https://github.com/komailmk/instagram-reach-forecasting
This repository provides a Python-based solution for forecasting Instagram reach using historical data and SARIMA modeling techniques.
https://github.com/komailmk/instagram-reach-forecasting
data-analysis data-visualizations machine-learning
Last synced: 8 months ago
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This repository provides a Python-based solution for forecasting Instagram reach using historical data and SARIMA modeling techniques.
- Host: GitHub
- URL: https://github.com/komailmk/instagram-reach-forecasting
- Owner: KomailMK
- License: epl-2.0
- Created: 2024-07-12T18:40:17.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-20T08:40:05.000Z (about 1 year ago)
- Last Synced: 2025-05-21T05:13:15.052Z (about 1 year ago)
- Topics: data-analysis, data-visualizations, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 15.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Instagram Reach Forecasting
This 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.
## Repository Structure
instagram-reach-forecasting/
│
├── data/
│ └── Instagram-Reach.csv # Your dataset
├── scripts/
│ ├── forecast_reach.py # Python script
│ └── forecast_reach.ipynb # Jupyter notebook
├── .gitignore # Git ignore file
├── LICENSE # License file (optional)
└── README.md # README file with instructions
## Usage
You 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.
## Setup Instructions
### Prerequisites
Ensure you have the following installed:
- Python 3.6 or higher
- pip (Python package installer)
- Jupyter Notebook (for running the notebook file)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/your-username/instagram-reach-forecasting.git
cd instagram-reach-forecasting
```
2. Install the required Python packages:
```bash
pip install -r requirements.txt
```
### Running the Python Script
1. Ensure your dataset (`Instagram-Reach.csv`) is in the `data` directory.
2. Run the script:
```bash
python scripts/forecast_reach.py
```
### Running the Jupyter Notebook
1. Ensure your dataset (`Instagram-Reach.csv`) is in the `data` directory.
2. Open the Jupyter Notebook:
```bash
jupyter notebook scripts/forecast_reach.ipynb
```
3. Run the cells in the notebook to perform the analysis and train the model.
## Project Description
This project performs the following steps:
1. **Data Import and Preprocessing**: Loading the dataset and checking for missing values, column information, and descriptive statistics.
2. **Data Analysis and Visualization**: Analyzing trends, distributions, and patterns in Instagram reach data using line charts, bar charts, and box plots.
3. **Feature Engineering**: Creating a day column and analyzing reach based on the days of the week.
4. **Model Training**: Training a SARIMA model to forecast Instagram reach.
5. **Forecasting**: Making predictions for future Instagram reach and visualizing the results.
6. **Saving and Loading the Model**: Saving the trained model and loading it for future use.
## Example Output
Below is an example of the forecasted Instagram reach for the next 30 days:
Date
2024-07-01 54000
2024-07-02 55000
2024-07-03 56000
...
2024-07-30 61000
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
## Contact
If you have any questions, feel free to reach out to me at komikhalils091@gmail.com.