Update README.md

README.md

@@ -1,72 +1,81 @@
-# DataFlow Pro
-Automating ML Workflows with Ease
-
-## Introduction
-The Automated ML is a Python application designed to automate the process of building, tuning, and evaluating machine learning models based on json provided in RTF/JSON?/TXT file format. <br>
-This application follows a structured flow to read the json file, extract dataset information, transform features, split data, build and tune models, and evaluate their performance.
-
-
-## Installation
-To use the Automated ML Pipeline, follow these steps:
-
-1. Clone this repository to your local machine: <br>
-```git clone https://github.com/Rupanshu-Kapoor/AutomateML.git```
-
-2. Install the required dependencies: <br>
-   `pip install -r requirements.txt`
-
-3. Run the application: <br>
-   `streamlit run app.py`
-
- 
- ## Steps to Use the Application:
-
- You can use the application in following two ways:
-
-### (A). Create Json and Train Model
-
-1. Upload the dataset on the tool on which you want to train the different model.
-2. Once the data is uploaded, you can preview the dataset.
-3. Select prediction parameters (prediction type, target variable, k-fold, etc.). 
-4. Select features to be used for prediction.
-5. When you select any feature, you can choose how to handle it. (rescaling, encoding, etc.)
-6. Select the model to be used for prediction.
-7. When you select any model, you can choose hyperparameters for tuning.
-8. Once all the parameters are selected, click on `Generate Json and Train Model` button.
-9. Application will generate the json file and train the model and display the results.
-
-### (B). Upload Json and Train Model
- 1. Upload the json file that contains all the dataset information.
- 2. Click on Train Models.
- 3. Application will train the model and display the results.
-
-## Working of the Application:
-The application performs the following tasks in sequence:
-1. **Read the JSON File and Parse JSON Content**: The RTF/JSON file is read, converted to plain text, and JSON content is extracted.
-2. **Extract Dataset Information**: Extract dataset information such as feature names, target variable, problem type (regression/classification), feature handling, etc.
-3. **Transform Features**: Features are transformed based on the specified feature handling methods.
-4. **Sample Data and Train-Test Split**: Data is sampled and split into training and testing sets.
-5. **Model Building**: Models are built based on the problem type (regression/classification).
-6. **Hyperparameter Tuning**: Hyperparameters of the models are tuned using grid search.
-7. **Model Evaluation**: Trained models are evaluated using specified evaluation metrics.
-<! --8. **Save Results**: Trained models and evaluation metrics are saved in the results/ directory. -->
-
-
-## Use Cases
-
-This application can be used for various use cases, including but not limited to:
-
-- Automated machine learning (AutoML) pipelines.
-- Data preprocessing and feature engineering tasks.
-- Model training and evaluation for regression or classification problems.
-- Hyperparameter tuning and model selection.
-- Experimentation with different datasets and configurations.
-
-## Future Work
-Possible future enhancements for the application include:
-
-- Adding support for additional data formats (e.g., CSV, Excel).
-- Implementing more advanced feature engineering techniques.
-- Incorporating more sophisticated model selection and evaluation methods.
-- Enhancing the user interface for easier interaction.
-- Integrating with external APIs or databases for data retrieval.
+---
+license: gpl-3.0
+title: DataFlowPro
+sdk: streamlit
+emoji: 🚀
+colorFrom: purple
+colorTo: pink
+short_description: Automating ML Workflows with Ease
+---
+# DataFlow Pro
+Automating ML Workflows with Ease
+
+## Introduction
+The Automated ML is a Python application designed to automate the process of building, tuning, and evaluating machine learning models based on json provided in RTF/JSON?/TXT file format. <br>
+This application follows a structured flow to read the json file, extract dataset information, transform features, split data, build and tune models, and evaluate their performance.
+
+
+## Installation
+To use the Automated ML Pipeline, follow these steps:
+
+1. Clone this repository to your local machine: <br>
+```git clone https://github.com/Rupanshu-Kapoor/AutomateML.git```
+
+2. Install the required dependencies: <br>
+   `pip install -r requirements.txt`
+
+3. Run the application: <br>
+   `streamlit run app.py`
+
+ 
+ ## Steps to Use the Application:
+
+ You can use the application in following two ways:
+
+### (A). Create Json and Train Model
+
+1. Upload the dataset on the tool on which you want to train the different model.
+2. Once the data is uploaded, you can preview the dataset.
+3. Select prediction parameters (prediction type, target variable, k-fold, etc.). 
+4. Select features to be used for prediction.
+5. When you select any feature, you can choose how to handle it. (rescaling, encoding, etc.)
+6. Select the model to be used for prediction.
+7. When you select any model, you can choose hyperparameters for tuning.
+8. Once all the parameters are selected, click on `Generate Json and Train Model` button.
+9. Application will generate the json file and train the model and display the results.
+
+### (B). Upload Json and Train Model
+ 1. Upload the json file that contains all the dataset information.
+ 2. Click on Train Models.
+ 3. Application will train the model and display the results.
+
+## Working of the Application:
+The application performs the following tasks in sequence:
+1. **Read the JSON File and Parse JSON Content**: The RTF/JSON file is read, converted to plain text, and JSON content is extracted.
+2. **Extract Dataset Information**: Extract dataset information such as feature names, target variable, problem type (regression/classification), feature handling, etc.
+3. **Transform Features**: Features are transformed based on the specified feature handling methods.
+4. **Sample Data and Train-Test Split**: Data is sampled and split into training and testing sets.
+5. **Model Building**: Models are built based on the problem type (regression/classification).
+6. **Hyperparameter Tuning**: Hyperparameters of the models are tuned using grid search.
+7. **Model Evaluation**: Trained models are evaluated using specified evaluation metrics.
+<! --8. **Save Results**: Trained models and evaluation metrics are saved in the results/ directory. -->
+
+
+## Use Cases
+
+This application can be used for various use cases, including but not limited to:
+
+- Automated machine learning (AutoML) pipelines.
+- Data preprocessing and feature engineering tasks.
+- Model training and evaluation for regression or classification problems.
+- Hyperparameter tuning and model selection.
+- Experimentation with different datasets and configurations.
+
+## Future Work
+Possible future enhancements for the application include:
+
+- Adding support for additional data formats (e.g., CSV, Excel).
+- Implementing more advanced feature engineering techniques.
+- Incorporating more sophisticated model selection and evaluation methods.
+- Enhancing the user interface for easier interaction.
+- Integrating with external APIs or databases for data retrieval.