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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


Data Visualization

Data Extraction


A basic understanding of Python

A basic understanding of statistical tools

A basic understanding of plotting in 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



Task 1: Load Libraries

Task 2: Fetch the Data


Simple Inspection

Task 3: Preprocess the Data

Task 4: Plot the Volume

Task 5: Plot the Highest Price



Task 6: Calculate the SMA for 10 and 20 Days

Task 7: Calculate the Autocorrelation Function

Task 8: Calculate the Heat Map