diff --git a/.dvc/.gitignore b/.dvc/.gitignore
new file mode 100644
index 00000000..528f30c7
--- /dev/null
+++ b/.dvc/.gitignore
@@ -0,0 +1,3 @@
+/config.local
+/tmp
+/cache
diff --git a/.dvc/config b/.dvc/config
new file mode 100644
index 00000000..f9aae639
--- /dev/null
+++ b/.dvc/config
@@ -0,0 +1,10 @@
+[core]
+    remote = my_s3_remote_2
+['remote "myremote"']
+    url = /tmp/dvc
+['remote "localremote"']
+    url = ../../../localremote
+['remote "myremote-amazon"']
+    url = ../arn:aws:s3:::bee-ml-dataset-jan-24
+['remote "my_s3_remote_2"']
+    url = ../arn:aws:s3:::dvc-projects-research-mrbestnaija1
diff --git a/.dvc/plots/confusion.json b/.dvc/plots/confusion.json
new file mode 100644
index 00000000..84ec022f
--- /dev/null
+++ b/.dvc/plots/confusion.json
@@ -0,0 +1,107 @@
+{
+    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
+    "data": {
+        "values": "<DVC_METRIC_DATA>"
+    },
+    "title": "<DVC_METRIC_TITLE>",
+    "facet": {
+        "field": "rev",
+        "type": "nominal"
+    },
+    "spec": {
+        "transform": [
+            {
+                "aggregate": [
+                    {
+                        "op": "count",
+                        "as": "xy_count"
+                    }
+                ],
+                "groupby": [
+                    "<DVC_METRIC_Y>",
+                    "<DVC_METRIC_X>"
+                ]
+            },
+            {
+                "impute": "xy_count",
+                "groupby": [
+                    "rev",
+                    "<DVC_METRIC_Y>"
+                ],
+                "key": "<DVC_METRIC_X>",
+                "value": 0
+            },
+            {
+                "impute": "xy_count",
+                "groupby": [
+                    "rev",
+                    "<DVC_METRIC_X>"
+                ],
+                "key": "<DVC_METRIC_Y>",
+                "value": 0
+            },
+            {
+                "joinaggregate": [
+                    {
+                        "op": "max",
+                        "field": "xy_count",
+                        "as": "max_count"
+                    }
+                ],
+                "groupby": []
+            },
+            {
+                "calculate": "datum.xy_count / datum.max_count",
+                "as": "percent_of_max"
+            }
+        ],
+        "encoding": {
+            "x": {
+                "field": "<DVC_METRIC_X>",
+                "type": "nominal",
+                "sort": "ascending",
+                "title": "<DVC_METRIC_X_LABEL>"
+            },
+            "y": {
+                "field": "<DVC_METRIC_Y>",
+                "type": "nominal",
+                "sort": "ascending",
+                "title": "<DVC_METRIC_Y_LABEL>"
+            }
+        },
+        "layer": [
+            {
+                "mark": "rect",
+                "width": 300,
+                "height": 300,
+                "encoding": {
+                    "color": {
+                        "field": "xy_count",
+                        "type": "quantitative",
+                        "title": "",
+                        "scale": {
+                            "domainMin": 0,
+                            "nice": true
+                        }
+                    }
+                }
+            },
+            {
+                "mark": "text",
+                "encoding": {
+                    "text": {
+                        "field": "xy_count",
+                        "type": "quantitative"
+                    },
+                    "color": {
+                        "condition": {
+                            "test": "datum.percent_of_max > 0.5",
+                            "value": "white"
+                        },
+                        "value": "black"
+                    }
+                }
+            }
+        ]
+    }
+}
diff --git a/.dvc/plots/confusion_normalized.json b/.dvc/plots/confusion_normalized.json
new file mode 100644
index 00000000..92c77739
--- /dev/null
+++ b/.dvc/plots/confusion_normalized.json
@@ -0,0 +1,112 @@
+{
+    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
+    "data": {
+        "values": "<DVC_METRIC_DATA>"
+    },
+    "title": "<DVC_METRIC_TITLE>",
+    "facet": {
+        "field": "rev",
+        "type": "nominal"
+    },
+    "spec": {
+        "transform": [
+            {
+                "aggregate": [
+                    {
+                        "op": "count",
+                        "as": "xy_count"
+                    }
+                ],
+                "groupby": [
+                    "<DVC_METRIC_Y>",
+                    "<DVC_METRIC_X>"
+                ]
+            },
+            {
+                "impute": "xy_count",
+                "groupby": [
+                    "rev",
+                    "<DVC_METRIC_Y>"
+                ],
+                "key": "<DVC_METRIC_X>",
+                "value": 0
+            },
+            {
+                "impute": "xy_count",
+                "groupby": [
+                    "rev",
+                    "<DVC_METRIC_X>"
+                ],
