/shark-tank-analysis

Explore the investment strategies of Sharks on Shark Tank India with this comprehensive data analysis repository. By examining investment data, we aim to uncover trends, preferences.

Primary LanguageJupyter Notebook

Shark Tank India Investment Analysis

Repository Overview

This repository explores the investment patterns of sharks on Shark Tank India. By analyzing investment data, we aim to uncover trends, preferences, and insights into the sharks' investment strategies.

Data

The dataset includes comprehensive information about investments made by each shark:

  • Industry Sector: The industry of the invested company.
  • Deal Size: The total investment amount.
  • Equity Percentage: The percentage of ownership acquired by the sharks.
  • Valuation: The company's valuation at the time of investment.
  • Episode Details: Information about the specific Shark Tank episode.
  • Shark Participation: Which sharks participated in the deal.

Data is primarily in CSV format, cleaned, and preprocessed for analysis. You can find it in the data directory.

Methodology

Our analysis follows a structured approach:

  1. Data Cleaning and Preprocessing: Handling missing values, inconsistencies, and outliers using scripts in the scripts directory.
  2. Exploratory Data Analysis (EDA): Unveiling data distributions, correlations, and key trends using Jupyter notebooks in the notebooks directory.
  3. Investment Pattern Analysis: Examining investment preferences based on industry, deal size, and equity percentage.
  4. Shark Performance Analysis: Evaluating individual shark performance using metrics like ROI.
  5. Visualization: Creating informative visualizations using Matplotlib and Seaborn to communicate findings effectively. Output plots are stored in the visualizations directory.

Key Findings

  • Shark Investment Preferences: Identifying preferred industries and deal sizes for each shark.
  • Investment Trends: Analyzing overall investment trends across different seasons.
  • Successful Investment Patterns: Discovering common characteristics of successful investments.
  • Shark Performance Comparison: Comparing the investment performance of different sharks.

Dependencies

To run the code, the following libraries are required:

  • pandas
  • numpy
  • plotly

Install dependencies using pip install -r requirements.txt.

Usage

To reproduce the analysis:

  1. Clone the repository: git clone https://github.com/yourusername/shark-tank-india-analysis.git
  2. Install dependencies: pip install -r requirements.txt
  3. Execute Jupyter notebooks in the notebooks directory.