Multi store sales prediction using Time series

ML Projects/Multi store sales prediction/Readme.md

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+# 🕒 Time Series Multi-Store Sales Prediction
+
+Accurately forecasting sales across multiple stores is critical for inventory planning, staffing, and decision-making in the retail sector. This project focuses on building a predictive model to estimate future sales using historical time series data from various stores.
+
+---
+
+## 📌 Project Overview
+
+Retail businesses often face challenges in predicting store-level sales due to fluctuating demand patterns, seasonal trends, promotions, and holidays. In this project, we:
+
+* Analyze historical sales data from multiple stores
+* Identify seasonality, trends, and external factors
+* Build predictive models using machine learning and time series techniques
+* Evaluate the models using standard regression metrics
+
+---
+
+## 🧠 Techniques & Models Used
+
+This project combines classical time series modeling with machine learning and deep learning techniques:
+
+* **Exploratory Data Analysis**: Trend, seasonality, and outlier detection
+* **Feature Engineering**: Lag features, rolling means, holiday flags, promotions
+* **Models**:
+
+  * XGBoost / LightGBM
+  * Facebook Prophet / NeuralProphet
+  * ARIMA / SARIMA
+  * LSTM / GRU (for sequence modeling)
+* **Evaluation Metrics**:
+
+  * RMSE, MAE, MAPE
+
+---
+
+## 📊 Dataset Overview
+
+* **Columns**:
+
+  * `store_id`: Unique store identifier
+  * `date`: Date of transaction
+  * `sales`: Daily sales
+  * `promotion`: Binary feature indicating promotional events
+  * `holiday`: Boolean feature for public holidays
+  * `store_type`, `region`, etc. (for categorical segmentation)
+
+* **Source**: \[Add link if from Kaggle or UCI, or write "synthetic dataset created for experimentation"]
+
+---
+
+## 🚀 Getting Started
+
+1. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+2. Run the Jupyter notebooks in order:
+
+   * `EDA.ipynb`: Data exploration and visualization
+   * `feature_engineering.ipynb`: Creating useful time-based features
+   * `model_training.ipynb`: Training and evaluation of models
+
+3. (Optional) Deploy using Streamlit or Flask to create an interactive dashboard.
+
+---
+
+## 📈 Results
+
+* XGBoost achieved **RMSE: XX.XX**, outperforming linear models.
+* Facebook Prophet handled seasonality and holidays effectively.
+* LSTM model captured temporal dependencies but required more tuning.
+
+Visualizations comparing **actual vs predicted sales** show the model's ability to forecast weekly and monthly trends for each store.
+
+---
+
+## 🛠️ Possible Improvements
+
+* Incorporate external features like weather or regional events
+* Implement hyperparameter tuning with Optuna or GridSearchCV
+* Use hierarchical/multivariate models (e.g., DeepAR, Temporal Fusion Transformers)
+* Real-time forecasting with API deployment
+
+---
+