🎯 Presenting my advanced project on Candidate Selection Prediction using Machine Learning.

🔍 Objective:

The project aims to predict ideal candidates for roles by analyzing skills, experience, and performance metrics. Using logistic regression and machine learning techniques, we aim to enhance hiring efficiency and identify top talent.

🕵 Project Insights:

1️⃣ Candidate speaking duration correlates positively with selection chances.

2️⃣ Candidates possessing specific skills are more likely to be selected.

3️⃣ Prompt candidate arrival at interviews improves selection prospects.

4️⃣ Optimal interview duration increases chances of selection.

5️⃣ Minimizing pauses during interviews enhances candidate suitability.

🛠️ Tools Utilized:

Python, Pandas, NumPy, scikit-learn for machine learning, and data analysis libraries to develop predictive models and insights.

👇 Explore the Results :

The logistic regression model emerged as the optimal choice, offering accurate candidate selection predictions. Check out the project here: [https://github.com/Bobby-Rawat/Candidate-Selection-Prediction/blob/main/Interview%20Status/Interview_status_pred.ipynb]

📈 Results:

Achieved significant accuracy in predicting candidate suitability, empowering recruiters with valuable insights for strategic hiring decisions.

#CandidateSelection #PredictiveAnalytics #LogisticRegression #MachineLearning #DataScience #RecruitmentAnalytics #HiringProcess #DataDrivenDecisions #InterviewInsights #HRAnalytics