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Verified signature## 📱 Mobile Price Prediction (NPR) | Quick Summary **Objective:** Predict smartphone prices in Nepal using a regression pipeline built on messy scraped data. ### 🛠️ The Tech Stack * **Data:** 109 cleaned records (from 127 raw) of mobile specs in Nepal. * **Features:** RAM, Storage, 5G, "Ultra/Pro" status, Foldable status, and a custom **Premium Score**. * **Top Model:** **Gradient Boosting Regressor** ($R^2 = 0.7487$, MAE $\approx$ NPR 31k). ### 🔑 Key Findings 1. **Brand is King:** Apple and Samsung (Ultra/Fold) drive the highest price premiums. 2. **Interaction Matters:** The relationship between `RAM x Storage` is a better predictor than either alone. 3. **Tiered Logic:** The model successfully categorizes phones from Budget (<20k) to Flagship (≥120k). ### 🏃 Use It in 3 Lines ```python import joblib bundle = joblib.load('mobile_price_model.pkl') # Input: [RAM, Storage, 5G, Ultra, Pro, Foldable, Interaction, Log_Store, Premium, Brand] price = bundle['model'].predict(sample_…b5cd72d -
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