DreamShift Industry Intelligence Dashboard

DreamShift Industry Intelligence Dashboard is a private, production-grade analytical platform built to process raw CRM data into actionable acquisition insights, geographic lead mapping, and predictive forecasts.

It brings together:

• Executive overview of active clients and tally leads
• Industry-specific lead-to-client conversion matrices
• Geospatial lead distribution heatmaps across Australia
• Machine learning-powered conversion probabilities and clustering
• Time-series forecasting for future client acquisition
• A sleek, premium dark-mode UI

Built and engineered by Navodhya Fernando.

---

Features

Automated Data Pipeline

Python ETL script that cleans, standardizes, and processes raw CRM CSV files into optimized JSON.

Geospatial Intelligence

Interactive map of Australia displaying lead density by state, featuring a custom caching and rendering engine to avoid SSR hydration issues.

Advanced Data Visualization

Dynamic, responsive donut charts and trend indicators using Recharts, designed specifically for a premium dark theme.

Lead-to-Client Velocity Matrix

Deep-dive tables calculating exact conversion rates, including dedicated tracking for high-quality leads.

Predictive Analytics

• Logistic Regression: Calculates conversion probabilities by industry
• K-Means Clustering: Identifies top converting user personas based on industry, state, and visa status
• Exponential Smoothing: Generates a 30-day forward-looking forecast of client acquisitions

---

Benefits

• Zero-Touch Reporting: Eliminates manual spreadsheet crunching with a unified Python-to-React pipeline
• Predictive Growth: Moves beyond historical reporting by using ML to identify high-converting personas and forecast future trends
• Executive Visibility: Consolidates complex CRM data into a clean, scannable command center
• Performant Architecture: Static JSON consumption via Next.js ensures fast load times and zero database latency on the frontend
• Bulletproof UI: Custom flexbox layouts and client-side mounting patterns prevent layout breaks across screen sizes

---

Tech Stack

Frontend

• Next.js 14+ App Router
• React
• TypeScript / JavaScript

UI System

• Tailwind CSS
• Recharts
• React Simple Maps
• Premium dark-mode interface

Data Pipeline

• Python 3
• Pandas
• NumPy

Machine Learning

• Scikit-Learn
• Statsmodels

Data Layer

• Static JSON generated through automated Python scripts

---

Why This Project Reflects My Engineering Profile

• Full-stack ownership across data science, ETL, machine learning, and frontend architecture
• Ability to translate complex backend calculations into a polished executive-facing dashboard
• Real-world engineering problem solving, including custom workarounds for third-party library limitations
• Clean separation of concerns, with Python handling heavy data processing and React handling presentation
• Fast, scalable frontend architecture powered by optimized static JSON

---

System Architecture

Architecture Components

flowchart TD
    RAW1[Clients.csv]
    RAW2[Leads.csv]
    PY[process_data.py<br/>Pandas + Scikit-Learn]
    JSON[(dashboard_data.json)]
    NEXT[Next.js App Router]
    UI[Premium Dark Mode UI]
    MAP[Geospatial Render]
    PRED[Predictive Models]

    RAW1 --> PY
    RAW2 --> PY
    PY -->|Cleans, Clusters, Forecasts| JSON
    JSON --> NEXT
    NEXT --> UI
    NEXT --> MAP
    NEXT --> PRED

Loading

---

How Data Flows

1. Raw CRM data, including Clients.csv and Leads.csv, is securely placed inside the raw_data folder.

2. The Python pipeline, process_data.py, runs and performs data cleaning, industry standardization, conversion calculations, clustering, and forecasting.

3. The pipeline outputs a single optimized dashboard_data.json file.

4. The JSON file is stored inside the Next.js frontend at:

public/data/dashboard_data.json

5. Next.js statically consumes the JSON file, resulting in fast, zero-latency data delivery.

6. React components render the executive overview, geospatial maps, industry metrics, and predictive intelligence sections.

---

Repository Structure

industry-dashboard/
  raw_data/
    # Ignored by git
    # Drop CRM CSV files here

  process_data.py
    # Python ETL and ML pipeline

  public/
    data/
      dashboard_data.json

  src/
    app/
      # Next.js pages, layouts, and global CSS

    components/
      section-1-overview/
        # KPI cards, summary tables, donut charts

      section-2-industry/
        # Geospatial map and deep-dive metrics

      section-3-predictive/
        # ML personas and forecast charts

---

Quick Start

Prerequisites

Make sure you have the following installed:

• Node.js 18+
• npm 10+
• Python 3.9+
• pip

---

Installation and Setup

1. Install Node Modules

npm install

2. Install Python Dependencies

pip install pandas numpy scikit-learn statsmodels

3. Provide Raw Data

Place the following files inside the /raw_data/ directory at the root of the project:

Clients.csv
Leads.csv

---

Running the Pipeline and Server

1. Process the Data

Run the Python script to generate the latest JSON file.

python process_data.py

2. Start the Frontend

npm run dev

Open the dashboard at:

http://localhost:3000

---

Build and Production Run

Build the Project

npm run build

Start the Production Server

npm run start

Important: Run python process_data.py before npm run build to make sure Next.js uses the latest data.

---

Security and Access Notes

This repository uses a strict .gitignore to ensure raw CRM data is never committed to version control.

The following files should remain private and excluded from Git:

raw_data/*.csv

Only aggregated and anonymized metrics inside dashboard_data.json are exposed to the frontend build process.

---

License

This software is private and proprietary.

See LICENSE for usage restrictions.