diff --git a/CHANGELOG.md b/CHANGELOG.md
index b366aa1..e53c6b8 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,11 @@
 # AWS-MLOps-module
 
+## [2.0.2] - 01/03/24
+* Added functionality for passing preprocessing script
+
+## [2.0.1] - 02/02/24
+* Updated retraining_schedule validation
+
 ## [2.0.0] - 21/12/23
 **BREAKING CHANGES**
 * Mandatory variable `resource_naming_prefix` has now been added.
diff --git a/README.md b/README.md
index 252b5dc..cd3fa7f 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,6 @@ This repo contains a terraform module with corresponding AWS resources that enab
 
 
 ## Example Usage
-
  ```
 module "MLOps" {
   source  = "github.com/crederauk/terraform-aws-mlops-module?ref=<MODULE_VERSION>"
@@ -22,6 +21,7 @@ module "MLOps" {
   algorithm_choice        = "classification"
   sagemaker_training_notebook_instance_type = "ml.m4.xlarge"
   inference_instance_count = 1
+  preprocessing_script_path = "terraform/preprocess_data.py"
   tags = {
     my-tag-key = "my-tag-value"
   }
@@ -67,6 +67,7 @@ No resources.
 | <a name="input_inference_instance_count"></a> [inference\_instance\_count](#input\_inference\_instance\_count) | The initial number of instances to serve the model endpoint | `number` | `1` | no |
 | <a name="input_inference_instance_type"></a> [inference\_instance\_type](#input\_inference\_instance\_type) | The instance type to be created for serving the model. Must be a valid EC2 instance type | `string` | `"ml.t2.medium"` | no |
 | <a name="input_model_target_variable"></a> [model\_target\_variable](#input\_model\_target\_variable) | The dependent variable (or 'label') that the model aims to predict. This should be a column name in the dataset. | `string` | n/a | yes |
+| <a name="input_preprocessing_script_path"></a> [preprocessing\_script\_path](#input\_preprocessing\_script\_path) | The path the user provides if they want to include their own data cleaning logic | `string` | `null` | no |
 | <a name="input_resource_naming_prefix"></a> [resource\_naming\_prefix](#input\_resource\_naming\_prefix) | Naming prefix to be applied to all resources created by this module | `string` | n/a | yes |
 | <a name="input_retrain_model_bool"></a> [retrain\_model\_bool](#input\_retrain\_model\_bool) | Boolean to indicate if the retraining pipeline shoud be added | `bool` | `false` | no |
 | <a name="input_retraining_schedule"></a> [retraining\_schedule](#input\_retraining\_schedule) | Cron expression for the model retraining frequency in the AWS format. See https://docs.aws.amazon.com/lambda/latest/dg/services-cloudwatchevents-expressions.html for details | `string` | `""` | no |
diff --git a/main.tf b/main.tf
index 685f88e..dcc025e 100644
--- a/main.tf
+++ b/main.tf
@@ -1,8 +1,9 @@
 module "s3" {
   source = "./modules/s3"
 
-  resource_naming_prefix = var.resource_naming_prefix
-  tags                   = var.tags
+  resource_naming_prefix    = var.resource_naming_prefix
+  tags                      = var.tags
+  preprocessing_script_path = var.preprocessing_script_path
 }
 
 module "sagemaker" {
@@ -26,15 +27,15 @@ module "sagemaker" {
   ecr_repo_uri             = "${module.ecr.repository.repository_url}:latest"
 
   # S3
-  config_s3_bucket      = module.s3.config_bucket.id
-  config_bucket_key_arn = module.s3.encryption_key.arn
-  data_s3_bucket        = var.data_s3_bucket
-  data_bucket_key_arn   = var.data_s3_bucket_encryption_key_arn
-  data_location_s3      = var.data_location_s3
-  model_s3_bucket       = module.s3.model_bucket.id
-  model_bucket_key_arn  = module.s3.encryption_key.arn
+  config_s3_bucket          = module.s3.config_bucket.id
+  config_bucket_key_arn     = module.s3.encryption_key.arn
+  data_s3_bucket            = var.data_s3_bucket
+  data_bucket_key_arn       = var.data_s3_bucket_encryption_key_arn
+  data_location_s3          = var.data_location_s3
+  model_s3_bucket           = module.s3.model_bucket.id
+  model_bucket_key_arn      = module.s3.encryption_key.arn
+  preprocessing_script_path = var.preprocessing_script_path
 }
-
 module "retraining_job" {
   count  = var.retrain_model_bool ? 1 : 0
   source = "./modules/glue"
diff --git a/mlops_ml_models/delete_sagemaker_endpoint.py b/mlops_ml_models/delete_sagemaker_endpoint.py
index 7741287..fe118d7 100644
--- a/mlops_ml_models/delete_sagemaker_endpoint.py
+++ b/mlops_ml_models/delete_sagemaker_endpoint.py
@@ -32,11 +32,8 @@ def delete_sagemaker_endpoint(endpoint_name: str) -> None:
         sagemaker_client.delete_endpoint(EndpointName=endpoint_name)
 
         # Delete endpoint configuration
-        sagemaker_client.delete_endpoint_config(
-            EndpointConfigName=endpoint_name
-        )
+        sagemaker_client.delete_endpoint_config(EndpointConfigName=endpoint_name)
 
-        print(f"Endpoint '{endpoint_name}' and its configuration have "
-              "been deleted.")
+        print(f"Endpoint '{endpoint_name}' and its configuration have " "been deleted.")
     else:
         print("Endpoint deletion cancelled.")
diff --git a/mlops_ml_models/deploy_model_endpoint.py b/mlops_ml_models/deploy_model_endpoint.py
index ce4052e..ce3b391 100644
--- a/mlops_ml_models/deploy_model_endpoint.py
+++ b/mlops_ml_models/deploy_model_endpoint.py
@@ -2,10 +2,15 @@
 
 
 def deploy_model(
-    model_name: str, model_type: str, model_s3_bucket: str, instance_type: str, endpoint_name,
-    role: str, inference_instance_count: int, image_uri: str
+    model_name: str,
+    model_type: str,
+    model_s3_bucket: str,
+    instance_type: str,
+    endpoint_name,
+    role: str,
+    inference_instance_count: int,
+    image_uri: str,
 ) -> None:
-
     """This script deploys the sagemaker endpoint using the tar.gz file
     saved in s3.
 
@@ -23,10 +28,7 @@ def deploy_model(
         image_uri=(image_uri),  # The ECR image you pushed
         model_data=model_file,  # Location of your serialized model
         role=role,
-        env={
-            "MODEL_NAME": model_name,
-            "MODEL_TYPE": model_type
-        }
+        env={"MODEL_NAME": model_name, "MODEL_TYPE": model_type},
     )
     model.deploy(
         initial_instance_count=inference_instance_count,
diff --git a/mlops_ml_models/finalize_and_save_model.py b/mlops_ml_models/finalize_and_save_model.py
index acbcadc..eb67b79 100644
--- a/mlops_ml_models/finalize_and_save_model.py
+++ b/mlops_ml_models/finalize_and_save_model.py
@@ -1,8 +1,7 @@
 import importlib
 
 
-def finalize_and_save_model(algorithm_choice: str, bestModel: str,
-                            model_name: str):
+def finalize_and_save_model(algorithm_choice: str, bestModel: str, model_name: str):
     """
     Finalizes the best model obtained from PyCaret and saves it locally.
 
diff --git a/mlops_ml_models/load_data.py b/mlops_ml_models/load_data.py
index 421ed06..c3bef86 100644
--- a/mlops_ml_models/load_data.py
+++ b/mlops_ml_models/load_data.py
@@ -18,7 +18,7 @@ def load_data(data_location: str) -> pd.DataFrame:
         df = pd.read_csv(data_location, low_memory=False)
         # Dropped unnamed columns. You should comment this portion out before
         # using the script if you dont have unamed columns
-        df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
+        df = df.loc[:, ~df.columns.str.contains("^Unnamed")]
         return df
     except Exception as e:
         print(f"Error loading data: {e}")
diff --git a/mlops_ml_models/models_template_notebook.ipynb b/mlops_ml_models/models_template_notebook.ipynb
index e9d7e71..b0ff651 100644
--- a/mlops_ml_models/models_template_notebook.ipynb
+++ b/mlops_ml_models/models_template_notebook.ipynb
@@ -2,181 +2,300 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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-      "Requirement already satisfied: pillow in /home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages (from imagehash==4.3.1->ydata-profiling) (10.0.1)\n",
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-      "\u001b[33mWARNING: visions 0.7.5 does not provide the extra 'type-image-path'\u001b[0m\u001b[33m\n",
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+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mRequirement already satisfied: pycaret in /opt/homebrew/lib/python3.11/site-packages (3.2.0)\n",
+      "Requirement already satisfied: category-encoders>=2.4.0 in /opt/homebrew/lib/python3.11/site-packages (from pycaret) (2.6.3)\n",
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+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mCollecting python-dotenv\n",
+      "  Downloading python_dotenv-1.0.0-py3-none-any.whl (19 kB)\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mInstalling collected packages: python-dotenv\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mSuccessfully installed python-dotenv-1.0.0\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mCollecting ydata-profiling\n",
+      "  Downloading ydata_profiling-4.6.4-py2.py3-none-any.whl.metadata (20 kB)\n",
+      "Requirement already satisfied: scipy<1.12,>=1.4.1 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (1.10.1)\n",
+      "Requirement already satisfied: pandas!=1.4.0,<3,>1.1 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (1.5.3)\n",
+      "Requirement already satisfied: matplotlib<3.9,>=3.2 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (3.6.0)\n",
+      "Requirement already satisfied: pydantic>=2 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (2.4.2)\n",
+      "Requirement already satisfied: PyYAML<6.1,>=5.0.0 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (6.0.1)\n",
+      "Requirement already satisfied: jinja2<3.2,>=2.11.1 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (3.1.2)\n",
+      "Collecting visions==0.7.5 (from visions[type_image_path]==0.7.5->ydata-profiling)\n",
+      "  Downloading visions-0.7.5-py3-none-any.whl (102 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.7/102.7 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: numpy<1.26,>=1.16.0 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (1.25.2)\n",
+      "Requirement already satisfied: htmlmin==0.1.12 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (0.1.12)\n",
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+      "Requirement already satisfied: tqdm<5,>=4.48.2 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (4.66.1)\n",
+      "Collecting seaborn<0.13,>=0.10.1 (from ydata-profiling)\n",
+      "  Downloading seaborn-0.12.2-py3-none-any.whl (293 kB)\n",
+      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m293.3/293.3 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hRequirement already satisfied: multimethod<2,>=1.4 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (1.10)\n",
+      "Requirement already satisfied: statsmodels<1,>=0.13.2 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (0.14.1)\n",
+      "Collecting typeguard<5,>=4.1.2 (from ydata-profiling)\n",
+      "  Downloading typeguard-4.1.5-py3-none-any.whl.metadata (3.7 kB)\n",
+      "Requirement already satisfied: imagehash==4.3.1 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (4.3.1)\n",
+      "Collecting wordcloud>=1.9.1 (from ydata-profiling)\n",
+      "  Downloading wordcloud-1.9.3-cp311-cp311-macosx_11_0_arm64.whl.metadata (3.4 kB)\n",
+      "Collecting dacite>=1.8 (from ydata-profiling)\n",
+      "  Downloading dacite-1.8.1-py3-none-any.whl.metadata (15 kB)\n",
+      "Requirement already satisfied: numba<0.59.0,>=0.56.0 in /opt/homebrew/lib/python3.11/site-packages (from ydata-profiling) (0.58.1)\n",
+      "Requirement already satisfied: PyWavelets in /opt/homebrew/lib/python3.11/site-packages (from imagehash==4.3.1->ydata-profiling) (1.4.1)\n",
+      "Requirement already satisfied: pillow in /opt/homebrew/lib/python3.11/site-packages (from imagehash==4.3.1->ydata-profiling) (10.0.1)\n",
+      "Requirement already satisfied: attrs>=19.3.0 in /opt/homebrew/lib/python3.11/site-packages (from visions==0.7.5->visions[type_image_path]==0.7.5->ydata-profiling) (23.1.0)\n",
+      "Requirement already satisfied: networkx>=2.4 in /opt/homebrew/lib/python3.11/site-packages (from visions==0.7.5->visions[type_image_path]==0.7.5->ydata-profiling) (3.1)\n",
