https://github.com/macielmk7/imdb-movie-analysis
Analyze IMDB movie data with Python and pandas. Discover trends in ratings and genre popularity over time. 📊📈 Explore insights with ease.
https://github.com/macielmk7/imdb-movie-analysis
businessintelligence decision-tree-classifier deep-learning docker etl-pipeline ggplot2 html imdb-dataset imdb-sentiment-analysis jupyter-notebooks lstm-neural-networks machine-learning nlp-library numpy rlanguage rmarkdown rstudio sentiment-analysis
Last synced: 3 months ago
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Analyze IMDB movie data with Python and pandas. Discover trends in ratings and genre popularity over time. 📊📈 Explore insights with ease.
- Host: GitHub
- URL: https://github.com/macielmk7/imdb-movie-analysis
- Owner: macielmk7
- License: mit
- Created: 2025-06-25T09:20:29.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-06-25T17:36:43.000Z (3 months ago)
- Last Synced: 2025-06-25T18:39:17.055Z (3 months ago)
- Topics: businessintelligence, decision-tree-classifier, deep-learning, docker, etl-pipeline, ggplot2, html, imdb-dataset, imdb-sentiment-analysis, jupyter-notebooks, lstm-neural-networks, machine-learning, nlp-library, numpy, rlanguage, rmarkdown, rstudio, sentiment-analysis
- Language: Jupyter Notebook
- Homepage: https://macielmk7.github.io
- Size: 1.87 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# IMDB Movie Analysis: A Beginner's Guide to Data Insights 🎬📊
## Table of Contents
- [Project Overview](#project-overview)
- [Getting Started](#getting-started)
- [Dataset Information](#dataset-information)
- [Technologies Used](#technologies-used)
- [Data Analysis Techniques](#data-analysis-techniques)
- [Visualization Techniques](#visualization-techniques)
- [How to Run the Project](#how-to-run-the-project)
- [Explore the Releases](#explore-the-releases)
- [Contributing](#contributing)
- [License](#license)
## Project Overview
The **IMDB Movie Analysis** project provides an insightful exploration of the IMDB movie dataset. This project leverages Python's powerful libraries, such as Pandas and Matplotlib, to perform data analysis and visualization. Users can gain valuable insights into movie trends, ratings, and other key metrics.
## Getting Started
To get started with this project, you will need to have Python installed on your machine. This project is beginner-friendly and serves as a great introduction to data analysis and visualization.
### Prerequisites
- Python 3.x
- Pandas
- Matplotlib
- Seaborn
You can install the required libraries using pip:
```bash
pip install pandas matplotlib seaborn
```
## Dataset Information
The dataset used in this project is sourced from Kaggle and includes various features related to movies, such as:
- Title
- Genre
- Year of Release
- Rating
- Duration
- Box Office Revenue
This dataset allows for comprehensive analysis and visualization of movie trends over the years.
## Technologies Used
This project utilizes the following technologies:
- **Python**: The primary programming language used for data analysis.
- **Pandas**: A library for data manipulation and analysis.
- **Matplotlib**: A plotting library for creating static, animated, and interactive visualizations.
- **Seaborn**: A statistical data visualization library based on Matplotlib.
## Data Analysis Techniques
In this project, we explore various data analysis techniques, including:
- **Descriptive Statistics**: Understanding the basic characteristics of the dataset.
- **Data Cleaning**: Handling missing values and correcting data types.
- **Group By Operations**: Aggregating data to find trends.
- **Correlation Analysis**: Understanding relationships between different features.
### Example Analysis
One of the analyses performed in this project is to find the average rating of movies by genre. This helps identify which genres tend to receive higher ratings.
```python
import pandas as pd
# Load the dataset
df = pd.read_csv('imdb_movies.csv')
# Group by genre and calculate the average rating
average_rating = df.groupby('Genre')['Rating'].mean()
print(average_rating)
```
## Visualization Techniques
Visualization is a key component of data analysis. This project employs several visualization techniques to present data insights clearly and effectively.
### Types of Visualizations Used
- **Bar Charts**: To compare average ratings across genres.
- **Histograms**: To show the distribution of movie ratings.
- **Scatter Plots**: To explore relationships between box office revenue and ratings.
### Example Visualization
Below is an example of how to create a bar chart to visualize average ratings by genre.
```python
import matplotlib.pyplot as plt
# Plotting average ratings
average_rating.plot(kind='bar', color='skyblue')
plt.title('Average Movie Ratings by Genre')
plt.xlabel('Genre')
plt.ylabel('Average Rating')
plt.show()
```
## How to Run the Project
To run the project, follow these steps:
1. Clone the repository to your local machine:
```bash
git clone https://github.com/macielmk7/IMDB-Movie-Analysis.git
```
2. Navigate to the project directory:
```bash
cd IMDB-Movie-Analysis
```
3. Run the Python scripts provided in the repository to perform data analysis and generate visualizations.
4. You can download the latest release from the [Releases section](https://github.com/macielmk7/IMDB-Movie-Analysis/releases) for any updates or additional files.
## Explore the Releases
For the latest updates and files related to this project, visit the [Releases section](https://github.com/macielmk7/IMDB-Movie-Analysis/releases). Here, you can find downloadable files that may enhance your experience with the project.
## Contributing
Contributions are welcome! If you would like to contribute to this project, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes.
4. Push to your branch.
5. Create a pull request.
Your contributions can help improve this project and make it more useful for others.
## License
This project is licensed under the MIT License. Feel free to use and modify it as you wish, but please provide appropriate credit to the original authors.
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By engaging with this project, you will enhance your skills in data analysis and visualization using Python. Dive into the world of movies and discover the stories behind the numbers!