Exploratory Data Analysis (EDA) on AI Job Dataset 📊

Purpose 🎯

This Jupyter notebook conducts an Exploratory Data Analysis (EDA) on the "AI Job Dataset" to uncover insights, patterns, and relationships within the data. The primary goals are to:

• Understand the structure and characteristics of the dataset 📋.
• Identify key trends, distributions, and correlations in AI-related job postings 🔍.
• Visualize relationships between variables, such as experience level and years of experience 📈.
• Prepare the data for potential future analysis or modeling by cleaning and transforming it 🧹.
• Answer specific questions about the dataset, such as the average years of experience by experience level ❓.

The notebook serves as an educational tool for data analysts and scientists to practice EDA techniques and gain insights into the AI job market 🚀.

Dataset 📂

• Source: The dataset is sourced from 'C:\Users\suryansh\Downloads\archive\ai_job_dataset.csv'.
• Description: Contains 15,000 job postings related to AI roles, with details on job titles, salaries, experience levels, employment types, company locations, and more.
• Columns:
  • job_id: Unique identifier for each job posting 🆔.
  • job_title: Title of the job (e.g., AI Research Scientist, Data Analyst) 💼.
  • salary_usd: Salary in USD (or other currency as specified) 💰.
  • salary_currency: Currency of the salary 💸.
  • experience_level: Level of experience (e.g., EN for Entry, SE for Senior) 📚.
  • employment_type: Type of employment (e.g., FT for Full-Time, PT for Part-Time) ⏰.
  • company_location: Location of the company 🌍.
  • company_size: Size of the company (S, M, L) 🏢.
  • employee_residence: Residence of the employee 🏠.
  • remote_ratio: Percentage of remote work allowed 🌐.
  • required_skills: List of required skills 🛠️.
  • education_required: Required education level 🎓.
  • years_experience: Years of experience required ⏳.
  • industry: Industry of the job 🏭.
  • posting_date: Date the job was posted 📅.
  • application_deadline: Application deadline ⏳.
  • job_description_length: Length of the job description 📜.
  • benefits_score: Score representing job benefits 🌟.
  • company_name: Name of the company 🏬.

Structure 🗂️

The notebook is organized into the following steps:

1. Imports and Reading Data 📥:
   • Imports necessary Python libraries (pandas, numpy, matplotlib, seaborn).
   • Loads the dataset into a pandas DataFrame.
2. Data Understanding 🔎:
   • Examines the DataFrame's shape, head, tail, data types, and descriptive statistics.
3. Data Preparation 🛠️:
   • Renames columns for consistency (e.g., Years_Experience to years_experience).
   • Drops irrelevant columns or duplicates.
   • Handles missing values and creates new features if needed.
4. Feature Understanding 📉:
   • Conducts univariate analysis using visualizations like value counts, KDE plots, and boxplots to explore individual feature distributions.
5. Feature Relationships 🔗:
   • Performs bivariate analysis with scatter plots, pairplots, and correlation heatmaps to identify relationships between variables.
6. Asking a Question ❓:
   • Analyzes the average years of experience by experience level and visualizes it using a horizontal bar plot.

Requirements 🛠️

To run the notebook, ensure the following are installed:

• Python: Version 3.x 🐍
• Libraries:
  • pandas: For data manipulation and analysis 📋.
  • numpy: For numerical operations 🔢.
  • matplotlib: For creating visualizations 📊.
  • seaborn: For enhanced statistical visualizations 🎨.

Install dependencies using:

pip install pandas numpy matplotlib seaborn