https://github.com/punitkumar4871/backpack_price_prediction
https://github.com/punitkumar4871/backpack_price_prediction
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/punitkumar4871/backpack_price_prediction
- Owner: punitkumar4871
- Created: 2025-07-17T17:45:48.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-17T17:46:29.000Z (11 months ago)
- Last Synced: 2025-07-17T20:41:37.148Z (11 months ago)
- Size: 414 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# `Backpack_Price_Prediction`
### `Backpack Price Prediction: Kaggle Competition`
#### `Overview`
This repository contains our submission for the Kaggle Competition: Backpack Price Prediction. The goal was to predict backpack prices based on various features using a structured machine learning approach. We performed exploratory data analysis, feature engineering, and trained multiple models to achieve accurate predictions. This project demonstrates end-to-end data science workflows, from preprocessing to model evaluation.
`Data Analysis & Preprocessing`
We conducted thorough data preparation to ensure high-quality inputs for modeling:
In-depth exploratory data analysis (EDA), including univariate and bivariate analysis to understand distributions and relationships.
Detection and handling of null values for data consistency.
Creation of additional columns for enhanced visualizations and insights.
Feature engineering by extracting meaningful information from existing attributes.
Analysis of correlations and relationships between variables to identify relevant features.
Dropping unnecessary columns to reduce noise and improve model performance.
`Model Training & Evaluation`
We experimented with various machine learning models and evaluated them using Mean Absolute Error (MAE):
## Model Emoji MAE
`Linear Regression 🏹 39.18`
`Ridge Regression 🏔️ 39.18`
`Lasso Regression 🔗 39.19`
`Elastic Net Regression ⚡ 39.19`
`Decision Tree 🌳 59.43`
`Random Forest 🌲 43.93`
`K-Means Clustering 📌 89.14`
`K-Nearest Neighbors (KNN) 👥 42.95`
`XGBoost 🚀 39.33`
## Insights & Learnings
Linear, Ridge, and Lasso regressions performed the best with an MAE of approximately 39.18, effectively capturing data relationships.
XGBoost was highly competitive at 39.33, showcasing its strength in structured data tasks.
Decision Tree (59.43) and K-Means (89.14) showed higher errors, possibly due to overfitting or unsuitability for regression.
Feature engineering and careful column selection were pivotal in boosting overall model performance.
`Next Steps`
Fine-tune hyperparameters for further improvements.
Explore ensemble techniques to enhance generalization.
Experiment with deep learning models for potential gains.
`Requirements`
Python 3.x
`Libraries: pandas, scikit-learn, xgboost, matplotlib/seaborn (for EDA and visualizations)`
Jupyter Notebook or similar for running the code
## `Installation and Setup`
Clone this repository.
Install dependencies: pip install -r requirements.txt (if provided, or install manually).
Download the Kaggle dataset and place it in the data/ folder.
Open the Jupyter Notebook to explore the analysis.
## `Usage`
Run the notebook cells sequentially to reproduce the EDA, preprocessing, and model training.
Adjust parameters in the code for custom experiments.
Submit predictions to Kaggle using the generated output.
## `Acknowledgments`
A huge shoutout to my amazing teammates Vaibhav Tamang and Yash Bhardwaj for their dedication and collaboration. Their expertise and teamwork made this project a success!
## `Contributing`
Feel free to fork the repo, suggest improvements, or submit pull requests for new features or optimizations.