Analyzed Shark Tank India data to uncover key trends, including episode counts, entrepreneur participation, brand involvement, industry dominance, and regional activity. Examined deal trends and investment patterns across seasons, highlighting the most and least invested sharks and top equity gains.
- Importing the required libraries
- Loading the Dataset
- Basic Understanding of Data
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Summary of Insights
- Seasons Overview
- Total Number of Episodes
- Entrepreneurs' Participation Count
- Brands per Episode
- Top Industries by Participation
- Most Dominant Sectors
- Gender Distribution of Entrepreneurs
- Age Groups of Entrepreneurs
- Top Participating States
- Top 5 States by Participation
- Most and Least Active Zones
- Deals Accepted vs Rejected
- Season 1: Higher Rejection Rates
- Season 2: More Accepted Deals
- Highest Deal Amounts in Season 2
- Highest Deal Amounts in Season 1
- Most and Least Expensive Episodes
- Most & Least Invested Sharks
- Highest Equity Gains: Peyush and Namita
Technologies Used
Developed using Python with:
- Pandas: Data manipulation and analysis
- NumPy: Numerical calculations
- Matplotlib & Seaborn: Data visualization
- Jupyter Notebook: Interactive analysis
CSV Dataset: Analyzed data from a CSV file using Pandas
- Shark Tank India
- Bar Charts
- Line Graphs
- Heatmaps
- Boxplots
- Pie Charts
- WordCloud
- Donut Chart
- Pandas Functions:
groupby,merge,describe,time_series_analysis,WordCloud - Feature Engineering: Creating and transforming features for improved analysis
- EDA: Univariate and Bivariate Analysis, Observations & Insights