Advance-Database-Systems-Feature-Project-final

Proposed Unique Features for the HRM Project AI-Enhanced Candidate Matching Proposed Unique Features for the HRM Project AI-Enhanced Candidate Matching

The AI-Enhanced Candidate Matching feature utilizes artificial intelligence algorithms to improve the efficiency and accuracy of the candidate selection process in Human Resource Management. Inspired by the research on the use of AI in HR processes, this feature aims to streamline the recruitment process by automatically matching candidate profiles with job requirements.

tables to be inputed: candidates profile, job requirement, matched candidate libraries: mostly python libraries nltk, matplotlib, tensorflow(if neccessary), and api libraries

Creating a complete AI-Enhanced Candidate Matching feature with a user interface (UI) involves several components, including backend logic, frontend design, and integration with the specified libraries. Below is a simplified example using Python and some common libraries for AI and UI development.

Import necessary libraries

import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords from sklearn.feature_extraction.text import CountVectorizer from sklearn.metrics.pairwise import cosine_similarity

Sample data (you can replace this with your database connectivity)

candidates_profile = {"candidate_1": "Experienced software engineer with expertise in Python and Java.", "candidate_2": "Entry-level data analyst skilled in data visualization using Matplotlib.", # Add more candidate profiles }

note: this is just a draft

job_requirement = "Looking for a software engineer proficient in Python and Java." Backend (Python Script):

Preprocessing functions

def preprocess(text): stop_words = set(stopwords.words('english')) words = word_tokenize(text) words = [word.lower() for word in words if word.isalnum() and word.lower() not in stop_words] return ' '.join(words)

Vectorization function

def vectorize_text(text): vectorizer = CountVectorizer().fit_transform([text, job_requirement]) vectors = vectorizer.toarray() return vectors[0], vectors[1]

Candidate matching function

def match_candidates(candidate_profiles): matches = {} job_requirement_vector, _ = vectorize_text(preprocess(job_requirement))

for candidate, profile in candidate_profiles.items():
    candidate_vector, _ = vectorize_text(preprocess(profile))
    similarity_score = cosine_similarity([job_requirement_vector], [candidate_vector])[0][0]
    matches[candidate] = similarity_score

# Sort candidates by similarity score
sorted_matches = sorted(matches.items(), key=lambda x: x[1], reverse=True)
return sorted_matches

Sample output

matched_candidates = match_candidates(candidates_profile) print("Matched Candidates:") for candidate, score in matched_candidates: print(f"{candidate}: {score}")

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