+      "Requirement already satisfied: tangled-up-in-unicode>=0.0.4 in /opt/homebrew/lib/python3.11/site-packages (from visions==0.7.5->visions[type_image_path]==0.7.5->ydata-profiling) (0.2.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/homebrew/lib/python3.11/site-packages (from jinja2<3.2,>=2.11.1->ydata-profiling) (2.1.3)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib<3.9,>=3.2->ydata-profiling) (1.1.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib<3.9,>=3.2->ydata-profiling) (0.12.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib<3.9,>=3.2->ydata-profiling) (4.43.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib<3.9,>=3.2->ydata-profiling) (1.4.5)\n",
+      "Requirement already satisfied: packaging>=20.0 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib<3.9,>=3.2->ydata-profiling) (23.2)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib<3.9,>=3.2->ydata-profiling) (3.1.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /opt/homebrew/lib/python3.11/site-packages (from matplotlib<3.9,>=3.2->ydata-profiling) (2.8.2)\n",
+      "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /opt/homebrew/lib/python3.11/site-packages (from numba<0.59.0,>=0.56.0->ydata-profiling) (0.41.1)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/lib/python3.11/site-packages (from pandas!=1.4.0,<3,>1.1->ydata-profiling) (2023.3.post1)\n",
+      "Requirement already satisfied: joblib>=0.14.1 in /opt/homebrew/lib/python3.11/site-packages (from phik<0.13,>=0.11.1->ydata-profiling) (1.3.2)\n",
+      "Requirement already satisfied: annotated-types>=0.4.0 in /opt/homebrew/lib/python3.11/site-packages (from pydantic>=2->ydata-profiling) (0.6.0)\n",
+      "Requirement already satisfied: pydantic-core==2.10.1 in /opt/homebrew/lib/python3.11/site-packages (from pydantic>=2->ydata-profiling) (2.10.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.1 in /opt/homebrew/lib/python3.11/site-packages (from pydantic>=2->ydata-profiling) (4.8.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/homebrew/lib/python3.11/site-packages (from requests<3,>=2.24.0->ydata-profiling) (3.3.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /opt/homebrew/lib/python3.11/site-packages (from requests<3,>=2.24.0->ydata-profiling) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/ethan.charlton-harrow/Library/Python/3.11/lib/python/site-packages (from requests<3,>=2.24.0->ydata-profiling) (1.26.18)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /opt/homebrew/lib/python3.11/site-packages (from requests<3,>=2.24.0->ydata-profiling) (2023.11.17)\n",
+      "Requirement already satisfied: patsy>=0.5.4 in /opt/homebrew/lib/python3.11/site-packages (from statsmodels<1,>=0.13.2->ydata-profiling) (0.5.6)\n",
+      "Requirement already satisfied: six in /opt/homebrew/lib/python3.11/site-packages (from patsy>=0.5.4->statsmodels<1,>=0.13.2->ydata-profiling) (1.16.0)\n",
+      "Downloading ydata_profiling-4.6.4-py2.py3-none-any.whl (357 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m357.8/357.8 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hDownloading dacite-1.8.1-py3-none-any.whl (14 kB)\n",
+      "Downloading typeguard-4.1.5-py3-none-any.whl (34 kB)\n",
+      "Downloading wordcloud-1.9.3-cp311-cp311-macosx_11_0_arm64.whl (168 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m168.3/168.3 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
+      "\u001b[?25h\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mInstalling collected packages: typeguard, dacite, wordcloud, visions, seaborn, ydata-profiling\n",
+      "  Attempting uninstall: visions\n",
+      "\u001b[33m    WARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33m    WARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33m    WARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m    Found existing installation: visions 0.7.4\n",
+      "    Uninstalling visions-0.7.4:\n",
+      "      Successfully uninstalled visions-0.7.4\n",
+      "  Attempting uninstall: seaborn\n",
+      "\u001b[33m    WARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33m    WARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33m    WARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m    Found existing installation: seaborn 0.13.0\n",
+      "    Uninstalling seaborn-0.13.0:\n",
+      "      Successfully uninstalled seaborn-0.13.0\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+      "pandas-profiling 3.2.0 requires joblib~=1.1.0, but you have joblib 1.3.2 which is incompatible.\n",
+      "pandas-profiling 3.2.0 requires visions[type_image_path]==0.7.4, but you have visions 0.7.5 which is incompatible.\u001b[0m\u001b[31m\n",
+      "\u001b[0mSuccessfully installed dacite-1.8.1 seaborn-0.12.2 typeguard-4.1.5 visions-0.7.5 wordcloud-1.9.3 ydata-profiling-4.6.4\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mCollecting shap\n",
+      "  Downloading shap-0.44.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (24 kB)\n",
+      "Requirement already satisfied: numpy in /opt/homebrew/lib/python3.11/site-packages (from shap) (1.25.2)\n",
+      "Requirement already satisfied: scipy in /opt/homebrew/lib/python3.11/site-packages (from shap) (1.10.1)\n",
+      "Requirement already satisfied: scikit-learn in /opt/homebrew/lib/python3.11/site-packages (from shap) (1.2.2)\n",
+      "Requirement already satisfied: pandas in /opt/homebrew/lib/python3.11/site-packages (from shap) (1.5.3)\n",
+      "Requirement already satisfied: tqdm>=4.27.0 in /opt/homebrew/lib/python3.11/site-packages (from shap) (4.66.1)\n",
+      "Requirement already satisfied: packaging>20.9 in /opt/homebrew/lib/python3.11/site-packages (from shap) (23.2)\n",
+      "Collecting slicer==0.0.7 (from shap)\n",
+      "  Downloading slicer-0.0.7-py3-none-any.whl (14 kB)\n",
+      "Requirement already satisfied: numba in /opt/homebrew/lib/python3.11/site-packages (from shap) (0.58.1)\n",
+      "Requirement already satisfied: cloudpickle in /opt/homebrew/lib/python3.11/site-packages (from shap) (3.0.0)\n",
+      "Requirement already satisfied: llvmlite<0.42,>=0.41.0dev0 in /opt/homebrew/lib/python3.11/site-packages (from numba->shap) (0.41.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/homebrew/lib/python3.11/site-packages (from pandas->shap) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /opt/homebrew/lib/python3.11/site-packages (from pandas->shap) (2023.3.post1)\n",
+      "Requirement already satisfied: joblib>=1.1.1 in /opt/homebrew/lib/python3.11/site-packages (from scikit-learn->shap) (1.3.2)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/homebrew/lib/python3.11/site-packages (from scikit-learn->shap) (3.2.0)\n",
+      "Requirement already satisfied: six>=1.5 in /opt/homebrew/lib/python3.11/site-packages (from python-dateutil>=2.8.1->pandas->shap) (1.16.0)\n",
+      "Downloading shap-0.44.0-cp311-cp311-macosx_11_0_arm64.whl (445 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m446.0/446.0 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
+      "\u001b[?25h\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mInstalling collected packages: slicer, shap\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/idna-3.6.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0mSuccessfully installed shap-0.44.0 slicer-0.0.7\n",
+      "\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/charset_normalizer-3.3.2.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\u001b[33mWARNING: Skipping /opt/homebrew/lib/python3.11/site-packages/urllib3-2.1.0.dist-info due to invalid metadata entry 'name'\u001b[0m\u001b[33m\n",
+      "\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n"
      ]
     }
    ],
@@ -224,31 +343,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "from sagemaker import get_execution_role\n",
     "from dotenv import load_dotenv\n",
     "from load_data import load_data\n",
-    "from split_data import split_data\n",
+    "from transfom_data import split_data, preprocess_df\n",
     "import importlib\n",
     "from save_model_to_s3 import save_model_to_s3\n",
     "from deploy_model_endpoint import deploy_model\n",
     "from finalize_and_save_model import finalize_and_save_model\n",
     "from delete_sagemaker_endpoint import delete_sagemaker_endpoint\n",
     "from ydata_profiling import ProfileReport\n",
-    "import shap\n"
+    "import shap\n",
+    "import pandas as pd"
    ]
   },
   {
@@ -263,16 +374,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n",
-      "streaming-data-platform-ml-data/ethan_data.csv classification y classification-proba-endpoint banking-classification s3://streaming-data-platform-ml-data/ethan_data.csv ml.m4.xlarge 135544376709.dkr.ecr.eu-west-1.amazonaws.com/mlops-classification-repo:latest AUC\n"
+      "None None None None None s3://None None None None\n"
      ]
     }
    ],
@@ -293,8 +402,9 @@
     "inference_instance_count = int(os.getenv(\"inference_instance_count\"))\n",
     "image_uri = os.getenv(\"ecr_repo_uri\")\n",
     "tuning_metric = os.getenv(\"tuning_metric\")\n",
+    "preprocessing_script_path = os.getenv(\"preprocessing_script_path\")\n",
     "\n",
-    "print(data_location_s3, algorithm_choice, target, endpoint_name, model_name, data_location, instance_type, image_uri, tuning_metric)\n"
+    "print(data_location_s3, algorithm_choice, target, endpoint_name, model_name, data_location, instance_type, image_uri, tuning_metric)"
    ]
   },
   {
@@ -306,21 +416,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Your installed version of s3fs is very old and known to cause\n",
-      "severe performance issues, see also https://github.com/dask/dask/issues/10276\n",
-      "\n",
-      "To fix, you should specify a lower version bound on s3fs, or\n",
-      "update the current installation.\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -342,86 +440,153 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
        "      <th>age</th>\n",
        "      <th>job</th>\n",
+       "      <th>marital</th>\n",
        "      <th>education</th>\n",
        "      <th>default</th>\n",
        "      <th>balance</th>\n",
        "      <th>housing</th>\n",
        "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
        "      <th>y</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>32</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-238</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>58</td>\n",
+       "      <td>management</td>\n",
+       "      <td>married</td>\n",
+       "      <td>tertiary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2143</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>261</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
        "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>34</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-478</td>\n",
        "      <td>1</td>\n",
+       "      <td>44</td>\n",
+       "      <td>technician</td>\n",
+       "      <td>single</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>29</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>151</td>\n",
        "      <td>1</td>\n",
+       "      <td>-1</td>\n",
        "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>32</td>\n",
-       "      <td>3</td>\n",
        "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>266</td>\n",
+       "      <td>33</td>\n",
+       "      <td>entrepreneur</td>\n",
+       "      <td>married</td>\n",
+       "      <td>secondary</td>\n",
+       "      <td>no</td>\n",
+       "      <td>2</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>76</td>\n",
        "      <td>1</td>\n",
+       "      <td>-1</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>36</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>13</td>\n",
-       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>47</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1506</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>92</td>\n",
        "      <td>1</td>\n",
+       "      <td>-1</td>\n",
        "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>23</td>\n",
-       "      <td>11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>486</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>33</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>single</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
+       "      <td>1</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>5</td>\n",
+       "      <td>may</td>\n",
+       "      <td>198</td>\n",
+       "      <td>1</td>\n",
+       "      <td>-1</td>\n",
