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📱 Mobile Price Classification using Machine Learning

📌 Project Overview

This project focuses on predicting the price range of mobile phones based on their specifications. We experimented with different machine learning models to compare their performance and find the most accurate one.

Dataset: Mobile Price Classification (Kaggle)

📊 Dataset Information

  • 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
  • n_cores: Number of cores of processor
  • pc: Primary Camera mega pixels
  • px_height: Pixel Resolution Height
  • ram: Random Access Memory in Megabytes
  • sc_h: Screen Height of mobile in cm
  • sc_w: Screen Width of mobile in cm
  • three_g: Has 3G or not
  • touch_screen: Has touch screen or not
  • wifi: Has wifi or not
  • talk_time: longest time that a single battery charge will last when you are
  • price_range (Target): This is the target variable with value of 0(low cost), 1(medium cost), 2(high cost) and 3(very high cost).

🔎 Feature Selection

  • Features with high correlation to the target variable were selected to train the models.
  • The Features is : battery_power, px_height, px_width, ram
  • This ensures better accuracy and reduces noise from irrelevant features.

🤖 Models Used & Accuracy

We trained and tested four ML models:

Model Accuracy (%)
Decision Tree 86.5
K-Nearest Neighbors 93.0
Support Vector Machine 96.75
Logistic Regression 97.25

✅ Logistic Regression achieved the highest accuracy (97.25%), making it the best-performing model in this project.