The objective of this project is to use the machine learning software Weka to experiment different data preprocessing techniques (data cleaning, data reduction, normalization...) and different data mining techniques (Frequent pattern mining techniques, supervised learning techniques, unsupervised learning techniques) on a selected dataset. - You should use the same project document to prepare your answer. A word file and a pdf file should be provided. - The number of students per project is 3 or 4 (maximum). You should fill the work distribution table provided at the end of this project. - Using/copying ideas from previous years will result in zero mark. - You should select one of the datasets from any Machine Learning Repositories: - http://archive.ics.uci.edu/ml - https://www.kaggle.com/datasets - - - The dataset may follow the following requirements - Number of instances: at least 300 - Number of attributes: at least 10 - The dataset should contain different types of attributes, some null values, some missing values, different scales of attributes... Note: You can modify the dataset manually by adding some attributes, removing some values, adding some data discrepancies ... Apply Apriori algorithm on your dataset with different support and confidence values (at least 3 different scenarios). Discuss the generated rules. Note: - You can use only a subset of the attributes of your dataset to apply A priori algorithm. - Add screenshots of the output of the algorithm for each scenario. - Choose the best scenario