Use the Amazon dataset attached with this assignment, Split the data set into training and testing, and explain in a small paragraph how should the data be prepared before implementing the machine learning model.

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2. Based on the algorithms you investigated in task 2, Choose any algorithm you find suitable, to solve Amazon's problem of classifying user reviews to spam or not spam.
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4. Build the machine learning model based on the algorithm you chose, over the data provided, show the model results including (accuracy, AUC, F-measure, confusion matrix).
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6. Explain to you colleages every step of building this model, focus on testing results, your explanations should be as markdown boxes inside your jupyter notebook.
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8. Based on your testing results critically asses how your machine learning model is effective for the mentioned scenario and data and useful for amazon, explain how effective it is to meet Amazon's goal in this project?
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10. Consider whether the output of the algorithm you have picked previously has the problems of (underfitting or over-fitting), provide explanation according to your results. Also, based on your results, examin the effectiveness of your model, do you think the algorithm you chose is suitable here according to this problem? Assess the effectiveness of this algorithm in general.
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