https://github.com/nagar2nd/imdb-movie-analysis-python

This project analyzes the IMDB Movies dataset using Python to uncover trends in genre popularity, ratings, and factors influencing movie success. Key insights include the relationship between budget and revenue, genre-specific performance, and trends in movie ratings over time. Libraries like Pandas, NumPy, Seaborn, and Matplotlib were used .
https://github.com/nagar2nd/imdb-movie-analysis-python

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This project analyzes the IMDB Movies dataset using Python to uncover trends in genre popularity, ratings, and factors influencing movie success. Key insights include the relationship between budget and revenue, genre-specific performance, and trends in movie ratings over time. Libraries like Pandas, NumPy, Seaborn, and Matplotlib were used .

• Host: GitHub
• URL: https://github.com/nagar2nd/imdb-movie-analysis-python
• Owner: Nagar2nd
• Created: 2024-11-24T16:36:14.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-25T07:19:21.000Z (16 days ago)
• Last Synced: 2024-12-25T08:25:24.612Z (16 days ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 3.74 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# **IMDB Movie Analysis**

## **Objective**  

This project explores and analyzes the IMDB Movies dataset to uncover insights into movie trends, genre popularity, and factors influencing movie success. Using Python and libraries like **Pandas**, **NumPy**, **Seaborn**, and **Matplotlib**, the analysis answers specific business questions and delivers actionable recommendations to help IMDB better understand its data.

---

## **Dataset**  

The dataset used in this analysis is available at:  

[IMDB Movies Dataset](https://drive.google.com/file/d/1lruT50ZWD4PtvDbIn4VnepZvSoeO9BrA/view?usp=sharing)  

Analysis Summary Link : 

https://docs.google.com/document/d/1X6Hy1KSsvCyovFm46JS9mvMUTsUnCGTb/edit

Each row in the dataset represents a movie, and the columns include attributes like budget, revenue, genre, ratings, and release year.

---

## **Key Libraries Used**  

1. **Pandas**: For data manipulation and cleaning.  

2. **NumPy**: For numerical operations and handling missing values.  

3. **Matplotlib**: For creating static visualizations.  

4. **Seaborn**: For advanced statistical visualizations.  

---

## **Steps and Questions Addressed**  

### **1. Data Overview and Basic Exploration**  

- Used `.info()` to examine data types and missing values.  

- Generated summary statistics using `.describe()` to understand numerical columns.  

- Identified potential data quality issues like missing values, incorrect data types, or outliers.  

### **2. Data Cleaning**  

- Handled missing values appropriately, depending on their significance.  

- Converted data types, such as dates and numeric fields, for accurate analysis.  

- Addressed outliers in numerical columns like budget and revenue.  

### **3. Univariate Analysis**  

- Analyzed the distribution of scores using histograms.  

- Identified the most common genres using bar charts.  

### **4. Bivariate Analysis**  

- **Budget vs. Revenue**: Used scatter plots to explore their relationship.  

- Analyzed how ratings vary by country using box plots.  

- Calculated correlation coefficients to determine relationships between scores, budgets, and revenues.  

### **5. Genre-Specific Analysis**  

- Determined the genre with the highest average rating.  

- Visualized the popularity of genres over time by plotting the number of movies released per genre each year.  

### **6. Year and Trend Analysis**  

- Examined how average movie ratings have changed over the years using line plots.  

- Identified years with the highest and lowest movie releases using bar plots.  

### **7. Multivariate Analysis**  

- Identified the most popular genres in each decade using bar plots.  

- Analyzed the relationship between genre, release year, and average ratings.  

---

## **Key Insights**  

1. **Genre Popularity**: Some genres consistently perform better in terms of ratings, while others dominate in terms of frequency.  

2. **Budget and Revenue Relationship**: A positive correlation exists between a movie's budget and its revenue, with high-budget movies generally earning more.  

3. **Trends Over Time**: Average ratings and genre popularity have varied significantly over the decades.  

---

## **Future Exploration**  

- Investigate the impact of directors, actors, or production studios on movie success.  

- Analyze geographic trends in movie ratings and revenues.  

- Examine the effect of runtime on audience satisfaction and ratings.  

---

## **How to Run the Project**  

1. Clone this repository:  

   ```bash

   git clone https://github.com/Nagar2nd/IMDB-Movie-Analysis-Python.git