backpack-prediction-challenge

work on kaggle competition for predicting prices of backpacks

Backpack Prediction Challenge (Playground Series - Season 5, Episode 2) – [Your Solution Title]

📌 Overview

This repository contains my solution for the Competition Name hosted on Kaggle. The goal of the competition was to [briefly describe the problem statement].

🏆 Final Ranking & Score

• Rank: [Your Rank]
• Score: [Your Score]
• Submission Type: [Single Model / Ensemble]

📂 Repository Structure

📂 your-repo-name/
│── 📁 data/                # Data files (not included in the repo, see instructions)
│── 📁 notebooks/           # Jupyter Notebooks for EDA & model training
│── 📁 src/                 # Python scripts for preprocessing, training, and inference
│── 📁 models/              # Trained models (not included in repo, see instructions)
│── 📁 results/             # Evaluation results and predictions
│── 📄 requirements.txt     # Dependencies for the project
│── 📄 train.py             # Training pipeline
│── 📄 inference.py         # Script for generating predictions
│── 📄 README.md            # Project documentation

🚀 Approach

1. Data Preprocessing

• [Describe feature engineering, handling missing values, encoding techniques, etc.]

2. Model Selection

• [Describe model(s) used, hyperparameters, and training strategy]

3. Evaluation & Post-Processing

• [Explain evaluation metrics and any post-processing steps applied]

💻 Getting Started

1. Install Dependencies

pip install -r requirements.txt

2. Download Data

Download the competition dataset from Kaggle and place it inside the data/ directory.

3. Train the Model

python train.py

4. Generate Predictions

python inference.py

📊 Results & Insights

• Best Model: [e.g., XGBoost, Transformer-based, CNN, etc.]
• Key Findings:
  • [Mention the most impactful feature or technique]
  • [Any unexpected challenges or insights]

📌 Future Improvements

• [Ideas for improving the model, if applicable]

📜 License

This project is licensed under the MIT License – see the LICENSE file for details.