We will Visualize a Superstore Dataset related to different categories of Office Supplies, Furniture & Technological products, by conducting EDA on the dataset through the use of scatter plots & heatmaps. After that, we will try to figure out the variables which are directly / indirectly related to Profit variable.

Read, Load & Understand the Data

#Load packages
# Read the file & display first 5 rows
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt6
import seaborn as sns
import warnings
warnings.filterwarnings(“ignore”, category=DeprecationWarning)
warnings.filterwarnings(“ignore”, category=FutureWarning)
super_store = pd.read_csv(“D:/ANCHIT/Python/SampleSuperstore.csv”)

Discussing on 5 Basic most used Tensor Operations

Deep learning allows us to carry out a very wide range of complicated tasks. In order to carry out our tasks effectively, we need a tool which is flexible. Pytorch gives us this option because of its simplicity. It provides accelerated operations using GPU’s (Graphical Processing Units). Due to this reason of Pytorch being a high performance library that it has gained its popularity. The below notebook consists of some essential functions which are very useful in carrying out tensor operations. These operations are used for multi-dimensional tensors & for arithmetic operations.

  • General Ops — Inverse
  • Creation Ops — Complex
  • Arithmetic Ops — Transpose
  • Mutating Ops — Add
  • Reduction Ops — Amax

We will discuss the examples of these 5 Basic functions & observe the errors. Before we begin, let’s install and import PyTorch

# Windows
# !pip install numpy torch==1.7.0+cpu torchvision==0.8.1+cpu torchaudio==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html
# Import torch and other required modules
import torch

1. General Operations — Inverse Function

First function we will be using is the ‘Inverse’ function.

a = torch.randn(2,3,3)

The above 'randn' function has created 3X3 square matrix with 2 outer most rows. …

A new and effective way of exploring and analyzing data

As a Data Scientist, it becomes very important on your part not only to work towards achieving the desired result but also able to understand it. This analyses has to be effectively communicated to your stake holders. Exploratory Data Analyses plays an important role to grab the basic understanding of the data.

In EDA, we perform all the necessary tasks to extract the relevant information out of our data which ranges from performing the tasks related to—

  1. Finding the Missing/ Null / Nan values
  2. Performing Statistical Analysis (through describe function)

Anchit Bhagat

M.Sc Data Analytics (QUB 2021-22 batch) l Experienced HR Analyst l Travel-freak l French beginner l

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