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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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