Exploratory Data Analysis (EDA) - IMDB Dataset Indian Offbeat Movies using Python (Pandas)

Link to the code - github.com/Ank...
🎥 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗘𝗗𝗔) 𝗼𝗻 𝗜𝗻𝗱𝗶𝗮𝗻 𝗢𝗳𝗳𝗯𝗲𝗮𝘁 𝗖𝗶𝗻𝗲𝗺𝗮 (𝗜𝗠𝗗𝗕 𝗗𝗮𝘁𝗮𝘀𝗲𝘁) 📊
I recently explored the IMDB dataset on Indian Offbeat Cinema, sourced from Kaggle, and uncovered some fascinating insights using Python and pandas! Here's what I found:
🔍 1. 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗥𝗮𝘁𝗶𝗻𝗴𝘀
Most movies have ratings centered around 6, with a bell-curve distribution.
Very few movies scored below 3 or above 9, indicating a concentration of average-rated films in this genre.
👥 2. 𝗔𝗰𝘁𝗼𝗿 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 (𝗧𝗼𝗽 10)
Mithun Chakraborty leads with the highest number of collaborations (240+), followed by Dharmendra and Ashok Kumar.
These actors have played a significant role in shaping Indian cinema through repeated partnerships.
🎬 3. 𝗧𝗼𝗽 10 𝗗𝗶𝗿𝗲𝗰𝘁𝗼𝗿𝘀 𝘄𝗶𝘁𝗵 𝗠𝗼𝘀𝘁 𝗠𝗼𝘃𝗶𝗲𝘀
Jayant Desai is at the top, directing over 50 movies, followed by Kanti Shah and Babubhai Mistry.
These prolific directors have significantly contributed to Indian offbeat cinema's diverse storytelling.
🎭 4. 𝗧𝗼𝗽 𝗚𝗲𝗻𝗿𝗲𝘀
Drama dominates as the most common genre, followed by Action and Romance.
Interestingly, niche genres like Musical and Adventure are relatively less represented.
⭐ 5. 𝗧𝗼𝗽 10 𝗠𝗼𝘃𝗶𝗲𝘀 𝗯𝘆 𝗥𝗮𝘁𝗶𝗻𝗴
Highly rated movies include Reflect and Ashok Vatika, showcasing unique narratives that resonate with audiences.
These films are examples of excellence in offbeat cinema, receiving top ratings.
📈 6. 𝗩𝗼𝘁𝗲𝘀 𝘃𝘀. 𝗥𝗮𝘁𝗶𝗻𝗴𝘀 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻
A positive correlation is evident: movies with higher votes tend to have better ratings.
However, some low-rated movies received a surprising number of votes, hinting at their controversial or polarizing nature.
I performed this analysis using Python pandas for data wrangling and Matplotlib/Seaborn for visualizations. 📊
✨ Let’s connect over data, cinema, and storytelling!
#Python #DataScience #IMDB #IndianCinema #OffbeatMovies #EDA #Kaggle #DataVisualization #StorytellingWithData #MovieLovers