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
- Cleaning the Weight and Ram columns and converting to integer and float format
- Extracting screen resolution column by using regular expressions
- Extracting cpu column
- 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 is knowing how feature is distrubuted with respect totarget variable
- Transforming categorical features into numerical values
- checking for the features for correlated or not by setting threshold value
- splitting the data into train data and test data
- Transforming train data from range of numerical values into between 0 to 1
- Building the model and train with different parameters by applying different algorithems
- Selecting the model which performs well
- Saving the model
- Creating the app using streamlit