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How to Predict Future Values Using Historical Data in Excel
This video provides a useful tutorial to how you can predict values using Forecast in Excel. If you have historical time-based data, you can use it to create a forecast. Science Mentor tries to learn you how to use Forecast options to predict future values in Excel.
published: 14 Nov 2022
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Finding Residuals and Predicted Values on Your Calculator
This goes along with the 3.2 Part 2 Notes
published: 03 Oct 2020
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How to predict a future value using FORECAST. ETS function in Excel - Office 365
To predict future values based on the recurring pattern observed in the historical data use FORECAST.ETS function. This function uses an exponential smoothing (ETS) algorithm to predict a future value on a timeline based on a series of existing historical values. The predicted value is a continuation of the historical values in the specified target date which should be a continuation of the timeline. You can use this function to predict future sales, inventory requirements or consumer trends.
To download the practice Excel file please click on the link below
https://www.findeasysolution.com/Downloads/MSExcel/Formulas/Forecast.ETS-Function.xlsx
To learn more about different Forecast functions in Excel please click on the link below
https://www.youtube.com/playlist?list=PL5WGXr--aIL_zD_E...
published: 18 Aug 2022
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Predict values with regression
The essence of linear regression is arguably the simplest form of ML: drawing a line through points. You might have done a simple form of this in your high school physics class: plot the results of a series of experiments on graph paper and then draw a line through as many points as possible (and include as many points below the line as above the line where the points don't fall on the line). That is a very form of linear regression. We will build on that conceptual foundation to address more complex situations, such as having points in more than two dimensions or even points whose relationship seems non-linear.
[eventID:7194]
published: 17 Apr 2020
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Predict Football Match Winners With Machine Learning And Python
In this video, we'll use machine learning to predict who will win football matches in the EPL.
We'll start by cleaning the EPL match data we scraped in the last video (Web Scraping Football Matches From The EPL With Python [part 1 of 2]). Don't worry if you missed the last video - you'll still be able to download the data.
We'll create predictors and train a machine learning model to predict the winner of each of the football matches.
Then we'll end by measuring error and making improvements.
You can find the data and code here - https://github.com/dataquestio/project-walkthroughs/tree/master/football_matches
Chapters
00:00 Introduction
00:59 Reading match data into pandas dataframe
02:58 Investigating missing data
05:55 Cleaning our data for machine learning
08:05 Creating predicto...
published: 09 May 2022
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Using a Linear Regression Model to Predict Future Values
Don’t forget to like, comment, and subscribe so you don’t miss future videos!
Share this video: https://youtu.be/LAXGKJKMH_I
This video demonstrates how to use Linear Regression Model (function) to Predict Future Values.
published: 29 Dec 2020
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96 predict vs predict proba | Scikit-learn Creating Machine Learning Models
published: 20 Mar 2021
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Forecast Function in Excel to predict future values by Chris Menard
The FORECAST Function in Excel will use current or existing data to predict or forecast future values. The Forecast function has three arguments. All are required. In this video, we will use the forecast function in three examples:
1) Existing sales for a company to predict future sales values by years
2) We will look at an automobile or car deprecation
3) World population - we will examine the world population for 2020 to 2050 in five-year increments.
Three arguments for the forecast function:
X Required. The data point for which you want to predict a value.
Known_y's Required. The dependent array or range of data.
Known_x's Required. The independent array or range of data.
FORECAST(x, known_y's, known_x's) is the syntax for the function.
Microsoft's support webpage for ...
published: 12 Feb 2020
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Predict The Stock Market With Machine Learning And Python
In this tutorial, we'll learn how to predict tomorrow's S&P 500 index price using historical data. We'll also learn how to avoid common issues that make most stock price models overfit in the real world.
We'll start by downloading S&P 500 prices using a package called yfinance. Then, we'll clean up the data with pandas, and get it ready for machine learning.
We'll train a random forest model and make predictions using backtesting. Then, we'll improve the model by adding predictors. We'll end with next steps you can use to improve the model on your own.
You can find an overview of the project and the code here - https://github.com/dataquestio/project-walkthroughs/tree/master/sp_500 .
If you enjoyed this tutorial, check out this link https://bit.ly/3O8MDef for free courses that will...
published: 23 May 2022
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How to predict values using linear regression in power bi | Power BI scenarios videos
Welcome to DWBIADDA's Power BI scenarios and questions and answers tutorial, as part of this lecture we will see,How to predict values using linear regression in power bi
published: 01 Sep 2020
4:26
How to Predict Future Values Using Historical Data in Excel
This video provides a useful tutorial to how you can predict values using Forecast in Excel. If you have historical time-based data, you can use it to create a ...
