# Automobile price prediction

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.