diff --git a/README.md b/README.md
index d904142..37845e2 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ pip install blobcity
 
 ``` Python
 import blobcity as bc
-bc.train("data.csv", "Y_column")
+bc.train(file="data.csv", target="Y_column")
 bc.spill("my_code.ipynb")
 ```
 `Y_column` is the name of the target column. The column must be present within the data provided. 
@@ -21,6 +21,20 @@ Support input data formats are `.csv` and `.xlsx`. Extension for other file form
 
 The `spill` function generates the model code with exhaustive documentation. Training code is also included for basic scikit-learn models. TensorFlow and other DNN models produce only the test / final use code. 
 
+## Use a Pandas Data Frame
+``` Python
+bc.train(df=my_df, target="Y_column")
+```
+
+If loading data from a Database or external system, create a DataFrame from your data source, and pass it directly to the `train` function.
+
+## From a URL
+``` Python
+bc.train(file="https://example.com/data.csv", target="Y_column")
+```
+
+The `file` parameter can be a local file, or a URL. The function will load the data from URL specified. The file at the URL must be either in CSV or XLSX format. The URL should be accessible publicly without authentication. 
+
 # Code Generation
 Multiple formats of code generation is supported by the framework. The `spill` function can be used to generate both `ipynb` and `py` files. The desired type is infered from the name of the output file. The code file will be created at the path specified. Relative or absolute file paths, are both supported. 
 
@@ -41,7 +55,7 @@ bc.spill("my_code.py", docs=True)
 ```
 Pass the optional `docs` parameter to generate Python code along with full code documentation. 
 
-# Specifying `X_Values`
+# Specifying Features
 Framework automatically performs a feature selection. All features are selected by default for feature selection.
 Framework is smart enough to remove ID / Primary key columns. 
 
@@ -51,3 +65,8 @@ Use the below code if you would like to manually specify the features to be used
 bc.train("data.csv", target="Y_value", features=["col1", "col2", "col3"])
 ```
 
+# Printing Model Stats
+``` Python
+model = bc.train(file='./test.csv')
+model.stats()
+```
diff --git a/blobcity/main/driver.py b/blobcity/main/driver.py
index 8202b74..585c04b 100644
--- a/blobcity/main/driver.py
+++ b/blobcity/main/driver.py
@@ -25,7 +25,7 @@
 from blobcity.utils import AutoFeatureSelection as AFS
 from blobcity.utils import writeYml
 from blobcity.main.modelSelection import modelSearch
-def train(file_path=None, df=None, target=None,features=None):
+def train(file=None, df=None, target=None,features=None):
     # this should internally create and a yml file. The yml file is used for generating the code in the future.
     # this should also store a pickle / tensorflow file based on the model used
     # Data read
@@ -33,8 +33,8 @@ def train(file_path=None, df=None, target=None,features=None):
     dc=DictClass()
     dc.resetVar()
     #data read
-    if file_path!=None:
-        dataframe= getDataFrameType(file_path, dc)
+    if file!=None:
+        dataframe= getDataFrameType(file, dc)
     else: 
         dataframe = df
         dc.addKeyValue('data_read',{"class":"df"})