        "      <td>0</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>no</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   age  job  education  default  balance  housing  loan  y\n",
-       "0   32    7          2        1     -238        1     0  0\n",
-       "1   34    4          2        0     -478        1     1  0\n",
-       "2   32    3          2        0      266        1     0  0\n",
-       "3   36    7          2        1       13        0     1  0\n",
-       "4   23   11          2        0      486        0     0  0"
+       "   Unnamed: 0  age           job  marital  education default  balance housing  \\\n",
+       "0           0   58    management  married   tertiary      no     2143     yes   \n",
+       "1           1   44    technician   single  secondary      no       29     yes   \n",
+       "2           2   33  entrepreneur  married  secondary      no        2     yes   \n",
+       "3           3   47   blue-collar  married    unknown      no     1506     yes   \n",
+       "4           4   33       unknown   single    unknown      no        1      no   \n",
+       "\n",
+       "  loan  contact  day month  duration  campaign  pdays  previous poutcome   y  \n",
+       "0   no  unknown    5   may       261         1     -1         0  unknown  no  \n",
+       "1   no  unknown    5   may       151         1     -1         0  unknown  no  \n",
+       "2  yes  unknown    5   may        76         1     -1         0  unknown  no  \n",
+       "3   no  unknown    5   may        92         1     -1         0  unknown  no  \n",
+       "4   no  unknown    5   may       198         1     -1         0  unknown  no  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -441,64 +606,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd0eeb9e5943413eb2a07921ad25a2c2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c5099049aa3e42f8aa5f839834430a95",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Render widgets:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b6106a9884b64e1b8763b7d3058015d7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "ename": "NameError",
+     "evalue": "name 'ProfileReport' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m profile \u001b[38;5;241m=\u001b[39m \u001b[43mProfileReport\u001b[49m(\n\u001b[1;32m      2\u001b[0m     df,\n\u001b[1;32m      3\u001b[0m     sort\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m      4\u001b[0m     html\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstyle\u001b[39m\u001b[38;5;124m\"\u001b[39m: {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfull_width\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m}},\n\u001b[1;32m      5\u001b[0m     title\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mData Exploration\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      6\u001b[0m     explorative\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m      7\u001b[0m )\n\u001b[1;32m      8\u001b[0m profile\u001b[38;5;241m.\u001b[39mto_widgets()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'ProfileReport' is not defined"
+     ]
     }
    ],
    "source": [
@@ -512,68 +632,48 @@
     "profile.to_widgets()\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h3>6. Data Cleaning and Feature Engineering Placeholder</h3> "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       age  job  education  default  balance  housing  loan  y\n",
-      "0       46    7          1        0     1666        1     0  0\n",
-      "1       30    7          2        0     3532        1     0  0\n",
-      "2       37    2          3        0     2905        1     0  1\n",
-      "3       37    3          2        0     -797        1     0  1\n",
-      "4       92    8          4        0      775        0     0  1\n",
-      "...    ...  ...        ...      ...      ...      ...   ... ..\n",
-      "44649   37    3          2        0      588        1     0  0\n",
-      "44650   41    4          2        0      239        1     0  0\n",
-      "44651   65    8          1        0      543        0     0  1\n",
-      "44652   50    2          2        0     1716        1     0  0\n",
-      "44653   40    2          3        0        0        0     0  1\n",
-      "\n",
-      "[44654 rows x 8 columns]        age  job  education  default  balance  housing  loan  y\n",
-      "44654   35    7          4        0     2298        0     0  0\n",
-      "44655   31    5          2        0      132        0     0  0\n",
-      "44656   50    4          2        0     1375        0     0  1\n",
-      "44657   30    1          3        0      734        1     0  0\n",
-      "44658   36    4          2        0     1305        1     0  1\n",
-      "...    ...  ...        ...      ...      ...      ...   ... ..\n",
-      "55813   42    2          3        0     -380        1     0  0\n",
-      "55814   18   11          1        0      608        0     0  1\n",
-      "55815   40    7          1        0      105        1     0  0\n",
-      "55816   31    2          2        0     4150        1     0  1\n",
-      "55817   35    2          2        0      910        0     0  0\n",
-      "\n",
-      "[11164 rows x 8 columns]\n"
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\KonradBachusz(Creder\\OneDrive - OneWorkplace\\Documents\\Projects\\internal\\terraform-aws-mlops-module\\mlops_ml_models\\models_template_notebook.ipynb Cell 12\u001b[0m line \u001b[0;36m1\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/KonradBachusz%28Creder/OneDrive%20-%20OneWorkplace/Documents/Projects/internal/terraform-aws-mlops-module/mlops_ml_models/models_template_notebook.ipynb#X14sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m             \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mFile does not exist\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/KonradBachusz%28Creder/OneDrive%20-%20OneWorkplace/Documents/Projects/internal/terraform-aws-mlops-module/mlops_ml_models/models_template_notebook.ipynb#X14sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m df\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/KonradBachusz%28Creder/OneDrive%20-%20OneWorkplace/Documents/Projects/internal/terraform-aws-mlops-module/mlops_ml_models/models_template_notebook.ipynb#X14sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m df\u001b[39m.\u001b[39mhead()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
      ]
     }
    ],
    "source": [
-    "# Split and shuffle data\n",
-    "train_data, test_data = split_data(df, shuffle=True)\n",
-    "print(train_data, test_data)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>6. Data Cleaning and Feature Engineering Placeholder</h3> \n"
+    "df=preprocess_df(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "# Split and shuffle data\n",
+    "train_data, test_data = split_data(df, shuffle=True)\n",
+    "print(train_data, test_data)"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -583,142 +683,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_fe6fe_row8_col1 {\n",
-       "  background-color: lightgreen;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_fe6fe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_fe6fe_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
-       "      <th id=\"T_fe6fe_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_fe6fe_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
-       "      <td id=\"T_fe6fe_row0_col1\" class=\"data row0 col1\" >123</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_fe6fe_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
-       "      <td id=\"T_fe6fe_row1_col1\" class=\"data row1 col1\" >y</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_fe6fe_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
-       "      <td id=\"T_fe6fe_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_fe6fe_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
-       "      <td id=\"T_fe6fe_row3_col1\" class=\"data row3 col1\" >(44654, 8)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_fe6fe_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
-       "      <td id=\"T_fe6fe_row4_col1\" class=\"data row4 col1\" >(44654, 8)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_fe6fe_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
-       "      <td id=\"T_fe6fe_row5_col1\" class=\"data row5 col1\" >(31257, 8)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_fe6fe_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
-       "      <td id=\"T_fe6fe_row6_col1\" class=\"data row6 col1\" >(13397, 8)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_fe6fe_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
-       "      <td id=\"T_fe6fe_row7_col1\" class=\"data row7 col1\" >7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_fe6fe_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
-       "      <td id=\"T_fe6fe_row8_col1\" class=\"data row8 col1\" >True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_fe6fe_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
-       "      <td id=\"T_fe6fe_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
-       "      <td id=\"T_fe6fe_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
-       "      <td id=\"T_fe6fe_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
-       "      <td id=\"T_fe6fe_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
-       "      <td id=\"T_fe6fe_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
-       "      <td id=\"T_fe6fe_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
-       "      <td id=\"T_fe6fe_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
-       "      <td id=\"T_fe6fe_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
-       "      <td id=\"T_fe6fe_row13_col1\" class=\"data row13 col1\" >10</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
-       "      <td id=\"T_fe6fe_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
-       "      <td id=\"T_fe6fe_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
-       "      <td id=\"T_fe6fe_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
-       "      <td id=\"T_fe6fe_row15_col1\" class=\"data row15 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
-       "      <td id=\"T_fe6fe_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
-       "      <td id=\"T_fe6fe_row16_col1\" class=\"data row16 col1\" >False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
-       "      <td id=\"T_fe6fe_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
-       "      <td id=\"T_fe6fe_row17_col1\" class=\"data row17 col1\" >clf-default-name</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_fe6fe_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
-       "      <td id=\"T_fe6fe_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
-       "      <td id=\"T_fe6fe_row18_col1\" class=\"data row18 col1\" >5013</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f00ccfd34c0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<pycaret.classification.oop.ClassificationExperiment at 0x7f00ccb1ccd0>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize data in PyCaret with all the defined parameters\n",
     "pycaret.setup(data=train_data, target=target, session_id=123)\n"
@@ -736,248 +703,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_720a4 th {\n",
-       "  text-align: left;\n",
-       "}\n",
