IMDb Movie Ratings Analysis

 IMDb Movie Ratings Analysis 

1. Introduction

Objective: Analyze IMDb movie ratings to uncover trends by year, genre, and average ratings.
Purpose: Provide insights into movie performance over time, popular genres, and ratings distribution to inform content creation and audience engagement strategies.

2. Project Workflow

1. Problem Definition:
   - Identify trends in IMDb movie ratings over the years.
   - Key questions:
     - How have movie ratings changed over the years?
     - Which genres consistently receive high ratings?
     - What is the distribution of ratings across movies?
2. Data Collection:
   - Source: IMDb datasets from Kaggle or IMDb's public database.
   - Example: A dataset containing attributes like `Title`, `Year`, `Genre`, `Rating`, and `Votes`.
3. Data Preprocessing:
   - Clean and preprocess the data for analysis.
   - Handle missing or duplicate data.
4. Analysis and Visualization:
   - Explore trends in ratings, genres, and other attributes.
5. Insights and Recommendations:
   - Provide actionable insights for filmmakers and content producers.

3. Technical Requirements

- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Visualization: Matplotlib, Seaborn, Plotly
  - Statistical Analysis: Scipy, Statsmodels

4. Implementation Steps

Step 1: Setup Environment

Install required libraries:
```
pip install pandas numpy matplotlib seaborn plotly statsmodels scipy
```

Step 2: Load and Explore Dataset

Load the IMDb movie dataset:
```
import pandas as pd

df = pd.read_csv('imdb_movie_ratings.csv')
```
Explore the dataset:
```
print(df.head())
print(df.info())
```

Step 3: Data Cleaning and Preprocessing

Clean and preprocess the data:
```
df.dropna(inplace=True)
df['Year'] = pd.to_numeric(df['Year'], errors='coerce')
df['Rating'] = pd.to_numeric(df['Rating'], errors='coerce')
```
Handle genres with multiple categories:
```
df['Primary Genre'] = df['Genre'].str.split(',').str[0]
```

Step 4: Analyze and Visualize Data

1. Trends in Ratings Over Years:
```
import matplotlib.pyplot as plt

avg_ratings_year = df.groupby('Year')['Rating'].mean()
avg_ratings_year.plot(kind='line', title='Average Ratings by Year')
plt.xlabel('Year')
plt.ylabel('Average Rating')
plt.show()
```
2. Genre-Wise Analysis:
```
import seaborn as sns

sns.barplot(data=df, x='Primary Genre', y='Rating', ci=None)
plt.title('Average Ratings by Genre')
plt.xticks(rotation=45)
plt.show()
```
3. Ratings Distribution:
```
sns.histplot(df['Rating'], kde=True, bins=10)
plt.title('Distribution of IMDb Ratings')
plt.xlabel('Rating')
plt.ylabel('Frequency')
plt.show()
```

Step 5: Generate Reports and Insights

Export summarized data and visualizations:
```
with pd.ExcelWriter('imdb_ratings_analysis_report.xlsx') as writer:
    avg_ratings_year.to_excel(writer, sheet_name='Yearly Ratings')
    df.groupby('Primary Genre')['Rating'].mean().to_excel(writer, sheet_name='Genre Ratings')
```
Save visualizations as images for reporting.

5. Expected Outcomes

1. Clear trends in IMDb movie ratings over the years.
2. Identification of high-performing genres.
3. Insights into the general distribution of movie ratings.

6. Additional Suggestions

- Advanced Analysis:
  - Explore correlations between ratings and the number of votes.
- Predictive Modeling:
  - Build a regression model to predict ratings based on genres, release years, and other factors.
- Dashboard Integration:
  - Create an interactive dashboard using Streamlit or Dash for real-time data exploration.