Regression

![enter image description here][1] #In this session • Regression Algorithms in Azure ML <br> • Create New ML Experiment<br> • Import auto data from UCI<br> • Add column name using python<br> • Clean missing data<br> • Select column (exclude column)<br> • Split data <br> • Add Linear Regression module <br> • Add Train Model <br> • Add Score Model <br> • Add Evaluate Model <br>   #Regression Algorithms in Azure ML   #Over view   #Working steps 1. Create New ML Experiment 2. Import auto data from UCI 3. Add column name using python 4. Clean missing data 5. Select column (exclude column) 6. Split data 7. Add Linear Regression module 8. Add Train Model 9. Add Score Model 10. Add Evaluate Model   #Data set #Home https://archive.ics.uci.edu/ml/datasets/Automobile #Data download https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data #Element column names/values https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names   #Data attribute - symboling: -3, -2, -1, 0, 1, 2, 3<br> - normalized-losses: continuous from 65 to 256<br> - make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi, nissan, peugot, plymouth, porsche, renault, saab, subaru, toyota, volkswagen, volvo - fuel-type: diesel, gas<br> - aspiration: std, turbo<br> - num-of-doors: four, two<br> - body-style: hardtop, wagon, sedan, hatchback, convertible<br> - drive-wheels: 4wd, fwd, rwd<br> - engine-location: front, rear<br> - wheel-base: continuous from 86.6 120.9<br> - length: continuous from 141.1 to 208.1<br> - width: continuous from 60.3 to 72.3<br> - height: continuous from 47.8 to 59.8<br> - curb-weight: continuous from 1488 to 4066<br> - engine-type: dohc, dohcv, l, ohc, ohcf, ohcv, rotor<br> - num-of-cylinders: eight, five, four, six, three, twelve, two<br> - engine-size: continuous from 61 to 326<br> - fuel-system: 1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi<br> - bore: continuous from 2.54 to 3.94<br> - stroke: continuous from 2.07 to 4.17<br> - compression-ratio: continuous from 7 to 23<br> - horsepower: continuous from 48 to 288<br> - peak-rpm: continuous from 4150 to 6600<br> - city-mpg: continuous from 13 to 49<br> - highway-mpg: continuous from 16 to 54<br> - price: continuous from 5118 to 45400<br> # Last steps - Add column name Python Script<br> - Clean missing data<br> - Select (exclude) column<br> - Split data<br> - Evaluation Metrics<br> ![enter image description here][2] # More information Using linear regression in Azure Machine Learning https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-linear-regression-in-azure <br><br> ---------- > This ML experiment is for [Microsoft Azure Machine Learning Course][101].<br> For the complete experiment list [Click here][102].<br> Laploy | laploy@gmail.com | 084 007 5544 | [www.laploy.com][103]<br> ![enter image description here][104] ---------- [101]: https://notebooks.azure.com/laploy/libraries/loyml/html/00001%20Sessions%20summary.ipynb [102]: https://gallery.cortanaintelligence.com/Home/Author?authorId=81E333F747E3429B55A3445E6714C36F60B397C13B4D0B07F34DEF1421F64D73 [103]: http://laploy.com [104]: https://raw.githubusercontent.com/laploy/mli/master//loy-small.jpg [1]: https://raw.githubusercontent.com/laploy/mli/master//12510-001.JPG [2]: https://raw.githubusercontent.com/laploy/mli/master//12510-002.JPG [11]: https://raw.githubusercontent.com/laploy/mli/master//loy-small.jpg