October 2024

In this project, I harnessed the power of Python and advanced machine learning techniques to develop a predictive model for loan default risks. The process began with comprehensive Exploratory Data Analysis (EDA) using Pandas, where I identified key insights and trends within the Credit Risk dataset, laying the foundation for effective model building.

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Following EDA, I performed data preprocessing, including handling missing values, normalizing features, and encoding categorical variables to ensure the dataset was clean and suitable for analysis. Using NumPy to uncover significant patterns that informed feature selection and engineering, I employed statistical methods such as Standard Deviation, Mean, Mode, and Median. Encoding processes included One-Hot, Rank, and Label Encoding. 

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The machine learning involved splitting the dataset into training and testing sets to validate model performance. I created four robust Classification Machine Learning Models: a Decision Tree, Logistic Regression, K-nearest neighbor, and perceptron. Hyperparameter tuning and cross-validation were conducted to optimize model performance, ensuring reliability and effectiveness.

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To enhance the interpretability of my findings, I utilized Matplotlib and Seaborn to create impactful visualizations that effectively conveyed data trends and model performance metrics.

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This project exemplified my proficiency in machine learning analytics and my ability to translate complex data into actionable insights, enabling data-driven decision-making in the financial sector.

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Business Opportunities:

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• Predictive Lending Solutions: With accurate predictions of loan defaults, there’s an opportunity to develop tailored lending solutions that mitigate risk for financial institutions. This could involve creating advanced risk assessment tools that leverage predictive analytics to refine credit scoring models and optimize lending criteria.

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• Financial Literacy Programs: Given the patterns identified in default risks across different demographics, there is potential to create targeted financial literacy programs. These programs could educate borrowers on responsible borrowing practices, thereby reducing default rates and improving financial outcomes for individuals.

 

Key Skills:

 

1. ​Exploratory Data Analysis (EDA)

2. Data Preprocessing with Pandas

3. Feature Engineering and Selection

4. Statistical Methods (Standard Deviation, Mean, Mode, Median) using NumPy

5. Supervised Machine Learning

6. Data Visualization with Matplotlib and Seaborn

7. Python Programming - Scikit-learn

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