This project focuses on data analytics and exploration of a dataset of a bank's telemarketing endeavors. The dataset comprises information related to the bank's telemarketing campaigns, primarily centered around promoting long-term deposit accounts, such as bonds and savings accounts. The bank often engaged in multiple interactions with potential customers before determining whether they would opt for a long-term deposit account.

The main goal of this project is to assist the bank in refining its telemarketing strategy for promoting long-term deposit accounts. By leveraging data analytics techniques, we aim to gain valuable insights from the dataset and provide actionable recommendations that can enhance the bank's marketing efforts.

1. Data Exploration: Thoroughly explore the dataset to gain a deep understanding of the available data and its characteristics.

2. Data Preprocessing: This step involves cleaning, transforming, and preparing the data for analysis, ensuring its quality and consistency.

3. Descriptive Analysis: Perform a descriptive analysis to extract meaningful statistics and insights from the dataset.

4. Predictive Modeling: Utilizing machine learning algorithms, build predictive models to identify potential leads for long-term deposit accounts.

Copy the data into the folder data. Copy the artifacts from here: Google Drive and put the files inside the artifacts folder. It's also possible to generate the artifacts by running the notebook notebooks/modeling.ipynb

1. Install the packages using the command pip install -r requirements.txt.
2. To run the backend server, use the command uvicorn app.main:app --reload.
3. To run the streamlit application, use the command streamlit run frontend/app.py.

Feel free to contact me for any feedback or suggestions or collaboration opportunities.

• Email: sifatnabil
• LinkedIn: sifat-nabil