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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+.vscode
+./flagged
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

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Makefile

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+install:
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+
+format:	
+	black *.py 
+
+train:
+	python train.py
+
+eval:
+	echo "## Model Metrics" > report.md
+	cat ./Results/metrics.txt >> report.md
+	
+	echo '\n## Confusion Matrix Plot' >> report.md
+	echo '![Confusion Matrix](./Results/model_results.png)' >> report.md
+	
+	cml comment create report.md
+		
+update-branch:
+	git config --global user.name $(USER_NAME)
+	git config --global user.email $(USER_EMAIL)
+	git commit -am "Update with new results"
+	git push --force origin HEAD:update
+
+hf-login: 
+	pip install -U "huggingface_hub[cli]"
+	git pull origin update
+	git switch update
+	huggingface-cli login --token $(HF) --add-to-git-credential
+
+push-hub: 
+	huggingface-cli upload kingabzpro/Drug-Classification ./App --repo-type=space --commit-message="Sync App files"
+	huggingface-cli upload kingabzpro/Drug-Classification ./Model /Model --repo-type=space --commit-message="Sync Model"
+	huggingface-cli upload kingabzpro/Drug-Classification ./Results /Metrics --repo-type=space --commit-message="Sync Model"
+
+deploy: hf-login push-hub
+
+all: install format train eval update-branch deploy

README.md

@@ -1,13 +1,22 @@
----
-title: Drug Classification
-emoji: 🐢
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 4.26.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# CICD-for-Machine-Learning
+
+Learn how to automate model training, evaluation, versioning, and deployment using GitHub Actions with the easiest MLOps guide available online.
+
+## Project Description
+In this project, we will be using scikit-learn pipelines to train our random forest algorithm and build a drug classifier. After training, we will automate the evaluation process using CML. Finally, we will build and deploy the web application to Hugging Face Hub. 
+
+From training to evaluation, the entire process will be automated using GitHub actions. All you have to do is push the code to your GitHub repository, and within two minutes, the model will be updated on Hugging Face with the updated app, model, and results.
+
+
+
+## Pipeline
+
+
+![CICD](./asset/CICD-pipeline.png)
+
+## Results
+| Model                  | Accuracy | F1 Score |
+|------------------------|----------|----------|
+| RandomForestClassifier | 97.0%    | 94.0%    |
+
+![CM](./Results/model_results.png)