+                "key": "<DVC_METRIC_Y>",
+                "value": 0
+            },
+            {
+                "joinaggregate": [
+                    {
+                        "op": "sum",
+                        "field": "xy_count",
+                        "as": "sum_y"
+                    }
+                ],
+                "groupby": [
+                    "<DVC_METRIC_Y>"
+                ]
+            },
+            {
+                "calculate": "datum.xy_count / datum.sum_y",
+                "as": "percent_of_y"
+            }
+        ],
+        "encoding": {
+            "x": {
+                "field": "<DVC_METRIC_X>",
+                "type": "nominal",
+                "sort": "ascending",
+                "title": "<DVC_METRIC_X_LABEL>"
+            },
+            "y": {
+                "field": "<DVC_METRIC_Y>",
+                "type": "nominal",
+                "sort": "ascending",
+                "title": "<DVC_METRIC_Y_LABEL>"
+            }
+        },
+        "layer": [
+            {
+                "mark": "rect",
+                "width": 300,
+                "height": 300,
+                "encoding": {
+                    "color": {
+                        "field": "percent_of_y",
+                        "type": "quantitative",
+                        "title": "",
+                        "scale": {
+                            "domain": [
+                                0,
+                                1
+                            ]
+                        }
+                    }
+                }
+            },
+            {
+                "mark": "text",
+                "encoding": {
+                    "text": {
+                        "field": "percent_of_y",
+                        "type": "quantitative",
+                        "format": ".2f"
+                    },
+                    "color": {
+                        "condition": {
+                            "test": "datum.percent_of_y > 0.5",
+                            "value": "white"
+                        },
+                        "value": "black"
+                    }
+                }
+            }
+        ]
+    }
+}
diff --git a/.dvc/plots/linear.json b/.dvc/plots/linear.json
new file mode 100644
index 00000000..970dc929
--- /dev/null
+++ b/.dvc/plots/linear.json
@@ -0,0 +1,116 @@
+{
+    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
+    "data": {
+        "values": "<DVC_METRIC_DATA>"
+    },
+    "title": "<DVC_METRIC_TITLE>",
+    "width": 300,
+    "height": 300,
+    "layer": [
+        {
+            "encoding": {
+                "x": {
+                    "field": "<DVC_METRIC_X>",
+                    "type": "quantitative",
+                    "title": "<DVC_METRIC_X_LABEL>"
+                },
+                "y": {
+                    "field": "<DVC_METRIC_Y>",
+                    "type": "quantitative",
+                    "title": "<DVC_METRIC_Y_LABEL>",
+                    "scale": {
+                        "zero": false
+                    }
+                },
+                "color": {
+                    "field": "rev",
+                    "type": "nominal"
+                }
+            },
+            "layer": [
+                {
+                    "mark": "line"
+                },
+                {
+                    "selection": {
+                        "label": {
+                            "type": "single",
+                            "nearest": true,
+                            "on": "mouseover",
+                            "encodings": [
+                                "x"
+                            ],
+                            "empty": "none",
+                            "clear": "mouseout"
+                        }
+                    },
+                    "mark": "point",
+                    "encoding": {
+                        "opacity": {
+                            "condition": {
+                                "selection": "label",
+                                "value": 1
+                            },
+                            "value": 0
+                        }
+                    }
+                }
+            ]
+        },
+        {
+            "transform": [
+                {
+                    "filter": {
+                        "selection": "label"
+                    }
+                }
+            ],
+            "layer": [
+                {
+                    "mark": {
+                        "type": "rule",
+                        "color": "gray"
+                    },
+                    "encoding": {
+                        "x": {
+                            "field": "<DVC_METRIC_X>",
+                            "type": "quantitative"
+                        }
+                    }
+                },
+                {
+                    "encoding": {
+                        "text": {
+                            "type": "quantitative",
+                            "field": "<DVC_METRIC_Y>"
+                        },
+                        "x": {
