alaeddinee21/RH-analystics

This project predicts employee promotions using a CatBoost classifier. It preprocesses data by filling missing values, scaling numerical features, and encoding categorical data. The model pipeline includes SMOTE for handling class imbalance, aiming to accurately identify employees likely to be promoted.

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RH Analytics

Overview

The RH Analytics project aims to streamline the employee promotion process for a large multinational corporation (MNC) by predicting eligible candidates for promotion to manager positions and below. The goal is to identify potential candidates at a checkpoint to expedite promotions and reduce delays.

Problem Statement

The client faces delays in the promotion cycle as the final promotions are only announced after training and evaluations. This project uses predictive analytics to identify candidates for promotion earlier in the process, based on various performance metrics and employee data.

Data

• Training Data: train.csv
• Test Data: test.csv

Evaluation Metric

The model's performance is evaluated using the F1 Score, which balances precision and recall.

Data Inspection

1. Data Loading: Read the CSV files using pandas.
2. Exploration: Conduct exploratory data analysis (EDA) to understand the distribution of data, missing values, and correlations.
3. Categorization: Classify variables into categories such as employee information, demographics, and performance metrics.

Data Preprocessing

1. Handling Missing Values: Fill missing values in categorical columns with the mode.
2. Feature Encoding:
   • Numerical Features: Scale numerical features using StandardScaler.
   • Categorical Features: One-hot encode categorical features.
   • Target Encoding: Apply target encoding to the region column.
3. Handling Imbalance: Use SMOTE to address class imbalance.

Model Pipeline

1. Preprocessing:
   • Numerical features: StandardScaler
   • Categorical features: OneHotEncoder
   • Target encoding: TargetEncoder
2. Model: CatBoostClassifier
3. Integration: Combine preprocessing and modeling using imbalanced pipeline.

Model Tuning

• Grid Search: Optimize hyperparameters using GridSearchCV.
• Metrics: Evaluate model performance using F1 Score.

Results

• Best Parameters: Parameters identified through GridSearchCV.
• Test F1 Score: F1 Score of the best model on the test set.
• Accuracy: Accuracy score of the final model.

Installation

pip install category_encoders xgboost ydata_profiling imblearn catboost

Code

The code performs the following steps:

1. Load and inspect the data.
2. Preprocess the data.
3. Define and tune the model pipeline.
4. Evaluate and report the results.

Usage

1. Load Data: Read the train.csv and test.csv files.
2. Preprocess Data: Handle missing values and encode features.
3. Train Model: Fit the model pipeline on training data.
4. Evaluate Model: Assess performance using F1 Score and other metrics.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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