# harshavardhannayaka20072007-png/movie-success-rate-prediction architecture

> Architecture diagram of the GitHub repository harshavardhannayaka20072007-png/movie-success-rate-prediction, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/harshavardhannayaka20072007-png/movie-success-rate-prediction
- Repository: https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction

## Overview

This repository is a notebook-based movie-success analysis and prediction project. Its documented workflow prepares movie data, explores and visualizes financial relationships, predicts worldwide revenue, and classifies movies against a megahit threshold while comparing approaches. The notebook and CSVs are the only runtime artifacts shown in the tree; source excerpts were unavailable, so links reflect documented activity rather than inspected code-level calls.

Main entry points: [`Top_Movies_Cleaned_Data.csv`](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Top_Movies_Cleaned_Data.csv) (Cleaned Movie Data), [`Untitled.ipynb`](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb) (Revenue Regression), [`Top Movies Data.csv`](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Top%20Movies%20Data.csv) (Original Movie Data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_preparation["Data Preparation"]
  node_raw_data["Original Movie Data"]
  node_cleaning["Clean Data<br/>[Untitled.ipynb]"]
  node_cleaned_data["Cleaned Movie Data"]
end

subgraph group_analysis["Exploration"]
  node_eda["Explore Relationships<br/>[Untitled.ipynb]"]
  node_visualization["Financial Visualizations<br/>[Untitled.ipynb]"]
end

subgraph group_prediction["Prediction"]
  node_revenue_model["Revenue Regression<br/>[Untitled.ipynb]"]
  node_success_model["Megahit Classification<br/>[Untitled.ipynb]"]
  node_comparison["Model Comparison<br/>[Untitled.ipynb]"]
  node_predictions["Movie Predictions<br/>[Untitled.ipynb]"]
end

node_analyst(("Analyst"))

node_analyst -->|"runs notebook"| node_cleaning
node_raw_data -->|"input data"| node_cleaning
node_cleaning -->|"prepares data"| node_cleaned_data
node_cleaned_data -->|"explores data"| node_eda
node_eda -->|"visualizes relationships"| node_visualization
node_cleaned_data -->|"predicts revenue"| node_revenue_model
node_cleaned_data -->|"classifies success"| node_success_model
node_revenue_model -->|"compared with"| node_comparison
node_success_model -->|"compared with"| node_comparison
node_revenue_model -->|"estimates revenue"| node_predictions
node_success_model -->|"classifies threshold"| node_predictions

click node_raw_data "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Top%20Movies%20Data.csv"
click node_cleaning "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb"
click node_cleaned_data "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Top_Movies_Cleaned_Data.csv"
click node_eda "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb"
click node_visualization "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb"
click node_revenue_model "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb"
click node_success_model "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb"
click node_comparison "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb"
click node_predictions "https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_raw_data,node_cleaning,node_cleaned_data toneBlue
class node_eda,node_visualization toneAmber
class node_revenue_model,node_success_model,node_comparison,node_predictions toneMint
class node_analyst toneIndigo
```

## Components

### Data Preparation

- [Original Movie Data](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Top%20Movies%20Data.csv): `Top Movies Data.csv`
- [Clean Data](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb): `Untitled.ipynb`
- [Cleaned Movie Data](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Top_Movies_Cleaned_Data.csv): `Top_Movies_Cleaned_Data.csv`

### Exploration

- [Explore Relationships](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb): `Untitled.ipynb`
- [Financial Visualizations](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb): `Untitled.ipynb`

### Prediction

- [Revenue Regression](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb): `Untitled.ipynb`
- [Megahit Classification](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb): `Untitled.ipynb`
- [Model Comparison](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb): `Untitled.ipynb`
- [Movie Predictions](https://github.com/harshavardhannayaka20072007-png/movie-success-rate-prediction/blob/main/Untitled.ipynb): `Untitled.ipynb`

### Other components

- **Analyst**

## Connections

- Analyst → Clean Data: runs notebook
- Original Movie Data → Clean Data: input data
- Clean Data → Cleaned Movie Data: prepares data
- Cleaned Movie Data → Explore Relationships: explores data
- Explore Relationships → Financial Visualizations: visualizes relationships
- Cleaned Movie Data → Revenue Regression: predicts revenue
- Cleaned Movie Data → Megahit Classification: classifies success
- Revenue Regression → Model Comparison: compared with
- Megahit Classification → Model Comparison: compared with
- Revenue Regression → Movie Predictions: estimates revenue
- Megahit Classification → Movie Predictions: classifies threshold