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:
- NaΓ―ve Bayes
- K-Nearest Neighbour
- Logistic Regression
- Decision Tree
- Random Forest
- XG Boost
- Light GBM
- Artificial Neural Network
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 β Not looking for job change
- 1 β Looking for a job change (binary classification)
- Download the code from Kaggle and put the
aug_train.csv
file in thedata
folder. - Execute the
preprocess.ipynb
notebook. - Execute the
model_implementation.ipynb
notebook. - EDA has been performed in
EDA.ipynb
notebook.
Comparing the accuracy of the models we implemented:
Comparing the F1 Score of the models we implemented:
- 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