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https://github.com/kavyachippada/hva
Mini-Hackathon 1.0
https://github.com/kavyachippada/hva
bigquery excel pandas powerbi sql
Last synced: 4 days ago
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Mini-Hackathon 1.0
- Host: GitHub
- URL: https://github.com/kavyachippada/hva
- Owner: kavyachippada
- Created: 2024-08-28T18:03:08.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2024-08-29T16:24:32.000Z (about 1 month ago)
- Last Synced: 2024-09-29T10:41:29.761Z (4 days ago)
- Topics: bigquery, excel, pandas, powerbi, sql
- Language: Jupyter Notebook
- Homepage: https://console.cloud.google.com/bigquery?sq=891619701453:0e4eaea46c524742b719b6e2a4848ac1
- Size: 1.01 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# IMDB Movies and TV Shows Analysis
## Overview
This project is part of the **Mini Hackathon 1.0 - Data Science Track**. The objective was to analyze a dataset featuring the top 1000 movies and TV shows from IMDB, focusing on data processing, analysis, and visualization to uncover meaningful insights.
## Project Structure
- **Data Preprocessing**: All preprocessing tasks were done using Kaggle. The dataset was cleaned and prepared for analysis.
- **SQL Queries**: The data analysis was conducted using SQL queries in BigQuery. The queries explore various aspects of the dataset, such as director impact, genre popularity, and the correlation between IMDB ratings and gross earnings.
- **Visualizations**: The visualizations were created in Power BI to effectively communicate the insights derived from the analysis.
## Key Areas of Analysis
1. **Director's Impact on Earnings**: Analysis of how different directors' movies performed in terms of gross earnings.
2. **Genre Popularity Over the Years**: Investigation into the evolution of genre popularity over time.
3. **Correlation Between IMDB Ratings and Commercial Success**: Exploration of the relationship between IMDB ratings and box office earnings.
4. **Impact of Movie Length on Ratings or Earnings**: Examination of how the duration of a movie influences its ratings or financial success.
5. **Actor Influence on Movie Success**: Analysis of the correlation between the presence of certain actors and movie performance.
6. **Release Date Analysis**: Study of the impact of a movie's release date on its success.
## How to Run
1. **Data Preprocessing**: Access the preprocessing notebook on Kaggle [[link](https://www.kaggle.com/code/kavyachippada/preprocessing/edit/run/194314284)].
2. **SQL Queries**: Use the provided SQL queries to explore the dataset in BigQuery [Link to bigquery](https://console.cloud.google.com/bigquery?sq=891619701453:0e4eaea46c524742b719b6e2a4848ac1).
3. **Power BI Visualizations**: Open the Power BI file to view the visualizations and interact with the data [Link to powerbi](https://app.powerbi.com/groups/me/reports/aebfe87b-8eed-4f61-b253-ed1bdf0f9175?ctid=4c49cc3b-7d9d-464e-8fc8-a10f91d8117a&pbi_source=linkShare).
## Submission
- **Loom Video**: [Link to video](https://www.loom.com/share/e1b17f8b41de42f98aa31c6908286531?sid=fb6b6e77-12d5-411c-9831-a8adc13fb305)
## Contact
For any questions or suggestions, please feel free to mail me ([email protected])