Analyzing IKEA Product Analysis and create Model for Product Price Prediction Using Linear Regression Model

IKEA is a furniture design company that specializes in the sale of flat-packed furniture, kitchen appliances, and other home accessories. IKEA is named after the initials of founder Ingvar Kamprad, Elmtaryd, the farm on which he grew up, and Agunnaryd, the nearby village.

IKEA has vision to create a better everyday life for the many people from their products. This vision goes beyond home furnishing, and to have a positive impact on the world – from the communities to help our customers live a more sustainable life at home.

IKEA constantly challenging themselves and others to make more from less without compromising on quality. Every day, everywhere, IKEA do their best to discover and eliminate unnecessary costs, because low prices are impossible without low costs.

There are some up and down for IKEA Net Income thorough the years, that probably caused by the amount of online selling, operational cost, and cost of goods sold.

Here we try to analyze IKEA Product based on it price, availability online, color option, and size dimension, and we also create modeling for product price prediction with Linear Regression. Goals for this project are to know what exactly affect the price of product, and find out what exactly can help increase the Net Income.

Import the used Library

import pandas as pd # for data processing import numpy as np # for array, linear algebra import matplotlib.pyplot as plt # to crreate stat and visualize data import seaborn as sns # to visualize data import matplotlib as mpl

Numpy, Matplotlib, Panda, Seaborn