+                            "field": "<DVC_METRIC_X>",
+                            "type": "quantitative"
+                        },
+                        "y": {
+                            "field": "<DVC_METRIC_Y>",
+                            "type": "quantitative"
+                        }
+                    },
+                    "layer": [
+                        {
+                            "mark": {
+                                "type": "text",
+                                "align": "left",
+                                "dx": 5,
+                                "dy": -5
+                            },
+                            "encoding": {
+                                "color": {
+                                    "type": "nominal",
+                                    "field": "rev"
+                                }
+                            }
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
diff --git a/.dvc/plots/scatter.json b/.dvc/plots/scatter.json
new file mode 100644
index 00000000..6e8cf5b4
--- /dev/null
+++ b/.dvc/plots/scatter.json
@@ -0,0 +1,104 @@
+{
+    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
+    "data": {
+        "values": "<DVC_METRIC_DATA>"
+    },
+    "title": "<DVC_METRIC_TITLE>",
+    "width": 300,
+    "height": 300,
+    "layer": [
+        {
+            "encoding": {
+                "x": {
+                    "field": "<DVC_METRIC_X>",
+                    "type": "quantitative",
+                    "title": "<DVC_METRIC_X_LABEL>"
+                },
+                "y": {
+                    "field": "<DVC_METRIC_Y>",
+                    "type": "quantitative",
+                    "title": "<DVC_METRIC_Y_LABEL>",
+                    "scale": {
+                        "zero": false
+                    }
+                },
+                "color": {
+                    "field": "rev",
+                    "type": "nominal"
+                }
+            },
+            "layer": [
+                {
+                    "mark": "point"
+                },
+                {
+                    "selection": {
+                        "label": {
+                            "type": "single",
+                            "nearest": true,
+                            "on": "mouseover",
+                            "encodings": [
+                                "x"
+                            ],
+                            "empty": "none",
+                            "clear": "mouseout"
+                        }
+                    },
+                    "mark": "point",
+                    "encoding": {
+                        "opacity": {
+                            "condition": {
+                                "selection": "label",
+                                "value": 1
+                            },
+                            "value": 0
+                        }
+                    }
+                }
+            ]
+        },
+        {
+            "transform": [
+                {
+                    "filter": {
+                        "selection": "label"
+                    }
+                }
+            ],
+            "layer": [
+                {
+                    "encoding": {
+                        "text": {
+                            "type": "quantitative",
+                            "field": "<DVC_METRIC_Y>"
+                        },
+                        "x": {
+                            "field": "<DVC_METRIC_X>",
+                            "type": "quantitative"
+                        },
+                        "y": {
+                            "field": "<DVC_METRIC_Y>",
+                            "type": "quantitative"
+                        }
+                    },
+                    "layer": [
+                        {
+                            "mark": {
+                                "type": "text",
+                                "align": "left",
+                                "dx": 5,
+                                "dy": -5
+                            },
+                            "encoding": {
+                                "color": {
+                                    "type": "nominal",
+                                    "field": "rev"
+                                }
+                            }
+                        }
+                    ]
+                }
+            ]
+        }
+    ]
+}
diff --git a/.dvc/plots/simple.json b/.dvc/plots/simple.json
new file mode 100644
index 00000000..1cebce9b
--- /dev/null
+++ b/.dvc/plots/simple.json
@@ -0,0 +1,31 @@
+{
+    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
+    "data": {
+        "values": "<DVC_METRIC_DATA>"
+    },
+    "title": "<DVC_METRIC_TITLE>",
+    "width": 300,
+    "height": 300,
+    "mark": {
+        "type": "line"
+    },
+    "encoding": {
+        "x": {
+            "field": "<DVC_METRIC_X>",
+            "type": "quantitative",
+            "title": "<DVC_METRIC_X_LABEL>"
+        },
+        "y": {
+            "field": "<DVC_METRIC_Y>",
+            "type": "quantitative",
