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It includes data preprocessing, exploratory data analysis, and time series forecasting using Facebook Prophet.\n\n## Data Sources\n\nThe project uses two main datasets:\n1. `train.csv`: Contains daily sales data for multiple stores\n2. `store.csv`: Contains additional information about each store\n\n## Key Features\n\n- Data cleaning and preprocessing\n- Exploratory Data Analysis (EDA) with visualizations\n- Time series forecasting using Facebook Prophet\n- Incorporation of holiday effects in the forecast\n\n## Requirements\n\n- Python 3.6+\n- pandas\n- numpy\n- matplotlib\n- seaborn\n- fbprophet\n\n## Usage\n\n1. Mount your Google Drive (if using Google Colab)\n2. Import the required libraries\n3. Load and preprocess the data\n4. Perform EDA\n5. Train the Prophet model\n6. Make predictions and visualize results\n\n## Main Functions\n\n- `sales_prediction(Store_ID, sales_df, holidays, periods)`: Generates sales predictions for a specific store\n\n## Future Improvements\n\n- Fine-tune the Prophet model parameters\n- Incorporate additional external factors that might affect sales\n- Implement cross-validation for model evaluation\n- Extend the analysis to include all stores in the dataset\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Framyacp14%2Fsalesforecasting","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Framyacp14%2Fsalesforecasting","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Framyacp14%2Fsalesforecasting/lists"}