Predicting-Recruitment-Outcomes-with-PyCaret

Table of Contents

1. Introduction
2. Dataset Description
3. Objective
4. Exploratory Data Analysis (EDA)
5. Feature Engineering
6. Modeling
7. Model Evaluation
8. Conclusion
9. Future Work
10. References

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Introduction

In this project, we aim to develop a model to predict HR recruitments based on various features related to candidates and recruitment processes. This notebook walks through the data analysis, feature engineering, model training, and evaluation phases.

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Dataset Description

The dataset used in this project includes multiple features that describe the attributes of candidates, jobs, and the recruitment process. The key features include:

• Feature 1: Description of Feature 1
• Feature 2: Description of Feature 2
• Target Variable: The target variable for this prediction problem is the recruitment outcome (e.g., whether the candidate was recruited or not).

You can elaborate on the source of the dataset, any missing values, or preprocessing steps taken.

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Objective

The objective of this project is to build a machine learning model to predict whether a candidate will be successfully recruited based on the available data.

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Exploratory Data Analysis (EDA)

In this section, we perform exploratory data analysis to uncover patterns and relationships in the data.

Data Distribution

• Describe key findings regarding data distribution, outliers, or skewness.

Correlation Analysis

• Discuss correlations between features and the target variable.

Visualizations

• Include key visualizations such as histograms, bar plots, and correlation heatmaps.

# Example of a heatmap code
import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(10,8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title("Correlation Heatmap of Features")
plt.show()