https://github.com/dawoodkhatri1/codealpha_credit_scoring_model
Develop a credit scoring model to predict the creditworthiness of individuals based on historical financial data. Utilize classification algorithms and assess the model's accuracy.
https://github.com/dawoodkhatri1/codealpha_credit_scoring_model
faker matplotlib numpy pandas pycharm-ide python sklearn
Last synced: 3 months ago
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Develop a credit scoring model to predict the creditworthiness of individuals based on historical financial data. Utilize classification algorithms and assess the model's accuracy.
- Host: GitHub
- URL: https://github.com/dawoodkhatri1/codealpha_credit_scoring_model
- Owner: dawoodkhatri1
- License: mit
- Created: 2024-07-13T08:59:10.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-14T10:23:47.000Z (almost 2 years ago)
- Last Synced: 2025-04-02T07:35:19.989Z (about 1 year ago)
- Topics: faker, matplotlib, numpy, pandas, pycharm-ide, python, sklearn
- Language: Python
- Homepage:
- Size: 14.6 KB
- Stars: 12
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CodeAlpha_Credit_Scoring_Model
You can run the code in Pycharm and vscode.
I divided the task into following things:
**Importing libraries:**
pandas: Used for data manipulation and analysis.
numpy: Provides support for large arrays and matrices.
matplotlib.pyplot: Used for plotting and visualization.
sklearn.model_selection: Includes tools for splitting the dataset and performing grid search.
sklearn.pipeline: Helps in creating a machine learning pipeline.
sklearn.compose: Allows combining different preprocessing steps.
sklearn.preprocessing: Contains various preprocessing utilities.
sklearn.linear_model: Provides regression algorithms like Ridge regression.
sklearn.metrics: Offers metrics for model evaluation.
Faker: Generates fake data.
**Initialize Faker and Data Generation:**
Faker is initialized to create realistic synthetic data.
np.random.seed(0) ensures reproducibility of random numbers.
fake_data dictionary contains:
> income: Normally distributed incomes with mean 50000 and standard deviation 15000.
>> age: Random integers between 20 and 70.
>>> credit_history: Random integers between 1 and 10.
>>>> credit_score: Random integers between 300 and 850.
**Data Conversion:**
> The generated data is converted to a pandas DataFrame for easier manipulation.
**Splitting Data:**
X contains the features (income, age, credit_history).
y contains the target variable (credit_score).
**Pipline Preprocessing:**
ColumnTransformer applies transformations to specified columns.
StandardScaler standardizes features by removing the mean and scaling to unit variance.
**Pipline Regression:**
Pipeline chains preprocessing and model fitting steps.
Ridge regression is used as the model.
**HyperParameter Tuning:**
GridSearchCV performs hyperparameter tuning using cross-validation.
parameters dictionary specifies the range of alpha values for Ridge regression.
**Train-Test Split and Prediction:**
train_test_split splits the data into training and testing sets (80% train, 20% test).
**Model Evaluation:**
r2_score and mean_squared_error evaluate the model's performance.
The best hyperparameters are printed.
**Prediction Function:**
predict_credit_score: Function to predict credit scores using the trained model.
A sample prediction is made using specified income, age, and credit_history.
**Visualization:**
plt.scatter creates a scatter plot to visualize the relationship between actual and predicted credit scores.
The output looks like this:
## License
[MIT License](LICENSE)