Automobile price prediction.

Automobile price data (Raw) sample data set is used for this experiment. First we prepare the data set by cleaning the data. Then we specify the features that we are used in this prediction. Then we train the model by using an algorithm which is Linear Regression algorithm. Finally, we score and test the model to predict new automobile prices. The output shows the predicted values for price and the known values from the test data. Here we compare the statistic of the predicted values for price with the known values from the test data. **Known values (Price):** Mean 12733.5625 Median 9541.5 Min 5195 Max 41315 Standard Deviation 8294.6886 Unique Values 44 Missing Values 0 Feature Type Numeric Label **Predicted values(Scored Labels)**: Mean 12437.776 Median 10208.7085 Min 5446.8479 Max 34960.6439 Standard Deviation 7323.458 Unique Values 46 Missing Values 0 Feature Type Numeric Score We can conclude this algorithm fits to predict the new prices of the automobiles.