+            "title": "<DVC_METRIC_Y_LABEL>",
+            "scale": {
+                "zero": false
+            }
+        },
+        "color": {
+            "field": "rev",
+            "type": "nominal"
+        }
+    }
+}
diff --git a/.dvc/plots/smooth.json b/.dvc/plots/smooth.json
new file mode 100644
index 00000000..42b1ecff
--- /dev/null
+++ b/.dvc/plots/smooth.json
@@ -0,0 +1,39 @@
+{
+    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
+    "data": {
+        "values": "<DVC_METRIC_DATA>"
+    },
+    "title": "<DVC_METRIC_TITLE>",
+    "mark": {
+        "type": "line"
+    },
+    "encoding": {
+        "x": {
+            "field": "<DVC_METRIC_X>",
+            "type": "quantitative",
+            "title": "<DVC_METRIC_X_LABEL>"
+        },
+        "y": {
+            "field": "<DVC_METRIC_Y>",
+            "type": "quantitative",
+            "title": "<DVC_METRIC_Y_LABEL>",
+            "scale": {
+                "zero": false
+            }
+        },
+        "color": {
+            "field": "rev",
+            "type": "nominal"
+        }
+    },
+    "transform": [
+        {
+            "loess": "<DVC_METRIC_Y>",
+            "on": "<DVC_METRIC_X>",
+            "groupby": [
+                "rev"
+            ],
+            "bandwidth": 0.3
+        }
+    ]
+}
diff --git a/.dvcignore b/.dvcignore
new file mode 100644
index 00000000..51973055
--- /dev/null
+++ b/.dvcignore
@@ -0,0 +1,3 @@
+# Add patterns of files dvc should ignore, which could improve
+# the performance. Learn more at
+# https://dvc.org/doc/user-guide/dvcignore
diff --git a/.env/my_key_github_ssh.pub b/.env/my_key_github_ssh.pub
new file mode 100644
index 00000000..d90d5d56
--- /dev/null
+++ b/.env/my_key_github_ssh.pub
@@ -0,0 +1 @@
+#deleted the ssh key
diff --git a/.github/workflows/cml.yaml b/.github/workflows/cml.yaml
new file mode 100644
index 00000000..3536e220
--- /dev/null
+++ b/.github/workflows/cml.yaml
@@ -0,0 +1,63 @@
+name: CML Report
+on: pull_request
+jobs:
+  run:
+    runs-on: [ubuntu-latest]
+    steps:
+      - uses: iterative/setup-cml@v2
+      - uses: iterative/setup-dvc@v1
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 2
+      # Needed for https://github.com/iterative/example-repos-dev/issues/225
+      - name: Installs JSON5
+        run: npm install -g json5
+      - name: Generate metrics report
+        env:
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          cml ci
+          if [ $GITHUB_REF = refs/heads/main ]; then
+            PREVIOUS_REF=HEAD~1
+          else
+            PREVIOUS_REF=main
+            git fetch origin main:main
+          fi
+
+          dvc pull eval
+          dvc plots diff $PREVIOUS_REF workspace \
+            --show-vega --targets ROC | json5 > vega.json
+          vl2svg vega.json roc.svg
+
+          dvc plots diff $PREVIOUS_REF workspace \
+            --show-vega --targets Precision-Recall | json5 > vega.json
+          vl2svg vega.json prc.svg
+
+          dvc plots diff $PREVIOUS_REF workspace \
+            --show-vega --targets Confusion-Matrix | json5 > vega.json
+          vl2svg vega.json confusion.svg
+
+          cp eval/plots/images/importance.png importance_workspace.png
+
+          git checkout $PREVIOUS_REF -- dvc.lock
+          cp eval/plots/images/importance.png importance_previous.png
+
+          dvc_report=$(dvc exp diff $PREVIOUS_REF --md)
+
+          cat <<EOF > report.md
+          # CML Report
+          ## Plots
+          ![ROC](./roc.svg)
+          ![Precision-Recall](./prc.svg)
+          ![Confusion Matrix](./confusion.svg)
+          #### Feature Importance: ${PREVIOUS_REF}
+          ![Feature Importance: ${PREVIOUS_REF}](./importance_previous.png)
+          #### Feature Importance: workspace
+          ![Feature Importance: workspace](./importance_workspace.png)
+
+          ## Metrics and Params
+          ### ${PREVIOUS_REF} → workspace
+          ${dvc_report}
+          EOF
+
+          cml comment create --publish --pr=false report.md
diff --git a/.gitignore b/.gitignore
index 186220be..9f5df164 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,4 +15,55 @@ __pycache__
 .ipynb_checkpoints
 
 # Venv
-dvc-venv
\ No newline at end of file
+dvc-venv
+
+# ssh keys
+env/*
+.env/**/*
+.env/*
+
+################################
+
+# From gittemplate import
+
+# Compiled source #
+###################
+*.com
+*.class
+*.dll
+*.exe
+*.o
+*.so
+
+# Packages #
+############
+# it's better to unpack these files and commit the raw source
+# git has its own built in compression methods
+*.7z
+*.dmg
+*.gz
+*.iso
+*.jar
+*.rar
+*.tar
+*.zip
+
+# Logs and databases #
+######################
+*.log
+*.sql
+*.sqlite
+
+# OS generated files #
+######################
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+
+# DVc tracks #
+/file.txt
+/datadir
+/file_data.txt
diff --git a/README.md b/README.md
index 5d3aa41d..41870ca4 100644
--- a/README.md
+++ b/README.md
@@ -5,9 +5,8 @@
 ### 1. Fork / Clone this repository
 
 ```bash
-git clone https://gitlab.com/iterative.ai/cse/tutorials/course-ds-base.git
+git clone https://github.com/mrbestnaija/gitworkflow-course-ds-base.git
