Predicts the new automobile prices based on different variables using Linear regression Algorithm.
Automobile price data (Raw), a sample data set is used for this prediction of automobile prices. Here, we prepare the data set by cleaning the missing data. Then we specify the features which we are going to use for the prediction. Then we train this predictive model by choosing an algorithm, which is a 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. We compare the statistics of the predicted values for price with the known values from the test data are shown below. **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 So we can conclude that this model best predicts the new prices of automobiles.