ANU UG/Degree 2nd & 4th Sem RV Results July/Aug 2023 @ Available Now

 Acharya Nagarjuna University (ANU) has released the revaluation results for the UG/Degree 2nd and 4th Sem examinations held in July/August 2023. Students who were not satisfied with their initial results can now check their revaluation results online.

Key Points

• ANU UG/Degree 2nd and 4th Sem Revaluation Results July/Aug 2023 are available online
• Students can check their revaluation results on the university's website or through the official mobile app
• The revaluation results are available in PDF format
• Students can also download their revaluation mark sheets online

 
 

 

How to Check Revaluation Results

Students can follow these steps to check their revaluation results online:

1. Go to the link given below
   
2. Click on the 'Results' tab
3. Select 'UG/Degree' from the drop-down menu
4. Select 'Revaluation' from the drop-down menu
5. Select '2nd Sem' or '4th Sem' from the drop-down menu
6. Enter your roll number and date of birth
7. Click on 'Submit'

The revaluation results will be displayed on the screen. Students can also download their revaluation mark sheets online.

 Acharya Nagarjuna University ANU UG/Degree 2nd  Sem Revaluation Results July/Aug 2023

 Acharya Nagarjuna University ANU UG/Degree 4th Sem Revaluation Results July/Aug 2023

 

data Presentation and Automation - Presenting findings and building applications on top of them

•  Presenting findings to stakeholders after successful data analysis and model development.
• Automating models to meet the demand for repeatable predictions and insights
• Implementing model scoring or creating applications for automatic updates of reports, Excel spreadsheets, or PowerPoint presentations.
• Emphasizing the importance of soft skills in the final stage of data science.
• Recommendation: Find dedicated books and information on the subject to enhance your skills.

Modeling - Build the models

 Model Building Process

1. Clean data and understanding of content are crucial.
2. Goals include better predictions, object classification, and system understanding.
3. Focused phase compared to exploratory analysis.
4. Outcomes determined by desired outcomes.
5. Below Figure illustrates model building components.

 Building a model is an iterative process. The way you build your model depends on whether you go with classic statistics or the somewhat more recent machine learning school, and the type of technique you want to use. Either way, most models consist of the following main steps:

 1. Model and variable selection

• Selecting variables and modeling technique based on exploratory analysis findings.
• Judgment required to choose the right model for a problem.
• Consideration of model performance and project requirements.
• Factors to consider: model's suitability for production environment, maintenance challenges, and model's ease of explanation.
• Action required once the model is developed.

2. Model execution

Once you’ve chosen a model you’ll need to implement it in code. Here are the two example 

Example1:

In the above code we provided how a linear regression model will be executed.

Example2:

 3. Model diagnostics and model comparison

• Multiple models are built and chosen based on multiple criteria.
• Holdout sample is used to evaluate the model after building.
• The model should work on unseen data.
• Only a fraction of the data is used for model estimation.
• The model is then unleashed on unseen data and error measures calculated.
• Multiple error measures are available, with the mean square error.