📊 Predictive Analytics Dashboard

A data visualization and predictive analytics dashboard for business intelligence. Features interactive charts, trend analysis, and ML-powered forecasting.

✨ Features

• � CSV File Upload - Upload your own data for custom analysis
• �📈 Interactive Visualizations - Dynamic charts powered by Plotly
• 🔮 ML-Powered Forecasting - Trend predictions using Linear & Polynomial regression
• 📊 Real-time KPIs - Key performance indicators with growth metrics
• 🎯 Automated Reports - Summary statistics and actionable insights
• 🎨 Modern Dark Theme - Sleek, professional UI design
• 🔄 Auto Column Detection - Automatically detects date, numeric, and category columns

🛠️ Tech Stack

Technology	Purpose
Python	Core programming language
Pandas	Data manipulation
Plotly	Interactive visualizations
scikit-learn	Machine learning models
Streamlit	Web dashboard framework

📁 Project Structure

Predictive Analytics Dashboard/
├── app.py                    # Main Streamlit application
├── requirements.txt          # Python dependencies
├── data/
│   └── sample_sales_data.csv # Sample business dataset
├── models/
│   └── forecaster.py         # ML forecasting models
├── utils/
│   ├── data_loader.py        # Data loading utilities
│   └── visualizations.py     # Plotly chart functions
└── README.md                 # Documentation

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip package manager

Installation

1. Clone the repository

   git clone https://github.com/DiwashPaudel/Predictive-Analytics-Dashboard.git
   cd Predictive-Analytics-Dashboard

2. Install dependencies

   pip install -r requirements.txt

3. Run the dashboard

   streamlit run app.py

4. Open in browser Navigate to http://localhost:8501

📊 Dashboard Features

KPI Cards

• Total Sales with growth percentage
• Total Revenue with growth percentage
• Total Customers count
• Average Revenue per Customer

Interactive Charts

• Daily Sales & Revenue trends (Area charts)
• Revenue by Product Category (Pie chart)
• Sales by Region (Bar chart)

ML Forecasting

• Select metric to forecast (Sales/Revenue/Customers)
• Adjustable forecast horizon (7-90 days)
• Choose between Linear or Polynomial models
• Confidence intervals visualization

Automated Reports

• Summary statistics table
• Key business insights
• Trend direction and strength analysis

🔧 Customization

Using Your Own Data

Replace data/sample_sales_data.csv with your own CSV file. Required columns:

• Date - Date in YYYY-MM-DD format
• Sales - Number of sales
• Revenue - Revenue amount
• Customers - Number of customers
• Region - Region name
• Product_Category - Category name

📄 License

This project is open source and available under the MIT License.

👨‍💻 Author

Diwash Paudel
AI & Machine Learning Engineer

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