This project analyzes a dataset of 5000 customers and their attributes related to bank loans. The goal is to explore the data, visualize important relationships, and gain insights into the bank's loan business.

We harnessed the power of the following tools and libraries to conduct our analysis:

• 🐍 Python
• 📊 Pandas for efficient data manipulation
• 📈 Matplotlib and Seaborn for stunning data visualizations
• 🔢 Numpy for robust numerical calculations

Our journey began with a thorough exploration of the dataset, where we:

• 👪 Loaded the dataset and meticulously inspected columns, data types, and null values.
• 📉 Calculated vital summary statistics such as mean, median, and quantiles.
• 📊 Crafted eye-catching histograms to delve into the distributions of various columns.
• 📈 Produced compelling box plots, scatter plots, and bar charts to visualize intriguing relationships within the data.

Our analytical prowess shone as we embarked on a series of insightful steps, including:

• 🕵️‍♂️ Investigating and adeptly handling invalid values lurking within the Experience column.
• 🔬 Calculating correlations between different features to unearth hidden connections.
• 🎯 Creating visually striking representations, focusing on pivotal columns such as Income, Loans, and Accounts.
• 🏷 Grouping data into meaningful categories, such as Education and Account types, and skillfully aggregating the results.
• 📊 Comparing the distributions of Income and Credit Card Averages by Loan status.
• 💡 Making astute observations about these data relationships to glean invaluable insights.

Our meticulous analysis has uncovered some key insights that can inform strategic decisions:

• 📚 Education levels are positively correlated with higher income, highlighting the importance of investing in higher education.
• 💳 Customers with personal loans tend to have higher credit card averages, suggesting potential cross-selling opportunities.
• 💰 Income distributions exhibit significant variations based on loan status, emphasizing the importance of assessing customers' financial stability.
• 👨‍👩‍👦‍👦 Larger families tend to have lower average incomes, which could be a crucial factor in loan eligibility.

• Author: Aishik Dasgupta

This project is licensed under the MIT License.

We welcome contributions from the community! If you'd like to enhance this project or fix issues, please follow these steps:

1. Fork the project.
2. Create your feature branch: git checkout -b feature/your-feature.
3. Commit your changes: git commit -m 'Add your feature'.
4. Push to the branch: git push origin feature/your-feature.
5. Submit a pull request.
6. For .csv file visit: (https://aishik-dasgupta.super.site) for contact details

Thank you for your valuable contributions in advance! 🙌

Feel free to explore our analysis, and may it guide you toward a brighter financial future! 🚀📈💼