Ordinal Encoding README added

Pre-Processing/Algorithms/Ordinal_Encoding/README.md

@@ -0,0 +1,72 @@
+# OrdinalEncoding
+
+`OrdinalEncoding` is a custom implementation of ordinal encoding for categorical variables. It assigns a unique integer value to each category, preserving the ordinal relationship if present.
+
+![Ordinal Encoder by Scikit-learn](images/ordinal_enc_scikit-learn_ex.png)
+
+## Features
+
+- **Null Check**: Provides a null check function to ensure input data does not contain null values before encoding.
+- **Fit Function**: Computes category mappings for each column based on unique values.
+- **Transform Function**: Transforms categorical variables into encoded integers using the computed mappings.
+- **Fit Transform**: Combines the fit and transform steps into a single method.
+
+## Methods
+
+- `null_check(data)`: Checks for null values in the input data.
+- `fit(data)`: Computes category mappings for each column based on unique values.
+- `transform(data)`: Transforms categorical variables into encoded integers using the computed mappings.
+- `fit_transform(data)`: Combines the fit and transform steps into a single method.
+
+## Exception Handling
+
+The class includes basic exception handling:
+- Raises a ValueError if input data contains null values.
+
+## Installation
+
+This implementation is a standalone Python class and does not require any specific installation. However, you need to have `numpy` and `pandas` installed in your environment.
+Just run these command in the Algorithms directory
+ - First, create a virtual environment. For that, ```virtualenv``` module is required.
+   ```
+   pip install virtualenv
+   ```
+   Then to create a virtual environement:
+   ```
+   virtualenv <environment_name>
+   ```
+   This creates a virtual environment inside Algorithms directory, which then needs to be activated
+   ```
+   py <environment_name>/Scripts/activate_this.py
+   ```
+ - Secondly, install all the required libraries
+   ```
+   pip install -r requirements.txt
+   ```
+
+## Testing
+
+1. Load all the required libraries and Custom OrdinalEncoding class
+
+![Load required libraries and StandardScaling Class](images/test_1.png)
+
+2. Create a custom data frame 
+
+![Create a custom dataframe](images/test_2.png)
+
+3. Create a OrdinalEncoding Class's object and call the desired functions
+
+![Creating object and calling functions](images/test_3.png)
+
+## Outputs
+
+![Original Data](images/out_1.png)
+
+![Scaled Data](images/out_2.png)
+
+### Notes
+1. **Ensure the class definition is available**: Include the `OrdinalEncoding` class in your script or save it in a module (e.g., `your_module.py`) and import it accordingly.
+2. **Adjust paths**: Make sure to adjust the import path if you save the class in a different module.
+3. **License**: Replace the placeholder in the License section with the actual license text or file link.
+
+This README provides a comprehensive overview of the `OrdinalEncoding` class, including its features, usage examples, and how to handle potential issues.

Pre-Processing/Algorithms/Ordinal_Encoding/testing/ordinal_encoding_test.py

@@ -17,13 +17,15 @@
 }
 df = pd.DataFrame(data)
 
+print(f"Original Data : \n{df}")
+
 # Initialize the custom OrdinalEncoder
 encoder = OrdinalEncoding()
 
 # Fit the encoder to the data and transform it
 try:
     encoded_df = encoder.fit_transform(df)
-    print("\nEncoded DataFrame:")
+    print("\nEncoded Data:")
     print(encoded_df)
 except ValueError as e:
     print(f"\nException: {e}")