Master DataFrame Creation in Pandas for EDA 🐼 | Vienna Hotels Series Part 5

## Exploratory Data Analysis Python Tutorial Series on Vienna Hotels - Part 5 of 6

In this video, we dive into multiple linear regression, adding more explanatory variables to enhance our model's ability to explain variations in price. We'll use Pandas to create a DataFrame, and filter and transform the data. We'll focus on incorporating variables such as hotel stars and ratings, turning stars into binary or dummy variables, and fitting a model to better understand hotel pricing.

🎓 For the blog post + code snippets from this video, visit: https://bit.ly/eda-python-tutorial-hotels-2

## See the full EDA with Python Tutorial Series

Part 1: https://www.youtube.com/watch?v=xJl4_wrWWw4
Part 2: https://www.youtube.com/watch?v=gsFLjlaWkOk
Part 3: https://www.youtube.com/watch?v=jWSAdhZJqhI
Part 4: https://www.youtube.com/watch?v=Pkso7J9osY8
Part 5: https://www.youtube.com/watch?v=3whIUhAb8js
Part 6: https://www.youtube.com/watch?v=ZxpV6ZRxvnw

## Timestamps

00:00 - Introduction to Multiple Linear Regression
00:10 - Adding More Explanatory Variables
00:25 - Goal: Explain More Variation in Price
00:40 - Using R Squared to Represent Variation
00:55 - Incorporating Hotel Stars and Ratings
01:10 - Using Stars as Binary Variables
01:25 - Filtering Data for Hotels with 3 to 4 Stars
01:55 - Preparing Data for Model Fitting
02:10 - Creating DataFrame for Analysis
02:30 - Renaming Columns for Clarity
03:00 - Adding Stars and Ratings to DataFrame
03:20 - Filtering DataFrame by Stars
03:55 - Ensuring Stars as Dummy Variables
04:10 - Using Pandas' Get Dummies Function
04:30 - Concatenating DataFrames
05:00 - Cleaning Data and Preparing for Model Fitting
06:00 - Dropping Unnecessary Columns
06:30 - Adjusting Distances for Accuracy
07:00 - Filtering Prices to Handle Influential Values
07:30 - Checking Descriptive Statistics
08:00 - Reviewing and Renaming Dummy Variables
09:00 - Discussing the Role of Dummy Variables in Regression
09:45 - Ensuring Data Readiness for Model Fitting
10:30 - Previewing Next Steps: Fitting the Model
11:00 - Break and Upcoming Content

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