Step 1: Collecting Resume Data

The first step in building our Resume Parser AI is to gather a dataset of resumes that we can use to train and test our model.

Understanding the Data Requirement

For a resume parser, we need:

• Resume text documents
• Job titles or categories (for matching purposes)
• Ideally, some labeled data indicating which resumes were successful for which positions

Using the Kaggle Resume Dataset

For this tutorial, we'll use the Resume Dataset available on Kaggle, which contains resume text along with job titles.

How to Download the Dataset

1. Go to Kaggle
2. Create an account if you don't have one
3. Search for "Resume Dataset" or use this direct link: https://www.kaggle.com/datasets/gauravduttakiit/resume-dataset
4. Click the "Download" button to get the dataset

Setting Up Your Project Environment

Let's start by setting up our project and installing the necessary libraries:

# Create a virtual environment (optional but recommended)
# In your command line/terminal:
# python -m venv resume_parser_env
# source resume_parser_env/bin/activate  # On Windows: resume_parser_env\Scripts\activate

# Install required packages
# pip install pandas numpy matplotlib seaborn nltk scikit-learn

# Create a Python script for our project
import os
import pandas as pd
import numpy as np

# Create project directories
os.makedirs('data', exist_ok=True)
os.makedirs('models', exist_ok=True)
os.makedirs('results', exist_ok=True)

print("Project setup complete!")
            

Loading the Dataset

After downloading the dataset, you'll need to load it into your Python environment:

# Assuming you've downloaded and extracted the dataset to a 'data' folder
# The exact filename might be different based on the Kaggle dataset

# Load the dataset
resume_data = pd.read_csv('data/resume_dataset.csv')

# Display the first few rows to understand the structure
print(resume_data.head())

# Check basic information about the dataset
print("\nDataset Information:")
print(resume_data.info())

# Check the distribution of job categories
print("\nJob Category Distribution:")
print(resume_data['Category'].value_counts())
            

Understanding the Dataset Structure

The Kaggle Resume Dataset typically contains:

• Resume text: The actual content of the resume
• Category: The job category or title the resume is for
• There might be additional fields depending on the specific dataset

Alternative Data Sources

If you don't want to use the Kaggle dataset, you have other options:

1. Create your own dataset: Collect sample resumes (with permission) and categorize them
2. Use public resume examples: Many career websites provide sample resumes
3. Synthetic data: Generate artificial resume data for practice purposes

Next Steps

Now that we have our dataset, we can move on to preprocessing the text data to make it suitable for machine learning algorithms. In the next step, we'll learn how to clean and tokenize the resume text using Natural Language Processing techniques.