{"id":22479662,"url":"https://github.com/djdurga/amazon-prime-dataset-project","last_synced_at":"2025-06-16T02:02:56.561Z","repository":{"id":261261222,"uuid":"883775159","full_name":"Djdurga/Amazon-Prime-Dataset-Project","owner":"Djdurga","description":null,"archived":false,"fork":false,"pushed_at":"2024-11-06T15:02:58.000Z","size":250,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T18:31:38.970Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Djdurga.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-11-05T14:51:54.000Z","updated_at":"2024-11-06T15:03:01.000Z","dependencies_parsed_at":"2024-11-05T16:20:22.506Z","dependency_job_id":"4aa1aff5-2eed-4a81-b54c-b000853d58c6","html_url":"https://github.com/Djdurga/Amazon-Prime-Dataset-Project","commit_stats":null,"previous_names":["djdurga/amazon-prime-dataset-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Djdurga/Amazon-Prime-Dataset-Project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Djdurga%2FAmazon-Prime-Dataset-Project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Djdurga%2FAmazon-Prime-Dataset-Project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Djdurga%2FAmazon-Prime-Dataset-Project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Djdurga%2FAmazon-Prime-Dataset-Project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Djdurga","download_url":"https://codeload.github.com/Djdurga/Amazon-Prime-Dataset-Project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Djdurga%2FAmazon-Prime-Dataset-Project/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260083830,"owners_count":22956406,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-12-06T15:16:25.677Z","updated_at":"2025-06-16T02:02:56.515Z","avatar_url":"https://github.com/Djdurga.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Amazon-Prime-Dataset-Project\n\n# Movie Dataset Analysis and Predictive Modeling\n\n## Table of Contents\n1. [Introduction](#introduction)\n2. [Data Overview](#data-overview)\n3. [Data Cleaning and Preprocessing](#data-cleaning-and-preprocessing)\n4. [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)\n5. [Correlation Analysis](#correlation-analysis)\n6. [Predictive Modeling](#predictive-modeling)\n7. [Conclusion](#conclusion)\n8. [Future Work](#future-work)\n\n\n## Introduction\nThe aim of this project is to analyze a movie dataset to understand trends and build a predictive model for IMDb ratings. The analysis will help identify key factors that influence movie ratings and how this information can be leveraged for better insights.\n\n## Data Overview\nThe dataset contains movie information with the following columns:\n- **title**: Name of the movie\n- **type**: Type of media (e.g., movie)\n- **genres**: Genres of the movie\n- **releaseYear**: Year of release\n- **imdbId**: IMDb ID for the movie\n- **imdbAverageRating**: IMDb average rating\n- **imdbNumVotes**: Number of votes on IMDb\n- **availableCountries**: Countries where the movie is available\n\n**Initial Observations**:\n- Basic data exploration indicates some missing values in columns such as `releaseYear` and `imdbNumVotes`.\n- The dataset includes both numerical and categorical data types.\n\n## Data Cleaning and Preprocessing\n- **Handling Missing Values**: Missing values in `releaseYear` were filled with the median, and `imdbNumVotes` were filled with zero.\n- **Data Type Adjustments**: Converted `releaseYear` to integer and `imdbNumVotes` to integer for consistency.\n- **Outlier Detection**: Outliers in `imdbAverageRating` were inspected but not removed as they represent valid data points.\n- **Feature Engineering**: No additional features were engineered at this stage, but future iterations may include feature extraction from `genres`.\n\n## Exploratory Data Analysis (EDA)\nVisualizations and analyses performed include:\n- **Distribution of IMDb Ratings**: Histogram plot showing the distribution of movie ratings.\n- **Movies Released Per Year**: Bar chart visualizing the number of movies released annually.\n- **Most Common Genres**: Bar plot highlighting the most frequently occurring movie genres.\n\n**Key Findings**:\n- The distribution of IMDb ratings is skewed towards higher values.\n- The number of movies released has fluctuated over the years, with notable peaks and declines.\n- Certain genres, such as `Drama` and `Comedy`, are more prevalent.\n\n## Correlation Analysis\nA correlation matrix was plotted to examine relationships between numerical features such as `releaseYear`, `imdbAverageRating`, and `imdbNumVotes`.\n\n**Insights**:\n- Weak positive correlation between `imdbNumVotes` and `imdbAverageRating`.\n- No strong correlation between `releaseYear` and `imdbAverageRating`, suggesting ratings are not time-dependent.\n\n## Predictive Modeling\n### Model Selection\nA linear regression model was chosen to predict IMDb ratings using `releaseYear` and `imdbNumVotes` as features.\n\n### Data Splitting\n- **Training Set**: 70% of the data\n- **Testing Set**: 30% of the data\n\n### Model Training and Evaluation\n- **Model Performance Metrics**:\n  - **Mean Squared Error (MSE)**: [Insert MSE value]\n  - **R-squared**: [Insert R-squared value]\n\n**Limitations**:\nThe linear regression model has limited predictive power, indicating that more complex models or additional features may be needed for better performance.\n\n## Conclusion\nThe project successfully explored the dataset and built a baseline predictive model for IMDb ratings. Key insights include:\n- The number of votes impacts IMDb ratings more than the year of release.\n- Popular genres such as `Drama` and `Comedy` dominate the dataset.\n\n## Future Work\n- **Model Improvements**: Explore more complex algorithms (e.g., Random Forest, Gradient Boosting).\n- **Feature Expansion**: Include `genres` and `availableCountries` for richer modeling.\n- **Additional Data**: Integrate movie budget, box office revenue, and critic reviews for better prediction accuracy.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjdurga%2Famazon-prime-dataset-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdjdurga%2Famazon-prime-dataset-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdjdurga%2Famazon-prime-dataset-project/lists"}