📊 Data Science Tasks:

Loan Approval, Employee Attrition, and Customer Segmentation

🔍 Overview

This repository contains three distinct data science python projects, each addressing a unique problem using advanced analytical techniques. The projects include:

1. Loan Approval Dataset Analysis: Explores applicant features to inform loan approval decisions.
2. Employee Attrition Prediction: Predicts employee attrition using machine learning models.
3. Mall Customer Segmentation Analysis: Segments customers into groups for targeted marketing strategies.

Each project is designed to uncover actionable insights and demonstrate proficiency in data cleaning, feature engineering, modeling, and visualization.

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📂 Projects

1. 📊 Loan Approval Dataset Analysis

• Goal: Analyze loan applicant data to identify patterns and relationships for better approval decisions.
• Key Tasks:
  • Data exploration and cleaning.
  • Feature engineering (Income_to_Loan_Ratio, EMI).
  • Visualizations (boxplots, histograms, scatterplots).
• Insights: Higher-income applicants tend to request larger loans; new features enhance risk assessment.
• Python libraries: Pandas, Numpy, Scikit-learn, Matplotlib, Seaborn.

2. 👥 Employee Attrition Prediction

• Goal: Predict employee attrition using machine learning models to identify at-risk employees.
• Key Tasks:
  • Data preparation (encoding, scaling).
  • Model training (KNN, Decision Tree, SVM, Random Forest, MLP).
  • Evaluation (accuracy, precision, recall).
• Insights: KNN performed best with balanced precision/recall; class imbalance was a challenge.
• Python libraries: Pandas, Scikit-learn, TensorFlow/Keras.

3. 🛍️ Mall Customer Segmentation Analysis

• Goal: Segment mall customers into distinct groups for targeted marketing.
• Key Tasks:
  • Data preparation (encoding, scaling, PCA).
  • Clustering (KMeans, Agglomerative, GMM, BIRCH).
  • Evaluation (Silhouette Score, Davies-Bouldin Index).
• Insights: KMeans was most effective for clear, interpretable clusters.
• Python libraries: Pandas, Numpy , Scikit-learn, Matplotlib, Seaborn.

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🛠️ Tech Stack

Key Libraries:

• Pandas: Data cleaning, transformation, and analysis.
• NumPy: Numerical computations.
• Scikit-learn: Machine learning models and clustering.
• TensorFlow/Keras: Deep learning implementation.
• Matplotlib & Seaborn: Data visualization.

Development:

• Google Colab for cloud-based execution and collaboration.

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🎯 Conclusion

This repository showcases a diverse range of data science tasks, from predictive modeling to unsupervised learning. Each project highlights problem-solving skills, technical proficiency, and the ability to derive meaningful insights from data. The results can be leveraged for decision-making in finance, HR, and marketing domains.

Explore the individual project folders for detailed documentation and code! 🚀