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This project focuses on the analysis of emotional patterns and their correlation with loan operations. It includes emotional pattern analysis, loan-emotion correlation, lending operation assessment, machine learning modeling, and overall visualization.

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DeekshithKGowda/Testcase_Cloudwalk

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Emotional Pattern Analysis and Loan-Emotion Correlation Dashboard

Overview

This project focuses on the analysis of emotional patterns and their correlation with loan operations. It includes emotional pattern analysis, loan-emotion correlation, lending operation assessment, machine learning modeling, and overall visualization. The goal is to understand how emotions impact loan decisions and to provide data-driven insights into lending operations.

Key Features

Emotional Pattern Analysis:

  • Identifies dominant emotional patterns over time.
  • Analyzes how emotions vary based on relationships, time of day, and other contexts.

Loan-Emotion Correlation:

  • Examines the relationship between emotional patterns and loan terms (amount, interest rate).
  • Identifies emotional factors influencing loan outcomes (approved, denied, default).

Lending Operation Assessment:

  • Evaluates the effectiveness of lending policies (interest rates, loan amounts) in terms of default rates.
  • Assesses loan disbursement, performance, and revenue trends over time.

Machine Learning:

  • Predicts loan terms based on emotional and contextual data.
  • Includes model interpretation using SHAP values.

Dashboard:

  • Visualizes emotional patterns and loan data using interactive graphs and slicers.
  • Provides insights into the relationship between emotions and loan amounts, emotional intensity over time, and other key factors.

Data

The project utilizes data from three primary sources:

  • Emotional Data: Captures users' emotional states (e.g., intensity, relationship, time of day).
  • Loan Data: Includes loan-related information (e.g., loan amount, interest rate, status).
  • User Data: Contains user-related attributes (e.g., credit score, approved/denied dates, credit limit).

Workflow

  1. Data Merging: Merged tables (loans, users, emotional_data) into a single DataFrame for analysis.
  2. Data Exploration: Conducted data exploration and identified missing values, outliers, and patterns.
  3. Data Preprocessing: Treated outliers, normalized the data, and handled missing values.
  4. Emotional Pattern Analysis: Performed visual analysis of emotional patterns and their contexts.
  5. Loan-Emotion Correlation: Analyzed correlations between emotional factors and loan outcomes.
  6. Lending Operation Assessment: Evaluated loan performance and profitability over time.
  7. Machine Learning: Built and evaluated predictive models for loan terms.
  8. Visualization: Created a comprehensive Power BI dashboard to visualize key metrics.

Tools and Technologies

  • Python: For data processing, analysis, and machine learning.
  • Pandas: Data manipulation and cleaning.
  • Power BI: For interactive data visualization and dashboard creation.
  • SQLite: Database for storing and querying loan, user, and emotional data.
  • Git: Version control and project management.

Key Visualizations

  • Emotional Pattern Dashboard: Includes interactive bubble and bar charts displaying the relationship between emotions, loan amounts, relationships, and time of day.
  • Slicers: Allow users to filter by relationship and primary emotion to explore specific patterns.

Additional Resources

  • All the required Python scripts, Jupyter Notebook, SQL query file, and Power BI dashboard file are included.
  • Detailed explanations of the code, SQL query, and visualizations are provided in three separate Word documents.

Future Enhancements

  • Implement advanced machine learning models for better loan term predictions.
  • Add additional filtering options to the Power BI dashboard.
  • Explore the integration of real-time emotional data for dynamic analysis.

About

This project focuses on the analysis of emotional patterns and their correlation with loan operations. It includes emotional pattern analysis, loan-emotion correlation, lending operation assessment, machine learning modeling, and overall visualization.

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