{"id":28276415,"url":"https://github.com/komailmk/instagram-reach-forecasting","last_synced_at":"2026-07-27T01:31:10.589Z","repository":{"id":248170900,"uuid":"827954478","full_name":"KomailMK/instagram-reach-forecasting","owner":"KomailMK","description":"This repository provides a Python-based solution for forecasting Instagram reach using historical data and SARIMA modeling techniques.","archived":false,"fork":false,"pushed_at":"2025-04-20T08:40:05.000Z","size":16169,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-05T21:54:12.589Z","etag":null,"topics":["data-analysis","data-visualizations","machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"epl-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/KomailMK.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2024-07-12T18:40:17.000Z","updated_at":"2025-04-20T08:40:08.000Z","dependencies_parsed_at":"2025-06-17T23:41:31.602Z","dependency_job_id":null,"html_url":"https://github.com/KomailMK/instagram-reach-forecasting","commit_stats":null,"previous_names":["komailmk/instagram-reach-forecasting"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/KomailMK/instagram-reach-forecasting","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Finstagram-reach-forecasting","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Finstagram-reach-forecasting/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Finstagram-reach-forecasting/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Finstagram-reach-forecasting/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/KomailMK","download_url":"https://codeload.github.com/KomailMK/instagram-reach-forecasting/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/KomailMK%2Finstagram-reach-forecasting/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35933751,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-07-20T02:08:10.276Z","status":"online","status_checked_at":"2026-07-26T02:00:06.503Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["data-analysis","data-visualizations","machine-learning"],"created_at":"2025-05-21T05:10:35.872Z","updated_at":"2026-07-27T01:31:10.522Z","avatar_url":"https://github.com/KomailMK.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Instagram Reach Forecasting\n\nThis repository contains code for forecasting Instagram reach using historical data and a SARIMA model. The project includes both a Python script for analysis and model training.\n\n## Repository Structure\ninstagram-reach-forecasting/   \n│   \n├── data/   \n│ └── Instagram-Reach.csv # Your dataset   \n├── scripts/   \n│ ├── forecast_reach.py # Python script   \n│ └── forecast_reach.ipynb # Jupyter notebook   \n├── .gitignore # Git ignore file   \n├── LICENSE # License file (optional)   \n└── README.md # README file with instructions   \n\n## Usage   \nYou can choose between the Python script (`forecast_reach.py`) and the Jupyter notebook (`forecast_reach.ipynb`), both containing identical code for running the forecasting analysis.The Jupyter notebook (`forecast_reach.ipynb`) offers a step-by-step approach with detailed explanations and visualizations, making it suitable for users who prefer an interactive and exploratory workflow.\n\n## Setup Instructions\n\n### Prerequisites\n\nEnsure you have the following installed:\n- Python 3.6 or higher\n- pip (Python package installer)\n- Jupyter Notebook (for running the notebook file)\n\n### Installation\n\n1. Clone the repository:\n    ```bash\n    git clone https://github.com/your-username/instagram-reach-forecasting.git\n    cd instagram-reach-forecasting\n    ```\n\n2. Install the required Python packages:\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n### Running the Python Script\n\n1. Ensure your dataset (`Instagram-Reach.csv`) is in the `data` directory.\n2. Run the script:\n    ```bash\n    python scripts/forecast_reach.py\n    ```\n\n### Running the Jupyter Notebook\n\n1. Ensure your dataset (`Instagram-Reach.csv`) is in the `data` directory.\n2. Open the Jupyter Notebook:\n    ```bash\n    jupyter notebook scripts/forecast_reach.ipynb\n    ```\n\n3. Run the cells in the notebook to perform the analysis and train the model.\n\n## Project Description\n\nThis project performs the following steps:\n1. **Data Import and Preprocessing**: Loading the dataset and checking for missing values, column information, and descriptive statistics.\n2. **Data Analysis and Visualization**: Analyzing trends, distributions, and patterns in Instagram reach data using line charts, bar charts, and box plots.\n3. **Feature Engineering**: Creating a day column and analyzing reach based on the days of the week.\n4. **Model Training**: Training a SARIMA model to forecast Instagram reach.\n5. **Forecasting**: Making predictions for future Instagram reach and visualizing the results.\n6. **Saving and Loading the Model**: Saving the trained model and loading it for future use.\n\n## Example Output\n\nBelow is an example of the forecasted Instagram reach for the next 30 days:\n\nDate   \n2024-07-01 54000    \n2024-07-02 55000    \n2024-07-03 56000    \n...   \n2024-07-30 61000   \n\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Contributing\n\nContributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.\n\n## Contact\n\nIf you have any questions, feel free to reach out to me at komikhalils091@gmail.com.\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkomailmk%2Finstagram-reach-forecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkomailmk%2Finstagram-reach-forecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkomailmk%2Finstagram-reach-forecasting/lists"}