 cd course-ds-base
-git checkout step-1
 ```
 
 
@@ -16,6 +15,7 @@ git checkout step-1
 Create virtual environment named `dvc-venv` (you may use other name)
 ```bash
 python3 -m venv dvc-venv
+echo "export PYTHONPATH=$PWD" >> dvc-venv/bin/activate
 source dvc-venv/bin/activate
 ```
 Install python libraries
@@ -31,9 +31,13 @@ Add Virtual Environment to Jupyter Notebook
 python -m ipykernel install --user --name=dvc-venv
 ``` 
 
-Configure ToC for jupyter notebook (optional)
+Configure ToC for jupyter notebook (optional)/Install the python package
 
 ```bash
+
+
+pip install jupyter_contrib_nbextensions
+
 jupyter contrib nbextension install --user
 jupyter nbextension enable toc2/main
 ```
diff --git a/arn:aws:s3:::bee-ml-dataset-jan-24/01/05828cebc2a54ad9a65b6660f3209c b/arn:aws:s3:::bee-ml-dataset-jan-24/01/05828cebc2a54ad9a65b6660f3209c
new file mode 100644
index 00000000..b1a2771f
--- /dev/null
+++ b/arn:aws:s3:::bee-ml-dataset-jan-24/01/05828cebc2a54ad9a65b6660f3209c
@@ -0,0 +1,119 @@
+sepal_length,sepal_width,petal_length,petal_width,sepal_length_to_sepal_width,petal_length_to_petal_width,target
+6.7,3.1,4.4,1.4,2.161290322580645,3.1428571428571432,1
+4.8,3.4,1.6,0.2,1.411764705882353,8.0,0
+4.4,3.2,1.3,0.2,1.375,6.5,0
+6.3,2.5,5.0,1.9,2.52,2.631578947368421,2
+6.4,3.2,4.5,1.5,2.0,3.0,1
+5.2,3.5,1.5,0.2,1.4857142857142858,7.5,0
+5.0,3.6,1.4,0.2,1.3888888888888888,6.999999999999999,0
+5.2,4.1,1.5,0.1,1.2682926829268295,15.0,0
+5.8,2.7,5.1,1.9,2.148148148148148,2.6842105263157894,2
+6.0,3.4,4.5,1.6,1.7647058823529411,2.8125,1
+6.7,3.1,4.7,1.5,2.161290322580645,3.1333333333333333,1
+5.4,3.9,1.3,0.4,1.3846153846153848,3.25,0
+5.4,3.7,1.5,0.2,1.4594594594594594,7.5,0
+5.5,2.4,3.7,1.0,2.291666666666667,3.7,1
+6.3,2.8,5.1,1.5,2.25,3.4,2
+6.4,3.1,5.5,1.8,2.064516129032258,3.055555555555556,2
+6.6,3.0,4.4,1.4,2.2,3.1428571428571432,1
+7.2,3.6,6.1,2.5,2.0,2.44,2
+5.7,2.9,4.2,1.3,1.9655172413793105,3.230769230769231,1
+7.6,3.0,6.6,2.1,2.533333333333333,3.1428571428571423,2
+5.6,3.0,4.5,1.5,1.8666666666666665,3.0,1
+5.1,3.5,1.4,0.2,1.457142857142857,6.999999999999999,0
+7.7,2.8,6.7,2.0,2.7500000000000004,3.35,2
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diff --git a/arn:aws:s3:::bee-ml-dataset-jan-24/42/24576f0267bf88902f87f0f6200967 b/arn:aws:s3:::bee-ml-dataset-jan-24/42/24576f0267bf88902f87f0f6200967
new file mode 100644
index 00000000..c6c10dc2
--- /dev/null
+++ b/arn:aws:s3:::bee-ml-dataset-jan-24/42/24576f0267bf88902f87f0f6200967
@@ -0,0 +1,151 @@
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diff --git a/arn:aws:s3:::bee-ml-dataset-jan-24/5d/03a1564b3038fc35a842f8e4bde491 b/arn:aws:s3:::bee-ml-dataset-jan-24/5d/03a1564b3038fc35a842f8e4bde491
new file mode 100644
index 00000000..1f34b3b5
--- /dev/null
+++ b/arn:aws:s3:::bee-ml-dataset-jan-24/5d/03a1564b3038fc35a842f8e4bde491
@@ -0,0 +1,151 @@
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new file mode 100644
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new file mode 100644
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diff --git a/arn:aws:s3:::bee-ml-dataset-jan-24/d5/33847a0ca14ca93752b1b1f1df349e b/arn:aws:s3:::bee-ml-dataset-jan-24/d5/33847a0ca14ca93752b1b1f1df349e
new file mode 100644
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diff --git a/arn:aws:s3:::bee-ml-dataset-jan-24/dd/ede7ba843927234678d5ec8d4f9f99.dir b/arn:aws:s3:::bee-ml-dataset-jan-24/dd/ede7ba843927234678d5ec8d4f9f99.dir
new file mode 100644
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diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/42/24576f0267bf88902f87f0f6200967 b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/42/24576f0267bf88902f87f0f6200967
new file mode 100644
index 00000000..c6c10dc2
--- /dev/null
+++ b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/42/24576f0267bf88902f87f0f6200967
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diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/5d/03a1564b3038fc35a842f8e4bde491 b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/5d/03a1564b3038fc35a842f8e4bde491
new file mode 100644
index 00000000..1f34b3b5
--- /dev/null
+++ b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/5d/03a1564b3038fc35a842f8e4bde491
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diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/70/44beeffac0f67cc0401146b26e8f3e b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/70/44beeffac0f67cc0401146b26e8f3e
new file mode 100644
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diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/d4/1d8cd98f00b204e9800998ecf8427e b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/d4/1d8cd98f00b204e9800998ecf8427e
new file mode 100644
index 00000000..e69de29b
diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/d5/33847a0ca14ca93752b1b1f1df349e b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/d5/33847a0ca14ca93752b1b1f1df349e
new file mode 100644
index 00000000..0b4a08e1
--- /dev/null
+++ b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/d5/33847a0ca14ca93752b1b1f1df349e
@@ -0,0 +1 @@
