download data set from hear :

https://archive.ics.uci.edu/ml/machine-learning-databases/autos/

https://archive.ics.uci.edu/ml/machine-learning-databases/autos/ There will be two files: names (with description of the data), data (dataset). This dataset has 26 features/attributes in total. It also has 205 examples/objects/data points. Prepare a dataset (subset) with the following characteristics: Tasks: 1. Select six features: At least two must features must be continuous. One of the continuous features must have missing values. The remaining four features must include at least one nominal feature with MISSING values. 2. Create a data set with 30 examples/datapoints with the above characteristics. 3. Replace the missing values (numeric and nominal) with techniques discussed in class 4. Discretize the feature PRICE into 3 bins. In other words, you are converting a regression problem into a classification problem. Run the decision tree algorithm (using any SW platform). J48 algorithm if you are using WEKA platform. 5. Consider any of the nominal feature values as a class feature and run the decision tree algorithm. Be sure to include price as a feature. In other words, you are still using 5 features and 1 class feature. Run the decision tree algorithm (using any SW platform)