comfortBenson/laptop_price_prediction_app

An End-to-End Machine Learning project that predicts the market value of laptops in Nigeria based on hardware specifications. This tool is designed to help buyers and sellers estimate fair prices in the local market (NGN). 💻 Laptop Price Prediction (Nigeria) Overview: Buying a laptop in Nigeria can be challenging due to price volatility and varying hardware configurations. This project uses a Random Forest Regressor to provide an intelligent price estimate by analyzing factors like Brand, RAM, Storage, and CPU/GPU types, converted to Naira using current market exchange rates.The model was trained on a dataset of laptops and their specification. LIVE DEMO: laptoppricepredictionapp-to… Project Structure: app.py # Main Streamlit application laptop_model.pkl # Trained Random Forest model encoders.pkl # Saved LabelEncoders for categorical data laptops.csv # Raw dataset requirements.txt # List of dependencies Laptop_Analysis.ipynb # Jupyter Notebook with EDA and Model Training Dataset: Source: Laptop Price Prediction Dataset Columns include Brand, RAM, Storage, Storage type, CPU brand, GPU brand and Screen Size. Tech Stack: Language: Python 3.12 Data Analysis: Pandas, NumPy Visualization: Matplotlib, Seaborn Machine Learning: Scikit-Learn (RandomForestRegressor, LabelEncoder) Deployment: Streamlit, GitHub This project demonstrates end-to-end data science skills including: Data cleaning & feature engineering Exploratory Data Analysis (EDA) Model training & evaluation Deployment using Streamlit WorkFlow: Data Cleaning: Handled missing values (specifically in GPU and Storage Type columns). ​Feature Engineering: - Extracted CPU_Brand and GPU_Brand from raw text. ​Converted prices to Naira (NGN) using a standard exchange rate. ​Exploratory Data Analysis (EDA): Identified correlations between RAM/Storage and price. ​Encoding: Used LabelEncoder to transform categorical variables for production stability. ​Model Training: Utilized RandomForestRegressor for its ability to handle non-linear relationships in hardware specs. Features Used for Modeling: Predictive Modeling: Estimates laptop prices with high acc …