Add documentation

DOCS.md

@@ -18,7 +18,7 @@ pip install mlModelSaver
 
 # mlModelSaver Example: Simple Linear Regression
 
-In this example, we demonstrate how to use **mlModelSaver** to export a simple linear regression model based on a notebook from [ml_models_deployments](https://github.com/jafarijason/ml_models_deployments/blob/master/notebooks/001.ipynb).
+In this example, we demonstrate how to use **mlModelSaver** to export a simple linear regression model based on a notebook from [ml_models_deployments](https://github.com/jafarijason/ml_models_deployments).
 
 ### Example Description
 
@@ -55,4 +55,77 @@ loadedModel = mlModelSaverInstance.exportModel(
         ]
     }
 )
+
+testData = [
+    {
+        "Temperature": 42,
+        "Advertising": 15,
+        "Discount": 5
+    }
+]
+
+# Create a DataFrame from the dictionary
+testDf = pd.DataFrame(testData)
+
+# this will be your model without export
+modelPredictSaleByTemperatureAdvertisingDiscountFit.predict( add_constant_column(testDf)
+
+# result 0    19590.46727 \n dtype: float64
+
+# this will be result of predict with exported model
+loadedModel.mlModelSavePredict(testDf)
+#result [{'Sales': 19590.467270313893}]
+
 ```
+
+## Supported Models
+
+Current supported models by **mlModelSaver**:
+
+```python
+supportedModels = {
+    "sm.OLS": {
+        "supported": True,
+        "normalPredictorFunction": "predict"
+    },
+    "sm.Logit": {
+        "supported": True,
+        "normalPredictorFunction": "predict"
+    },
+    "sklearn.neighbors.KNeighborsClassifier": {
+        "supported": True,
+        "normalPredictorFunction": "predict_proba"
+    },
+}
+
+
+
+## Next Steps
+
+- [-] **Support More Models** **WIP**: Extend **mlModelSaver** to support various types of models beyond simple linear regression, such as decision trees, neural networks, and ensemble methods.
+
+- [-] **Additional Examples**: Provide diverse examples demonstrating the use of **mlModelSaver** with different machine learning models and data preprocessing techniques.
+
+- [] **Video Tutorial**: Create a comprehensive video tutorial demonstrating how to train models, use **mlModelSaver** for saving and deployment, and integrate with popular frameworks like Flask and FastAPI.
+
+- [] **Save Models to S3**: Implement functionality to save models directly to Amazon S3 for scalable and reliable storage, ensuring robust deployment in cloud environments.
+
+
+### Support More Models
+
+Expand **mlModelSaver** to handle a variety of machine learning models beyond simple linear regression. Example models could include decision trees, support vector machines, and deep learning models. Ensure that each model type integrates seamlessly with **mlModelSaver**'s saving and deployment functionalities.
+
+### Save Models to S3
+
+Enhance **mlModelSaver** to include options for saving models directly to Amazon S3. This feature ensures that models are stored securely and can be accessed from any location, facilitating deployment across distributed systems and cloud environments.
+
+### Additional Examples
+
+Include a range of examples demonstrating **mlModelSaver**'s capabilities across different use cases. Examples should cover various scenarios such as regression, classification, and time series forecasting. Provide clear instructions and code snippets for each example, showcasing how to prepare data, train models, and deploy them using **mlModelSaver**.
+
+### Video Tutorial
+
+Produce a video tutorial that guides users through the entire process of using **mlModelSaver**. The tutorial should include steps for training a model, integrating with **mlModelSaver** for saving and loading, and deploying the model using popular web frameworks like Flask or FastAPI. Emphasize best practices and common pitfalls to help users maximize efficiency and reliability in their machine learning projects.
+
+These next steps will enhance **mlModelSaver**'s usability and scalability, enabling users to leverage advanced machine learning models effectively in production environments.
+