This video provides a useful tutorial to how you can predict values using Forecast in Excel. If you have historical time-based data, you can use it to create a forecast. Science Mentor tries to learn you how to use Forecast options to predict future values in Excel.
https://wn.com/How_To_Predict_Future_Values_Using_Historical_Data_In_Excel
This video provides a useful tutorial to how you can predict values using Forecast in Excel. If you have historical time-based data, you can use it to create a forecast. Science Mentor tries to learn you how to use Forecast options to predict future values in Excel.
- published: 14 Nov 2022
- views: 905
9:29
How to predict a future value using FORECAST. ETS function in Excel - Office 365
To predict future values based on the recurring pattern observed in the historical data use FORECAST.ETS function. This function uses an exponential smoothing (...
To predict future values based on the recurring pattern observed in the historical data use FORECAST.ETS function. This function uses an exponential smoothing (ETS) algorithm to predict a future value on a timeline based on a series of existing historical values. The predicted value is a continuation of the historical values in the specified target date which should be a continuation of the timeline. You can use this function to predict future sales, inventory requirements or consumer trends.
To download the practice Excel file please click on the link below
https://www.findeasysolution.com/Downloads/MSExcel/Formulas/Forecast.ETS-Function.xlsx
To learn more about different Forecast functions in Excel please click on the link below
https://www.youtube.com/playlist?list=PL5WGXr--aIL_zD_E3ilYtMGscPtqRqdhj
https://wn.com/How_To_Predict_A_Future_Value_Using_Forecast._Ets_Function_In_Excel_Office_365
To predict future values based on the recurring pattern observed in the historical data use FORECAST.ETS function. This function uses an exponential smoothing (ETS) algorithm to predict a future value on a timeline based on a series of existing historical values. The predicted value is a continuation of the historical values in the specified target date which should be a continuation of the timeline. You can use this function to predict future sales, inventory requirements or consumer trends.
To download the practice Excel file please click on the link below
https://www.findeasysolution.com/Downloads/MSExcel/Formulas/Forecast.ETS-Function.xlsx
To learn more about different Forecast functions in Excel please click on the link below
https://www.youtube.com/playlist?list=PL5WGXr--aIL_zD_E3ilYtMGscPtqRqdhj
- published: 18 Aug 2022
- views: 6689
54:28
Predict values with regression
The essence of linear regression is arguably the simplest form of ML: drawing a line through points. You might have done a simple form of this in your high scho...
The essence of linear regression is arguably the simplest form of ML: drawing a line through points. You might have done a simple form of this in your high school physics class: plot the results of a series of experiments on graph paper and then draw a line through as many points as possible (and include as many points below the line as above the line where the points don't fall on the line). That is a very form of linear regression. We will build on that conceptual foundation to address more complex situations, such as having points in more than two dimensions or even points whose relationship seems non-linear.
[eventID:7194]
https://wn.com/Predict_Values_With_Regression
The essence of linear regression is arguably the simplest form of ML: drawing a line through points. You might have done a simple form of this in your high school physics class: plot the results of a series of experiments on graph paper and then draw a line through as many points as possible (and include as many points below the line as above the line where the points don't fall on the line). That is a very form of linear regression. We will build on that conceptual foundation to address more complex situations, such as having points in more than two dimensions or even points whose relationship seems non-linear.
[eventID:7194]
- published: 17 Apr 2020
- views: 269
44:43
Predict Football Match Winners With Machine Learning And Python
In this video, we'll use machine learning to predict who will win football matches in the EPL.
We'll start by cleaning the EPL match data we scraped in the las...
In this video, we'll use machine learning to predict who will win football matches in the EPL.
We'll start by cleaning the EPL match data we scraped in the last video (Web Scraping Football Matches From The EPL With Python [part 1 of 2]). Don't worry if you missed the last video - you'll still be able to download the data.
We'll create predictors and train a machine learning model to predict the winner of each of the football matches.
Then we'll end by measuring error and making improvements.
You can find the data and code here - https://github.com/dataquestio/project-walkthroughs/tree/master/football_matches
Chapters
00:00 Introduction
00:59 Reading match data into pandas dataframe
02:58 Investigating missing data
05:55 Cleaning our data for machine learning
08:05 Creating predictors for machine learning
14:00 Creating our initial machine learning model
22:34 Improving precision with rolling averages
31:07 Retraining our machine learning model
34:08 Combining home and away predictions
42:12 Recap and next steps
------------------
Join 1M+ Dataquest learners today!
Master data skills and change your life.
Sign up for free: https://bit.ly/3O8MDef
#Dataquest #Tutorial #DataScience #MachineLearning #WebScraping #Python
https://wn.com/Predict_Football_Match_Winners_With_Machine_Learning_And_Python
In this video, we'll use machine learning to predict who will win football matches in the EPL.