notebook.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>Sex</th>\n",
+       "      <th>BP</th>\n",
+       "      <th>Cholesterol</th>\n",
+       "      <th>Na_to_K</th>\n",
+       "      <th>Drug</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>176</th>\n",
+       "      <td>48</td>\n",
+       "      <td>M</td>\n",
+       "      <td>HIGH</td>\n",
+       "      <td>NORMAL</td>\n",
+       "      <td>10.446</td>\n",
+       "      <td>drugA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>61</td>\n",
+       "      <td>F</td>\n",
+       "      <td>HIGH</td>\n",
+       "      <td>HIGH</td>\n",
+       "      <td>25.475</td>\n",
+       "      <td>DrugY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>65</th>\n",
+       "      <td>68</td>\n",
+       "      <td>F</td>\n",
+       "      <td>NORMAL</td>\n",
+       "      <td>NORMAL</td>\n",
+       "      <td>27.050</td>\n",
+       "      <td>DrugY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Age Sex      BP Cholesterol  Na_to_K   Drug\n",
+       "176   48   M    HIGH      NORMAL   10.446  drugA\n",
+       "119   61   F    HIGH        HIGH   25.475  DrugY\n",
+       "65    68   F  NORMAL      NORMAL   27.050  DrugY"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "drug_df = pd.read_csv(\"Data/drug.csv\")\n",
+    "drug_df = drug_df.sample(frac=1)\n",
+    "drug_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X = drug_df.drop(\"Drug\", axis=1).values\n",
+    "y = drug_df.Drug.values\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.3, random_state=125\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;encoder&#x27;, OrdinalEncoder(),\n",
+       "                                                  [1, 2, 3]),\n",
+       "                                                 (&#x27;num_imputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;),\n",
+       "                                                  [0, 4]),\n",
+       "                                                 (&#x27;num_scaler&#x27;,\n",
+       "                                                  StandardScaler(), [0, 4])])),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 RandomForestClassifier(n_estimators=10, random_state=125))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;encoder&#x27;, OrdinalEncoder(),\n",
+       "                                                  [1, 2, 3]),\n",
+       "                                                 (&#x27;num_imputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;),\n",
+       "                                                  [0, 4]),\n",
+       "                                                 (&#x27;num_scaler&#x27;,\n",
+       "                                                  StandardScaler(), [0, 4])])),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 RandomForestClassifier(n_estimators=10, random_state=125))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessing: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessing: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;encoder&#x27;, OrdinalEncoder(), [1, 2, 3]),\n",
+       "                                (&#x27;num_imputer&#x27;,\n",
+       "                                 SimpleImputer(strategy=&#x27;median&#x27;), [0, 4]),\n",
+       "                                (&#x27;num_scaler&#x27;, StandardScaler(), [0, 4])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">encoder</label><div class=\"sk-toggleable__content fitted\"><pre>[1, 2, 3]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num_imputer</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 4]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num_scaler</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 4]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=10, random_state=125)</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('preprocessing',\n",
+       "                 ColumnTransformer(transformers=[('encoder', OrdinalEncoder(),\n",
+       "                                                  [1, 2, 3]),\n",
+       "                                                 ('num_imputer',\n",
+       "                                                  SimpleImputer(strategy='median'),\n",
+       "                                                  [0, 4]),\n",
+       "                                                 ('num_scaler',\n",
+       "                                                  StandardScaler(), [0, 4])])),\n",
+       "                ('model',\n",
+       "                 RandomForestClassifier(n_estimators=10, random_state=125))])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler\n",
+    "\n",
+    "cat_col = [1,2,3]\n",
+    "num_col = [0,4]\n",
+    "\n",
+    "transform = ColumnTransformer(\n",
+    "    [\n",
+    "        (\"encoder\", OrdinalEncoder(), cat_col),\n",
+    "        (\"num_imputer\", SimpleImputer(strategy=\"median\"), num_col),\n",
+    "        (\"num_scaler\", StandardScaler(), num_col),\n",
+    "    ]\n",
+    ")\n",
+    "pipe = Pipeline(\n",
+    "    steps=[\n",
+    "        (\"preprocessing\", transform),\n",
+    "        (\"model\", RandomForestClassifier(n_estimators=10, random_state=125)),\n",
+    "    ]\n",
+    ")\n",
+    "pipe.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy:  90.0% F1:  0.85\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, f1_score\n",
+    "\n",
+    "predictions = pipe.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, predictions)\n",
+    "f1 = f1_score(y_test, predictions, average=\"macro\")\n",
+    "\n",
+    "print(\"Accuracy: \", str(round(accuracy, 2) * 100) + \"%\", \"F1: \", round(f1, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Write metrics to file\n",
+    "with open(\"Results/metrics.txt\", \"w\") as outfile:\n",
+    "    outfile.write(f\"\\nAccuracy = {round(accuracy,2)}, F1 Score = {round(f1,2)}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
+    "\n",
+    "predictions = pipe.predict(X_test)\n",
+    "cm = confusion_matrix(y_test, predictions, labels=pipe.classes_)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=pipe.classes_)\n",
+    "disp.plot()\n",
+    "plt.savefig(\"Results/model_results.png\", dpi=120)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import skops.io as sio\n",
+    "\n",
+    "sio.dump(pipe, \"Model/drug_pipeline.skops\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;encoder&#x27;, OrdinalEncoder(),\n",
+       "                                                  [1, 2, 3]),\n",
+       "                                                 (&#x27;num_imputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;),\n",
+       "                                                  [0, 4]),\n",
+       "                                                 (&#x27;num_scaler&#x27;,\n",
+       "                                                  StandardScaler(), [0, 4])])),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 RandomForestClassifier(n_estimators=10, random_state=125))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessing&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;encoder&#x27;, OrdinalEncoder(),\n",
+       "                                                  [1, 2, 3]),\n",
+       "                                                 (&#x27;num_imputer&#x27;,\n",
+       "                                                  SimpleImputer(strategy=&#x27;median&#x27;),\n",
+       "                                                  [0, 4]),\n",
+       "                                                 (&#x27;num_scaler&#x27;,\n",
+       "                                                  StandardScaler(), [0, 4])])),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 RandomForestClassifier(n_estimators=10, random_state=125))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessing: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessing: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;encoder&#x27;, OrdinalEncoder(), [1, 2, 3]),\n",
+       "                                (&#x27;num_imputer&#x27;,\n",
+       "                                 SimpleImputer(strategy=&#x27;median&#x27;), [0, 4]),\n",
+       "                                (&#x27;num_scaler&#x27;, StandardScaler(), [0, 4])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">encoder</label><div class=\"sk-toggleable__content fitted\"><pre>[1, 2, 3]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num_imputer</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 4]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num_scaler</label><div class=\"sk-toggleable__content fitted\"><pre>[0, 4]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=10, random_state=125)</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('preprocessing',\n",
+       "                 ColumnTransformer(transformers=[('encoder', OrdinalEncoder(),\n",
+       "                                                  [1, 2, 3]),\n",
+       "                                                 ('num_imputer',\n",
+       "                                                  SimpleImputer(strategy='median'),\n",
+       "                                                  [0, 4]),\n",
+       "                                                 ('num_scaler',\n",
+       "                                                  StandardScaler(), [0, 4])])),\n",
+       "                ('model',\n",
+       "                 RandomForestClassifier(n_estimators=10, random_state=125))])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sio.load(\"Model/drug_pipeline.skops\", trusted=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PY312",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,6 @@
+pandas
+scikit-learn
+numpy
+matplotlib
+skops
+black

