📱 Mobile Price Classification - Machine Learning Project

This project is a classification task aimed at predicting the price category of a mobile phone based on its specifications using various machine learning algorithms.

The dataset is clean, balanced, and contains various features such as battery power, RAM, screen size, and more.

📂 Dataset

The dataset used is from Kaggle - Mobile Price Classification.
It includes 2000 samples with 20 numerical features, and the target is a price range from 0 (lowest) to 3 (highest).

🚀 Project Workflow

1. Data Preprocessing

   • Feature inspection and EDA
   • Checked distributions and correlations
   • Feature scaling using StandardScaler

2. Model Training

   • Trained multiple models:
     • Logistic Regression
     • K-Nearest Neighbors (KNN)
     • Support Vector Machine (SVM)
     • Decision Tree
     • Random Forest

3. Hyperparameter Tuning

   • Used GridSearchCV to tune Logistic Regression (but accuracy dropped)

4. Final Model

   • Best performance achieved by Logistic Regression with 97% accuracy
   • GridSearch tuning result: 88%, so default configuration was used

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✅ Result

• Final model: LogisticRegression()
• Accuracy: 97%
• No further feature engineering was needed
• Project completed using pure Scikit-learn