+{"f1_score": 0.9305555555555555}
\ No newline at end of file
diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/dd/ede7ba843927234678d5ec8d4f9f99.dir b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/dd/ede7ba843927234678d5ec8d4f9f99.dir
new file mode 100644
index 00000000..6ed4e592
--- /dev/null
+++ b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/dd/ede7ba843927234678d5ec8d4f9f99.dir
@@ -0,0 +1 @@
+[{"md5": "d41d8cd98f00b204e9800998ecf8427e", "relpath": "data1.csv"}, {"md5": "d41d8cd98f00b204e9800998ecf8427e", "relpath": "data2.csv"}, {"md5": "d41d8cd98f00b204e9800998ecf8427e", "relpath": "data3.csv"}]
\ No newline at end of file
diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/de/44a28d8aa5da6d35dc3778e613449d b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/de/44a28d8aa5da6d35dc3778e613449d
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diff --git a/arn:aws:s3:::dvc-projects-research-mrbestnaija1/f9/53ee125de2bd311a3f846acfac349c b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/f9/53ee125de2bd311a3f846acfac349c
new file mode 100644
index 00000000..548c0a05
--- /dev/null
+++ b/arn:aws:s3:::dvc-projects-research-mrbestnaija1/f9/53ee125de2bd311a3f846acfac349c
@@ -0,0 +1,33 @@
+sepal_length,sepal_width,petal_length,petal_width,sepal_length_to_sepal_width,petal_length_to_petal_width,target
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diff --git a/datadir.dvc b/datadir.dvc
new file mode 100644
index 00000000..44f53dd4
--- /dev/null
+++ b/datadir.dvc
@@ -0,0 +1,5 @@
+outs:
+- md5: ddede7ba843927234678d5ec8d4f9f99.dir
+  size: 0
+  nfiles: 3
+  path: datadir
diff --git a/dvc.lock b/dvc.lock
new file mode 100644
index 00000000..6f876095
--- /dev/null
+++ b/dvc.lock
@@ -0,0 +1,152 @@
+schema: '2.0'
+stages:
+  data_load:
+    cmd: python src/stages/data_load.py --config=params.yaml
+    deps:
+    - path: src/stages/data_load.py
+      md5: 7e8c530e135da91b31ed742d95b7288c
+      size: 1084
+    params:
+      params.yaml:
+        base:
+          random_state: 42
+          log_level: INFO
+        data_load:
+          dataset_csv: data/raw/iris.csv
+    outs:
+    - path: data/raw/iris.csv
+      md5: 4224576f0267bf88902f87f0f6200967
+      size: 2757
+      isexec: true
+  featurize:
+    cmd: python src/stages/featurize.py --config=params.yaml
+    deps:
+    - path: data/raw/iris.csv
+      md5: 4224576f0267bf88902f87f0f6200967
+      size: 2757
+    - path: src/stages/featurize.py
+      md5: d1cc78e9ae6c9a43099cf2b43e377975
+      size: 1395
+    params:
+      params.yaml:
+        base:
+          random_state: 42
+          log_level: INFO
+        featurize:
+          features_path: data/processed/featured_iris.csv
+          target_column: target
+    outs:
+    - path: data/processed/featured_iris.csv
+      md5: 5d03a1564b3038fc35a842f8e4bde491
+      size: 7260
+      isexec: true
+  data_split:
+    cmd: python src/stages/data_split.py --config=params.yaml
+    deps:
+    - path: data/processed/featured_iris.csv
+      md5: 5d03a1564b3038fc35a842f8e4bde491
+      size: 7260
+    - path: src/stages/data_split.py
+      md5: 146a803b3261f01f798da85b49cfe00e
+      size: 1401
+    params:
+      params.yaml:
+        base:
+          random_state: 42
+          log_level: INFO
+        data_split:
+          test_size: 0.21
+          trainset_path: data/processed/train_iris.csv
+          testset_path: data/processed/test_iris.csv
+        featurize:
+          features_path: data/processed/featured_iris.csv
+          target_column: target
+    outs:
+    - path: data/processed/test_iris.csv
+      md5: f953ee125de2bd311a3f846acfac349c
+      size: 1575
+      isexec: true
+    - path: data/processed/train_iris.csv
+      md5: 0105828cebc2a54ad9a65b6660f3209c
+      size: 5641
+      isexec: true
+  train:
+    cmd: python src/stages/train.py --config=params.yaml
+    deps:
+    - path: data/processed/test_iris.csv
+      md5: f953ee125de2bd311a3f846acfac349c
+      size: 1575
+    - path: data/processed/train_iris.csv
+      md5: 0105828cebc2a54ad9a65b6660f3209c
+      size: 5641
+    - path: src/stages/train.py
+      md5: e755fbd9d95efacf4ded17ebc3c93dc2
+      size: 1564
+    params:
+      params.yaml:
+        base:
+          random_state: 42
+          log_level: INFO
+        train:
+          cv: 3
+          estimator_name: logreg
+          estimators:
+            logreg:
+              param_grid:
+                C:
+                - 0.001
+                max_iter:
+                - 100
+                solver:
+                - lbfgs
+                multi_class:
+                - multinomial
+            svm:
+              param_grid:
+                C:
+                - 0.1
+                - 1.0
+                kernel:
+                - rbf
+                - linear
+                gamma:
+                - scale
+                degree:
+                - 3
+                - 5
+          model_path: models/model.joblib
+    outs:
+    - path: models/model.joblib
+      md5: de44a28d8aa5da6d35dc3778e613449d
+      size: 2883
+      isexec: true
+  evaluate:
