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Automobile Price Prediction
This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is more risky (or less), this symbol is adjusted by moving it up (or down) the scale. Actuarians call this process “symboling”. A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.
The third factor is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/speciality, etc…), and represents the average loss per car per year.
Note: Several of the attributes in the database could be used as a “class” attribute.
Please bring it on whatever inferences you can get it and Make a Price Prediction Model.
This dataset consist of data From 1985 Ward’s Automotive Yearbook. Here are the sources
1) 1985 Model Import Car and Truck Specifications, 1985 Ward’s Automotive Yearbook.
2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038
3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037
Datasets is available Azure ML Studio Saved Datasets > Samples > Automobile Price Data (Raw)
Telco Customer Churn Prediction
For Telco companies it is key to attract new customers and at the same time avoid contract terminations (=churn) to grow their revenue generating base. Looking at churn, different reasons trigger customers to terminate their contracts, for example better price offers, more interesting packages, bad service experiences or change of customers’ personal situations.
Telcos apply machine learning models to predict churn on an individual customer basis and take counter measures such as discounts, special offers or other gratifications to keep their customers. A customer churn analysis is a typical classification problem within the domain of supervised learning.
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