train.py

@@ -0,0 +1,67 @@
+import pandas as pd
+import skops.io as sio
+from sklearn.compose import ColumnTransformer
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.metrics import accuracy_score, f1_score
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OrdinalEncoder, StandardScaler
+
+# Loading the Data
+drug_df = pd.read_csv("Data/drug.csv")
+drug_df = drug_df.sample(frac=1)
+
+#  Train Test Split
+from sklearn.model_selection import train_test_split
+
+X = drug_df.drop("Drug", axis=1).values
+y = drug_df.Drug.values
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=125
+)
+
+# Pipeline
+cat_col = [1, 2, 3]
+num_col = [0, 4]
+
+transform = ColumnTransformer(
+    [
+        ("encoder", OrdinalEncoder(), cat_col),
+        ("num_imputer", SimpleImputer(strategy="median"), num_col),
+        ("num_scaler", StandardScaler(), num_col),
+    ]
+)
+pipe = Pipeline(
+    steps=[
+        ("preprocessing", transform),
+        ("model", RandomForestClassifier(n_estimators=10, random_state=125)),
+    ]
+)
+
+# Training
+pipe.fit(X_train, y_train)
+
+# Model Evaluation
+predictions = pipe.predict(X_test)
+accuracy = accuracy_score(y_test, predictions)
+f1 = f1_score(y_test, predictions, average="macro")
+
+print("Accuracy:", str(round(accuracy, 2) * 100) + "%", "F1:", round(f1, 2))
+
+# Confusion Matrix Plot
+import matplotlib.pyplot as plt
+from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
+
+predictions = pipe.predict(X_test)
+cm = confusion_matrix(y_test, predictions, labels=pipe.classes_)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=pipe.classes_)
+disp.plot()
+plt.savefig("./Results/model_results.png", dpi=120)
+
+# Write metrics to file
+with open("./Results/metrics.txt", "w") as outfile:
+    outfile.write(f"\nAccuracy = {round(accuracy, 2)}, F1 Score = {round(f1, 2)}")
+
+# Saving the model file
+sio.dump(pipe, "./Model/drug_pipeline.skops")