https://github.com/rijul007/classification-on-imbalanced-data-using-python

Machine Learning Python project aimed at classifying imbalanced data by employing advanced techniques to refine model accuracy and performance.
https://github.com/rijul007/classification-on-imbalanced-data-using-python

data-science machine-learning python

Last synced: 6 months ago
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Machine Learning Python project aimed at classifying imbalanced data by employing advanced techniques to refine model accuracy and performance.

• Host: GitHub
• URL: https://github.com/rijul007/classification-on-imbalanced-data-using-python
• Owner: rijul007
• Created: 2024-09-11T12:38:34.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2024-09-14T12:30:06.000Z (almost 2 years ago)
• Last Synced: 2024-12-30T04:27:24.849Z (over 1 year ago)
• Topics: data-science, machine-learning, python
• Language: Python
• Homepage: https://nbviewer.org/gist/rijul007/94fab12ca87d1ddb1ddbe32b6e34704e
• Size: 877 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Classification on Imbalanced Data using Python

## Objective

This project aims to develop a classification model on an imbalanced dataset. The primary goal is to handle the challenges associated with imbalanced class distribution, particularly in predicting the minority class effectively.

## Dataset Used

The dataset used for this project is from [Statso](https://statso.io/training-models-on-imbalanced-data-case-study/). It contains insurance policy records, including customer demographics, vehicle details, and the `claim_status` target variable. The data has 58,592 entries and 41 columns, with a significant imbalance between the classes (claims vs. no claims).

## Analysis Technique

To address the class imbalance, the following techniques were used extensively throughout the analysis:

1. **Data Preprocessing**:

   - **Exploratory Data Analysis (EDA)**: The data was explored to understand the distribution of numerical and categorical features.

   - **Handling Missing Values**: Checked for missing values and confirmed none were present in the dataset.

   - **Data Visualization**: Plots were generated to examine the class imbalance and feature distributions.

2. **Class Imbalance Handling**:

   - **Oversampling**: Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the classes. This was crucial because the dataset showed a heavy imbalance, with significantly fewer claims (`claim_status = 1`) than non-claims (`claim_status = 0`).

   - **Resampling**: By creating synthetic samples of the minority class, we ensured that both classes were equally represented in the training data, preventing the model from becoming biased towards the majority class.

3. **Feature Selection**:

   - Analyzed the importance of both numerical and categorical variables using feature importance metrics. Key features like `policy_id`, `subscription_length`, and `customer_age` were identified as the most influential in predicting the claim status.

4. **Model Building**:

   - **Random Forest Classifier**: Chosen for its robustness in handling imbalanced data. The model was trained on the balanced dataset.

   - **Evaluation Metrics**: Precision, Recall, and F1-Score were prioritized over accuracy, as these metrics provide a better understanding of the model’s performance on imbalanced datasets. Additionally, the Area Under the ROC Curve (AUC-ROC) was used to evaluate the overall performance.

## Result

The model achieved a balanced F1-Score, indicating an effective handling of the minority class. Precision and recall metrics showed a significant improvement after balancing the dataset.

The detailed results and analysis can be found in the [Jupyter Notebook here](https://nbviewer.org/gist/rijul007/94fab12ca87d1ddb1ddbe32b6e34704e).