For requirement 3 you are encouraged to use more than one analysis technique. For example, you might
Question:
For requirement 3you are encouraged to use more than one analysis technique. For example, you might use clustering to find groups within the data and then perform a linear regression on some variables within the groups. Or, you might use logistic regression to establish a baseline classification performance and then apply a neural network to see if you can improve performance.
Requirement 4can be involved in any part of the project, such as data itself, data exploration, and data analysis. You may use a bar to visualise a categorical variable or a histogram for a numerical variable.
Note that you need to include necessary instructions and explanations in your notebook file to demonstrate that you have met these requirements.
Here are a few suggestions for your portfolio 4:
- Make use of linear regression as a predictive model and improve it using polynomial regression. Find important features using the RFE technique.
- Make use of various classification/prediction/clustering techniques from the unit
- Use various criteria (or metrics) for evaluation: e.g. use of Mean Square Error (MSE), Mean Absolute Error (MAE), and R-squared (r2) for regression problem. Use of accuracy, F-score, Area Under the ROC curve (AUC) for classification problem.
- First, implement a simple algorithm (or model) as a baseline and then improve the baseline using more complex models/techniques.
- Do parameter analysis to find out which configuration of parameters gives the best model's performance. For example, the performance under different k for the KNN algorithm.
Here is the DATASET: https://www.kaggle.com/datasets/deepcontractor/cyber-security-salaries/data
please do it in python notebook.
please do it to your best, i want to compare it to my portfolio/ This is not an assignment just practice
Managerial Economics
ISBN: 978-0133020267
7th edition
Authors: Paul Keat, Philip K Young, Steve Erfle