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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.
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 1: Load Libraries
Task 2: Fetch the Data
2
Simple Inspection
Task 3: Preprocess the Data
Task 4: Plot the Volume
Task 5: Plot the Highest Price
3
Analysis
Task 6: Calculate the SMA for 10 and 20 Days
Task 7: Calculate the Autocorrelation Function
Task 8: Calculate the Heat Map
Congratulations!
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.