+    cmd: python src/stages/evaluate.py --config=params.yaml
+    deps:
+    - path: data/processed/test_iris.csv
+      md5: f953ee125de2bd311a3f846acfac349c
+      size: 1575
+    - path: models/model.joblib
+      md5: de44a28d8aa5da6d35dc3778e613449d
+      size: 2883
+    - path: src/stages/evaluate.py
+      md5: eab9636bc1bf222815f1941a3abfc99e
+      size: 2492
+    params:
+      params.yaml:
+        base:
+          random_state: 42
+          log_level: INFO
+        evaluate:
+          reports_dir: reports
+          metrics_file: metrics.json
+          confusion_matrix_image: confusion_matrix.png
+    outs:
+    - path: reports/confusion_matrix.png
+      md5: 7044beeffac0f67cc0401146b26e8f3e
+      size: 25286
+      isexec: true
+    - path: reports/metrics.json
+      md5: d533847a0ca14ca93752b1b1f1df349e
+      size: 32
+      isexec: true
diff --git a/dvc.yaml b/dvc.yaml
new file mode 100644
index 00000000..68c96bff
--- /dev/null
+++ b/dvc.yaml
@@ -0,0 +1,68 @@
+# DAG of all the stages in the pipeline
+stages:
+# The first stage of the pipeline
+  data_load:
+    cmd: python src/stages/data_load.py --config=params.yaml
+    deps:
+    - src/stages/data_load.py
+    params:
+    - base
+    - data_load
+    outs:
+    - data/raw/iris.csv
+# The second stage of the pipeline
+  featurize:
+    cmd: python src/stages/featurize.py --config=params.yaml
+    deps:
+    - data/raw/iris.csv
+    - src/stages/featurize.py
+    params:
+    - base
+    - featurize
+    outs:
+    - data/processed/featured_iris.csv
+# The third stage of the pipeline
+  data_split:
+    cmd: python src/stages/data_split.py --config=params.yaml
+    deps:
+    - data/processed/featured_iris.csv
+    - src/stages/data_split.py
+    params:
+    - base
+    - data_split
+    - featurize
+    outs:
+    - data/processed/test_iris.csv
+    - data/processed/train_iris.csv
+# The fourth stage of the pipeline
+  train:
+    cmd: python src/stages/train.py --config=params.yaml
+    deps:
+    - data/processed/test_iris.csv
+    - data/processed/train_iris.csv
+    - src/stages/train.py
+    params:
+    - base
+    - train
+    outs:
+    - models/model.joblib
+# The fifth stage of the pipeline
+  evaluate:
+    cmd: python src/stages/evaluate.py --config=params.yaml
+    deps:
+    - models/model.joblib
+    - data/processed/test_iris.csv
+    - src/stages/evaluate.py
+
+    params:
+    - base
+    - evaluate
+
+    outs: 
+    - 'reports/confusion_matrix.png'
+    
+
+    metrics:
+    - reports/metrics.json:
+      
+        cache: false
diff --git a/env/.gitignore b/env/.gitignore
new file mode 100644
index 00000000..3797191b
--- /dev/null
+++ b/env/.gitignore
@@ -0,0 +1,36 @@
+# Compiled source #
+###################
+*.com
+*.class
+*.dll
+*.exe
+*.o
+*.so
+
+# Packages #
+############
+# it's better to unpack these files and commit the raw source
+# git has its own built in compression methods
+*.7z
+*.dmg
+*.gz
+*.iso
+*.jar
+*.rar
+*.tar
+*.zip
+
+# Logs and databases #
+######################
+*.log
+*.sql
+*.sqlite
+
+# OS generated files #
+######################
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
\ No newline at end of file
diff --git a/env/my_key_github_ssh b/env/my_key_github_ssh
new file mode 100644
index 00000000..005b8a39
--- /dev/null
+++ b/env/my_key_github_ssh
@@ -0,0 +1,49 @@
+-----BEGIN OPENSSH PRIVATE KEY-----
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+-----END OPENSSH PRIVATE KEY-----
diff --git a/file_data.txt.dvc b/file_data.txt.dvc
new file mode 100644
index 00000000..b125fafc
--- /dev/null
+++ b/file_data.txt.dvc
@@ -0,0 +1,5 @@
+outs:
+- md5: d41d8cd98f00b204e9800998ecf8427e
+  size: 0
+  path: file_data.txt
+  isexec: true
diff --git a/gitworkflow-course-ds-base b/gitworkflow-course-ds-base
new file mode 160000
index 00000000..53842777
--- /dev/null
+++ b/gitworkflow-course-ds-base
@@ -0,0 +1 @@
+Subproject commit 53842777e06be196a8392ae116c91a9f2b32315b
diff --git a/lineapy-trial-prototype.ipynb b/lineapy-trial-prototype.ipynb
new file mode 100644
index 00000000..a1b86c0b
--- /dev/null
+++ b/lineapy-trial-prototype.ipynb
@@ -0,0 +1,1228 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: lineapy in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (0.2.3)\n",
+      "Requirement already satisfied: jinja2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (2.11.2)\n",
+      "Requirement already satisfied: pandas in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (1.3.2)\n",
+      "Requirement already satisfied: pydantic in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (1.8.2)\n",
+      "Requirement already satisfied: networkx in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (2.5)\n",
+      "Requirement already satisfied: SQLAlchemy<2.0.0,>=1.4 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (1.4.47)\n",
+      "Requirement already satisfied: requests in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (2.25.1)\n",
+      "Requirement already satisfied: alembic==1.8.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (1.8.0)\n",
+      "Requirement already satisfied: IPython>=7.0.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (7.19.0)\n",
