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.
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.
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.
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.
- Clone the repository:
git clone <repository-link>
- Navigate to the project directory:
cd client-subscription-classification
- Install the required libraries:
pip install -r requirements.txt
- Run the Jupyter notebooks to see the data exploration and model development process.
This project is licensed under the MIT License - see the LICENSE file for details.
For any inquiries or feedback, please reach out to lokeshsinha746@gmail.com.
Feel free to explore the code and contribute to the project. Let's continue making data-driven decisions!