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Stock Market Data Visualization Using Python

PROJECT

Stock Market Data Visualization Using Python

Python has a significant collection of libraries that are useful for statistical computation and visualization. This project will use Python tools and techniques to visualize stock market data.

Stock Market Data Visualization Using Python

You will learn to:

Use Python programming for data analysis

Import data in Pandas DataFrames

Plot data using Plotly and Matplotlib

Apply functions on stock market data

Skills

Data Visualization

Data Extraction

Prerequisites

A basic understanding of Python

A basic understanding of statistical tools

A basic understanding of plotting in Python

Technology

Python

Project Description

Stock markets are constantly changing. It is difficult to predict the stock market’s fluctuations because of the many factors at play. Similarly, it is difficult to create models to consider such variability. However, due to recent advances in machine learning and computing, machines can now process large amounts of data. This enables us to use stock exchange data from the past to analyze trends and predict future changes.

This project will leverage Python to analyze stock data from the last 13 years. Our focus will be on the NIFTY-50 stock market (2008–2021) dataset publicly available on Kaggle. However, because there are around 50 different files and stocks available in the dataset, we’ll only select one file to visualize in this project.

Project Tasks

1

Preliminaries

Task 0: Project Setup and Library Import

Task 1: Load and Explore the NIFTY-50 Stock Market Dataset

2

Simple Inspection

Task 2: Preprocess the Data

Task 3: Plot the Volume

Task 4: Visualize Daily Highest Prices

3

Analysis

Task 5: Calculate and Plot the Simple Moving Average (SMA)

Task 6: Calculate the Autocorrelation Function

Task 7: Calculate the Heat Map

Congratulations!

has successfully completed the Guided ProjectStock Market Data Visualization Using Python

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