Loptop-price-prediction-end-to-end-project-using-ecommerce-website-data

In this project, I tried to create a model that can predict the price of a laptop based on the criteria of the desired laptop by using the data of the available laptops that I collected from the Digikala website, the largest e-commerce site in Iran.

This project is a part of my portfolio, showcasing my skills. The main code isn't public, but I'm open to collaboration! Interested? Email me at mr.raz2002@gmail.com

Note: Since the price of laptops in Iran is constantly changing, I used Mage for this project, a modern tool for build and orchestrate data pipelines that fetch and prepare data and retrain model based on newset data.

Watch Demo here :

https://meysamraz-laptop-price-prediction-project.streamlit.app/

Watch Demo here heroku version :

update : heroku may shutdown free hosting so if this link didnt work use link above

https://loptop-price-prediction.herokuapp.com/

Predict price is in Rial (Iran's currency)

Project Overview :

1 - Collect Data

To collect my data, I used Digikala's secret api. I was able to collect the data I wanted (available laptops with prices) with a simple fitler.

• Collect Laptop Main Data

  • id : ID registered for laptop in Digikala
  • title_fa : The name of the laptop in Farsi
  • title_en : The name of the laptop in English
  • price : Laptop price in Rial (Iranian currency)
  • image_url : Laptop photo
  • brand : Laptop brand

• Collect Laptop Details Data

  • cpu manufacturer : Laptop cpu manufacturer
  • cpu series : The cpu series used in the laptop
  • cpu model : The cpu model used in the laptop
  • ram : Laptop RAM capacity
  • ram type : The type of RAM used in the laptop
  • internal storage : Internal storage capacity of the laptop
  • internal storage type : The type of internal storage in loptop
  • gpu manufacturer : Laptop gpu manufacturer
  • gpu model : The gpu model used in the laptop
  • screen resolution : Laptop screen resolution
  • ports : Ports used in laptops

• Merge Collected data

• Remove duplicated rows

• Save data into csv file

2 - Take a Look at Data :

After collecting the data, I started checking the collected data to make sure it was collected correctly

• Check shape of data

• Check is there any null value

• Check data types

• Check number of unique values in each column

3 - Cleaning Data

Like all machine learning projects, the data doesn't arrive perfect and ready for prediction. At this point, I started cleaning the collected data.

• Convert brands name from persian to english

• Convert ram from persian to english digits

• Clean and convert internal storage to english

• Convert and clean internal storage to english

• Convert and clean laptops screen size

• Clean laptops resolution

4 - EDA

For the next step, which is Feature engineering, it was necessary to get information about the data. In this step, I analyzed and explored the data.

• Laptops price distribution

• Number of laptops of each brand

• Number of cpu of each cpu manufacturer

• Number of laptops for each ram group

• Number of laptops for each ram type group

• Number of laptops with diffrent internal storage

• Number of laptops for each internal storage group

• Number of laptops with diffrent screen sizes

• Number of laptop with diffrent screen resolution

5 - Feature Engineering

In this step, I prepared the features for training the model

• Remove outliers base on laptops price using z score

• Convert screen resolution to number

• Extract Gaming brands from title (asus rog , acer nitro ...)

• Remove brand with only one laptop

• Extract clean gpu model from gpu model column

• Remove laptops with only less than 3 model gpu

• Label endcoding cleand gpu models

• Convert internal storage from tb and gb to mg

• Label encoding internal storage type

• Convert ram from str to int

• Extract port count

• Label encoding ram type

• Label encoding cpu series

• One hot encoding brand - cpu manufacturer - gpu manufacturer (nominal categorical variables)

6 - Feature Selection

In this step, I chose the features needed to train the model

• Check correlation

• Mutual information regression

7 - Model training

8 - Hyperparameter tuning

9 - Cross validation

10 - Save model

I pickeld model for use in the gui environment

10 - Create Data Pipline

I used Mage (moderen and easier version of Airflow), to ETL data from Digikala everyday and retrain model based on newest data and export model

11 - Website

To create website, I used streamlit formwork, a powerful formwork that allows me to create the desired user interface completely using Python.

12 - Deploy

I used Heroku a cloud platform as a service which provide a free hosting to deploy my app on it. it's and amzaing platform gave me so much flexbilte to deploy your apps

Libraries and FrameWorks Used in the Project

• streamlit
• pandas
• numpy
• matplotlib
• seaborn
• termcolor
• scikit-learn
• scipy
• xgboost
• mage-ai