https://github.com/20101301-alina-hasan/mobile-price-prediction-ai

Here we use basic ML models to learn from already categorized mobile phone prices and then predict prices of mobile phones not introduced to its learning scheme.
https://github.com/20101301-alina-hasan/mobile-price-prediction-ai

artificial-intelligence decision-tree jupyter k-nearest-neighbor knn logistic-regression machine-learning mobile-price-prediction naive-bayes price-prediction python

Last synced: 7 months ago
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Here we use basic ML models to learn from already categorized mobile phone prices and then predict prices of mobile phones not introduced to its learning scheme.

• Host: GitHub
• URL: https://github.com/20101301-alina-hasan/mobile-price-prediction-ai
• Owner: 20101301-Alina-Hasan
• Created: 2024-02-26T01:23:55.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-27T14:22:11.000Z (over 1 year ago)
• Last Synced: 2025-01-22T03:24:45.362Z (9 months ago)
• Topics: artificial-intelligence, decision-tree, jupyter, k-nearest-neighbor, knn, logistic-regression, machine-learning, mobile-price-prediction, naive-bayes, price-prediction, python
• Language: Jupyter Notebook
• Homepage:
• Size: 317 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# CSE422: Artificial Intelligence | BRACU

Mobile phones are available in a wide range of costs, features such as internal memory or battery power, and other criteria. A key component of consumer strategy is the estimate and forecast of prices. Our study focuses on training machine learning models using readily accessible information on the numerous features and price ranges of mobile phones in the market to make the prices of newer products determinable. The models were trained on the [Mobile Price Classification](https://www.kaggle.com/datasets/iabhishekofficial/mobile-price-classification) dataset provided on *Kaggle* by *Abhishek Sharma*, a  *Maropost* Data Scientist.

The machine learning models used in our Jupyter Notebook are provided below:

* K-Nearest Neighbor

* Logistic Regression

* Naive Bayes

* Decision Tree

The results of the study are illustrated as follows: 

![Accuracy V Algorithm](Accuracy_against_Algorithm.png)

To learn more read up on it in our report: [Lab Report on Mobile Price Prediction](https://github.com/20101301-Alina-Hasan/Mobile-Price-Prediction-AI/blob/fdf4f28eb345d65392714d30c1b687eac3ea8ca4/Lab%20Report_%20Mobile%20Price%20Prediction.pdf)