README.md

Laptop_Price_Prediction

Importing required libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

• Reading the data
• Checking for duplicated values and null values

Data Cleaning

• Cleaning the Weight and Ram columns and converting to integer and float format
• Extracting screen resolution column by using regular expressions
• Extracting cpu column

Univariate analysis

• perform the univariate analysis i.e. how the each feature is distributed
• if any value treaten as null value replace it with mode of perticular feature

Bivariate analysis

• Bivariate analysis is knowing how feature is distrubuted with respect totarget variable

Encoding

• Transforming categorical features into numerical values

Feature selection

• checking for the features for correlated or not by setting threshold value

splitting the data

• splitting the data into train data and test data

standerdization

• Transforming train data from range of numerical values into between 0 to 1

Model building

• Building the model and train with different parameters by applying different algorithems
• Selecting the model which performs well
• Saving the model

App creation

• Creating the app using streamlit