Objective : Predict a price range, indicating how high the price is, using K-Nearest Neighbors algorithm.
Abstract: There is a new mobile company and the CEO wants to give a tough fight to big companies like Apple, Samsung etc. He has no idea of how to estimate the price of mobiles his company creates. A simple assumption of the prices will not be profitable in this competitive world. To solve this problem he collects sales data of mobile phones of various companies. Thus, the CEO has hired you to find out some relation between features of a mobile phone(eg:- RAM, Internal Memory etc) and its selling price
Problem Statement: Predict a price range, indicating how high the price is, using K-Nearest Neighbors algorithm.
Dataset Information:
Column : Description battery_power : Total energy a battery can store in one time measured in mAh clock_speed : The speed at which microprocessor executes instructions fc : Front Camera megapixels int_memory : Internal Memory in Gigabytes m_dep : Mobile Depth in cm mobile_wt : Weight of the mobile phone n_cores : Number of cores of a processor pc : Primary Camera megapixels px_height : Pixel Resolution Height px_width : Pixel Resolution Width ram : Random Access Memory in MegaBytes sc_h : Screen Height of mobile in cm sc_w : Screen Width of mobile in cm talk_time : The longest time that a single battery charge will last when you are price_range : This is the target variable with the value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).
Scope: ● Prepare and analyse data, treat outliers and missing values ● Check the distribution of key numerical variables ● Training a KNN with data and check it’s performance ● Getting an optimized number of neighbours
Learning Outcome: The students will get a better understanding of how the variables are linked to each other and how the EDA approach will help them gain more insights and knowledge about the data that we have and classify the data into similar groups using KNN algorithm.