Cloud Counselage Pvt. Ltd. Machine Learning Internship

This repository contains all Jupyter Notebooks, reports, and supporting files developed as part of the Machine Learning Internship at Cloud Counselage Pvt. Ltd. The primary focus is on building predictive models for student placement and graduation year using real-world datasets and various machine learning techniques.

Table of Contents

• About
• Repository Structure
• Key Features & Insights
• Getting Started
• Usage
• License
• Contact

About

This repository showcases the work completed during the internship, featuring two major machine learning tasks:

• Student's Year of Graduation Prediction: Predicts the expected graduation year of students based on provided parameters.
• Student's Placement Prediction Model: Predicts the placement status of a student using features like college name, CGPA, machine learning knowledge, and speaking skills.

Repository Structure

.
├── Task 1: Student's Year of Graduation Prediction Model/
│   ├── Cloud Counselage Pvt Ltd. Machine Learning Intern Task 1- Year of Graduation Prediction Model.ipynb
│   ├── Readme.md
│   └── summary.txt
├── Task 2: Student's Placement Prediction Model/
│   ├── Cloud Counselage Pvt. Ltd. Machine Learning Intern Task 2 - Placement Prediction Model .ipynb
│   ├── Readme.md
│   └── summary.txt
└── ... (other reports, images, and files)

Key Features & Insights

Task 1: Student's Year of Graduation Prediction

• Objective: To estimate a student's graduation year based on various demographic and event participation data.
• Notable Insights Visualized:
  • Count of attendees by their city.
  • Count by gender.
  • Event information sources.
  • Willingness to know more about the organization.
  • Recommendation likelihood.

View Task 1 Notebook

Task 2: Student's Placement Prediction Model

• Objective: To predict student placement status based on institutional and personal skill factors.
• Key Factors: College attended, CGPA, ML knowledge, speaking skills.
• Notable Insights Visualized:
  • Sources of event information.
  • Graduation year distribution.
  • College-wise and designation-wise attendee counts.
  • Ticket type analysis.

View Task 2 Notebook

Getting Started

Prerequisites

• Python 3.x
• Jupyter Notebook
• Recommended: Create a virtual environment

Installation

Clone the repository:

git clone https://github.com/ADVAIT135/Cloud-Counselage-Pvt-Ltd-Machine-Learning-Internship.git
cd Cloud-Counselage-Pvt-Ltd-Machine-Learning-Internship

Install required packages (if any requirements.txt or list provided in the notebooks):

pip install -r requirements.txt

or manually install as per notebook instructions.

Usage

Open the notebooks using Jupyter:

jupyter notebook

Navigate to the respective task folders and explore the notebooks. Each notebook contains explanations, code, and visualizations.

License

This project is licensed under the MIT License.

Contact

• Author: ADVAIT135
• Project Link: Cloud Counselage ML Internship Repository

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