https://github.com/thekartikeyamishra/predictive-sales-analytics
The Predictive Sales Analytics tool aims to help MSMEs forecast future sales using historical data. This advanced version leverages Machine Learning for accurate predictions and provides a dashboard to visualize sales trends, seasonality, and predictions.
https://github.com/thekartikeyamishra/predictive-sales-analytics
joblib machine-learning matplotlib pandas python scikit-learn streamlit
Last synced: 3 months ago
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The Predictive Sales Analytics tool aims to help MSMEs forecast future sales using historical data. This advanced version leverages Machine Learning for accurate predictions and provides a dashboard to visualize sales trends, seasonality, and predictions.
- Host: GitHub
- URL: https://github.com/thekartikeyamishra/predictive-sales-analytics
- Owner: thekartikeyamishra
- Created: 2024-12-23T12:10:03.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-12-23T12:16:56.000Z (10 months ago)
- Last Synced: 2025-02-17T06:41:52.572Z (8 months ago)
- Topics: joblib, machine-learning, matplotlib, pandas, python, scikit-learn, streamlit
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predictive-Sales-Analytics
The Predictive Sales Analytics tool aims to help MSMEs forecast future sales using historical data. This advanced version leverages Machine Learning for accurate predictions and provides a dashboard to visualize sales trends, seasonality, and predictions.
### **Predictive Sales Analytics**
The **Predictive Sales Analytics** tool aims to help MSMEs forecast future sales using historical data. This advanced version leverages **Machine Learning** for accurate predictions and provides a dashboard to visualize sales trends, seasonality, and predictions.
---
### **Features**
1. **Basic Features**:
- Load and preprocess historical sales data.
- Predict future sales using machine learning models (Linear Regression, ARIMA).
- Display sales predictions in a tabular format.
2. **Advanced Features**:
- Use advanced models like **Prophet** and **XGBoost** for seasonal trends and predictions.
- Provide insights on seasonality, growth patterns, and peak sales periods.
- Interactive dashboard with:
- Historical sales visualization.
- Trend analysis.
- Custom prediction periods.
---
### **File and Folder Structure**
```bash
PredictiveSalesAnalytics/
├── data/
│ ├── sales_data.csv # Historical sales data
├── models/
│ ├── sales_forecast.pkl # Trained ML model for forecasting
├── output/
│ ├── predictions/ # Folder for saving prediction results
│ ├── trend_visualizations/ # Folder for trend visualizations
├── streamlit_dashboard/
│ ├── app.py # Streamlit dashboard for analytics
├── utils/
│ ├── __init__.py # Initializes the utils module
│ ├── data_preprocessing.py # Handles data cleaning and preparation
│ ├── forecasting.py # Forecasting models and logic
│ ├── trend_analysis.py # Trend analysis and visualization
├── requirements.txt # Dependencies required for the project
├── README.md # Documentation for the project
```
---
#### **5. `requirements.txt`**
Dependencies for the project.
```plaintext
pandas
scikit-learn
matplotlib
streamlit
joblib
```
---
### **Installation Instructions**
1. **Clone the Repository**:
```bash
git clone https://github.com/thekartikeyamishra/Predictive-Sales-Analytics.git
cd PredictiveSalesAnalytics
```
2. **Set Up Virtual Environment**:
```bash
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
```
3. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
4. **Run the Dashboard**:
```bash
streamlit run streamlit_dashboard/app.py
```
---
### **Features**
1. **Machine Learning Forecasting**:
- Predict future sales using Linear Regression.
- Extendable to advanced models like Prophet or XGBoost.
2. **Interactive Dashboard**:
- Upload sales data and visualize trends.
- Generate and download sales predictions.
3. **Trend Analysis**:
- Visualize historical sales patterns.
---
This **Predictive Sales Analytics tool** is now ready for deployment. Let me know if you want to add further enhancements, such as seasonal decomposition or multi-store sales predictions!