# URGENT!! Need help with some work. **URL:** \ **Category:** KNIME Analytics Platform\ **Tags:** machine-learning\ **Created:** [July 2, 2024, 12:52pm UTC](https://forum.knime.com/t/urgent-need-help-with-some-work/80839 "2024-07-02T12:52:07Z")\ **Posts on this page:** 1\ **Showing post:** 3
**Author:** ![mlauber71](https://forum-cdn.knime.com/user_avatar/forum.knime.com/mlauber71/32/73590_2.png) [@mlauber71](https://forum.knime.com/u/mlauber71)\ **Post date:** [July 10, 2024, 8:56am UTC](https://forum.knime.com/t/urgent-need-help-with-some-work/80839/3 "2024-07-10T08:56:00Z")
@Amy_Read1 for this dataset I have [set up some examples about how to do machine learning here](https://forum.knime.com/t/how-do-know-if-your-dataset-is-good-before-using-ml-algorithms/77228/7). Maybe you can check this out. > **[Heart Disease - Machine Learning Case - Comparing Algorithms – mlauber71](https://hub.knime.com/mlauber71/spaces/Machine_Learning/ml_binary/Heart%20Disease%20-%20Machine%20Learning%20Case%20-%20Comparing%20Algorithms~0dXvsD0vMrv_w6Fw/current-state)** > > Binary Classification - use Python XGBoost package and other nodes to build model and deploy that thru KNIME Python nodes prepare data with vtreat package in t… For Algorithms that need normalization and just numeric data you might also take a look at the data preparation in the Metanode “BINARY\_RPROP\_MLP”. This will involve converting the string columns to numeric ones (you could also [employ vtreat for that](https://medium.com/p/efcaf58fa783)), to normalize the data and while doing that preserve the option to re-do this for new data. It does look a little bit complicated first but you can just use the example for other data, provided your target variable is named “Target”. ![image](https://forum-cdn.knime.com/uploads/default/original/3X/f/d/fd7204dcc5af03dab1f59d0f847b154eb1d3afc9.png) SVM with Parameter optimization: > **[SVM Exercise with Parameter Optimization – knime](https://hub.knime.com/knime/spaces/Educators%20Alliance/Guide%20to%20Intelligent%20Data%20Science/Exercises/Chapter9_SVM/SVM_Solution~rnzxfDElze4t7RRd/current-state)** > > Exercise for SVM. Classification of 2D silhouette attributes with SVM classifier. Oprimize the c parameter for the margin hardness with parameter optimization … You might want to discuss again what you would do with kNN. Is this to cluster the data. You can try and find an optimal number of clusters: > [@iteration over k values using variables in the K Nearest Neighbor node](https://forum.knime.com/t/iteration-over-k-values-using-variables-in-the-k-nearest-neighbor-node/13070/2): > > Maybe like this. I have not checked if the results from the Iris sample data would be of any practical use in this scenario. [image] [kn\_example\_loop\_over\_nearest\_neighbor.knwf](https://forum-cdn.knime.com/uploads/default/original/2X/9/94310d8704e1618dbea9d4abe1522dedc90b7cc0.knwf) (68.9 KB) --- _[View the full topic](https://forum.knime.com/t/urgent-need-help-with-some-work/80839)._