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You are working as a car salesman and you would like to develop a model to predict the total dollar amount that customers are willing to pay given the following attributes: Customer Name, Customer e-mail, Country, Gender, Age, Annual Salary, Credit Card Debt, Net Worth. The model should predict: Car Purchase Amount
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The Chicago Crime dataset contains a summary of the reported crimes occurred in the City of Chicago from 2001 to 2017. Dataset contains the following columns: ID, Case Number, Date, Block address, IUCR: The Illinois Unifrom Crime Reporting code, Primary Type, Description, Location Description, Arrest, Domestic, Beat, District, Ward, Community Area, FBI Code, X Coordinate, Y Coordinate, Year, Updated On, Latitude, Longitude, Location. The model should forecast the future trend.
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Predicting Avocado prices using facebook prophet. Some relevant columns in the dataset: Date - The date of the observation, AveragePrice, type - conventional or organic, year, Region, Total Volume, 4046 - Total number of avocados with PLU 4046 sold, 4225, 4770.
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In this case study, you have been provided with images of traffic signs and the goal is to train a Deep Network to classify them. The dataset contains 43 different classes of images. The network used is called Le-Net that was presented by Yann LeCun http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
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Detect spam e-mails using Naive Bayes. The SMS Spam Collection is a set of SMS tagged messages that have been collected for SMS Spam research. It contains one set of SMS messages in English of 5,574 messages, tagged acording being ham (legitimate) or spam.
All the project from Udemy course: Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks taught by Dr. Ryan Ahmed