README.md

ISDM-CDSSI-Hackathon-Code-4-Change

The project aims to create a predictive model for financial risk in Indian women-led households, introducing a new financial vulnerability index. It will utilize exploratory data analysis (EDA) for insights and visualize hotspots of financial vulnerability across India. This effort seeks to enhance financial resilience and empower these households.

https://www.isdm.org.in/isdm-code-for-change

https://www.linkedin.com/posts/indian-school-of-development-management---isdm_code-for-change-rsvp-activity-7199277891661012992-iMfv?utm_source=share&utm_medium=member_desktop

https://www.linkedin.com/posts/samrudhi-shete-807a9320a_datascience-socialimpact-hackathon-activity-7199780450712625153-oOO7?utm_source=share&utm_medium=member_desktop

Problem Statement :

https://drive.google.com/file/d/17tXpKGaFbKowS8ex2fb91GmhY1qwhTQR/view https://drive.google.com/file/d/1qGKPnNiICJErxzRZXwJtqzEqJCSSJVRH/view https://drive.google.com/file/d/17tXpKGaFbKowS8ex2fb91GmhY1qwhTQR/view

Dataset :

https://drive.google.com/file/d/1ukaTvnp_Fm2je4gh3AwORW6fxvISRXqi/view

Solution:

Step 1 : Data Understanding

Preprocessing Steps:

1)Data Cleaning: Removed irrelevant columns and handled missing values through imputation or deletion.

2)Feature Engineering: Created new features by combining or transforming existing ones to improve model performance.

3)Normalization/Standardization: Scaled numerical features to ensure they have a similar range, preventing dominance by certain features.

4)Encoding Categorical Variables: Converted categorical variables into numerical format using techniques like one-hot encoding or label encoding.

Step 2 : EDA

Results

Step 3 : Model Selection

Algorithm Choices: Random Forest, Logistic Regression, Naive Bayes, SVM.

Evaluation Criteria: Selected based on performance metrics (accuracy, precision, recall).

Step 4 : Model Development

Feature Scaling: Normalized numerical features for better model performance.

Model Training: Trained selected algorithms using training data.

Step 5 : Model Evaluation

Performance Metrics: Evaluated models on testing data using metrics like accuracy, precision, recall, and F1-score.

Cross-Validation: Applied cross-validation techniques to ensure robustness of models.

Step 6 : Model Selection and Tuning

Hyperparameter Tuning: Optimized model parameters using techniques like grid search.

Final Model Selection: Chose Random Forest as the best-performing model based on evaluation results

Step 7:Results

Accuracy:

Random Forest: 85% accuracy

Logistic Regression: 78% accuracy

Naive Bayes: 72% accuracy

SVM: 80% accuracy

Visualize financial vulnerability hotspots across India

CONCLUSION

Our analysis identified critical factors impacting financial vulnerability in women-headed households, including education levels and household income. The Random Forest model demonstrated the highest accuracy in predicting financial vulnerability, enabling targeted interventions. Predictive analytics can play a vital role in empowering women in rural India by directing resources effectively. Understanding financial vulnerability can inform policy decisions and interventions aimed at socio-economic empowerment.