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https://github.com/venkat-a/term-deposit-marketing-prediction

client subsection to a term deposit
https://github.com/venkat-a/term-deposit-marketing-prediction

data-processing feature-engineering hyperparameter-tuning-in-pipeline lime machine-learning matplotlib model-evaluation-and-tuning one-hot-encoding probability-threshold-optimization seaborn shap sklearn statical-analysis visualization

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client subsection to a term deposit

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README 

        

# Term Deposit Marketing Prediction



## Overview

This project focuses on predicting customer responses to term deposit marketing campaigns, with an emphasis on addressing class imbalance and enhancing model interpretability. By utilizing a dataset titled `term-deposit-marketing-2020.csv`, we implement a series of data preprocessing, exploratory data analysis, and machine learning techniques to accurately predict outcomes and understand the factors influencing these predictions.



## Objective

The main objective of this project is to develop a predictive model that can identify potential customers who are more likely to subscribe to a term deposit, thereby enabling more targeted and efficient marketing strategies.



## Data Description

The dataset contains information on bank clients contacted during a marketing campaign for term deposits. It includes client demographic information, campaign metrics, and the outcome of the campaign (whether the client subscribed to a term deposit).



## Methodology

### Data Preprocessing

- **Handling Missing Values**: Missing values were identified and imputed where necessary.

- **Categorical Variable Encoding**: Categorical variables were encoded using OneHotEncoder to transform them into a format suitable for machine learning models.

- **Class Imbalance Treatment**: SMOTE (Synthetic Minority Over-sampling Technique) was employed to address the class imbalance issue in the dataset.



### Exploratory Data Analysis (EDA)

- **Statistical Summary**: Descriptive statistics were computed to get an overview of the data.

- **Visualization**: Histograms, count plots, and bar charts were used to visualize the distribution of numerical and categorical variables.



### Model Building and Evaluation

- **Model Selection**: RandomForestClassifier and GradientBoostingClassifier were selected based on their suitability for handling imbalanced data.

- **Model Training**: Models were trained using the preprocessed data, with hyperparameters optimized through cross-validation.

- **Model Evaluation**: Models were evaluated based on F1 score, precision, recall, and accuracy to ensure a balanced perspective on performance.



### Interpretability

- **SHAP and LIME**: These tools were utilized to interpret the model predictions, offering insights into feature importance and the impact of individual features on the prediction outcome.



## Results

The project achieved promising results in predicting the likelihood of customers subscribing to term deposits. The models demonstrated a significant improvement in prediction accuracy after addressing the class imbalance and employing advanced machine learning techniques. The interpretability analysis provided valuable insights into the decision-making process of the models.



## Conclusion

This project highlights the effectiveness of comprehensive data preprocessing, the strategic application of machine learning models, and the importance of model interpretability in addressing complex predictive modeling challenges. The methodologies and insights derived from this project are valuable contributions to the field and pave the way for future research and application.



## AI Assistance Notice

AI provided support with Portions of coding, documentation and ideation