Skip to content

README.md

Latest commit

 

History

History
73 lines (50 loc) · 1.9 KB

README.md

File metadata and controls

73 lines (50 loc) · 1.9 KB

📊 AMCAT Job Dataset Analysis

A data analysis project exploring the relationship between salaries, academic performance, job locations, experience, and skills using the AMCAT job dataset.

✨ Overview

This project dives into the AMCAT Job Dataset to uncover patterns in salary distribution, academic achievements, job cities, and experience levels. We use Python, Pandas, and Seaborn to draw data-driven insights.

📂 Dataset

  • Filename: AMCET Dataset.csv
  • Records: 3,998 entries
  • Columns: 39 attributes

🧾 Key Features

  • Salary
  • Gender
  • 10/12th/Degree Scores
  • College Tier & Specialization
  • Skills
  • Job City
  • Work Experience

✅ No missing values or duplicate records

🎯 Project Goals

  • Analyze salary distribution across various dimensions
  • Assess how GPA, skills, and gender affect pay
  • Understand the impact of job city on earnings
  • Study correlation between work experience and salary

🔍 Key Insights

  • Salaries are right-skewed, with a few high earners
  • Higher GPA and more skills generally lead to better pay 💸
  • Candidates from top-tier colleges and CS/IT specializations earn more
  • Cities like Bangalore offer higher salary packages
  • Work experience has a strong positive correlation with earnings

🛠️ Tools & Technologies

  • 🐍 Python 3.x
  • 📊 Pandas, NumPy
  • 📈 Seaborn & Matplotlib
  • 📓 Jupyter Notebook

📌 How to Run

Prerequisites

Make sure you have Python 3.x installed on your system.

Installation

# Clone this repo
git clone https://github.com/KumarRaju1313/AMCET_EDA.git
cd AMCET_EDA

# Install required packages
pip install -r requirements.txt

# Open and run the notebook
jupyter notebook AMCAT_EDA.ipynb

Required Libraries

pip install pandas numpy matplotlib seaborn jupyter