https://github.com/sinha532/termdeposit-classification-datascience-project

A Minor Project , which I have worked on a classsification Project aimed at predicting whether customers of a bank would subscribe to a term deposit using Data science skills.
https://github.com/sinha532/termdeposit-classification-datascience-project

Last synced: 2 months ago
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A Minor Project , which I have worked on a classsification Project aimed at predicting whether customers of a bank would subscribe to a term deposit using Data science skills.

• Host: GitHub
• URL: https://github.com/sinha532/termdeposit-classification-datascience-project
• Owner: Sinha532
• Created: 2024-06-05T08:24:54.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-06-05T08:29:08.000Z (8 months ago)
• Last Synced: 2024-06-05T09:50:44.876Z (8 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 85.9 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Client Subscription Classification Project

I worked on a classification project aimed at predicting whether clients would subscribe to a term deposit. The highest accuracy I achieved was with the **Decision Tree**, reaching an impressive 90% accuracy score.

## 🔍 Project Overview:

The project's goal was to develop machine learning models capable of accurately predicting client subscriptions based on various features. To achieve this, I:

- Thoroughly explored the provided dataset to understand the underlying patterns.

- Created and fine-tuned multiple classification models.

- Evaluated model performance to ensure robust and reliable predictions.

## 🛠 Technologies and Techniques:

Throughout this project, I leveraged several key tools and methodologies, including:

- **Data Visualization:** Utilizing libraries such as Seaborn and Matplotlib to uncover insights from the data.

- **Model Development:** Building and evaluating models using techniques like Logistic Regression, Decision Trees, and Random Forests.

- **Prediction and Evaluation:** Making predictions on test datasets and using performance metrics to validate model accuracy.

## 📂 Repository Structure:

- `data/`: Contains the dataset used for training and testing the models.

- `notebooks/`: Jupyter notebooks with the code for data exploration, model development, and evaluation.

- `results/`: Outputs and results from the model evaluations.

- `README.md`: Project overview and instructions.

## 🚀 Getting Started:

1. Clone the repository: `git clone `

2. Navigate to the project directory: `cd client-subscription-classification`

3. Install the required libraries: `pip install -r requirements.txt`

4. Run the Jupyter notebooks to see the data exploration and model development process.

## 📄 License:

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 📧 Contact:

For any inquiries or feedback, please reach out to [email protected].

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Feel free to explore the code and contribute to the project. Let's continue making data-driven decisions!