Problem Statement

In this dataset , data collected are the sales data of mobile phones of various companies . In the competitive mobilephone market you cannot simply assume things . In this problem to estimate the price of mobiles this company creates we have to predict the price range indicationg how high the price is by finding out some relation between the features of a mobile phone.

Dataset

The dataset used is the Mobile Price Classification .

The class labels are:

1. battery_power: ->Total energy a battery can store in one time measured in mAh
2. blue: ->Has bluetooth or not
3. clock_speed: ->speed at which microprocessor executes instructions
4. dual_sim: ->Has dual sim support or not
5. fc: ->Front Camera mega pixels
6. four_g ->Has 4G or not
7. int_memory: ->Internal Memory in Gigabytes
8. m_dep: ->Mobile Depth in cm
9. mobile_wt ->Weight of mobile phone
10. n_cores ->Number of cores of processor
11. pc ->Primary Camera mega pixels
12. px_height: ->Pixel Resolution Height
13. px_width ->Pixel Resolution Width
14. ram ->Random Access Memory in Megabytes
15. sc_h ->Screen Height of mobile in cm
16. sc_w ->Screen Width of mobile in cm
17. talk_time ->longest time that a single battery charge will last when you are
18. three_g ->Has 3G or not
19. touch_screen ->Has touch screen or not
20. wifi ->Has wifi or not
21. price_range: ->Gives price range for the mobile

Model(s) Used

1. K Nearest Neighbours: This model is most useful in data which can be linearly separated. It simply finds the "K nearest neighbours" and uses the hoghest class occurence as the final class preidction.

2. Naive Bayes: Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.

3. Decision Tree Classifier: This model identifies the most informative attribute at every level and uses it to make a tree. The final tree can then be used as a simple if-else statement to identify the final prediction.

4. Logistic Regression: Logistic regression predicts the output of a categorical dependent variable. Therefore the outcome must be a categorical or discrete value. It can be either Yes or No, 0 or 1, true or False, etc. but instead of giving the exact value as 0 and 1, it gives the probabilistic values which lie between 0 and 1.