https://github.com/neerajcodes888/a-novel-used-car-price-prediction-model-based-on-lindenoise

Welcome to the LinDenoise Repository! LinDenoise offers a smart solution for cleaning noisy data in regression tasks. Integrated seamlessly within the widely-used scikit-learn framework, it effortlessly enhances data quality while improving predictive accuracy
https://github.com/neerajcodes888/a-novel-used-car-price-prediction-model-based-on-lindenoise

car-price-prediction deep-learning ipynb-notebook machine-learning numpy pandas python3 visualization

Last synced: about 2 months ago
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Welcome to the LinDenoise Repository! LinDenoise offers a smart solution for cleaning noisy data in regression tasks. Integrated seamlessly within the widely-used scikit-learn framework, it effortlessly enhances data quality while improving predictive accuracy

• Host: GitHub
• URL: https://github.com/neerajcodes888/a-novel-used-car-price-prediction-model-based-on-lindenoise
• Owner: neerajcodes888
• Created: 2024-05-05T20:19:18.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-16T17:01:26.000Z (8 months ago)
• Last Synced: 2024-11-13T08:42:06.690Z (about 2 months ago)
• Topics: car-price-prediction, deep-learning, ipynb-notebook, machine-learning, numpy, pandas, python3, visualization
• Language: Jupyter Notebook
• Homepage:
• Size: 4.61 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
🚗 A Novel Used Car Price Prediction Model Based on LinDenoise 📊



  



Table of Contents 📜




  1. Introduction •

  2. Installation •

  3. Usage •

  4. Contributing •

  5. License



Introduction 🌟




Welcome to the README for my project, "A Novel Used Car Price Prediction Model Based on LinDenoise." 🚀 This was my final semester college project, developed to revolutionize the used car market by offering advanced price prediction capabilities. Leveraging the innovative LinDenoise algorithm, we provide more accurate and reliable predictions, aiding both buyers and sellers in making informed decisions. 🛠️



Installation ⚙️




To use our model, follow these installation steps:



1. Clone this repository to your local machine.

2. Install the required dependencies by running:

   ```bash

   pip install -r requirements.txt

   ```