📱 Mobile Price Range Prediction

AlmaBetter Verfied Project - AlmaBetter School

Problem Statement

There are many things we consider before buying a mobile as we used our mobile for various purpose like connecting with our family & Office Colleagues, playing games, taking a photos to keep our memory alive. So, this such specifications such as RAM, internal memory, Wi-Fi, 3G/4G connectivity etc. plays important role to buy a mobile. To analysis of this important factor from time to time and come up with the best setoff specifications and price ranges so that people will buy the mobile. Hence through the various ML modules we will help the company to estimate the price of mobiles according to feature so the maximum amount of sell will be possible.

Data Description:-

• Battery_power - Total energy a battery can store in one time measured in mAh
• Blue - Has bluetooth or not
• Clock_speed - speed at which microprocessor executes instructions
• Dual_sim - Has dual sim support or not
• Fc - Front Camera mega pixels
• Four_g - Has 4G or not
• Int_memory - Internal Memory in Gigabytes
• M_dep - Mobile Depth in cm
• Mobile_wt - Weight of mobile phone
• N_cores - Number of cores of processor
• Pc - Primary Camera mega pixels
• Px_height - Pixel Resolution Height
• Px_width - Pixel Resolution Width
• Ram - Random Access Memory in Mega
• Touch_screen - Has touch screen or not
• Wifi - Has wifi or not
• Sc_h - Screen Height of mobile in cm
• Sc_w - Screen Width of mobile in cm
• Talk_time - longest time that a single battery charge will last over a call
• Three_g - Has 3G or not
• Wifi - Has wifi or not
• Price_range - This is the target variable with value of 0(low cost), 1(medium cost),2(high cost) and 3(very high cost)

💾 Project Files Description

This Project includes :-

Google Colab NoteBook

Project Summary (with the GitHub Repo link inside)

Presentation Video

Data Source:

• Dataset - Dataset taken from AlmaBetter

Summary

• Model implementation involved fitting various machine learning models, such as KNN, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, Xgboost and Stacking to make predictions on mobile phone prices.

• Determined that logistic regression, Stacking models performed exceptionally well in predicting mobile phone prices, showcasing their effectiveness in this context.