README.md

Car-predicion-model

Problem Statement

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:

• Which variables are significant in predicting the price of a car
• How well those variables describe the price of a car

Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

Business Goal

You are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.

Results

Inference :

1. Toyota seemed to be favored car company.
2. Number of gas fueled cars are more than diesel.
3. sedan is the top car type prefered.

Inference :

1. It seems that the symboling with 0 and 1 values have high number of rows (i.e. They are most sold.)
2. The cars with -1 symboling seems to be high priced (as it makes sense too, insurance risk rating -1 is quite good). But it seems that symboling with 3. value has the price range similar to -2 value. There is a dip in price at symboling 1.

Inference :

1. ohc Engine type seems to be most favored type.
2. ohcv has the highest price range (While dohcv has only one row), ohc and ohcf have the low price range.

Inference :

1. Jaguar and Buick seem to have highest average price.
2. diesel has higher average price than gas.
3. hardtop and convertible have higher average price.

Inference :

1. doornumber variable is not affacting the price much. There is no sugnificant difference between the categories in it.
2. It seems aspiration with turbo have higher price range than the std(though it has some high values outside the whiskers.)

#### Inference :

1. Very few datapoints for enginelocation categories to make an inference.
2. Most common number of cylinders are four, six and five. Though eight cylinders have the highest price range.
3. mpfi and 2bbl are most common type of fuel systems. mpfi and idi having the highest price range. But there are few data for other categories to derive any meaningful inference
4. A very significant difference in drivewheel category. Most high ranged cars seeme to prefer rwd drivewheel.

#### Inference :

1. carwidth, carlength and curbweight seems to have a poitive correlation with price.
2. carheight doesn't show any significant trend with price.

Model Conclusion

Inference :

1. R-sqaured and Adjusted R-squared (extent of fit) - 0.899 and 0.896 - 90% variance explained.
2. F-stats and Prob(F-stats) (overall model fit) - 308.0 and 1.04e-67(approx. 0.0) - Model fir is significant and explained 90% variance is just not by chance.
3. p-values - p-values for all the coefficients seem to be less than the significance level of 0.05. - meaning that all the predictors are statistically significant.