We'll start by cleaning the EPL match data we scraped in the last video (Web Scraping Football Matches From The EPL With Python [part 1 of 2]). Don't worry if you missed the last video - you'll still be able to download the data.
We'll create predictors and train a machine learning model to predict the winner of each of the football matches.
Then we'll end by measuring error and making improvements.
You can find the data and code here - https://github.com/dataquestio/project-walkthroughs/tree/master/football_matches
Chapters
00:00 Introduction
00:59 Reading match data into pandas dataframe
02:58 Investigating missing data
05:55 Cleaning our data for machine learning
08:05 Creating predictors for machine learning
14:00 Creating our initial machine learning model
22:34 Improving precision with rolling averages
31:07 Retraining our machine learning model
34:08 Combining home and away predictions
42:12 Recap and next steps
------------------
Join 1M+ Dataquest learners today!
Master data skills and change your life.
Sign up for free: https://bit.ly/3O8MDef
#Dataquest #Tutorial #DataScience #MachineLearning #WebScraping #Python
- published: 09 May 2022
- views: 136914
2:51
Using a Linear Regression Model to Predict Future Values
Don’t forget to like, comment, and subscribe so you don’t miss future videos!
Share this video: https://youtu.be/LAXGKJKMH_I
This video demonstrates how t...
Don’t forget to like, comment, and subscribe so you don’t miss future videos!
Share this video: https://youtu.be/LAXGKJKMH_I
This video demonstrates how to use Linear Regression Model (function) to Predict Future Values.
https://wn.com/Using_A_Linear_Regression_Model_To_Predict_Future_Values
Don’t forget to like, comment, and subscribe so you don’t miss future videos!
Share this video: https://youtu.be/LAXGKJKMH_I
This video demonstrates how to use Linear Regression Model (function) to Predict Future Values.
- published: 29 Dec 2020
- views: 924
7:53
Forecast Function in Excel to predict future values by Chris Menard
The FORECAST Function in Excel will use current or existing data to predict or forecast future values. The Forecast function has three arguments. All are requir...
The FORECAST Function in Excel will use current or existing data to predict or forecast future values. The Forecast function has three arguments. All are required. In this video, we will use the forecast function in three examples:
1) Existing sales for a company to predict future sales values by years
2) We will look at an automobile or car deprecation
3) World population - we will examine the world population for 2020 to 2050 in five-year increments.
Three arguments for the forecast function:
X Required. The data point for which you want to predict a value.
Known_y's Required. The dependent array or range of data.
Known_x's Required. The independent array or range of data.
FORECAST(x, known_y's, known_x's) is the syntax for the function.
Microsoft's support webpage for the Forecast Function
https://support.office.com/en-us/article/forecast-function-50ca49c9-7b40-4892-94e4-7ad38bbeda99
#msexcel #forecastfunction #excelfunctions #exceltips #forecastexamples #chrismenard #forecast
Chris Menard's website:
https://chrismenardtraining.com
And make sure you subscribe to my channel!
-- EQUIPMENT USED ---------------------------------
○ My camera – https://amzn.to/3vdgF5E
○ Microphone - https://amzn.to/3gphDXh
○ Camera tripod – https://amzn.to/3veN6Rg
○ Studio lights - https://amzn.to/3vaxyy5
○ Dual monitor mount stand - https://amzn.to/3vbZSjJ
○ Web camera – https://amzn.to/2Tg75Sn
○ Shock mount - https://amzn.to/3g96FGj
○ Boom Arm - https://amzn.to/3g8cNi6
-- SOFTWARE USED ---------------------------------
○ Screen recording – Camtasia – https://chrismenardtraining.com/camtasia
○ Screenshots – Snagit – https://chrismenardtraining.com/snagit
○ YouTube keyword search – TubeBuddy – https://www.tubebuddy.com/chrismenard
DISCLAIMER: Links included in this description might be affiliate links. If you purchase a product or service with the links I provide, I may receive a small commission. There is no additional charge to you! Thank you for supporting my channel, so I can continue to provide you with free content each week!
https://wn.com/Forecast_Function_In_Excel_To_Predict_Future_Values_By_Chris_Menard
The FORECAST Function in Excel will use current or existing data to predict or forecast future values. The Forecast function has three arguments. All are required. In this video, we will use the forecast function in three examples:
1) Existing sales for a company to predict future sales values by years
2) We will look at an automobile or car deprecation
3) World population - we will examine the world population for 2020 to 2050 in five-year increments.
Three arguments for the forecast function:
X Required. The data point for which you want to predict a value.
Known_y's Required. The dependent array or range of data.
Known_x's Required. The independent array or range of data.
FORECAST(x, known_y's, known_x's) is the syntax for the function.