-       "#T_720a4_row0_col0, #T_720a4_row0_col3, #T_720a4_row1_col0, #T_720a4_row1_col1, #T_720a4_row1_col2, #T_720a4_row1_col3, #T_720a4_row1_col4, #T_720a4_row1_col5, #T_720a4_row1_col6, #T_720a4_row1_col7, #T_720a4_row2_col0, #T_720a4_row2_col1, #T_720a4_row2_col2, #T_720a4_row2_col3, #T_720a4_row2_col4, #T_720a4_row2_col5, #T_720a4_row2_col6, #T_720a4_row2_col7, #T_720a4_row3_col0, #T_720a4_row3_col1, #T_720a4_row3_col2, #T_720a4_row3_col3, #T_720a4_row3_col4, #T_720a4_row3_col5, #T_720a4_row3_col6, #T_720a4_row3_col7, #T_720a4_row4_col0, #T_720a4_row4_col1, #T_720a4_row4_col2, #T_720a4_row4_col3, #T_720a4_row4_col4, #T_720a4_row4_col5, #T_720a4_row4_col6, #T_720a4_row4_col7, #T_720a4_row5_col0, #T_720a4_row5_col1, #T_720a4_row5_col2, #T_720a4_row5_col3, #T_720a4_row5_col4, #T_720a4_row5_col5, #T_720a4_row5_col6, #T_720a4_row5_col7, #T_720a4_row6_col0, #T_720a4_row6_col1, #T_720a4_row6_col2, #T_720a4_row6_col3, #T_720a4_row6_col4, #T_720a4_row6_col5, #T_720a4_row6_col6, #T_720a4_row6_col7, #T_720a4_row7_col0, #T_720a4_row7_col1, #T_720a4_row7_col2, #T_720a4_row7_col3, #T_720a4_row7_col4, #T_720a4_row7_col5, #T_720a4_row7_col6, #T_720a4_row7_col7, #T_720a4_row8_col0, #T_720a4_row8_col1, #T_720a4_row8_col2, #T_720a4_row8_col3, #T_720a4_row8_col4, #T_720a4_row8_col5, #T_720a4_row8_col6, #T_720a4_row8_col7, #T_720a4_row9_col0, #T_720a4_row9_col1, #T_720a4_row9_col2, #T_720a4_row9_col3, #T_720a4_row9_col4, #T_720a4_row9_col5, #T_720a4_row9_col6, #T_720a4_row9_col7, #T_720a4_row10_col0, #T_720a4_row10_col1, #T_720a4_row10_col2, #T_720a4_row10_col3, #T_720a4_row10_col4, #T_720a4_row10_col5, #T_720a4_row10_col6, #T_720a4_row10_col7, #T_720a4_row11_col0, #T_720a4_row11_col1, #T_720a4_row11_col2, #T_720a4_row11_col4, #T_720a4_row11_col5, #T_720a4_row11_col6, #T_720a4_row11_col7, #T_720a4_row12_col0, #T_720a4_row12_col1, #T_720a4_row12_col2, #T_720a4_row12_col3, #T_720a4_row12_col4, #T_720a4_row12_col5, #T_720a4_row12_col6, #T_720a4_row12_col7, #T_720a4_row13_col0, #T_720a4_row13_col1, #T_720a4_row13_col2, #T_720a4_row13_col3, #T_720a4_row13_col4, #T_720a4_row13_col5, #T_720a4_row13_col6, #T_720a4_row13_col7 {\n",
-       "  text-align: left;\n",
-       "}\n",
-       "#T_720a4_row0_col1, #T_720a4_row0_col2, #T_720a4_row0_col4, #T_720a4_row0_col5, #T_720a4_row0_col6, #T_720a4_row0_col7, #T_720a4_row11_col3 {\n",
-       "  text-align: left;\n",
-       "  background-color: yellow;\n",
-       "}\n",
-       "#T_720a4_row0_col8, #T_720a4_row1_col8, #T_720a4_row2_col8, #T_720a4_row3_col8, #T_720a4_row4_col8, #T_720a4_row5_col8, #T_720a4_row6_col8, #T_720a4_row7_col8, #T_720a4_row8_col8, #T_720a4_row9_col8, #T_720a4_row10_col8, #T_720a4_row11_col8, #T_720a4_row12_col8 {\n",
-       "  text-align: left;\n",
-       "  background-color: lightgrey;\n",
-       "}\n",
-       "#T_720a4_row13_col8 {\n",
-       "  text-align: left;\n",
-       "  background-color: yellow;\n",
-       "  background-color: lightgrey;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_720a4\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_720a4_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
-       "      <th id=\"T_720a4_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
-       "      <th id=\"T_720a4_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
-       "      <th id=\"T_720a4_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
-       "      <th id=\"T_720a4_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
-       "      <th id=\"T_720a4_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
-       "      <th id=\"T_720a4_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
-       "      <th id=\"T_720a4_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
-       "      <th id=\"T_720a4_level0_col8\" class=\"col_heading level0 col8\" >TT (Sec)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row0\" class=\"row_heading level0 row0\" >rf</th>\n",
-       "      <td id=\"T_720a4_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
-       "      <td id=\"T_720a4_row0_col1\" class=\"data row0 col1\" >0.7979</td>\n",
-       "      <td id=\"T_720a4_row0_col2\" class=\"data row0 col2\" >0.8768</td>\n",
-       "      <td id=\"T_720a4_row0_col3\" class=\"data row0 col3\" >0.8246</td>\n",
-       "      <td id=\"T_720a4_row0_col4\" class=\"data row0 col4\" >0.7828</td>\n",
-       "      <td id=\"T_720a4_row0_col5\" class=\"data row0 col5\" >0.8031</td>\n",
-       "      <td id=\"T_720a4_row0_col6\" class=\"data row0 col6\" >0.5958</td>\n",
-       "      <td id=\"T_720a4_row0_col7\" class=\"data row0 col7\" >0.5968</td>\n",
-       "      <td id=\"T_720a4_row0_col8\" class=\"data row0 col8\" >1.4370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row1\" class=\"row_heading level0 row1\" >dt</th>\n",
-       "      <td id=\"T_720a4_row1_col0\" class=\"data row1 col0\" >Decision Tree Classifier</td>\n",
-       "      <td id=\"T_720a4_row1_col1\" class=\"data row1 col1\" >0.7855</td>\n",
-       "      <td id=\"T_720a4_row1_col2\" class=\"data row1 col2\" >0.7904</td>\n",
-       "      <td id=\"T_720a4_row1_col3\" class=\"data row1 col3\" >0.8169</td>\n",
-       "      <td id=\"T_720a4_row1_col4\" class=\"data row1 col4\" >0.7685</td>\n",
-       "      <td id=\"T_720a4_row1_col5\" class=\"data row1 col5\" >0.7919</td>\n",
-       "      <td id=\"T_720a4_row1_col6\" class=\"data row1 col6\" >0.5709</td>\n",
-       "      <td id=\"T_720a4_row1_col7\" class=\"data row1 col7\" >0.5721</td>\n",
-       "      <td id=\"T_720a4_row1_col8\" class=\"data row1 col8\" >0.0650</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row2\" class=\"row_heading level0 row2\" >et</th>\n",
-       "      <td id=\"T_720a4_row2_col0\" class=\"data row2 col0\" >Extra Trees Classifier</td>\n",
-       "      <td id=\"T_720a4_row2_col1\" class=\"data row2 col1\" >0.7729</td>\n",
-       "      <td id=\"T_720a4_row2_col2\" class=\"data row2 col2\" >0.8611</td>\n",
-       "      <td id=\"T_720a4_row2_col3\" class=\"data row2 col3\" >0.7977</td>\n",
-       "      <td id=\"T_720a4_row2_col4\" class=\"data row2 col4\" >0.7600</td>\n",
-       "      <td id=\"T_720a4_row2_col5\" class=\"data row2 col5\" >0.7783</td>\n",
-       "      <td id=\"T_720a4_row2_col6\" class=\"data row2 col6\" >0.5458</td>\n",
-       "      <td id=\"T_720a4_row2_col7\" class=\"data row2 col7\" >0.5465</td>\n",
-       "      <td id=\"T_720a4_row2_col8\" class=\"data row2 col8\" >1.1060</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row3\" class=\"row_heading level0 row3\" >lightgbm</th>\n",
-       "      <td id=\"T_720a4_row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
-       "      <td id=\"T_720a4_row3_col1\" class=\"data row3 col1\" >0.7632</td>\n",
-       "      <td id=\"T_720a4_row3_col2\" class=\"data row3 col2\" >0.8401</td>\n",
-       "      <td id=\"T_720a4_row3_col3\" class=\"data row3 col3\" >0.7791</td>\n",
-       "      <td id=\"T_720a4_row3_col4\" class=\"data row3 col4\" >0.7551</td>\n",
-       "      <td id=\"T_720a4_row3_col5\" class=\"data row3 col5\" >0.7668</td>\n",
-       "      <td id=\"T_720a4_row3_col6\" class=\"data row3 col6\" >0.5263</td>\n",
-       "      <td id=\"T_720a4_row3_col7\" class=\"data row3 col7\" >0.5267</td>\n",
-       "      <td id=\"T_720a4_row3_col8\" class=\"data row3 col8\" >0.7680</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row4\" class=\"row_heading level0 row4\" >knn</th>\n",
-       "      <td id=\"T_720a4_row4_col0\" class=\"data row4 col0\" >K Neighbors Classifier</td>\n",
-       "      <td id=\"T_720a4_row4_col1\" class=\"data row4 col1\" >0.7564</td>\n",
-       "      <td id=\"T_720a4_row4_col2\" class=\"data row4 col2\" >0.8402</td>\n",
-       "      <td id=\"T_720a4_row4_col3\" class=\"data row4 col3\" >0.8639</td>\n",
-       "      <td id=\"T_720a4_row4_col4\" class=\"data row4 col4\" >0.7110</td>\n",
-       "      <td id=\"T_720a4_row4_col5\" class=\"data row4 col5\" >0.7800</td>\n",
-       "      <td id=\"T_720a4_row4_col6\" class=\"data row4 col6\" >0.5129</td>\n",
-       "      <td id=\"T_720a4_row4_col7\" class=\"data row4 col7\" >0.5253</td>\n",
-       "      <td id=\"T_720a4_row4_col8\" class=\"data row4 col8\" >0.1020</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row5\" class=\"row_heading level0 row5\" >gbc</th>\n",
-       "      <td id=\"T_720a4_row5_col0\" class=\"data row5 col0\" >Gradient Boosting Classifier</td>\n",
-       "      <td id=\"T_720a4_row5_col1\" class=\"data row5 col1\" >0.7392</td>\n",
-       "      <td id=\"T_720a4_row5_col2\" class=\"data row5 col2\" >0.8125</td>\n",
-       "      <td id=\"T_720a4_row5_col3\" class=\"data row5 col3\" >0.7601</td>\n",
-       "      <td id=\"T_720a4_row5_col4\" class=\"data row5 col4\" >0.7297</td>\n",
-       "      <td id=\"T_720a4_row5_col5\" class=\"data row5 col5\" >0.7445</td>\n",
-       "      <td id=\"T_720a4_row5_col6\" class=\"data row5 col6\" >0.4785</td>\n",
-       "      <td id=\"T_720a4_row5_col7\" class=\"data row5 col7\" >0.4790</td>\n",
-       "      <td id=\"T_720a4_row5_col8\" class=\"data row5 col8\" >1.0040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row6\" class=\"row_heading level0 row6\" >ada</th>\n",
-       "      <td id=\"T_720a4_row6_col0\" class=\"data row6 col0\" >Ada Boost Classifier</td>\n",
-       "      <td id=\"T_720a4_row6_col1\" class=\"data row6 col1\" >0.7252</td>\n",
-       "      <td id=\"T_720a4_row6_col2\" class=\"data row6 col2\" >0.7954</td>\n",
-       "      <td id=\"T_720a4_row6_col3\" class=\"data row6 col3\" >0.7574</td>\n",
-       "      <td id=\"T_720a4_row6_col4\" class=\"data row6 col4\" >0.7116</td>\n",
-       "      <td id=\"T_720a4_row6_col5\" class=\"data row6 col5\" >0.7338</td>\n",
-       "      <td id=\"T_720a4_row6_col6\" class=\"data row6 col6\" >0.4504</td>\n",
-       "      <td id=\"T_720a4_row6_col7\" class=\"data row6 col7\" >0.4514</td>\n",
-       "      <td id=\"T_720a4_row6_col8\" class=\"data row6 col8\" >0.3740</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row7\" class=\"row_heading level0 row7\" >ridge</th>\n",
-       "      <td id=\"T_720a4_row7_col0\" class=\"data row7 col0\" >Ridge Classifier</td>\n",
-       "      <td id=\"T_720a4_row7_col1\" class=\"data row7 col1\" >0.7174</td>\n",
-       "      <td id=\"T_720a4_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
-       "      <td id=\"T_720a4_row7_col3\" class=\"data row7 col3\" >0.7720</td>\n",
-       "      <td id=\"T_720a4_row7_col4\" class=\"data row7 col4\" >0.6961</td>\n",
-       "      <td id=\"T_720a4_row7_col5\" class=\"data row7 col5\" >0.7320</td>\n",
-       "      <td id=\"T_720a4_row7_col6\" class=\"data row7 col6\" >0.4349</td>\n",
-       "      <td id=\"T_720a4_row7_col7\" class=\"data row7 col7\" >0.4376</td>\n",
-       "      <td id=\"T_720a4_row7_col8\" class=\"data row7 col8\" >0.0250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row8\" class=\"row_heading level0 row8\" >lda</th>\n",
-       "      <td id=\"T_720a4_row8_col0\" class=\"data row8 col0\" >Linear Discriminant Analysis</td>\n",
-       "      <td id=\"T_720a4_row8_col1\" class=\"data row8 col1\" >0.7174</td>\n",
-       "      <td id=\"T_720a4_row8_col2\" class=\"data row8 col2\" >0.7577</td>\n",
-       "      <td id=\"T_720a4_row8_col3\" class=\"data row8 col3\" >0.7720</td>\n",
-       "      <td id=\"T_720a4_row8_col4\" class=\"data row8 col4\" >0.6961</td>\n",
-       "      <td id=\"T_720a4_row8_col5\" class=\"data row8 col5\" >0.7320</td>\n",
-       "      <td id=\"T_720a4_row8_col6\" class=\"data row8 col6\" >0.4349</td>\n",
-       "      <td id=\"T_720a4_row8_col7\" class=\"data row8 col7\" >0.4376</td>\n",
-       "      <td id=\"T_720a4_row8_col8\" class=\"data row8 col8\" >0.0390</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row9\" class=\"row_heading level0 row9\" >lr</th>\n",
-       "      <td id=\"T_720a4_row9_col0\" class=\"data row9 col0\" >Logistic Regression</td>\n",
-       "      <td id=\"T_720a4_row9_col1\" class=\"data row9 col1\" >0.7134</td>\n",
-       "      <td id=\"T_720a4_row9_col2\" class=\"data row9 col2\" >0.7486</td>\n",
-       "      <td id=\"T_720a4_row9_col3\" class=\"data row9 col3\" >0.7750</td>\n",
-       "      <td id=\"T_720a4_row9_col4\" class=\"data row9 col4\" >0.6900</td>\n",
-       "      <td id=\"T_720a4_row9_col5\" class=\"data row9 col5\" >0.7300</td>\n",
-       "      <td id=\"T_720a4_row9_col6\" class=\"data row9 col6\" >0.4268</td>\n",
-       "      <td id=\"T_720a4_row9_col7\" class=\"data row9 col7\" >0.4302</td>\n",
-       "      <td id=\"T_720a4_row9_col8\" class=\"data row9 col8\" >0.8080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row10\" class=\"row_heading level0 row10\" >nb</th>\n",
-       "      <td id=\"T_720a4_row10_col0\" class=\"data row10 col0\" >Naive Bayes</td>\n",
-       "      <td id=\"T_720a4_row10_col1\" class=\"data row10 col1\" >0.6981</td>\n",
-       "      <td id=\"T_720a4_row10_col2\" class=\"data row10 col2\" >0.7604</td>\n",
-       "      <td id=\"T_720a4_row10_col3\" class=\"data row10 col3\" >0.8049</td>\n",
-       "      <td id=\"T_720a4_row10_col4\" class=\"data row10 col4\" >0.6633</td>\n",
-       "      <td id=\"T_720a4_row10_col5\" class=\"data row10 col5\" >0.7272</td>\n",
-       "      <td id=\"T_720a4_row10_col6\" class=\"data row10 col6\" >0.3963</td>\n",
-       "      <td id=\"T_720a4_row10_col7\" class=\"data row10 col7\" >0.4057</td>\n",
