Netflix Stock Price Analysis Dashboard Using Power BI

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Netflix Stock Price Analysis Dashboard Using Power BI

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Overview
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Detail Description

1. Abstract

Stock market analysis is an important activity for investors and financial analysts to understand market trends and make informed investment decisions. Stock prices change continuously based on market demand, company performance, and various economic factors. Analyzing historical stock data helps identify patterns, trends, and fluctuations in stock performance.

This project focuses on analyzing Netflix stock price data using Microsoft Power BI. Netflix is a popular OTT (Over-The-Top) streaming platform that provides movies, web series, and other digital content through the internet. The dataset used in this project contains historical stock information such as date, opening price, highest price, lowest price, closing price, adjusted closing price, and trading volume.

The project begins with importing the stock dataset in CSV format into Power BI. After loading the data, the dataset is explored and prepared for analysis. Data preparation includes checking data types, identifying missing values, and understanding the structure of the dataset.

A Date Master Table can also be created to extract additional information such as year, month, day, and weekday from the date column. This helps in performing time-based analysis of stock price movements.

Finally, interactive visualizations and dashboards are created to analyze trends in Netflix stock prices over time. The dashboard helps users understand stock performance, price fluctuations, and trading activity through graphical representations.

This project demonstrates how Power BI can be used to analyze financial data and create meaningful visual insights from stock market datasets.

2. Objectives

The main objectives of this project are:

To analyze Netflix stock price data using Power BI.
To import CSV datasets into Power BI.
To explore and understand financial market datasets.
To perform data cleaning and preparation using Power Query Editor.
To create a Date Master Table for time-based analysis.
To analyze stock price trends over time.
To create interactive dashboards and visual reports.
To demonstrate the use of Business Intelligence tools for financial data analysis.

3. Existing System

Traditionally, stock data analysis is performed using manual calculations or simple spreadsheets.

However, this system has several limitations:

Difficult to analyze large historical stock datasets.
Hard to identify trends and patterns in stock prices.
Limited visualization capabilities.
Time-consuming analysis process.

Because of these limitations, modern data visualization and business intelligence tools like Power BI are required for efficient stock data analysis.

4. Proposed System

The proposed system is a Power BI-based stock price analysis dashboard.

In this system:

Stock price data is imported from a CSV file.
Data is explored and prepared using Power Query Editor.
Important variables such as opening price, closing price, highest price, lowest price, and volume are analyzed.
A Date Master Table is created for better time-based analysis.
Interactive visualizations are created to study stock price trends and fluctuations.

This system helps users better understand financial data and stock market trends through visual dashboards.

5. Implementation Procedure

The project is implemented in the following steps:

Step 1: Import Dataset

Import the Netflix stock price dataset in CSV format into Power BI.

Step 2: Load Data

Load the dataset into the Power BI workspace.

Step 3: Explore Data

Review the dataset fields such as:
Date
Opening Price
Highest Price
Lowest Price
Closing Price
Adjusted Closing Price
Volume

Step 4: Data Preparation

Open Power Query Editor to clean and prepare the dataset.

Step 5: Data Cleaning

Check for missing values or errors in the dataset.
Ensure correct data types for numerical and date variables

Step 6: Create Date Master Table

Extract components such as:
Year
Month
Day
Weekday

from the date column for time-based analysis.

Step 7: Create Visualizations

Build visualizations such as:
Line charts for stock price trends
Bar charts for trading volume
Time-based charts for monthly or yearly trends

6. Software Requirements

The software required for this project includes:

Operating System

Windows / Linux / macOS

Software

Microsoft Power BI Desktop

Data Format

CSV File

7. Hardware Requirements

The hardware required for this project includes:

Processor: Intel i3 / i5 or higher
RAM: Minimum 4 GB
Storage: Minimum 100 GB
System: Laptop / Desktop Computer

8. Advantages of the Project

Helps analyze stock price trends and fluctuations.
Provides visual insights into financial data.
Enables interactive dashboards for better understanding.
Supports analysis of large financial datasets.
Helps investors understand market performance.
Reduces manual effort in analyzing stock data.
Demonstrates the use of Power BI in financial analytics.
Improves decision-making through data visualization.

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