Project Title

Netflix Dashboard and Netflix Stock Prediction

Dashboard Link : https://app.powerbi.com/reportEmbed?reportId=f0523dd1-673c-43fe-9ccd-074bd4e3ce8f&autoAuth=true&ctid=8d7b5b23-182d-4b77-9dea-a0821c7073c1

Table of Contents

• Project Overview
• Data Source
• Objectives
• Features and Insights
• Data Transformation
• Power BI Visualizations
• Conclusion
• Future Enhancements
• How to Use

Project Overview

• This Power BI project focuses on analyzing Netflix's stock performance and exploring trends in movie genres based on showtime data. The dashboard is designed to provide insights into how Netflix's stock price fluctuates over time and how different movie genres perform during specific times of the day or year.

• Using Power BI’s interactive visualizations, users can explore historical stock data to track trends, identify patterns, and predict future stock movements. Additionally, the dashboard delves into Netflix's content, offering a breakdown of popular movie genres by showtime, enabling users to discover correlations between viewing times and genre preferences.

Data Source

The data used in this project is sourced from [ SQL database]. The dataset contains information about [brief description of what the data represents].

• File Name: [Credits, Netflix_titles,Netflix stock prediction, Titles]

• Key Columns: [Date column ,released year,]

Objectives

PART-1(Netflix stock prediction)

• To find Top-5 dates with highest stock price.
• To find Top-5 dates with lowest stock price.
• Netflix stock cummulative returns.
• Netflix stock daily returns.
• Moving average fo stock. PART-2
• Show ratings by year.
• Trending watching times.

Features and Insights

The dashboard includes the following features and provides insights such as:

• [Feature 1: e.g., Interactive charts to filter data by year]
• [Feature 2: e.g., Scrollers show movies name by year]
• [Feature 3: e.g., Gauge chart to show ratings]
• [Feature 4: e.g., use clustered column chart to show watching time by year]
• [Feature 5: e.g. use stacked bar chart to show Top-6 genres by show time]

Data Transformation

Data preparation and transformation steps involved:

• Data Cleaning: [ data cleaning steps, e.g., handling missing values, removing duplicates]
• Data Modeling: [e.g., Relationship setup between different tables]

Power BI Visualizations

This project uses a variety of Power BI visualizations, including:

• Stacked Bar chart /Clustered Column Charts: [ to show watching time and show time by year figures by region]

• Multi row card: [to show movie name and year]

• Tables: [to show top-5 highest stock price and lowest stock price]

• Slicers: [to filter data by year]

• Area chart:[to show high and low dates]

Conclusion

The Power BI dashboard successfully highlights . This project helps make data-driven decisions based on clear visualizations and actionable insights.

main conclusion or takeaway from your analysis

1.1) Top-5 dates with Highest stock price

1.2) Top-5 dates with lowest stock price

1.3) Lowest-6 stock price compared by open date

1.4) Highest-6 stock price compared by open date

1.5) Watching time by year

1.6)Top-6 genres by show time

1.7)

1.8)

How to Use

• Open the Power BI file

• Navigate through different tabs/pages to explore the data insights.
• Use slicers and filters to interact with the dashboard.
• Key metrics are highlighted for quick reference, while detailed views are available in data tables.