-       "      <td id=\"T_720a4_row10_col8\" class=\"data row10 col8\" >0.0250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row11\" class=\"row_heading level0 row11\" >qda</th>\n",
-       "      <td id=\"T_720a4_row11_col0\" class=\"data row11 col0\" >Quadratic Discriminant Analysis</td>\n",
-       "      <td id=\"T_720a4_row11_col1\" class=\"data row11 col1\" >0.5874</td>\n",
-       "      <td id=\"T_720a4_row11_col2\" class=\"data row11 col2\" >0.7706</td>\n",
-       "      <td id=\"T_720a4_row11_col3\" class=\"data row11 col3\" >0.9672</td>\n",
-       "      <td id=\"T_720a4_row11_col4\" class=\"data row11 col4\" >0.5496</td>\n",
-       "      <td id=\"T_720a4_row11_col5\" class=\"data row11 col5\" >0.7009</td>\n",
-       "      <td id=\"T_720a4_row11_col6\" class=\"data row11 col6\" >0.1749</td>\n",
-       "      <td id=\"T_720a4_row11_col7\" class=\"data row11 col7\" >0.2687</td>\n",
-       "      <td id=\"T_720a4_row11_col8\" class=\"data row11 col8\" >0.0260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row12\" class=\"row_heading level0 row12\" >svm</th>\n",
-       "      <td id=\"T_720a4_row12_col0\" class=\"data row12 col0\" >SVM - Linear Kernel</td>\n",
-       "      <td id=\"T_720a4_row12_col1\" class=\"data row12 col1\" >0.5373</td>\n",
-       "      <td id=\"T_720a4_row12_col2\" class=\"data row12 col2\" >0.0000</td>\n",
-       "      <td id=\"T_720a4_row12_col3\" class=\"data row12 col3\" >0.5561</td>\n",
-       "      <td id=\"T_720a4_row12_col4\" class=\"data row12 col4\" >0.4982</td>\n",
-       "      <td id=\"T_720a4_row12_col5\" class=\"data row12 col5\" >0.4522</td>\n",
-       "      <td id=\"T_720a4_row12_col6\" class=\"data row12 col6\" >0.0746</td>\n",
-       "      <td id=\"T_720a4_row12_col7\" class=\"data row12 col7\" >0.0887</td>\n",
-       "      <td id=\"T_720a4_row12_col8\" class=\"data row12 col8\" >0.0920</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_720a4_level0_row13\" class=\"row_heading level0 row13\" >dummy</th>\n",
-       "      <td id=\"T_720a4_row13_col0\" class=\"data row13 col0\" >Dummy Classifier</td>\n",
-       "      <td id=\"T_720a4_row13_col1\" class=\"data row13 col1\" >0.5001</td>\n",
-       "      <td id=\"T_720a4_row13_col2\" class=\"data row13 col2\" >0.5000</td>\n",
-       "      <td id=\"T_720a4_row13_col3\" class=\"data row13 col3\" >0.0000</td>\n",
-       "      <td id=\"T_720a4_row13_col4\" class=\"data row13 col4\" >0.0000</td>\n",
-       "      <td id=\"T_720a4_row13_col5\" class=\"data row13 col5\" >0.0000</td>\n",
-       "      <td id=\"T_720a4_row13_col6\" class=\"data row13 col6\" >0.0000</td>\n",
-       "      <td id=\"T_720a4_row13_col7\" class=\"data row13 col7\" >0.0000</td>\n",
-       "      <td id=\"T_720a4_row13_col8\" class=\"data row13 col8\" >0.0200</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f00ccb1cc70>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Train and evaluate the performance of all estimators available in the model library using cross-validation.\n",
     "bestModel = pycaret.compare_models()\n"
@@ -1003,223 +731,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "#T_774dc_row10_col0, #T_774dc_row10_col1, #T_774dc_row10_col2, #T_774dc_row10_col3, #T_774dc_row10_col4, #T_774dc_row10_col5, #T_774dc_row10_col6 {\n",
-       "  background: yellow;\n",
-       "}\n",
-       "</style>\n",
-       "<table id=\"T_774dc\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_774dc_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
-       "      <th id=\"T_774dc_level0_col1\" class=\"col_heading level0 col1\" >AUC</th>\n",
-       "      <th id=\"T_774dc_level0_col2\" class=\"col_heading level0 col2\" >Recall</th>\n",
-       "      <th id=\"T_774dc_level0_col3\" class=\"col_heading level0 col3\" >Prec.</th>\n",
-       "      <th id=\"T_774dc_level0_col4\" class=\"col_heading level0 col4\" >F1</th>\n",
-       "      <th id=\"T_774dc_level0_col5\" class=\"col_heading level0 col5\" >Kappa</th>\n",
-       "      <th id=\"T_774dc_level0_col6\" class=\"col_heading level0 col6\" >MCC</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th class=\"index_name level0\" >Fold</th>\n",
-       "      <th class=\"blank col0\" >&nbsp;</th>\n",
-       "      <th class=\"blank col1\" >&nbsp;</th>\n",
-       "      <th class=\"blank col2\" >&nbsp;</th>\n",
-       "      <th class=\"blank col3\" >&nbsp;</th>\n",
-       "      <th class=\"blank col4\" >&nbsp;</th>\n",
-       "      <th class=\"blank col5\" >&nbsp;</th>\n",
-       "      <th class=\"blank col6\" >&nbsp;</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_774dc_row0_col0\" class=\"data row0 col0\" >0.7306</td>\n",
-       "      <td id=\"T_774dc_row0_col1\" class=\"data row0 col1\" >0.8003</td>\n",
-       "      <td id=\"T_774dc_row0_col2\" class=\"data row0 col2\" >0.7663</td>\n",
-       "      <td id=\"T_774dc_row0_col3\" class=\"data row0 col3\" >0.7151</td>\n",
-       "      <td id=\"T_774dc_row0_col4\" class=\"data row0 col4\" >0.7398</td>\n",
-       "      <td id=\"T_774dc_row0_col5\" class=\"data row0 col5\" >0.4613</td>\n",
-       "      <td id=\"T_774dc_row0_col6\" class=\"data row0 col6\" >0.4625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
-       "      <td id=\"T_774dc_row1_col0\" class=\"data row1 col0\" >0.7191</td>\n",
-       "      <td id=\"T_774dc_row1_col1\" class=\"data row1 col1\" >0.7866</td>\n",
-       "      <td id=\"T_774dc_row1_col2\" class=\"data row1 col2\" >0.7618</td>\n",
-       "      <td id=\"T_774dc_row1_col3\" class=\"data row1 col3\" >0.7017</td>\n",
-       "      <td id=\"T_774dc_row1_col4\" class=\"data row1 col4\" >0.7305</td>\n",
-       "      <td id=\"T_774dc_row1_col5\" class=\"data row1 col5\" >0.4383</td>\n",
-       "      <td id=\"T_774dc_row1_col6\" class=\"data row1 col6\" >0.4399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
-       "      <td id=\"T_774dc_row2_col0\" class=\"data row2 col0\" >0.7271</td>\n",
-       "      <td id=\"T_774dc_row2_col1\" class=\"data row2 col1\" >0.8030</td>\n",
-       "      <td id=\"T_774dc_row2_col2\" class=\"data row2 col2\" >0.7806</td>\n",
-       "      <td id=\"T_774dc_row2_col3\" class=\"data row2 col3\" >0.7052</td>\n",
-       "      <td id=\"T_774dc_row2_col4\" class=\"data row2 col4\" >0.7410</td>\n",
-       "      <td id=\"T_774dc_row2_col5\" class=\"data row2 col5\" >0.4543</td>\n",
-       "      <td id=\"T_774dc_row2_col6\" class=\"data row2 col6\" >0.4569</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
-       "      <td id=\"T_774dc_row3_col0\" class=\"data row3 col0\" >0.7169</td>\n",
-       "      <td id=\"T_774dc_row3_col1\" class=\"data row3 col1\" >0.7887</td>\n",
-       "      <td id=\"T_774dc_row3_col2\" class=\"data row3 col2\" >0.7626</td>\n",
-       "      <td id=\"T_774dc_row3_col3\" class=\"data row3 col3\" >0.6987</td>\n",
-       "      <td id=\"T_774dc_row3_col4\" class=\"data row3 col4\" >0.7293</td>\n",
-       "      <td id=\"T_774dc_row3_col5\" class=\"data row3 col5\" >0.4338</td>\n",
-       "      <td id=\"T_774dc_row3_col6\" class=\"data row3 col6\" >0.4356</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
-       "      <td id=\"T_774dc_row4_col0\" class=\"data row4 col0\" >0.7278</td>\n",
-       "      <td id=\"T_774dc_row4_col1\" class=\"data row4 col1\" >0.8010</td>\n",
-       "      <td id=\"T_774dc_row4_col2\" class=\"data row4 col2\" >0.7767</td>\n",
-       "      <td id=\"T_774dc_row4_col3\" class=\"data row4 col3\" >0.7075</td>\n",
-       "      <td id=\"T_774dc_row4_col4\" class=\"data row4 col4\" >0.7405</td>\n",
-       "      <td id=\"T_774dc_row4_col5\" class=\"data row4 col5\" >0.4555</td>\n",
-       "      <td id=\"T_774dc_row4_col6\" class=\"data row4 col6\" >0.4577</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
-       "      <td id=\"T_774dc_row5_col0\" class=\"data row5 col0\" >0.7284</td>\n",
-       "      <td id=\"T_774dc_row5_col1\" class=\"data row5 col1\" >0.8076</td>\n",
-       "      <td id=\"T_774dc_row5_col2\" class=\"data row5 col2\" >0.7857</td>\n",
-       "      <td id=\"T_774dc_row5_col3\" class=\"data row5 col3\" >0.7049</td>\n",
-       "      <td id=\"T_774dc_row5_col4\" class=\"data row5 col4\" >0.7431</td>\n",
-       "      <td id=\"T_774dc_row5_col5\" class=\"data row5 col5\" >0.4568</td>\n",
-       "      <td id=\"T_774dc_row5_col6\" class=\"data row5 col6\" >0.4598</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
-       "      <td id=\"T_774dc_row6_col0\" class=\"data row6 col0\" >0.7194</td>\n",
-       "      <td id=\"T_774dc_row6_col1\" class=\"data row6 col1\" >0.7914</td>\n",
-       "      <td id=\"T_774dc_row6_col2\" class=\"data row6 col2\" >0.7774</td>\n",
-       "      <td id=\"T_774dc_row6_col3\" class=\"data row6 col3\" >0.6967</td>\n",
-       "      <td id=\"T_774dc_row6_col4\" class=\"data row6 col4\" >0.7348</td>\n",
-       "      <td id=\"T_774dc_row6_col5\" class=\"data row6 col5\" >0.4389</td>\n",
-       "      <td id=\"T_774dc_row6_col6\" class=\"data row6 col6\" >0.4419</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
-       "      <td id=\"T_774dc_row7_col0\" class=\"data row7 col0\" >0.7114</td>\n",
-       "      <td id=\"T_774dc_row7_col1\" class=\"data row7 col1\" >0.7819</td>\n",
-       "      <td id=\"T_774dc_row7_col2\" class=\"data row7 col2\" >0.7798</td>\n",
-       "      <td id=\"T_774dc_row7_col3\" class=\"data row7 col3\" >0.6858</td>\n",
-       "      <td id=\"T_774dc_row7_col4\" class=\"data row7 col4\" >0.7298</td>\n",
-       "      <td id=\"T_774dc_row7_col5\" class=\"data row7 col5\" >0.4227</td>\n",
-       "      <td id=\"T_774dc_row7_col6\" class=\"data row7 col6\" >0.4268</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
-       "      <td id=\"T_774dc_row8_col0\" class=\"data row8 col0\" >0.7197</td>\n",
-       "      <td id=\"T_774dc_row8_col1\" class=\"data row8 col1\" >0.7983</td>\n",
-       "      <td id=\"T_774dc_row8_col2\" class=\"data row8 col2\" >0.7804</td>\n",
-       "      <td id=\"T_774dc_row8_col3\" class=\"data row8 col3\" >0.6958</td>\n",
-       "      <td id=\"T_774dc_row8_col4\" class=\"data row8 col4\" >0.7357</td>\n",
-       "      <td id=\"T_774dc_row8_col5\" class=\"data row8 col5\" >0.4394</td>\n",
-       "      <td id=\"T_774dc_row8_col6\" class=\"data row8 col6\" >0.4427</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
-       "      <td id=\"T_774dc_row9_col0\" class=\"data row9 col0\" >0.7091</td>\n",
-       "      <td id=\"T_774dc_row9_col1\" class=\"data row9 col1\" >0.7808</td>\n",
-       "      <td id=\"T_774dc_row9_col2\" class=\"data row9 col2\" >0.7663</td>\n",
-       "      <td id=\"T_774dc_row9_col3\" class=\"data row9 col3\" >0.6875</td>\n",
-       "      <td id=\"T_774dc_row9_col4\" class=\"data row9 col4\" >0.7248</td>\n",
-       "      <td id=\"T_774dc_row9_col5\" class=\"data row9 col5\" >0.4183</td>\n",
-       "      <td id=\"T_774dc_row9_col6\" class=\"data row9 col6\" >0.4210</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
-       "      <td id=\"T_774dc_row10_col0\" class=\"data row10 col0\" >0.7210</td>\n",
-       "      <td id=\"T_774dc_row10_col1\" class=\"data row10 col1\" >0.7940</td>\n",
-       "      <td id=\"T_774dc_row10_col2\" class=\"data row10 col2\" >0.7738</td>\n",
-       "      <td id=\"T_774dc_row10_col3\" class=\"data row10 col3\" >0.6999</td>\n",
-       "      <td id=\"T_774dc_row10_col4\" class=\"data row10 col4\" >0.7349</td>\n",
-       "      <td id=\"T_774dc_row10_col5\" class=\"data row10 col5\" >0.4419</td>\n",
-       "      <td id=\"T_774dc_row10_col6\" class=\"data row10 col6\" >0.4445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th id=\"T_774dc_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
-       "      <td id=\"T_774dc_row11_col0\" class=\"data row11 col0\" >0.0070</td>\n",
-       "      <td id=\"T_774dc_row11_col1\" class=\"data row11 col1\" >0.0089</td>\n",
-       "      <td id=\"T_774dc_row11_col2\" class=\"data row11 col2\" >0.0082</td>\n",
-       "      <td id=\"T_774dc_row11_col3\" class=\"data row11 col3\" >0.0085</td>\n",
-       "      <td id=\"T_774dc_row11_col4\" class=\"data row11 col4\" >0.0058</td>\n",
-       "      <td id=\"T_774dc_row11_col5\" class=\"data row11 col5\" >0.0140</td>\n",
