IMDB Top 1000 — Power BI Dashboard
Power BI • Excel cleaning • Data storytelling
I created this project to showcase my data visualization skills on my portfolio. Originally,
I planned to highlight work in Tableau, but since I no longer have access to the paid version,
I decided to learn Power BI instead through this hands-on project. My school provides access to Power BI,
which gave me the chance to practice the full process: cleaning raw data in Excel, building visuals in Power BI,
and structuring an interactive report.
I chose to analyze IMDB’s Top 1000 movies dataset because I have a personal interest in film and found it to be both a rich and
engaging source of information. The report opens with a set of KPI cards highlighting essential statistics such as average ratings,
gross revenue, vote counts, and year ranges. As you continue through the report, you can explore visuals including top-grossing films,
rating and critic score trends over time, comparisons of box office success versus audience reception by decade, and distributions of
genres across decades. The final section even includes slicers that allow viewers to filter results by genre or decade to explore the
data from different perspectives.
If the embed requires sign-in or doesn’t load, open the PDF above.
What I Built
- Data preparation in Excel: cleaned raw fields by converting Gross to numeric values, fixing year formats, and creating a new Decade field for time-based analysis.
- Key metrics: designed 15 KPI cards to surface essentials from the dataset (average rating, total/average gross, vote counts, and year ranges).
- Visual breakdowns: built charts for the Top 10 Highest-Grossing movies, a trend of IMDB Rating by Year, and a combo chart comparing Gross (columns) vs. Average Rating (line) by decade using a secondary axis.
- Genre & decade analysis: added visuals showing how genres are distributed across decades, plus a bubble scatter of Gross vs. IMDB Rating (bubble size = No_of_Votes) to see how commercial performance aligns with audience reception.
- Interactive design: included slicers for Genre and Decade so viewers can filter the report and explore different cuts of the data.
Notes
- Dataset: IMDB Top 1000 on Kaggle (public). No sensitive data was used.
- For visuals that need a single genre per film, I treat the first listed genre as the primary one for simplicity. This can bias toward early-alphabet genres.
- Built in Power BI (web) on macOS; data cleaning in Excel app.
- The embedded report may require sign-in depending on device/tenant settings.
Screenshots
