Categories
Machine Learning

Pose some questions that you want to analyze. Formulate hypotheses to test your

Pose some questions that you want to analyze.
Formulate hypotheses to test your questions.
Perform the “classic” data analysis steps ( ANOVA, data cleaning, EDA, dependent variable selection, regression, feature selection, classification, etc.) to determine if your hypothesis is supported.
WRITE A REPORT ABOUT WHAT YOU HAVE DONE WITH CONCLUSION MADE.
Submit commented code in .ipynb file. Submit the design of your experiment, hypotheses and conclusions as .pdf file

Categories
Machine Learning

Pose some questions that you want to analyze. Formulate hypotheses to test your

Pose some questions that you want to analyze.
Formulate hypotheses to test your questions.
Perform the “classic” data analysis steps ( ANOVA, data cleaning, EDA, dependent variable selection, regression, feature selection, classification, etc.) to determine if your hypothesis is supported.
WRITE A REPORT ABOUT WHAT YOU HAVE DONE WITH CONCLUSION MADE.
Submit commented code in .ipynb file. Submit the design of your experiment, hypotheses and conclusions as .pdf file

Categories
Machine Learning

Pose some questions that you want to analyze. Formulate hypotheses to test your

Pose some questions that you want to analyze.
Formulate hypotheses to test your questions.
Perform the “classic” data analysis steps ( ANOVA, data cleaning, EDA, dependent variable selection, regression, feature selection, classification, etc.) to determine if your hypothesis is supported.
WRITE A REPORT ABOUT WHAT YOU HAVE DONE WITH CONCLUSION MADE.
Submit commented code in .ipynb file. Submit the design of your experiment, hypotheses and conclusions as .pdf file

Categories
Machine Learning

Pose some questions that you want to analyze. Formulate hypotheses to test your

Pose some questions that you want to analyze.
Formulate hypotheses to test your questions.
Perform the “classic” data analysis steps ( ANOVA, data cleaning, EDA, dependent variable selection, regression, feature selection, classification, etc.) to determine if your hypothesis is supported.
WRITE A REPORT ABOUT WHAT YOU HAVE DONE WITH CONCLUSION MADE.
Submit commented code in .ipynb file. Submit the design of your experiment, hypotheses and conclusions as .pdf file

Categories
Machine Learning

I have a running code and implementation in python for iris clustring method and

I have a running code and implementation in python for iris clustring method and image clustring method and I just to need write a report about the algorithim and the code.

Categories
Machine Learning

Title: implementation of SimCLR self-supervised learning method for pretraining

Title: implementation of SimCLR self-supervised learning method for pretraining robust feature extractors
(2500 words minimum)

Categories
Machine Learning

The project idea is: TIME SERIES ANALYSIS FOR GREEN ENERENERGY CONSUMPTION The

The project idea is: TIME SERIES ANALYSIS FOR GREEN ENERENERGY CONSUMPTION
The data must be based on the time data.
Please use the ML which only covers in the Learning Objectives. Not too advance.
The Machine Learning code will be delivered in the Jupiter Notebook.
Please include the final presentation in powerpoint.
The project has 3 parts
Jobs to be Done (JTBD Framework), Buyer Persona, Discovery Hypothesis – You can complete 2 out of 3 by tomorrow. Please complete them in details. You can see the attachment for examples.
OBJECTIVE The objective would be to understand and Predict the Next 3 years’ total Energy Consumption based on past trends using Different Time Series Visualization/Analysis Methods (Autocorrelation, Seasonality, Trends and Residual) and Prediction Methods ranging from Arima, Dickey Fuller, Regression Analysis to FB Prophet and Evaluating each of the models the on different Evaluation Metrics (RMSE).
There are 3 main deadlines
All written requirements (not including wireframes) – 20 hours
You can complete the wireframes in next 12 hours
The code and final product next 3 days

Categories
Machine Learning

Please send me your project idea once you accept it. The data must be based on t

Please send me your project idea once you accept it.
The data must be based on the time data.
Please use the ML which only covers in the Learning Objectives. Not too advance.
The Machine Learning code will be delivered in the Jupiter Notebook.
Please include the final presentation in powerpoint
There are 2 main deadlines
All written requirements (not including wireframes) – 11/29 – You can complete the wireframes on 11/30
The code and final product 12/7
You can use this idea: TIME SERIES ANALYSIS FOR GREEN ENERGY CONSUMPTION
OBJECTIVE
The objective would be to understand and Predict the Next 3 years’ total Energy Consumption based on past trends using Different Time Series Visualization/Analysis Methods (Autocorrelation, Seasonality, Trends and Residual) and Prediction Methods ranging from Arima, Dickey Fuller, Regression Analysis to FB Prophet and Evaluating each of the models the on different Evaluation Metrics (RMSE).

Categories
Machine Learning

The project idea is: TIME SERIES ANALYSIS FOR GREEN ENERENERGY CONSUMPTION The

The project idea is: TIME SERIES ANALYSIS FOR GREEN ENERENERGY CONSUMPTION The data must be based on the time data.
Please use the ML which only covers in the Learning Objectives. Not too advance.
The Machine Learning code will be delivered in the Jupiter Notebook.
Please include the final presentation in powerpoint. The project has 3 parts
Jobs to be Done (JTBD Framework), Buyer Persona, Discovery Hypothesis – You can complete 2 out of 3 by tomorrow. Please complete them in details. You can see the attachment for examples.
OBJECTIVE The objective would be to understand and Predict the Next 3 years’ total Energy Consumption based on past trends using Different Time Series Visualization/Analysis Methods (Autocorrelation, Seasonality, Trends and Residual) and Prediction Methods ranging from Arima, Dickey Fuller, Regression Analysis to FB Prophet and Evaluating each of the models the on different Evaluation Metrics (RMSE).
There are 3 main deadlines
All written requirements (not including wireframes) – 20 hours
You can complete the wireframes in next 12 hours
The code and final product next 3 days

Categories
Machine Learning

Please send me your project idea once you accept it. The data must be based on t

Please send me your project idea once you accept it.
The data must be based on the time data.
Please use the ML which only covers in the Learning Objectives. Not too advance.
The Machine Learning code will be delivered in the Jupiter Notebook.
Please include the final presentation in powerpoint
There are 2 main deadlines
All written requirements (not including wireframes) – 11/29 – You can complete the wireframes on 11/30
The code and final product 12/7
You can use this idea: TIME SERIES ANALYSIS FOR GREEN ENERGY CONSUMPTION
OBJECTIVE
The objective would be to understand and Predict the Next 3 years’ total Energy Consumption based on past trends using Different Time Series Visualization/Analysis Methods (Autocorrelation, Seasonality, Trends and Residual) and Prediction Methods ranging from Arima, Dickey Fuller, Regression Analysis to FB Prophet and Evaluating each of the models the on different Evaluation Metrics (RMSE).