Pricing Optimization & Dynamic Pricing with Machine Learning

What you'll learn

⚡ Learn the basic fundamentals of pricing optimization and dynamic pricing
⚡ Learn about pricing strategies like cost, value, competitor based pricing, different factors that affect pricing like supply, demand, and production cost
⚡ Learn how to calculate price elasticity of demand
⚡ Learn how to clean pricing dataset by handling missing values, removing duplicates, and detecting potential outliers
⚡ Learn how to analyze relationship between price and quantity sold
⚡ Learn how to analyze and compare competitor prices
⚡ Learn how to conduct feature importance analysis using Random Forest Regressor
⚡ Learn how to build pricing optimization model using XGBoost Regressor
⚡ Learn how to build pricing optimization model using Gradient Boosting Regressor
⚡ Learn how to forecast demand using CatBoost Regressor
⚡ Learn how to analyse product pricing segment using K Means Clustering
⚡ Learn how to optimize flight ticket price using Linear Programming
⚡ Learn how to optimize hotel price with reinforcement learning and implement Deep Q Network
⚡ Learn how to build dynamic pricing model using Extra Trees Regressor
⚡ Learn how to create dynamic pricing simulation with real time data
⚡ Learn how to build AI pricing intelligence agent using Gemini

Requirements

❗ No previous experience in machine learning is required
❗ Basic knowledge in Python and pricing analytics

Description

This course contains the use of artificial intelligence
Disclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor.
Welcome to Pricing Optimization & Dynamic Pricing with Machine Learning course. This is a comprehensive project based course where you will learn how to optimize price, forecast demand, and implement dynamic pricing strategies. This course is a perfect combination between pricing analytics and machine learning, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in data science. In the introduction session, you will learn the basic fundamentals of pricing optimization and dynamic pricing, such as getting to know AI and machine learning applications in pricing and also understanding how pricing optimization models work. Then, in the next section, we will learn about foundations of pricing strategies, for examples strategies like cost based pricing, value based pricing, competitor based pricing, we will learn about factors that affect pricing like supply, demand, production cost, labor cost, material cost, in addition, this section also covers essential concepts like willingness to pay, fix vs variable cost, margin vs volume, and price sensitivity. Afterward, in the next section, we are going to learn about price elasticity of demand and additionally, we are also going to calculate price elasticity using sample data and interpret how changes in price affect customer demand. Then, we will start the project, firstly we are going to download pricing datasets from Kaggle which is a platform that provides a wide range of high quality datasets across different industries. After downloading the data, we are going to clean the data by handling missing values, removing duplicates, and detecting potential outliers. After that, we are going to analyze the correlation between price and quantity sold and visualize their relationship using a scatter plot. This will enable us to identify whether changes in price are associated with changes in demand. Following that, we are going to analyze and compare competitor prices by dividing our prices into three categories, below competitors, at market price, and above competitors. By doing so, we will be able to evaluate our competitor pricing positions. Before building a pricing optimization model using machine learning, we are going to conduct feature importance analysis. The objective is to find which features have the strongest correlation with the target variable and determine which factors are most relevant for pricing decisions. Then, we are going to build a pricing optimization model using XGBoost and Gradient Boosting Regressor. These models will predict the quantity sold, and to find the optimal price, we will multiply each candidate price by its predicted quantity to calculate expected revenue, then, we will select the price that generates the highest expected revenue. In the next section, we are going to forecast demand using the Catboost regressor. The objective is to predict demand under different market conditions so we can make more informed pricing decisions and adjust prices based on expected demand. Following that, we are going to analyze product pricing segments using unsupervised machine learning, specifically, K Means Clustering. By doing so, we will be able to identify groups of products with similar pricing and demand characteristics and develop appropriate pricing strategies for each segment. Next, we are going to optimize flight ticket prices using linear programming and also optimize hotel prices using reinforcement learning. These enable us to make optimal pricing decisions under different constraints and changing market conditions. In the next section, we are going to build a dynamic pricing model using Extra Trees Regressor and we are also going to create a dynamic pricing simulation using real time data, enabling the system to adjust prices based on current supply and demand conditions. Lastly, at the end of the course, we are going to build an AI agent for pricing intelligence. The agent will be able to perform web search to find competitor prices, identify price benchmarks, and analyze demand to provide data driven pricing recommendations.
First of all, before getting into the course, we need to ask this question to ourselves, why should we learn about pricing optimization and why should we use machine learning to optimize price. Well, here is my answer, pricing optimization helps businesses find the most optimal pricing point where they can maximize revenue. If the price is too low, revenue is not fully optimized, while setting the price too high can reduce demand and the number of units sold. Machine learning helps us analyze historical data and demand patterns to identify the pricing point that provides the best balance.
Below are things that you can expect to learn from this course
✨ Learn the basic fundamentals of pricing optimization and dynamic pricing
✨ Learn about pricing strategies like cost, value, competitor based pricing, different factors that affect pricing like supply, demand, and production cost
✨ Learn how to calculate price elasticity of demand
✨ Learn how to clean pricing dataset by handling missing values, removing duplicates, and detecting potential outliers
✨ Learn how to analyze relationship between price and quantity sold
✨ Learn how to analyze and compare competitor prices
✨ Learn how to conduct feature importance analysis using Random Forest Regressor
✨ Learn how to build pricing optimization model using XGBoost Regressor
✨ Learn how to build pricing optimization model using Gradient Boosting Regressor
✨ Learn how to forecast demand using CatBoost Regressor
✨ Learn how to analyse product pricing segment using K Means Clustering
✨ Learn how to optimize flight ticket price using Linear Programming
✨ Learn how to optimize hotel price with reinforcement learning and implement Deep Q Network
✨ Learn how to build dynamic pricing model using Extra Trees Regressor
✨ Learn how to create dynamic pricing simulation with real time data
✨ Learn how to build AI pricing intelligence agent using Gemini

Who this course is for

⭐ Pricing analysts who are interested in creating and implementing data driven pricing strategy
⭐ Data scientists who are interested in building pricing optimization models using machine learning

Homepage

https://www.udemy.com/course/pricing-optimization-dynamic-pricing-with-machine-learning

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