Microsoft's support webpage for the Forecast Function
https://support.office.com/en-us/article/forecast-function-50ca49c9-7b40-4892-94e4-7ad38bbeda99
#msexcel #forecastfunction #excelfunctions #exceltips #forecastexamples #chrismenard #forecast
Chris Menard's website:
https://chrismenardtraining.com
And make sure you subscribe to my channel!
-- EQUIPMENT USED ---------------------------------
○ My camera – https://amzn.to/3vdgF5E
○ Microphone - https://amzn.to/3gphDXh
○ Camera tripod – https://amzn.to/3veN6Rg
○ Studio lights - https://amzn.to/3vaxyy5
○ Dual monitor mount stand - https://amzn.to/3vbZSjJ
○ Web camera – https://amzn.to/2Tg75Sn
○ Shock mount - https://amzn.to/3g96FGj
○ Boom Arm - https://amzn.to/3g8cNi6
-- SOFTWARE USED ---------------------------------
○ Screen recording – Camtasia – https://chrismenardtraining.com/camtasia
○ Screenshots – Snagit – https://chrismenardtraining.com/snagit
○ YouTube keyword search – TubeBuddy – https://www.tubebuddy.com/chrismenard
DISCLAIMER: Links included in this description might be affiliate links. If you purchase a product or service with the links I provide, I may receive a small commission. There is no additional charge to you! Thank you for supporting my channel, so I can continue to provide you with free content each week!
- published: 12 Feb 2020
- views: 45882
35:55
Predict The Stock Market With Machine Learning And Python
In this tutorial, we'll learn how to predict tomorrow's S&P 500 index price using historical data. We'll also learn how to avoid common issues that make most st...
In this tutorial, we'll learn how to predict tomorrow's S&P 500 index price using historical data. We'll also learn how to avoid common issues that make most stock price models overfit in the real world.
We'll start by downloading S&P 500 prices using a package called yfinance. Then, we'll clean up the data with pandas, and get it ready for machine learning.
We'll train a random forest model and make predictions using backtesting. Then, we'll improve the model by adding predictors. We'll end with next steps you can use to improve the model on your own.
You can find an overview of the project and the code here - https://github.com/dataquestio/project-walkthroughs/tree/master/sp_500 .
If you enjoyed this tutorial, check out this link https://bit.ly/3O8MDef for free courses that will help you master data skills.
Chapters
00:00 - Introduction
01:28 - Downloading S&P 500 price data
03:30 - Cleaning and visualizing our stock market data
04:29 - Setting up our target for machine learning
08:19 - Training an initial machine learning model
17:01 - Building a backtesting system
23:05 - Adding additional predictors to our model
28:45 - Improving our model
33:37 - Summary and next steps with the model
---------------------------------
Join 1M+ Dataquest learners today!
Master data skills and change your life.
Sign up for free: https://bit.ly/3O8MDef
https://wn.com/Predict_The_Stock_Market_With_Machine_Learning_And_Python
In this tutorial, we'll learn how to predict tomorrow's S&P 500 index price using historical data. We'll also learn how to avoid common issues that make most stock price models overfit in the real world.
We'll start by downloading S&P 500 prices using a package called yfinance. Then, we'll clean up the data with pandas, and get it ready for machine learning.
We'll train a random forest model and make predictions using backtesting. Then, we'll improve the model by adding predictors. We'll end with next steps you can use to improve the model on your own.
You can find an overview of the project and the code here - https://github.com/dataquestio/project-walkthroughs/tree/master/sp_500 .
If you enjoyed this tutorial, check out this link https://bit.ly/3O8MDef for free courses that will help you master data skills.
Chapters
00:00 - Introduction
01:28 - Downloading S&P 500 price data
03:30 - Cleaning and visualizing our stock market data
04:29 - Setting up our target for machine learning
08:19 - Training an initial machine learning model
17:01 - Building a backtesting system
23:05 - Adding additional predictors to our model
28:45 - Improving our model
33:37 - Summary and next steps with the model
---------------------------------
Join 1M+ Dataquest learners today!
Master data skills and change your life.
Sign up for free: https://bit.ly/3O8MDef
- published: 23 May 2022
- views: 514338
4:48
How to predict values using linear regression in power bi | Power BI scenarios videos
Welcome to DWBIADDA's Power BI scenarios and questions and answers tutorial, as part of this lecture we will see,How to predict values using linear regression i...
Welcome to DWBIADDA's Power BI scenarios and questions and answers tutorial, as part of this lecture we will see,How to predict values using linear regression in power bi
https://wn.com/How_To_Predict_Values_Using_Linear_Regression_In_Power_Bi_|_Power_Bi_Scenarios_Videos
Welcome to DWBIADDA's Power BI scenarios and questions and answers tutorial, as part of this lecture we will see,How to predict values using linear regression in power bi
- published: 01 Sep 2020
- views: 4601