-       "      <td id=\"T_774dc_row11_col6\" class=\"data row11 col6\" >0.0136</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f00ccb1dc60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n",
-      "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "bestModel = pycaret.tune_model(bestModel, n_iter = 10, optimize = tuning_metric)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
-      "                       criterion='gini', max_depth=None, max_features='sqrt',\n",
-      "                       max_leaf_nodes=None, max_samples=None,\n",
-      "                       min_impurity_decrease=0.0, min_samples_leaf=1,\n",
-      "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
-      "                       n_estimators=100, n_jobs=-1, oob_score=False,\n",
-      "                       random_state=123, verbose=0, warm_start=False)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# View the model's hyperparameter\n",
     "print(bestModel)\n"
@@ -1236,27 +759,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a80ac25ea3bb4a059f8d9e065ed7b973",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Evaluate model: Display UI analyzing Hyperparameters, Confusion Matrix, Class Report, etc.\n",
-    "pycaret.evaluate_model(bestModel)\n"
+    "pycaret.evaluate_model(bestModel)"
    ]
   },
   {
@@ -1270,19 +778,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Transformation Pipeline and Model Successfully Saved\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Finalising model and save the model to current directory\n",
     "final_model = finalize_and_save_model(algorithm_choice, bestModel, model_name)\n"
@@ -1290,174 +790,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style type=\"text/css\">\n",
-       "</style>\n",
-       "<table id=\"T_131b5\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th class=\"blank level0\" >&nbsp;</th>\n",
-       "      <th id=\"T_131b5_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
-       "      <th id=\"T_131b5_level0_col1\" class=\"col_heading level0 col1\" >Accuracy</th>\n",
-       "      <th id=\"T_131b5_level0_col2\" class=\"col_heading level0 col2\" >AUC</th>\n",
-       "      <th id=\"T_131b5_level0_col3\" class=\"col_heading level0 col3\" >Recall</th>\n",
-       "      <th id=\"T_131b5_level0_col4\" class=\"col_heading level0 col4\" >Prec.</th>\n",
-       "      <th id=\"T_131b5_level0_col5\" class=\"col_heading level0 col5\" >F1</th>\n",
-       "      <th id=\"T_131b5_level0_col6\" class=\"col_heading level0 col6\" >Kappa</th>\n",
-       "      <th id=\"T_131b5_level0_col7\" class=\"col_heading level0 col7\" >MCC</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th id=\"T_131b5_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
-       "      <td id=\"T_131b5_row0_col0\" class=\"data row0 col0\" >Random Forest Classifier</td>\n",
-       "      <td id=\"T_131b5_row0_col1\" class=\"data row0 col1\" >0.8152</td>\n",
-       "      <td id=\"T_131b5_row0_col2\" class=\"data row0 col2\" >0.8959</td>\n",
-       "      <td id=\"T_131b5_row0_col3\" class=\"data row0 col3\" >0.8396</td>\n",
-       "      <td id=\"T_131b5_row0_col4\" class=\"data row0 col4\" >0.8008</td>\n",
-       "      <td id=\"T_131b5_row0_col5\" class=\"data row0 col5\" >0.8197</td>\n",
-       "      <td id=\"T_131b5_row0_col6\" class=\"data row0 col6\" >0.6304</td>\n",
-       "      <td id=\"T_131b5_row0_col7\" class=\"data row0 col7\" >0.6311</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7f00ce4934f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>age</th>\n",
-       "      <th>job</th>\n",
-       "      <th>education</th>\n",
-       "      <th>default</th>\n",
-       "      <th>balance</th>\n",
-       "      <th>housing</th>\n",
-       "      <th>loan</th>\n",
-       "      <th>y</th>\n",
-       "      <th>prediction_label</th>\n",
-       "      <th>prediction_score</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>44654</th>\n",
-       "      <td>35</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2298</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44655</th>\n",
-       "      <td>31</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>132</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44656</th>\n",
-       "      <td>50</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1375</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44657</th>\n",
-       "      <td>30</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>734</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44658</th>\n",
-       "      <td>36</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1305</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.531</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       age  job  education  default  balance  housing  loan  y  \\\n",
-       "44654   35    7          4        0     2298        0     0  0   \n",
-       "44655   31    5          2        0      132        0     0  0   \n",
-       "44656   50    4          2        0     1375        0     0  1   \n",
-       "44657   30    1          3        0      734        1     0  0   \n",
-       "44658   36    4          2        0     1305        1     0  1   \n",
-       "\n",
-       "       prediction_label  prediction_score  \n",
-       "44654                 1             0.590  \n",
-       "44655                 1             0.960  \n",
-       "44656                 0             0.610  \n",
-       "44657                 0             1.000  \n",
-       "44658                 1             0.531  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "final_prediction = pycaret.predict_model(final_model, data=test_data)\n",
     "final_prediction.head()\n"
@@ -1505,104 +840,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ExactExplainer explainer: 11165it [21:57,  8.43it/s]                           \n"
-     ]
-    },
-    {
-     "data": {
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e=null!==i?i.baseLanes|n:n,t.lanes=t.childLanes=1073741824,t.memoizedState={baseLanes:e,cachePool:null,transitions:null},t.updateQueue=null,Ca(Al,Ol),Ol|=e,null;t.memoizedState={baseLanes:0,cachePool:null,transitions:null},r=null!==i?i.baseLanes:n,Ca(Al,Ol),Ol|=r}else null!==i?(r=i.baseLanes|n,t.memoizedState=null):r=n,Ca(Al,Ol),Ol|=r;return wu(e,t,a,n),t.child}function Cu(e,t){var n=t.ref;(null===e&&null!==n||null!==e&&e.ref!==n)&&(t.flags|=512,t.flags|=2097152)}function Tu(e,t,n,r,a){var i=La(n)?Pa:Ma.current;return i=za(t,i),Si(t,a),n=So(e,t,n,r,i,a),r=Eo(),null===e||_u?(ai&&r&&ei(t),t.flags|=1,wu(e,t,n,a),t.child):(t.updateQueue=e.updateQueue,t.flags&=-2053,e.lanes&=~a,Hu(e,t,a))}function Mu(e,t,n,r,a){if(La(n)){var i=!0;Da(t)}else i=!1;if(Si(t,a),null===t.stateNode)Vu(e,t),Wi(t,n,r),Hi(t,n,r,a),r=!0;else if(null===e){var o=t.stateNode,u=t.memoizedProps;o.props=u;var l=o.context,s=n.contextType;s=\"object\"==typeof s&&null!==s?Ei(s):za(t,s=La(n)?Pa:Ma.current);var c=n.getDerivedStateFromProps,f=\"function\"==typeof c||\"function\"==typeof o.getSnapshotBeforeUpdate;f||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==r||l!==s)&&Vi(t,o,r,s),Pi=!1;var p=t.memoizedState;o.state=p,Ri(t,r,o,a),l=t.memoizedState,u!==r||p!==l||Na.current||Pi?(\"function\"==typeof c&&(Ii(t,n,c,r),l=t.memoizedState),(u=Pi||Bi(t,n,u,r,p,l,s))?(f||\"function\"!=typeof o.UNSAFE_componentWillMount&&\"function\"!=typeof o.componentWillMount||(\"function\"==typeof o.componentWillMount&&o.componentWillMount(),\"function\"==typeof o.UNSAFE_componentWillMount&&o.UNSAFE_componentWillMount()),\"function\"==typeof o.componentDidMount&&(t.flags|=4194308)):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),t.memoizedProps=r,t.memoizedState=l),o.props=r,o.state=l,o.context=s,r=u):(\"function\"==typeof o.componentDidMount&&(t.flags|=4194308),r=!1)}else{o=t.stateNode,Li(e,t),u=t.memoizedProps,s=t.type===t.elementType?u:gi(t.type,u),o.props=s,f=t.pendingProps,p=o.context,l=\"object\"==typeof(l=n.contextType)&&null!==l?Ei(l):za(t,l=La(n)?Pa:Ma.current);var d=n.getDerivedStateFromProps;(c=\"function\"==typeof d||\"function\"==typeof o.getSnapshotBeforeUpdate)||\"function\"!=typeof o.UNSAFE_componentWillReceiveProps&&\"function\"!=typeof o.componentWillReceiveProps||(u!==f||p!==l)&&Vi(t,o,r,l),Pi=!1,p=t.memoizedState,o.state=p,Ri(t,r,o,a);var h=t.memoizedState;u!==f||p!==h||Na.current||Pi?(\"function\"==typeof d&&(Ii(t,n,d,r),h=t.memoizedState),(s=Pi||Bi(t,n,s,r,p,h,l)||!1)?(c||\"function\"!=typeof o.UNSAFE_componentWillUpdate&&\"function\"!=typeof o.componentWillUpdate||(\"function\"==typeof o.componentWillUpdate&&o.componentWillUpdate(r,h,l),\"function\"==typeof o.UNSAFE_componentWillUpdate&&o.UNSAFE_componentWillUpdate(r,h,l)),\"function\"==typeof o.componentDidUpdate&&(t.flags|=4),\"function\"==typeof o.getSnapshotBeforeUpdate&&(t.flags|=1024)):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=h),o.props=r,o.state=h,o.context=l,r=s):(\"function\"!=typeof o.componentDidUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=4),\"function\"!=typeof o.getSnapshotBeforeUpdate||u===e.memoizedProps&&p===e.memoizedState||(t.flags|=1024),r=!1)}return Nu(e,t,n,r,i,a)}function Nu(e,t,n,r,a,i){Cu(e,t);var o=0!=(128&t.flags);if(!r&&!o)return a&&Ra(t,n,!1),Hu(e,t,i);r=t.stateNode,bu.current=t;var u=o&&\"function\"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&o?(t.child=Ki(t,e.child,null,i),t.child=Ki(t,null,u,i)):wu(e,t,u,i),t.memoizedState=r.state,a&&Ra(t,n,!0),t.child}function Pu(e){var t=e.stateNode;t.pendingContext?Aa(0,t.pendingContext,t.pendingContext!==t.context):t.context&&Aa(0,t.context,!1),ro(e,t.containerInfo)}function zu(e,t,n,r,a){return di(),hi(a),t.flags|=256,wu(e,t,n,r),t.child}var Lu,Ou,Au,Fu,Du={dehydrated:null,treeContext:null,retryLane:0};function Ru(e){return{baseLanes:e,cachePool:null,transitions:null}}function ju(e,t,n){var r,a=t.pendingProps,o=uo.current,u=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&o)),r?(u=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(o|=1),Ca(uo,1&o),null===e)return si(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:\"$!\"===e.data?t.lanes=8:t.lanes=1073741824,null):(l=a.children,e=a.fallback,u?(a=t.mode,u=t.child,l={mode:\"hidden\",children:l},0==(1&a)&&null!==u?(u.childLanes=0,u.pendingProps=l):u=Rs(l,a,0,null),e=Ds(e,a,n,null),u.return=t,e.return=t,u.sibling=e,t.child=u,t.child.memoizedState=Ru(n),t.memoizedState=Du,e):Uu(t,l));if(null!==(o=e.memoizedState)&&null!==(r=o.dehydrated))return function(e,t,n,r,a,o,u){if(n)return 256&t.flags?(t.flags&=-257,Iu(e,t,u,r=fu(Error(i(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(o=r.fallback,a=t.mode,r=Rs({mode:\"visible\",children:r.children},a,0,null),(o=Ds(o,a,u,null)).flags|=2,r.return=t,o.return=t,r.sibling=o,t.child=r,0!=(1&t.mode)&&Ki(t,e.child,null,u),t.child.memoizedState=Ru(u),t.memoizedState=Du,o);if(0==(1&t.mode))return Iu(e,t,u,null);if(\"$!\"===a.data){if(r=a.nextSibling&&a.nextSibling.dataset)var l=r.dgst;return r=l,Iu(e,t,u,r=fu(o=Error(i(419)),r,void 0))}if(l=0!=(u&e.childLanes),_u||l){if(null!==(r=Pl)){switch(u&-u){case 4:a=2;break;case 16:a=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:a=32;break;case 536870912:a=268435456;break;default:a=0}0!==(a=0!=(a&(r.suspendedLanes|u))?0:a)&&a!==o.retryLane&&(o.retryLane=a,Ni(e,a),rs(r,e,a,-1))}return gs(),Iu(e,t,u,r=fu(Error(i(421))))}return\"$?\"===a.data?(t.flags|=128,t.child=e.child,t=Ms.bind(null,e),a._reactRetry=t,null):(e=o.treeContext,ri=sa(a.nextSibling),ni=t,ai=!0,ii=null,null!==e&&(Qa[Ya++]=Ka,Qa[Ya++]=Za,Qa[Ya++]=Ga,Ka=e.id,Za=e.overflow,Ga=t),(t=Uu(t,r.children)).flags|=4096,t)}(e,t,l,a,r,o,n);if(u){u=a.fallback,l=t.mode,r=(o=e.child).sibling;var s={mode:\"hidden\",children:a.children};return 0==(1&l)&&t.child!==o?((a=t.child).childLanes=0,a.pendingProps=s,t.deletions=null):(a=As(o,s)).subtreeFlags=14680064&o.subtreeFlags,null!==r?u=As(r,u):(u=Ds(u,l,n,null)).flags|=2,u.return=t,a.return=t,a.sibling=u,t.child=a,a=u,u=t.child,l=null===(l=e.child.memoizedState)?Ru(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},u.memoizedState=l,u.childLanes=e.childLanes&~n,t.memoizedState=Du,a}return e=(u=e.child).sibling,a=As(u,{mode:\"visible\",children:a.children}),0==(1&t.mode)&&(a.lanes=n),a.return=t,a.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=a,t.memoizedState=null,a}function Uu(e,t){return(t=Rs({mode:\"visible\",children:t},e.mode,0,null)).return=e,e.child=t}function Iu(e,t,n,r){return null!