fixed docs

Author: pottekkat

README.md

@@ -88,7 +88,7 @@ print(data_type_handler.change_file_type(
     type_fields))
 
 
-preprocessing_code = '''python
+preprocessing_code = '''
 from pyspark.ml import Pipeline
 from pyspark.sql.functions import (
     mean, col, split,
@@ -198,14 +198,14 @@ print(model_builder.create_model(
 ```
 # Function APIs
 
-## DatabaseApi
+## Database API
 
 ### read_resume_files
 
 ```python
 read_resume_files(pretty_response=True)
 ```
-* `pretty_response`: return indented string to visualization 
+* `pretty_response`: returns indented `string` for visualization(default: `True`, returns `dict` if `False`)
 (default `True`, if `False`, return dict)
 
 ### read_file
@@ -214,156 +214,167 @@ read_resume_files(pretty_response=True)
 read_file(filename, skip=0, limit=10, query={}, pretty_response=True)
 ```
 
-* `filename` : filename of file
-* `skip`: number of rows amount to skip in pagination (default `0`)
-* `limit`: number of rows to return in pagination (default `10`)
-(max setted in `20` rows per request)
-* `query`: query to make in mongo (default empty query)
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `filename` : name of file
+* `skip`: number of rows  to skip in pagination(default: `0`)
+* `limit`: number of rows to return in pagination(default: `10`)
+(maximum is set at `20` rows per request)
+* `query`: query to make in MongoDB(default: `empty query`)
+* `pretty_response`: returns indented `string` for visualization(default: `True`, returns `dict` if `False`)
 
 ### create_file
 
 ```python
 create_file(filename, url, pretty_response=True)
 ```
 
-* `filename`: filename of file to be created
-* `url`: url to csv file
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `filename`: name of file to be created
+* `url`: url to CSV file
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 ### delete_file
 
 ```python
 delete_file(filename, pretty_response=True)
 ```
 
-* `filename`: file filename to be deleted
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `filename`: name of the file to be deleted
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
-## Projection
+## Projection API
 
 ### create_projection
 
 ```python
 create_projection(filename, projection_filename, fields, pretty_response=True)
 ```
 
-* `filename`: filename of file to make projection
-* `projection_filename`: filename used to create projection
+* `filename`: name of the file to make projection
+* `projection_filename`: name of file used to create projection
 * `fields`: list with fields to make projection 
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
-## DataTypeHandler
+## Data type handler API 
 
 ### change_file_type
 
 ```python
 change_file_type(filename, fields_dict, pretty_response=True)
 ```
 
-* `filename`: filename of file
+* `filename`: name of file
 * `fields_dict`: dictionary with `field`:`number` or `field`:`string` keys  
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
+
+## Histogram API
 
-## Histogram
 ### create_histogram
+
 ```python
 create_histogram(filename, histogram_filename, fields, 
                  pretty_response=True)
 ```
 
-* `filename`: filename of file to make histogram
-* `histogram_filename`: filename used to create histogram
+* `filename`: name of file to make histogram
+* `histogram_filename`: name of file used to create histogram
 * `fields`: list with fields to make histogram 
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
+
+## t-SNE API
 
-## Tsne
 ### create_image_plot
+
 ```python
 create_image_plot(tsne_filename, parent_filename,
                   label_name=None, pretty_response=True)
 ```
 
-* `parent_filename`: filename of file to make histogram
-* `tsne_filename`: filename used to create image plot
-* `label_name`: label name to dataset with labeled tuples (default `None`, to 
+* `parent_filename`: name of file to make histogram
+* `tsne_filename`: name of file used to create image plot
+* `label_name`: label name to dataset with labeled tuples (default: `None`, to 
 datasets without labeled tuples) 
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 ### read_image_plot_filenames
+
 ```python
 read_image_plot_filenames(pretty_response=True)
 ```
 
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 ### read_image_plot
+
 ```python
 read_image_plot(tsne_filename, pretty_response=True)
 ```
 
