Skip to content

Latest commit

 

History

History
25 lines (16 loc) · 2.01 KB

README.md

File metadata and controls

25 lines (16 loc) · 2.01 KB

📈 Bank Marketing Campaign: Evaluating Classifier Algorithms for Term Deposit Subscriptions Prediction

Welcome to the GitHub repository for a data analytics project focused on evaluating and identifying the best classifier algorithm for predicting client term deposit subscriptions in a Kaggle bank marketing dataset.

📊 Dataset Overview:

This project centers around an extensive analysis of the bank marketing dataset, obtained from Kaggle. The dataset contains a wealth of information about client demographics, past marketing campaigns, and various attributes that influence their subscription decisions.

💡 Objective:

The primary objective of this project is to assess and compare different classifier algorithms to determine the most accurate and reliable model for predicting whether a client will subscribe to a term deposit or not. By leveraging the dataset's features and the power of machine learning, we aim to provide valuable insights for campaign optimization and strategic decision-making.

🔬 Evaluation of Classifier Algorithms:

Throughout this project, we explore and evaluate a range of classifier algorithms, including but not limited to logistic regression, decision trees, and k-neighbors. By implementing these algorithms and carefully tuning their hyperparameters, we strive to identify the algorithm that yields the highest prediction accuracy and performance.

 

📑 Project Highlights:

  • Thorough exploration and analysis of the Kaggle bank marketing dataset.
  • Implementation and evaluation of various classifier algorithms.
  • Tuning of hyperparameters for optimal model performance.
  • Documentation of methodologies, results, and code for transparency and reproducibility.

Conclusion

We tested this dataset using 3 different classifiers (LogisticRegression, KNeighborsClassifier, and DecisionTreeClassifier) to see which classifiers generates the most accurate prediction. From our cross-validation, LogisticRegression generates the most accurate prediction, which is 90% of accuracy.