This project involves exploring two datasets: IPL (Indian Premier League) cricket matches and movies. The analysis includes various queries and insights extracted from the datasets using Python libraries such as NumPy and pandas.
- NumPy
- pandas
The project utilizes two datasets:
ipl-matches.csv: Contains data related to IPL cricket matches.movies.csv: Contains data related to movies.
- Final Winners: Identified champions of each IPL season.
- Super Over Finishes: Analyzed rare occurrences of matches decided by super overs.
- CSK in Kolkata: Examined Chennai Super Kings' performance in Kolkata venues.
- Toss Impact: Analyzed the correlation between toss wins and match wins.
- High-rated Movies: Identified critically acclaimed movies with significant viewer engagement.
- Action Movie Ratings: Explored the quality of action movies based on audience ratings.
- Player of the Match: Recognized outstanding players in crucial IPL matches.
- Toss Decision Trends: Visualized teams' preferences regarding toss decisions.
- Team Performance: Evaluated each IPL team's participation and success rate.
- Batsman Rankings: Ranked IPL batsmen based on their performance.
- Virat Kohli's Last Match: Identified the venue and opponent of Virat Kohli's most recent match in Delhi.
To replicate the analysis or explore the datasets further, follow these steps:
- Clone this repository to your local machine.
- Ensure you have Python installed along with the required libraries: NumPy and pandas.
- Open the Jupyter Notebook (
exploration.ipynb) to view the analysis and code implementation.
- Incorporate additional datasets for comparative analysis.
- Enhance visualization techniques for better insights.
- Implement machine learning models for predictive analysis.