🔍 HR Analytics – Likeliness of a Data Scientist to switch job

📝 Description

This project aims at classifying the profiles of the candidates to determine their likeliness to switch jobs based on a candidates demographics and job profile.

We apply the following algorithms to make this classification:

1. Naïve Bayes
2. K-Nearest Neighbour
3. Logistic Regression
4. Decision Tree
5. Random Forest
6. XG Boost
7. Light GBM
8. Artificial Neural Network

📦 Dataset

The dataset for this project is taken from Kaggle. Some details about the dataset:

• 19K rows
• 14 features
• Binary targets (whether or not the candidate is willing to switch jobs)

More information about the dataset

- enrollee_id: Unique ID for candidate
- city: City code
- city_development_index : Development index of the city (scaled)
- gender: Gender of candidate
- relevent_experience: Relevant experience of candidate
- enrolled_university: Type of University course enrolled if any
- education_level: Education level of candidate
- major_discipline: Education major discipline of candidate
- experience: Candidate total experience in years
- company_size: No of employees in current employer's company
- company_type : Type of current employer
- lastnewjob: Difference in years between previous job and current job
- training_hours: training hours completed
- target: 
  - 0 – Not looking for job change
  - 1 – Looking for a job change (binary classification)

⚙️ Setup

1. Download the code from Kaggle and put the aug_train.csv file in the data folder.
2. Execute the preprocess.ipynb notebook.
3. Execute the model_implementation.ipynb notebook.
4. EDA has been performed in EDA.ipynb notebook.

📊 Results

Comparing the accuracy of the models we implemented:

Comparing the F1 Score of the models we implemented:

⚠️ Requirements

• pandas==2.2.1
• seaborn==0.13.2
• scikit-learn==1.4.1.post1
• xgboost==2.0.3
• lightgbm==4.3.0
• tensorflow==2.16.1
• imblearn==0.0
• matplotlib==3.8.3