plots to guide marketing cohorts and roi.

Learn how cohort retention analysis measures user retention and differs from churn analysis, and identify factors like time spent, feature adoption, average order value, and customer service that influence outcomes.

Learn to compute invoice cohort metrics from min and max dates, derive year, month, and day, build a cohort index and pivot, compute retention, and visualize results with a heatmap.

Compute monthly retention by cohort through unique customer counts, derive the monthly customer count and the percentage of customers, and visualize the retention trend with a seaborn line plot.

Explore plotting customer revenue over time by grouping data by invoice month to compute total revenue per cohort, and visualizing the results with a bar plot in seaborn and matplotlib.

Plot the monthly revenue and customer count by configuring axes, labels, fonts, and annotations, then render the chart and prepare for a pareto chart next.

Use Pareto filters to identify top customers and their products, compute total spending, apply logarithmic transformations, and prepare data for k-means clustering.

Apply log1p transformations to customer data and run k-means clustering to identify cohorts. Evaluate inertia across cluster counts and plot the elbow curve to select the optimal number of clusters.

Apply k-means clustering, evaluate inertia with the elbow method to identify four clusters, and build a centered data frame linking products, spendings, and their logs to customer clusters.

Learn how the RFM model—recency, frequency, monetary value—segments customers by purchase patterns, scores them across three categories, and informs upsell strategies and incentives.

Build the RFM table by computing min and max invoice dates to derive the last day, then calculate recency, frequency, monetary value, and perform quartile segmentation with RFM score.