Job Recommendation System

The Job Recommendation System is a machine learning model designed to provide personalized job recommendations for individuals based on their resumes. Using natural language processing techniques and cosine similarity, the system matches the skills and experiences listed in resumes with job descriptions to identify the most relevant job opportunities.

How It Works

1. Data Collection: The system requires two main datasets - resumes and job descriptions. Resumes are typically collected from job seekers, while job descriptions can be sourced from job boards or company websites.

2. Preprocessing: Both resumes and job descriptions undergo preprocessing steps to clean the text and remove noise. This includes removing HTML tags, punctuation, and stop words, as well as stemming or lemmatization to reduce words to their base form.

3. Feature Engineering: Text data is converted into numerical representations using the TF-IDF (Term Frequency-Inverse Document Frequency) technique. This creates feature vectors that capture the importance of each word in the documents.

4. Similarity Calculation: Cosine similarity is then used to measure the similarity between each resume and job description. Cosine similarity calculates the cosine of the angle between two vectors, providing a measure of similarity regardless of the vector's magnitude.

5. Recommendation Generation: For each resume, the system identifies the job descriptions with the highest cosine similarity scores. These jobs are recommended to the user as potential matches based on their skills and experiences.

Methods

• TF-IDF Vectorization: Converts text data into numerical vectors based on the importance of each word in the document corpus.
• Cosine Similarity: Measures the similarity between two vectors by calculating the cosine of the angle between them.
• Data Preprocessing: Cleans and preprocesses text data to remove noise and prepare it for analysis.

Algorithms Used

The following algorithms are used in the Job Recommendation System:

• TF-IDF (Term Frequency-Inverse Document Frequency): Used for feature engineering to convert text data into numerical vectors.
• Cosine Similarity: Used to measure the similarity between resumes and job descriptions.

Datasets

• Resumes: Contains information about job seekers' skills, experiences, education, and other relevant details.
• Job Descriptions: Includes detailed descriptions of job roles, responsibilities, required skills, qualifications, and other job-specific information.

Usage

1. Data Preparation: Prepare your resume dataset and job description dataset in CSV format.
2. Model Training: Run the preprocessing steps and calculate similarity scores between resumes and job descriptions.
3. Recommendation Generation: Generate job recommendations for each resume based on the highest similarity scores.
4. Evaluation: Evaluate the quality of recommendations based on user feedback or predefined metrics such as accuracy, precision, recall, and F1 score.