You need to determine and analyze a data set in which you can apply the algorithms we have seen in the lesson 1. Give statistical information about your data set. 2. You need to obtain 4 different datasets by applying 4 feature selection methods to your dataset. In this case, you can use statistical and/or machine learning feature selection methods. 2.1. Explain the reason for choosing the feature selection methods you use. 2.2. Explain the working principle of the feature selection methods you use. 3. Divide each dataset into an optimum training and test dataset. It In this section, explain how you determined the optimum training and test dataset size. 4. Apply the algorithms we saw in the lesson to each data set. At this point, you need to apply all algorithms suitable for your data set for each of the 4 different data sets you will obtain from the second item 5. Explain the working principle of the algorithms you use. 6. Determine the appropriate evaluation metrics for your data set and get results. For example, Accuracy, Sensitivity etc. for classification. You need to use metrics. MAE, MSE, RMSE etc. for regression analysis. You need to use metrics. Explain which algorithm performs better based on the results you have obtained. 7. You have obtained the most effective feature selection methods you have used. Comment on the results. NOT The above-mentioned 7 items should be explained one by one in your report.. Write the access link for the source from which you get the data. All py. Upload your files with the extension, data sets and report to the system in last name_name okulNo.zip format. If similar assignments are detected, the visa grade of the relevant students is Zero (0) will be accepted as Determine and analyze a data set in which you can apply the algorithms we have seen in the lesson. required. 1. Give statistical information about your data set. 2. Obtaining 4 different datasets by applying 4 feature selection methods to your dataset. required. In this case, statistical and/or machine learning feature selection you can use methods. 2.1. Explain the reason for choosing the feature selection methods you use. 2.2. Explain the working principle of the feature selection methods you use. 3. Divide each dataset into an optimum training and test dataset. It In this section, explain how you determined the optimum training and test dataset size. 4. Apply the algorithms we saw in the lesson to each data set. At this point the second for each of the 4 different data sets you will obtain from the item, You have to apply all algorithms. 5. Explain the working principle of the algorithms you use. 6. Determine the appropriate evaluation metries for your data set and get results. Sample as Accuraey. Sensitivity etc. for classification, using metrics required. MAE. MSE, RMSE ete. for regression analysis, using metrics required. Which algorithm should be used according to the results you have obtained? Explain how it performs better. 7. You have obtained the most effective feature selection methods you have used. Comment on the results