💼 Fortivus

Fortivus is a futuristic approach to predicting whether a customer will subscribe to a bank’s term deposit plan, built using a Logistic Regression model on real marketing data.

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🖼️ Preview

📄 View Full Project Report (PDF)

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📌 Problem Statement

The dataset is related to direct marketing campaigns of a Portuguese banking institution.
The marketing campaigns were based on phone calls. Often, more than one contact with the same client was required in order to determine if the product (bank term deposit) would be subscribed (‘yes’) or not subscribed (‘no’).

In this project, we build a predictive model to decide whether a campaign will be successful in getting a client to sign up for the term deposits.

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📂 Dataset

The dataset is in CSV format and can be downloaded here:
🔗 bank.csv

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📑 Data Description

Bank client data

• age: numeric
• job: type of job (admin., blue-collar, entrepreneur, etc.)
• marital: marital status (divorced, married, single, unknown)
• education: education level (unknown, secondary, primary, tertiary)
• default: has credit in default? (yes/no)
• balance: average yearly balance in euros
• housing: has housing loan? (yes/no)
• loan: has personal loan? (yes/no)

Last contact details

• contact: communication type (unknown, telephone, cellular)
• day: last contact day of the month
• month: last contact month of year (jan–dec)
• duration: last contact duration in seconds

Other attributes

• campaign: number of contacts performed during this campaign (includes last contact)
• pdays: days since the client was last contacted from a previous campaign (-1 means not contacted before)
• previous: number of contacts performed before this campaign
• poutcome: outcome of the previous marketing campaign (failure, nonexistent, success)

Target variable

• y: has the client subscribed a term deposit? (yes/no)

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🎯 Objectives

✔️ Clean the data and drop irrelevant columns.
✔️ Perform EDA: univariate and bivariate analysis.
✔️ Derive new features, remove outliers, handle multicollinearity.
✔️ Visualize distributions and correlations.
✔️ Perform feature engineering to extract meaningful features.
✔️ Build a Logistic Regression model.
✔️ Evaluate the model using accuracy, precision, recall, and ROC‑AUC.

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📈 Model Evaluation

• Predictions compared using y_test and y_pred.
• VIF scores checked for multicollinearity.
• p‑values of features reviewed before finalizing.
• Evaluation metrics include:
  • ✅ Accuracy
  • ✅ Precision
  • ✅ Recall
  • ✅ ROC‑AUC

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🛠 Tech Stack

• Python 3.8+
• Pandas, NumPy for data handling
• Matplotlib, Seaborn for visualization
• Scikit‑Learn for modeling
• StatsModels for statistical analysis

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🚀 Installation

1. Clone this repository:

   git clone https://github.com/dj-ayush/Fortivus.git
   cd Fortivus

2. Install dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook BankMarketing.ipynb

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▶️ Usage

• Open BankMarketing.ipynb.
• Run all cells step by step:
  • Data cleaning
  • EDA and feature engineering
  • Logistic Regression model training
  • Model evaluation
• Modify or extend as needed.

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🤝 Contributing

Contributions are welcome!
If you’d like to enhance Fortivus, fork the repository and submit a pull request.

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💡 Fortivus — futuristic insights for smarter banking decisions.