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ACAD PULSE – AI-Powered Data Analytics Platform

A self-serve analytics platform that simplifies data analysis for technical and non-technical users.

Overview

CSV, Excel and JSON datasets are ingested and cleaned automatically to reduce manual effort and error, then explored through visualisations of patterns, distributions and relationships. A Flask backend runs ML-based predictive analytics with Matplotlib, Seaborn and Plotly dashboards.

Problem Statement

Analytics tools assume the user can already clean data and choose a model. Everyone else stops at the spreadsheet, and the data never gets analysed at all.

Proposed Solution

Automate the tedious half — ingestion, cleaning, preprocessing — and present exploration and prediction through dashboards, so the technical barrier sits with the tool rather than the user.

Key Features

  • Multi-Format Data Upload

    Accepts CSV, Excel, and JSON datasets.

  • Automated Cleaning & Preprocessing

    Reduces manual effort and error.

  • Exploratory Data Analysis

    Visualizes patterns, distributions, and relationships.

  • ML-Based Predictive Analytics

    Flask backend with Matplotlib/Seaborn/Plotly dashboards.

Technologies Used

Machine Learning

  • Scikit-Learn
  • Pandas
  • NumPy
  • Model comparison

Visualisation

  • Matplotlib
  • Seaborn
  • Plotly

Application

  • Flask
  • Bootstrap
  • SQLite

Project Workflow

  1. 1

    Upload

    A dataset is ingested in CSV, Excel or JSON form.

  2. 2

    Cleaning

    Preprocessing runs automatically on the raw data.

  3. 3

    Exploration

    Distributions and relationships are visualised.

  4. 4

    Prediction

    ML models produce predictive analytics.

  5. 5

    Dashboard

    Results are presented as interactive charts.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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