Laptop Prices Predictor

• Designed a web app that predicts the price of the laptop given the configurations.
• Scraped the laptops data from kaggle using python and Opendatasets package.
• Developed Linear, Lasso, and Random Forest Regressors using GridsearchCV to get the best model.
• Deployed the Machine Learning model using streamlit library on Heroku.

Links and Resources Used

Streamlit Library: https://www.streamlit.io/

Dataset Link: https://www.kaggle.com/ionaskel/laptop-prices/

heroku Platform: https://www.heroku.com/

Packages: pandas, numpy, matplotlib, seaborn, sklearn, pickle, streamlit

Feature Engineering

We go through all the features one by one and keep adding new features. I have made the following changes and created new variables:

• RAM - Made columns for Ram Capacity in GB
  
• Processor - Made columns for Name of the Processor, Type of the Processor, Generation
  
• Operating System - Parsed the Operating System from this column and made a new column
  
• Storage - Made new columns for the type of Disk Drive and the capacity of the Disk Drive
  
• Display - Made new columns for the size of the laptop(in inches) and touchscreen
  
• Description - Made new columns for the company and IPS
  

Data Preprocessing

There are a few columns which are categorical here but they actually contain numerical values.So we need to convert few categorical columns to numerical columns. These are ram, weight, hdd, ssd.

Model Building

Traditional Method

Used scikit-learn library for the Machine Learning tasks. Applied label encoding and converted the categorical variables into numerical ones.Then I splited the data into training and test sets with a test size of 20%. I tried different regression models ( Linear Regression, Random Forest Regression, XGBoost) and evaluated them using Mean Absolute Error.

Model Deployment

• I have deployed the model using Streamlit library on Heroku which is a Platform As A Service(PAAS)

• Web application: https://laptop-price-predictor-yg.herokuapp.com/