 * tsne_filename: filename of a created image plot
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 ### delete_image_plot
+
 ```python
 delete_image_plot(tsne_filename, pretty_response=True)
 ```
 
 * `tsne_filename`: filename of a created image plot
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
+
+## PCA API
 
-## Pca
 ### create_image_plot
+
 ```python
 create_image_plot(tsne_filename, parent_filename,
                   label_name=None, pretty_response=True)
 ```
 
-* `parent_filename`: filename of file to make histogram
+* `parent_filename`: name of file to make histogram
 * `pca_filename`: filename used to create image plot
-* `label_name`: label name to dataset with labeled tuples (default `None`, to 
+* `label_name`: label name to dataset with labeled tuples (default: `None`, to 
 datasets without labeled tuples) 
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 ### read_image_plot_filenames
+
 ```python
 read_image_plot_filenames(pretty_response=True)
 ```
 
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 ### read_image_plot
+
 ```python
 read_image_plot(pca_filename, pretty_response=True)
 ```
 
 * `pca_filename`: filename of a created image plot
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 ### delete_image_plot
+
 ```python
 delete_image_plot(pca_filename, pretty_response=True)
 ```
 
 * `pca_filename`: filename of a created image plot
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
-## ModelBuilder
+## Model builder API
 
 ### create_model
 
@@ -372,12 +383,12 @@ create_model(training_filename, test_filename, preprocessor_code,
              model_classificator, pretty_response=True)
 ```
 
-* `training_filename`: filename to be used in training
-* `test_filename`: filename to be used in test
-* `preprocessor_code`: python3 code for pyspark preprocessing model
-* `model_classificator`: list of initial from classificators to be used in model
-* `pretty_response`: return indented string to visualization 
-(default `True`, if `False`, return dict)
+* `training_filename`: name of file to be used in training
+* `test_filename`: name of file to be used in test
+* `preprocessor_code`: Python3 code for pyspark preprocessing model
+* `model_classificator`: list of initial classificators to be used in model
+* `pretty_response`: returns indented `string` for visualization 
+(default: `True`, returns `dict` if `False`)
 
 #### model_classificator
 
@@ -395,15 +406,15 @@ create_model(training_filename, test_filename, preprocessor_code, ["lr", "nb"])
 
 #### preprocessor_code environment
 
-The python 3 preprocessing code must use the environment instances in bellow:
+The Python 3 preprocessing code must use the environment instances as below:
 
 * `training_df` (Instantiated): Spark Dataframe instance training filename
 * `testing_df`  (Instantiated): Spark Dataframe instance testing filename
 
-The preprocessing code must instantiate the variables in below, all instances must be transformed by pyspark VectorAssembler:
+The preprocessing code must instantiate the variables as below, all instances must be transformed by pyspark VectorAssembler:
 
-* `features_training` (Not Instantiated): Spark Dataframe instance for train the model
-* `features_evaluation` (Not Instantiated): Spark Dataframe instance for evaluating trained model accuracy
+* `features_training` (Not Instantiated): Spark Dataframe instance for training the model
+* `features_evaluation` (Not Instantiated): Spark Dataframe instance for evaluating trained model
 * `features_testing` (Not Instantiated): Spark Dataframe instance for testing the model
 
 In case you don't want to evaluate the model, set `features_evaluation` as `None`.
@@ -414,8 +425,7 @@ In case you don't want to evaluate the model, set `features_evaluation` as `None
 self.fields_from_dataframe(dataframe, is_string)
 ```
 
-This method returns string or number fields as a string list from a DataFrame.
+This method returns `string` or `number` fields as a `string` list from a DataFrame.
 
 * `dataframe`: DataFrame instance
-* `is_string`: Boolean parameter, if `True`, the method returns the string DataFrame fields, otherwise, returns the numbers DataFrame fields.
-
+* `is_string`: Boolean parameter(if `True`, the method returns the string DataFrame fields, otherwise, returns the numbers DataFrame fields)