Part 3 of 3, Creating a Regression Model in Python
Using the past to predict the future! Say hello to part 3 of 3 in this series on regression modeling with python! In blog 1, I covered the important processing steps prior to creating a linear regression model. In blog 2, I showed you how to create the actual regression model along with demonstrating how to reuse the model with new data. In this blog I will cover how to check for post-linear model creation assumptions: Homoscedasticity & Normality in Residuals.
Part 2 of 3, Creating a Regression Model in Python
Using the past to predict the future! Say hello to part 2 of 3 in this series on regression modeling with python! In blog 1, I covered the important processing steps prior to creating a linear regression model. In this blog I build from that foundation by creating the actual regression model along with demonstrating how to reuse the model with new data. In blog 3 I will cover how to check for the post-linear model creation assumptions.
Part 1 of 3, Creating a Regression Model in Python
Using the past to predict the future! Say hello to Regression Modeling! In this three-part series I will show you how to create, use, and check the validity of a regression model with python. To effectively cover the topic, I have broken the topic into the following parts.
Getting Presentation Ready Formats with Aggregate Functions
In a previous post I showed how to use .groupby() with .agg()to summarize large amounts of data (see here). Furthermore, I provided a solution to formatting the output of .agg() functions which can be tricky for the Python beginner given the additional “layer” aggregate functions create when displaying the output. In this post I will provide a solution to a related problem, formatting the output of an aggregate function when you are looking to add different formats to outputs that resides in the same column or row. For the old excel pro…
In my previous post I introduced you to some of the basics when viewing, cleaning and transforming your data using Dataframes (see post). In this post I go a step further by showing you how to summarize your data using .groupby(). Like my previous posts, this post is for the beginner, perhaps an old Excel pro looking to make the jump from Excel to Python and needing a gentle introduction to Dataframes. Making the transition from Excel to Python or incorporating Python into your analytic repertoire can be daunting. Mastering Python Dataframes is the right first step in this…
In my previous post I covered my background and goals for Data Science Bootcamp… Becoming a Data Alchemist (see post). Now I am going to introduce you to Python and one of its core tools for working with data, the Dataframe. Having been schooled in C.P.G. Marketing in Corporate America, I have gained a certain degree of bias for Excel and performing analysis the “Excel way”. However, in bootcamp, it’s the “Python way” or the highway! As I am learning this new environment, I am finding myself constantly comparing the keystrokes required to perform certain tasks in Excel vs. the…
Recently I enrolled in Flatirons Data Science Bootcamp. My learning goals for the program are simple: Data Alchemy: The magical process of transforming data into insights.
My background is in Corporate America, C.P.G Marketing, with a heavy focus on leading teams in New Product Innovation. I have my M.B.A in Marketing and have had several analysts related responsibilities.
Why Data Science?
Innovation! Specifically, using data to drive innovation! Data and Innovation are two words that are rarely combined, but to me this combination is the next frontier in innovation. How do companies and or inventors take the overwhelming amounts…
Innovation Leader and Insight Enthusiast !