This project involves an in-depth analysis of the "Shark Tank India" dataset to extract insights into the startups and deals featured on the show.
- Introduction: Brief overview of the project and dataset.
- Insights: Summary of key findings from the EDA process.
- Data: Details about the dataset columns and structure.
- Analysis: Detailed analysis of startup pitches and deal outcomes.
- Visualizations: Graphical representations of important data points.
- Conclusion: Concluding remarks on the analysis conducted.
- Python
- Pandas
- Matplotlib
- Seaborn
- Jupyter Notebook
It delves into various aspects of the show, aiming to uncover interesting patterns and trends related to:
- Pitch Distribution: Analyze the number of pitches per season, identify potential variations over time.
- Industry Breakdown: Explore the distribution of pitches across different industries.
- Presenter Demographics: Investigate the demographics of entrepreneurs presenting on the show, including gender representation.
- Deal Success Rates: Examine the overall success rate of securing deals and analyze factors potentially influencing it (e.g., industry, investment ask).
- Shark Investment Patterns: Explore investment trends for each Shark, including their average investment amount and preferred industries.
- Data Acquisition and Cleaning: Describe the process of obtaining the Shark Tank dataset and any cleaning steps performed (e.g., handling missing values, formatting inconsistencies).
- Exploratory Analysis: Explain the tools and libraries used (e.g., pandas, Seaborn, matplotlib) for data exploration and visualization techniques employed (e.g., bar charts, pie charts, histograms).
- Key Findings: Summarize the most significant insights you discovered from your analysis.
Clone the project
git clone https://github.com/takshitharao07/Shark_Tank_EDAor click Download ZIP in right panel of repository and extract it.
Open latest version of notebook in Jupyter Notebook. Run each cell to reproduce the analysis and view insights.
