filmov
tv
EDA with Python & Pandas (3/6): Build a Simple Linear Regression Model
Показать описание
## Exploratory Data Analysis Python Tutorial Series on Vienna Hotels - Part 3 of 6
In this video, we continue our exploration of parameterized linear regression, a fundamental technique widely used in data analysis.
Building upon the previous videos, we delve into the nuances of building and interpreting a parameterized linear regression model. We discuss the significance of alpha and beta coefficients and the scenarios where a simple linear regression model can be more effective than complex machine learning models, especially when presenting to a business audience.
We demonstrate the process of fitting a linear regression model using statsmodels, emphasizing the importance of the intercept and slope coefficients. We cover the concept of binary variables and their impact on regression models, highlighting how different categories can affect our predictions. The video also explains the significance of residuals and RMSE (Root Mean Squared Error) in evaluating model performance.
By the end of the session, you'll have a clear understanding of how to implement and interpret a simple linear regression model, the importance of minimizing residuals, and the role of RMSE in regression analysis. We also touch upon the importance of confidence intervals and their interpretation in the context of model coefficients.
## See the full EDA with Python Tutorial Series
## Timestamps
00:00 - Introduction to Parameterized Linear Regression
00:06 - Building and Interpreting Linear Regression Models
00:18 - Importance of Alpha and Beta Coefficients
00:28 - Scenarios for Choosing Linear Regression over Complex Models
00:57 - Fitting a Linear Regression Model using Statsmodels
01:37 - Binary Variables and Their Impact
02:10 - Understanding Residuals and RMSE
03:45 - Computing and Interpreting RMSE
04:40 - Exploring Confidence Intervals
06:45 - Preparing for Multiple Linear Regression in Future Sessions
If you enjoyed this video, please like and subscribe to our channel! Leave a comment if there's a topic you'd like us to cover next.
See you in the next video!
In this video, we continue our exploration of parameterized linear regression, a fundamental technique widely used in data analysis.
Building upon the previous videos, we delve into the nuances of building and interpreting a parameterized linear regression model. We discuss the significance of alpha and beta coefficients and the scenarios where a simple linear regression model can be more effective than complex machine learning models, especially when presenting to a business audience.
We demonstrate the process of fitting a linear regression model using statsmodels, emphasizing the importance of the intercept and slope coefficients. We cover the concept of binary variables and their impact on regression models, highlighting how different categories can affect our predictions. The video also explains the significance of residuals and RMSE (Root Mean Squared Error) in evaluating model performance.
By the end of the session, you'll have a clear understanding of how to implement and interpret a simple linear regression model, the importance of minimizing residuals, and the role of RMSE in regression analysis. We also touch upon the importance of confidence intervals and their interpretation in the context of model coefficients.
## See the full EDA with Python Tutorial Series
## Timestamps
00:00 - Introduction to Parameterized Linear Regression
00:06 - Building and Interpreting Linear Regression Models
00:18 - Importance of Alpha and Beta Coefficients
00:28 - Scenarios for Choosing Linear Regression over Complex Models
00:57 - Fitting a Linear Regression Model using Statsmodels
01:37 - Binary Variables and Their Impact
02:10 - Understanding Residuals and RMSE
03:45 - Computing and Interpreting RMSE
04:40 - Exploring Confidence Intervals
06:45 - Preparing for Multiple Linear Regression in Future Sessions
If you enjoyed this video, please like and subscribe to our channel! Leave a comment if there's a topic you'd like us to cover next.
See you in the next video!
0:40:22
Exploratory Data Analysis with Pandas Python
0:32:13
Exploratory Data Analysis in Pandas | Python Pandas Tutorials
0:28:52
Exploratory Data Analysis in Python using pandas
0:38:37
Data Cleaning in Pandas | Python Pandas Tutorials
0:18:38
Exploratory Data Analysis with Python | PANDAS
0:29:59
Exploratory Data Analysis (EDA) Using Python | Python Data Analysis | Python Training | Edureka
0:05:59
Exploratory Data Analysis (EDA) with python || pandas_profiling (ProfileReport)
0:15:54
Exploratory Data Analysis (EDA) with Python using Pandas
0:00:57
Do FASTER Python Exploratory Data Analysis with this!
0:00:43
How to learn Python fast for data analysis? #dataanalyst #python #pandas #numpy #matplotlib
0:22:53
EDA with Python & Pandas (5/6): Create Pandas DataFrame
0:10:34
Python Exploratory Data Analysis (EDA) using Pandas and Seaborn - Flights Dataset
0:00:35
Pandas Style to Color your Data
0:29:56
Exploratory Data Analysis With Pandas || Python Machine Learning PT.1
0:12:50
Fast and Effective Exploratory Data Analysis (EDA) With Python and Pandas Profiling for Data Science
0:44:55
Economic Data Analysis Project with Python Pandas - Data scraping, cleaning and exploration!
0:00:53
Will Polars replace Pandas for Data Science?
0:00:51
Python interview questions (Pandas) #ai #machinelearning #datascience #dataanalytics #dataengineers
0:30:49
PYTHON EXPLORATORY DATA ANALYSIS | EDA | Pandas [End To End Project-Car Dataset]
0:23:05
EDA with Python & Pandas (6/6): Build a Multiple Regression Model
0:27:35
EDA with pandas using the House Price Data: Part 1
0:00:58
Clean MESSY String Data in Pandas
0:08:12
Python PandasGUI For Exploratory Data Analysis (EDA)| Data Analysis Using Mouse |PandasGUI Tutorial
0:22:11
Several Techniques Of EDA In Python Using Pandas | Matplotlib | Seaborn
Комментарии