Job-Recommender-System - Data Analysis & Visualisation

A job recommender system was developed involving extensive data analysis and visualization. Clustering techniques were implemented to group job preferences and skills, enhancing recommendation accuracy. Four algorithms were applied to refine recommendations, leveraging data insights to match candidates with suitable roles effectively.

Import Necessary Libraries & read the CSV file

• Pandas - working with dataset
• NLTK - preprocessing the data
• SK Learn - Implement ML algorithms
• Word Cloud - Data Analysis
• Seaborn - Visualization
• Matplotlib - Visualization

Cleaning & Preprocessing of data

• Remove unwanted columns
• Remove Duplicates
• Drop empty columns
• Data mining extract feature from our text using TF-IDF

Data Visualization

• Top 5 Demand Jobs in IT

Data Analysis

• Most Used Skill in IT - Word Cloud

Clustering

• k-means Algorithm
• Classify the clustering by giving names

Implementing ML Algorithms & Evaluate

• Logistic Regression
• KNN
• Decision Tree
• Support Vector Machine
• Accuracy comparison between 4 algorithms

Evaluating the distance metrics

• Cosine Similarity
• Euclidean Distance

Final Recommendation

• Cosine Similarity
• SVM