Browse Notebooks

A regression is among one of the most commonly used methods when it comes to analysts trying to describe the result of an event, such as the effect of different characteristics on housing prices, which is what we are going to focus on in this Notebook.

In this Project, we will learn about conjoint analysis and its application when introducing a new product into the market. Rank-based conjoint analysis is carried out in this Project using multiple linear regression.

This notebook describes probability distributions often used in simulation modeling, demonstrates how to generate random variables from these distributions, and visualizes the distributions through their density functions and histograms from sampled data.

In this Notebook, we are going to explore ways we can use Python to look for and visualize critical paths in projects.

In this Project, we use Monte Carlo simulation and optimization tools to determine the optimal expected profits under different scenarios.

In this project, we will lead you through a combination of text processing and marketing that mainly focus on the content optimization aspect: the recommender system and let you understand how the system actually works.

In this Project, we will focus on combining RFM (Recency, Frequency, and Monetary value) analysis with clustering analysis to to identify different market segments.

Scraping of websites is a very useful activity especially to achieve recruiting or marketing tasks. Using Python you can make this process smoother, using your time to focus on those profiles that have critical peculiarities.

This is the second of a series of two notebooks on the topic of Sales Forecast. Through this series, we want to showcase one of the many ways that one can follow exloring and forecasting time series data.

This is the first of a series of two notebooks on the topic of Sales Forecast. Through this series, we want to showcase one of the many ways that one can follow exloring and forecasting time series data.

In this Project, we will be introducing eight basic techniques that can be applied to most of the data sets that you will encounter. Our goal is to transform extremely messy data into less messy data.

In this Project, we focus on RFM (Recency, Frequency, and Monetary value) analysis. This type of analysis is used when historical data for existing customers are available.

In this Project, we will review the economic order quantity (EOQ) model and its application when demand uncertainty is introduced.

In this Notebook, we are going to explore ways to apply Monte Carlo Simulations in predicting a project's completion date and assessing a project's risk.

In this project, we will lead you through examples to talk about why creating different version can bring more profits to the companies while the customers are still willing to pay for it.

In this notebook, we are going to explore ways that one can apply Discrete Event Simulation when making resource planning decisions for Emergency Rooms (ER).

In this Project, we will focus on using Python and Pandas to retrieve historical stock prices from Yahoo Finance to perform primary analysis of stock returns and calculate key financial statistics or moments