https://github.com/shrutiijoshi/shark-tank-india-analysis

It shows entrepreneurs making business presentations to a panel of investors or sharks, who decide whether to invest in their company.
https://github.com/shrutiijoshi/shark-tank-india-analysis

analysis kaggle-dataset matplotlib-pyplot pandas-python python seaborn

Last synced: 9 months ago
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It shows entrepreneurs making business presentations to a panel of investors or sharks, who decide whether to invest in their company.

• Host: GitHub
• URL: https://github.com/shrutiijoshi/shark-tank-india-analysis
• Owner: Shrutiijoshi
• Created: 2025-01-18T05:33:24.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-01-28T06:52:15.000Z (10 months ago)
• Last Synced: 2025-01-28T07:28:57.048Z (10 months ago)
• Topics: analysis, kaggle-dataset, matplotlib-pyplot, pandas-python, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 239 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Shark Tank India Analysis

 It shows entrepreneurs making business presentations to a panel of investors or sharks, who decide whether to invest in their company.

 ![Banner](https://theindiasaga.com/wp-content/uploads/2024/02/6-lessons-that-entrepreneurs-can-learn-from-Shark-Tank-India.jpg)

##  Overview :

Shark Tank India is an entrepreneurial reality show where startups pitch their businesses to a panel of investors (Sharks) for funding. This analysis focuses on understanding various aspects of the business pitches, including:

- The relationship between equity offered and amount asked.

- Analyzing the type of businesses that received funding.

- Exploring the Sharks’ investment behavior and preferences.

## Dataset :

The dataset used for this analysis contains information on:

- Pitcher Information: Names of the entrepreneurs and their business ideas.

- Deal Information: Investment amounts, equity offered, and the Sharks who invested.

- Business Categories: Types of businesses and industries featured on the show.

- Investor Information: Details about the Sharks (e.g., amount invested, percentage equity offered).

- **Dataset Source** : [Shark Tank India Dataset](https://www.kaggle.com/datasets/shivavashishtha/shark-tank-india-dataset/data)

## Technologies used :

- Python: For data cleaning, analysis, and visualization.

- Pandas: Data manipulation and analysis.

- Matplotlib / Seaborn: Data visualization.

- Kaggle Notebook: Interactive analysis and visualization.

## Data Analysis :

This project utilizes various techniques for data analysis and visualization, including:

- Exploratory Data Analysis (EDA): Identifying trends, distributions, and relationships in the data.

- Visualization: Using libraries like Matplotlib, Seaborn, and Plotly to create insightful visualizations, including bar charts, scatter plots, and heatmaps.

- Business Analysis: Examining which brands or ideas attracting more investors.

- Regression Analysis: Investigating the relationship between the amount of money asked and the equity offered.

## Key Insights :

Through this analysis, some of the key insights include:

- The relationship between the amount asked and equity offered.

- Sharks’ preferences for certain types of businesses, industries, and investment amounts.

- An analysis of which Sharks have made the most investments and their investment preferences.