==r&&hi(r),Ki(t,e.child,null,n),(e=Uu(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function $u(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),ki(e.return,t,n)}function Bu(e,t,n,r,a){var i=e.memoizedState;null===i?e.memoizedState={isBackwards:t,rendering:null,renderingStartTime:0,last:r,tail:n,tailMode:a}:(i.isBackwards=t,i.rendering=null,i.renderingStartTime=0,i.last=r,i.tail=n,i.tailMode=a)}function Wu(e,t,n){var r=t.pendingProps,a=r.revealOrder,i=r.tail;if(wu(e,t,r.children,n),0!=(2&(r=uo.current)))r=1&r|2,t.flags|=128;else{if(null!==e&&0!=(128&e.flags))e:for(e=t.child;null!==e;){if(13===e.tag)null!==e.memoizedState&&$u(e,n,t);else if(19===e.tag)$u(e,n,t);else if(null!==e.child){e.child.return=e,e=e.child;continue}if(e===t)break e;for(;null===e.sibling;){if(null===e.return||e.return===t)break e;e=e.return}e.sibling.return=e.return,e=e.sibling}r&=1}if(Ca(uo,r),0==(1&t.mode))t.memoizedState=null;else switch(a){case\"forwards\":for(n=t.child,a=null;null!==n;)null!==(e=n.alternate)&&null===lo(e)&&(a=n),n=n.sibling;null===(n=a)?(a=t.child,t.child=null):(a=n.sibling,n.sibling=null),Bu(t,!1,a,n,i);break;case\"backwards\":for(n=null,a=t.child,t.child=null;null!==a;){if(null!==(e=a.alternate)&&null===lo(e)){t.child=a;break}e=a.sibling,a.sibling=n,n=a,a=e}Bu(t,!0,n,null,i);break;case\"together\":Bu(t,!1,null,null,void 0);break;default:t.memoizedState=null}return t.child}function Vu(e,t){0==(1&t.mode)&&null!==e&&(e.alternate=null,t.alternate=null,t.flags|=2)}function Hu(e,t,n){if(null!==e&&(t.dependencies=e.dependencies),Rl|=t.lanes,0==(n&t.childLanes))return null;if(null!==e&&t.child!==e.child)throw Error(i(153));if(null!==t.child){for(n=As(e=t.child,e.pendingProps),t.child=n,n.return=t;null!==e.sibling;)e=e.sibling,(n=n.sibling=As(e,e.pendingProps)).return=t;n.sibling=null}return t.child}function qu(e,t){if(!ai)switch(e.tailMode){case\"hidden\":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case\"collapsed\":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function Qu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=14680064&a.subtreeFlags,r|=14680064&a.flags,a.return=e,a=a.sibling;else for(a=e.child;null!==a;)n|=a.lanes|a.childLanes,r|=a.subtreeFlags,r|=a.flags,a.return=e,a=a.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function Yu(e,t,n){var r=t.pendingProps;switch(ti(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return Qu(t),null;case 1:case 17:return La(t.type)&&Oa(),Qu(t),null;case 3:return r=t.stateNode,ao(),Ea(Na),Ea(Ma),co(),r.pendingContext&&(r.context=r.pendingContext,r.pendingContext=null),null!==e&&null!==e.child||(fi(t)?t.flags|=4:null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==ii&&(us(ii),ii=null))),Ou(e,t),Qu(t),null;case 5:oo(t);var a=no(to.current);if(n=t.type,null!==e&&null!=t.stateNode)Au(e,t,n,r,a),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(i(166));return Qu(t),null}if(e=no(Ji.current),fi(t)){r=t.stateNode,n=t.type;var o=t.memoizedProps;switch(r[pa]=t,r[da]=o,e=0!=(1&t.mode),n){case\"dialog\":Ur(\"cancel\",r),Ur(\"close\",r);break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",r);break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],r);break;case\"source\":Ur(\"error\",r);break;case\"img\":case\"image\":case\"link\":Ur(\"error\",r),Ur(\"load\",r);break;case\"details\":Ur(\"toggle\",r);break;case\"input\":K(r,o),Ur(\"invalid\",r);break;case\"select\":r._wrapperState={wasMultiple:!!o.multiple},Ur(\"invalid\",r);break;case\"textarea\":ae(r,o),Ur(\"invalid\",r)}for(var l in me(n,o),a=null,o)if(o.hasOwnProperty(l)){var s=o[l];\"children\"===l?\"string\"==typeof s?r.textContent!==s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",s]):\"number\"==typeof s&&r.textContent!==\"\"+s&&(!0!==o.suppressHydrationWarning&&Xr(r.textContent,s,e),a=[\"children\",\"\"+s]):u.hasOwnProperty(l)&&null!=s&&\"onScroll\"===l&&Ur(\"scroll\",r)}switch(n){case\"input\":q(r),J(r,o,!0);break;case\"textarea\":q(r),oe(r);break;case\"select\":case\"option\":break;default:\"function\"==typeof o.onClick&&(r.onclick=Jr)}r=a,t.updateQueue=r,null!==r&&(t.flags|=4)}else{l=9===a.nodeType?a:a.ownerDocument,\"http://www.w3.org/1999/xhtml\"===e&&(e=ue(n)),\"http://www.w3.org/1999/xhtml\"===e?\"script\"===n?((e=l.createElement(\"div\")).innerHTML=\"<script><\\/script>\",e=e.removeChild(e.firstChild)):\"string\"==typeof r.is?e=l.createElement(n,{is:r.is}):(e=l.createElement(n),\"select\"===n&&(l=e,r.multiple?l.multiple=!0:r.size&&(l.size=r.size))):e=l.createElementNS(e,n),e[pa]=t,e[da]=r,Lu(e,t,!1,!1),t.stateNode=e;e:{switch(l=be(n,r),n){case\"dialog\":Ur(\"cancel\",e),Ur(\"close\",e),a=r;break;case\"iframe\":case\"object\":case\"embed\":Ur(\"load\",e),a=r;break;case\"video\":case\"audio\":for(a=0;a<Fr.length;a++)Ur(Fr[a],e);a=r;break;case\"source\":Ur(\"error\",e),a=r;break;case\"img\":case\"image\":case\"link\":Ur(\"error\",e),Ur(\"load\",e),a=r;break;case\"details\":Ur(\"toggle\",e),a=r;break;case\"input\":K(e,r),a=G(e,r),Ur(\"invalid\",e);break;case\"option\":default:a=r;break;case\"select\":e._wrapperState={wasMultiple:!!r.multiple},a=R({},r,{value:void 0}),Ur(\"invalid\",e);break;case\"textarea\":ae(e,r),a=re(e,r),Ur(\"invalid\",e)}for(o in me(n,a),s=a)if(s.hasOwnProperty(o)){var c=s[o];\"style\"===o?ge(e,c):\"dangerouslySetInnerHTML\"===o?null!=(c=c?c.__html:void 0)&&fe(e,c):\"children\"===o?\"string\"==typeof c?(\"textarea\"!==n||\"\"!==c)&&pe(e,c):\"number\"==typeof c&&pe(e,\"\"+c):\"suppressContentEditableWarning\"!==o&&\"suppressHydrationWarning\"!==o&&\"autoFocus\"!==o&&(u.hasOwnProperty(o)?null!=c&&\"onScroll\"===o&&Ur(\"scroll\",e):null!=c&&b(e,o,c,l))}switch(n){case\"input\":q(e),J(e,r,!1);break;case\"textarea\":q(e),oe(e);break;case\"option\":null!=r.value&&e.setAttribute(\"value\",\"\"+V(r.value));break;case\"select\":e.multiple=!!r.multiple,null!=(o=r.value)?ne(e,!!r.multiple,o,!1):null!=r.defaultValue&&ne(e,!!r.multiple,r.defaultValue,!0);break;default:\"function\"==typeof a.onClick&&(e.onclick=Jr)}switch(n){case\"button\":case\"input\":case\"select\":case\"textarea\":r=!!r.autoFocus;break e;case\"img\":r=!0;break e;default:r=!1}}r&&(t.flags|=4)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return Qu(t),null;case 6:if(e&&null!=t.stateNode)Fu(e,t,e.memoizedProps,r);else{if(\"string\"!=typeof r&&null===t.stateNode)throw 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0!=(128&t.flags)?(t.lanes=n,t):((r=null!==r)!=(null!==e&&null!==e.memoizedState)&&r&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&uo.current)?0===Fl&&(Fl=3):gs())),null!==t.updateQueue&&(t.flags|=4),Qu(t),null);case 4:return ao(),Ou(e,t),null===e&&Br(t.stateNode.containerInfo),Qu(t),null;case 10:return xi(t.type._context),Qu(t),null;case 19:if(Ea(uo),null===(o=t.memoizedState))return 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Z=this.mainGroup.selectAll(\".force-bar-array-divider-pos\").data(this.currUsedFeatures);Z.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-pos\").merge(Z).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].posyBottom]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[0].brighter(1.2)})),Z.exit().remove();var X=this.mainGroup.selectAll(\".force-bar-array-divider-neg\").data(this.currUsedFeatures);X.enter().append(\"path\").attr(\"class\",\"force-bar-array-divider-neg\").merge(X).attr(\"d\",(function(e){var n=(0,Re.map)((0,Re.range)(O),(function(n){return[t[n].xmapScaled,t[n].features[e].negyTop]}));return Y(n)})).attr(\"fill\",\"none\").attr(\"stroke-width\",1).attr(\"stroke\",(function(){return n.colors[1].brighter(1.5)})),X.exit().remove();for(var J=function(e,t,n,r,a){var 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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n",
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-      "findfont: Generic family 'sans-serif' not found because none of the following families were found: Arial, Liberation Sans, Bitstream Vera Sans, sans-serif\n"
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",
-      "text/plain": [
-       "<Figure size 800x430 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_features = test_data.copy().drop(target, axis=1)\n",
     "\n",
@@ -1623,37 +863,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<div id='iZ408T9BTDK51P8TQC4F7'>\n",
-       "<div style='color: #900; text-align: center;'>\n",
-       "  <b>Visualization omitted, Javascript library not loaded!</b><br>\n",
-       "  Have you run `initjs()` in this notebook? If this notebook was from another\n",
-       "  user you must also trust this notebook (File -> Trust notebook). If you are viewing\n",
-       "  this notebook on github the Javascript has been stripped for security. If you are using\n",
-       "  JupyterLab this error is because a JupyterLab extension has not yet been written.\n",
-       "</div></div>\n",
-       " <script>\n",
-       "   if (window.SHAP) SHAP.ReactDom.render(\n",
-       "    SHAP.React.createElement(SHAP.AdditiveForceVisualizer, {\"outNames\": [\"f(x)\"], \"baseValue\": 0.59, \"outValue\": 1.0, \"link\": \"identity\", \"featureNames\": [\"age\", \"job\", \"education\", \"default\", \"balance\", \"housing\", \"loan\"], \"features\": {\"0\": {\"effect\": 0.11699999999999999, \"value\": 35.0}, \"1\": {\"effect\": 0.07366666666666674, \"value\": 7.0}, \"2\": {\"effect\": -0.026333333333333223, \"value\": 4.0}, \"4\": {\"effect\": 0.08299999999999999, \"value\": 2298.0}, \"5\": {\"effect\": 0.13216666666666665, \"value\": 0.0}, \"6\": {\"effect\": 0.030499999999999985, \"value\": 0.0}}, \"plot_cmap\": \"RdBu\", \"labelMargin\": 20}),\n",
-       "    document.getElementById('iZ408T9BTDK51P8TQC4F7')\n",
-       "  );\n",
-       "</script>"
-      ],
-      "text/plain": [
-       "<shap.plots._force.AdditiveForceVisualizer at 0x7f00b70ceb30>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "shap_values_array = shap_values.values[0]\n",
     "\n",
@@ -1671,21 +883,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n",
-      "-------!"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# deploy model to sagemaker endpoint\n",
     "deploy_model(model_name, algorithm_choice, model_s3_bucket, instance_type, endpoint_name, role, inference_instance_count, image_uri)\n"
@@ -1701,33 +901,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Are you sure you want to delete the endpoint 'classification-proba-endpoint'? Type 'Yes' to confirm: Yes\n",
-      "Endpoint 'classification-proba-endpoint' and its configuration have been deleted.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "delete_sagemaker_endpoint(endpoint_name)\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1741,9 +925,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.10.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/mlops_ml_models/save_model_to_s3.py b/mlops_ml_models/save_model_to_s3.py
index 1895541..d6e4432 100644
--- a/mlops_ml_models/save_model_to_s3.py
+++ b/mlops_ml_models/save_model_to_s3.py
@@ -2,9 +2,7 @@
 import boto3
 
 
-def save_model_to_s3(
-    trained_model_name: str, bucket_name: str
-) -> None:
+def save_model_to_s3(trained_model_name: str, bucket_name: str) -> None:
     """This saves the tar.gz model in an s3 bucket
 
     Args:
@@ -16,5 +14,5 @@ def save_model_to_s3(
 
     s3 = boto3.client("s3")
     s3.upload_file(
-        f"{trained_model_name}.tar.gz", bucket_name,
-        f"{trained_model_name}.tar.gz")
+        f"{trained_model_name}.tar.gz", bucket_name, f"{trained_model_name}.tar.gz"
+    )
diff --git a/mlops_ml_models/split_data.py b/mlops_ml_models/transfom_data.py
similarity index 53%
rename from mlops_ml_models/split_data.py
rename to mlops_ml_models/transfom_data.py
index b7b8738..48776ef 100644
--- a/mlops_ml_models/split_data.py
+++ b/mlops_ml_models/transfom_data.py
@@ -3,7 +3,7 @@
 
 def split_data(df: pd.DataFrame, shuffle: bool) -> pd.DataFrame:
     """This script split the data into test_data and train_data,
-    with optinal shuffle function
+    with optional shuffle function
 
     Note:
         Remember that this function returns 2 values, therefore using,
@@ -30,3 +30,26 @@ def split_data(df: pd.DataFrame, shuffle: bool) -> pd.DataFrame:
         return train_data, test_data
     except Exception as e:
         print(f"Error loading data: {e}")
+
+
+def preprocess_df(df, preprocessing_script_path):
+    """This is a placeholder function to import the preprocess_data function
+    if it has been uploaded into s3 when the preprocessing_script_path is provided by the user.
