Exploratory analysis on Video Games Sales data
Name : ABUBAKKAR SIDDIQUE
Qualification : Bachelor of Engineering (BE)
Stream : Computer Science and Engineer (CSE)
University : Visvesvaraya Institute of Technology
Location : Bangalore, KA, INDIA
This Project is to perform the analysis on the Video Games Sales dataset.
Here we use various libraries of Python for visualization of Data.
The Dataset which is Used in Project is from Data World (👈 Click to Download)
The Libraries I used in Project are:
- Matplotlib Explore here
- Seaborn Explore here
- Numpy Explore here
- Pandas Explore here
- Jovian Explore here
To install all required libraries, run the following Command:
pip install matplotlib seaborn numpy pandas plotly jovian --upgrade
Know About Data visualization:
The Data visualization is the graphic representation of data. It involves producing images that communicate relationships among the represented data to viewers. Visualizing data is an essential part of data analysis and machine learning. In this tutorial, we'll use Python libraries Matplotlib and Seaborn to learn and apply some popular data visualization techniques.
Their Following Tasks are Implemented in the Project:
- Data Preparation and Cleaning
- Exploratory Analysis and Visualization
- Asking and Answering Questions
- Inferences and Conclusion
- References and Future Work
Let's Get Dive into the Project !!
project_name = "analysis-on-videogames-sales-data"
!pip install jovian --upgrade -q
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
import numpy as np
from plotly.offline import init_notebook_mode,iplot
import pandas as pd
Data Preparation and Cleaning
Here various modes of displaying dataset which is in CSV format.
First step is to load the data using pandas read_csv function. the data is stored in mutidimensional table called as dataframe.