Update README.md

README.md

@@ -16,4 +16,50 @@ short_description: Streamlit template space
 Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
 If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+forums](https://discuss.streamlit.io).
+# No Code Text Classifier Tool
+
+This tool will help you to perform training, evaluation & prediction of Text Classification task without knowing any kind of code. You have to define the dataset directory and create your model and perform predictions without any issue. In the backend, this will automatically perform text preprocessing, model training etc. You can also perform hyperparameter techniques to get the best model through experiments. Let's get started.
+
+Install the pakage
+```python
+pip install NoCodeTextClassifier
+```
+
+### Training the Text Classification
+
+Define the datapath
+```python
+data_path = "dataset.csv"
+```
+Clean the Text dataset and transform the label into number
+```python
+# It will take datapath, text feature and target feature
+process = process(data_path,'email','class')
+df = process.processing()
+print(df.head())
+```
+Convert the text feature into numerical vector. You can apply multiple vectorization such as TfIdfVectorizer, CountVectorizer.
+```python
+Vectorization = Vectorization(df,'clean_text')
+TfidfVectorizer = Vectorization.TfidfVectorizer(max_features= 10000)
+print(TfidfVectorizer.toarray())
+```
+Split the dataset into training and testing
+```python
+X_train, X_test, y_train, y_test = process.split_data(TfidfVectorizer.toarray(), df['labeled_target'])
+print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
+```
+Perform training with various models such as Naive Bayers, Decision Tree, Logistic Regression, and others. After training, you will see the evalution of the trained model.
+```python
+models = Models(X_train=X_train,X_test = X_test, y_train = y_train, y_test = y_test)
+models.DecisionTree()
+```
+
+### For Inferencing with text data
+
+For prediction of the text data with the trained model, try this.
+```python
+text = input("Enter your text:\n")
+inference.prediction(text)
+```