+    Args:
+    df:
+    preprocessing_script_path: Path to the data preprocessing script declared in user's repo
+
+    Returns:
+        df: dataframe that has been processed or unchanged depending
+        if the preprocessing_script_path has been provided
+    """
+    if preprocessing_script_path:
+        try:
+            print("Loading file")
+            from preprocess_data import preprocess_data
+
+            df = preprocess_data(df)
+            assert isinstance(df, pd.DataFrame)
+        except ImportError:
+            print("File does not exist")
+    return df
diff --git a/modules/s3/main.tf b/modules/s3/main.tf
index a4400b6..5cc30cf 100644
--- a/modules/s3/main.tf
+++ b/modules/s3/main.tf
@@ -3,7 +3,8 @@
 # The model bucket will contain the model artifact
 # The config-bucket is used to store ipynb files, python files and other configuration files
 locals {
-  file_path = "${path.module}/../../mlops_ml_models"
+  preprocessing_script_path = var.preprocessing_script_path
+  file_path                 = "${path.module}/../../mlops_ml_models"
   files_to_upload = concat(
     tolist(fileset(local.file_path, "*.ipynb")),
     tolist(fileset(local.file_path, "*.py")),
@@ -63,3 +64,12 @@ resource "random_string" "s3_suffix" {
   special = false
   upper   = false
 }
+
+resource "aws_s3_object" "preprocessing_script_path" {
+  count  = var.preprocessing_script_path != null ? 1 : 0
+  bucket = aws_s3_bucket.model_buckets[1].id
+  key    = "preprocess_data.py"
+  source = var.preprocessing_script_path
+  etag   = filemd5(local.preprocessing_script_path)
+  tags   = var.tags
+}
\ No newline at end of file
diff --git a/modules/s3/variables.tf b/modules/s3/variables.tf
index 3e5998a..02b8e34 100644
--- a/modules/s3/variables.tf
+++ b/modules/s3/variables.tf
@@ -12,4 +12,8 @@ variable "tags" {
   type        = map(string)
 }
 
-
+variable "preprocessing_script_path" {
+  description = "The path the user provides if they want to include their own data cleaning logic"
+  type        = string
+  default     = null
+}
\ No newline at end of file
diff --git a/modules/sagemaker/main.tf b/modules/sagemaker/main.tf
index cfd98e9..c965645 100644
--- a/modules/sagemaker/main.tf
+++ b/modules/sagemaker/main.tf
@@ -13,16 +13,17 @@ resource "aws_sagemaker_notebook_instance_lifecycle_configuration" "training_not
     {
       config_s3_bucket = var.config_s3_bucket
       env = {
-        data_location_s3         = "${var.data_s3_bucket}${var.data_location_s3}"
-        target                   = var.model_target_variable
-        algorithm_choice         = var.algorithm_choice
-        endpoint_name            = local.endpoint_name
-        model_name               = local.model_name
-        model_s3_bucket          = var.model_s3_bucket
-        inference_instance_type  = var.inference_instance_type
-        inference_instance_count = var.inference_instance_count
-        ecr_repo_uri             = var.ecr_repo_uri
-        tuning_metric            = var.tuning_metric
+        data_location_s3          = "${var.data_s3_bucket}${var.data_location_s3}"
+        target                    = var.model_target_variable
+        algorithm_choice          = var.algorithm_choice
+        endpoint_name             = local.endpoint_name
+        model_name                = local.model_name
+        model_s3_bucket           = var.model_s3_bucket
+        inference_instance_type   = var.inference_instance_type
+        inference_instance_count  = var.inference_instance_count
+        ecr_repo_uri              = var.ecr_repo_uri
+        tuning_metric             = var.tuning_metric
+        preprocessing_script_path = var.preprocessing_script_path
       }
     }
   ))
diff --git a/modules/sagemaker/templates/startupscript.sh.tftpl b/modules/sagemaker/templates/startupscript.sh.tftpl
index 6be12e0..28a3865 100644
--- a/modules/sagemaker/templates/startupscript.sh.tftpl
+++ b/modules/sagemaker/templates/startupscript.sh.tftpl
@@ -15,4 +15,4 @@ ${key}=${value}
 %{ endfor ~}
 EOF
 
-exit 0
+exit 0
\ No newline at end of file
diff --git a/modules/sagemaker/variables.tf b/modules/sagemaker/variables.tf
index f8b676a..b75e56f 100644
--- a/modules/sagemaker/variables.tf
+++ b/modules/sagemaker/variables.tf
@@ -88,3 +88,9 @@ variable "config_bucket_key_arn" {
   description = "The ARN of the KMS key using which notebook scripts are encrypted in S3."
   type        = string
 }
+
+variable "preprocessing_script_path" {
+  description = "The path the user provides if they want to include their own data cleaning logic"
+  type        = string
+  default     = null
+}
\ No newline at end of file
diff --git a/preprocess_data.py b/preprocess_data.py
new file mode 100644
index 0000000..cf807ef
--- /dev/null
+++ b/preprocess_data.py
@@ -0,0 +1,18 @@
+import pandas as pd
+
+
+def preprocess_data(df):
+    """This placeholder function is supposed to mock some dataframe pre-processing to b used in unit testing
+    Args:
+        df: input dataframe
+    Returns:
+    df: processed dataframe"""
+
+    # One-hot-encode categorical columns
+    df = pd.get_dummies(data=df, columns=["col1", "col2"])
+
+    # Create some dummy columns
+    df["col4"] = df["col3"] + 23
+    df["col5"] = (df["col3"] + 100) / df["col4"]
+
+    return df
diff --git a/pycaret_image_files/prediction_script.py b/pycaret_image_files/prediction_script.py
index d24b336..8ec20d5 100644
--- a/pycaret_image_files/prediction_script.py
+++ b/pycaret_image_files/prediction_script.py
@@ -14,8 +14,8 @@
 # Instantiate Flask app
 app = Flask(__name__)
 
-MODEL_NAME = os.getenv('MODEL_NAME')
-MODEL_TYPE = os.getenv('MODEL_TYPE')
+MODEL_NAME = os.getenv("MODEL_NAME")
+MODEL_TYPE = os.getenv("MODEL_TYPE")
 
 # Define the model path
 # When you configure the model, you will need to specify the S3 location of
@@ -32,8 +32,7 @@
 
 @app.route("/ping", methods=["GET"])
 def ping():
-    return flask.Response(response="\n", status=200,
-                          mimetype="application/json")
+    return flask.Response(response="\n", status=200, mimetype="application/json")
 
 
 # Define an endpoint for making predictions
@@ -47,7 +46,7 @@ def predict():
     logging.info(df)
 
     # Make predictions using the loaded model
-    if (MODEL_TYPE == "classification"):
+    if MODEL_TYPE == "classification":
         prediction = model.predict_proba(df)
     else:
         prediction = model.predict(df)
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..905c8bb
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,2 @@
+[flake8]
+max-line-length = 160
\ No newline at end of file
diff --git a/tests/test_load_data.py b/tests/test_load_data.py
index 2438faf..58fae9e 100644
--- a/tests/test_load_data.py
+++ b/tests/test_load_data.py
@@ -16,11 +16,9 @@ def mock_df() -> pd.DataFrame:
         pd.DataFrame: dataframe created from the script. Should be the same as
         in the resources.
     """
-    return pd.DataFrame({
-        'col1': [1, 2, 3],
-        'col2': ['A', 'B', 'C'],
-        'col3': [4.5, 5.5, 6.5]
-    })
+    return pd.DataFrame(
+        {"col1": [1, 2, 3], "col2": ["A", "B", "C"], "col3": [4.5, 5.5, 6.5]}
+    )
 
 
 def test_load_data(mock_df: pd.DataFrame) -> None:
@@ -30,6 +28,6 @@ def test_load_data(mock_df: pd.DataFrame) -> None:
     Args:
         mock_df (pd.DataFrame): Mock data generated from mock_df function
     """
-    with patch('pandas.read_csv', return_value=mock_df):
+    with patch("pandas.read_csv", return_value=mock_df):
         result = load_data("mlops_ml_models/tests/resources/sample.csv")
     pd.testing.assert_frame_equal(result, mock_df)
diff --git a/tests/test_transform_data.py b/tests/test_transform_data.py
new file mode 100644
index 0000000..3b87ffd
--- /dev/null
+++ b/tests/test_transform_data.py
@@ -0,0 +1,68 @@
+from mlops_ml_models.transfom_data import split_data, preprocess_df
+import pandas as pd
+import pytest
+
+
+@pytest.fixture
+def mock_df() -> pd.DataFrame:
+    """This creates a mock dataframe based on the data
+    entered in the columns below. The data in the mock
+    dataframe is the same data that we have in the csv file in the
+    resources section The aim of this is to be able to test if the
+    load_data.py file returns a the same dataframe as what we have here.
+
+    Returns:
+        pd.DataFrame: dataframe created from the script. Should be the same as
+        in the resources.
+    """
+    return pd.DataFrame(
+        {
+            "col1": [1, 2, 3, 1, 2, 3, 1, 2, 3, 1],
+            "col2": ["A", "B", "C", "A", "B", "C", "A", "B", "C", "A"],
+            "col3": [4.5, 5.5, 6.5, 4.5, 5.5, 6.5, 4.5, 5.5, 6.5, 6.5],
+        }
+    )
+
+
+def test_split_data_shuffle(mock_df: pd.DataFrame) -> None:
+    """This Test compares if the split_data correctly splits a dataframe into 80% and 20% of rows with shuffling.
+
+    Args:
+        mock_df (pd.DataFrame): Mock data generated from mock_df function
+    """
+    train_data, test_data = split_data(mock_df, shuffle=True)
+    assert len(train_data) == 8 and len(test_data) == 2
+
+
+def test_split_data(mock_df: pd.DataFrame) -> None:
+    """This Test compares if the split_data correctly splits a dataframe into 80% and 20% of rows with no shuffling.
+
+    Args:
+        mock_df (pd.DataFrame): Mock data generated from mock_df function
+    """
+    train_data, test_data = split_data(mock_df, shuffle=False)
+    assert list(train_data["col1"]) == [1, 2, 3, 1, 2, 3, 1, 2] and list(
+        test_data["col1"]
+    ) == [3, 1]
+
+
+def test_preprocess_df(mock_df: pd.DataFrame) -> None:
+    """This test checks if the pre-processing function can be imported and execute a custom script to update the dataframe.
+
+    Args:
+    mock_df: mock dataframe"""
+    preprocessing_script_path = "tests\\preprocess_data.py"
+    df = preprocess_df(mock_df, preprocessing_script_path)
+
+    assert len(df.columns) == 9
+
+
+def test_preprocess_df_no_path(mock_df: pd.DataFrame) -> None:
+    """This test checks if the pre-processing function doesn't change the data if the preprocessing_script_path is not present.
+
+    Args:
+    mock_df: mock dataframe"""
+    preprocessing_script_path = None
+    df = preprocess_df(mock_df, preprocessing_script_path)
+
+    assert df.equals(mock_df)
diff --git a/variables.tf b/variables.tf
index 56f6e31..9e44d68 100644
--- a/variables.tf
+++ b/variables.tf
@@ -97,3 +97,9 @@ variable "tuning_metric" {
   description = "The metric user want to focus when tuning hyperparameter"
   type        = string
 }
+
+variable "preprocessing_script_path" {
+  description = "The path the user provides if they want to include their own data cleaning logic"
+  type        = string
+  default     = null
+}
\ No newline at end of file