+      "Requirement already satisfied: isort in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (5.9.3)\n",
+      "Requirement already satisfied: rich in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (12.4.4)\n",
+      "Requirement already satisfied: click>=8.0.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (8.1.3)\n",
+      "Requirement already satisfied: pyyaml in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (5.3.1)\n",
+      "Requirement already satisfied: fsspec in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (2022.7.1)\n",
+      "Requirement already satisfied: nbconvert<7.0.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (6.0.7)\n",
+      "Requirement already satisfied: nbformat in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (5.0.8)\n",
+      "Requirement already satisfied: cloudpickle in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (1.6.0)\n",
+      "Requirement already satisfied: asttokens in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (2.2.1)\n",
+      "Requirement already satisfied: black in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (21.7b0)\n",
+      "Requirement already satisfied: typing-extensions>=4.0.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from lineapy) (4.3.0)\n",
+      "Requirement already satisfied: importlib-metadata in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from alembic==1.8.0->lineapy) (2.0.0)\n",
+      "Requirement already satisfied: Mako in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from alembic==1.8.0->lineapy) (1.2.4)\n",
+      "Requirement already satisfied: importlib-resources in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from alembic==1.8.0->lineapy) (5.7.1)\n",
+      "Requirement already satisfied: appnope in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (0.1.0)\n",
+      "Requirement already satisfied: jedi>=0.10 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (0.17.1)\n",
+      "Requirement already satisfied: pygments in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (2.7.2)\n",
+      "Requirement already satisfied: pickleshare in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (0.7.5)\n",
+      "Requirement already satisfied: traitlets>=4.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (5.0.5)\n",
+      "Requirement already satisfied: pexpect>4.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (4.8.0)\n",
+      "Requirement already satisfied: decorator in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (4.4.2)\n",
+      "Requirement already satisfied: backcall in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (0.2.0)\n",
+      "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (3.0.8)\n",
+      "Requirement already satisfied: setuptools>=18.5 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from IPython>=7.0.0->lineapy) (50.3.1.post20201107)\n",
+      "Requirement already satisfied: bleach in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (3.2.1)\n",
+      "Requirement already satisfied: testpath in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (0.4.4)\n",
+      "Requirement already satisfied: jupyter-core in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (4.6.3)\n",
+      "Requirement already satisfied: pandocfilters>=1.4.1 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (1.4.3)\n",
+      "Requirement already satisfied: defusedxml in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (0.6.0)\n",
+      "Requirement already satisfied: mistune<2,>=0.8.1 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (0.8.4)\n",
+      "Requirement already satisfied: jupyterlab-pygments in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (0.1.2)\n",
+      "Requirement already satisfied: nbclient<0.6.0,>=0.5.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (0.5.1)\n",
+      "Requirement already satisfied: entrypoints>=0.2.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbconvert<7.0.0->lineapy) (0.3)\n",
+      "Requirement already satisfied: MarkupSafe>=0.23 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from jinja2->lineapy) (1.1.1)\n",
+      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbformat->lineapy) (3.2.0)\n",
+      "Requirement already satisfied: ipython-genutils in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbformat->lineapy) (0.2.0)\n",
+      "Requirement already satisfied: greenlet!=0.4.17 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from SQLAlchemy<2.0.0,>=1.4->lineapy) (2.0.2)\n",
+      "Requirement already satisfied: six in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from asttokens->lineapy) (1.15.0)\n",
+      "Requirement already satisfied: tomli<2.0.0,>=0.2.6 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from black->lineapy) (1.2.3)\n",
+      "Requirement already satisfied: regex>=2020.1.8 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from black->lineapy) (2020.10.15)\n",
+      "Requirement already satisfied: mypy-extensions>=0.4.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from black->lineapy) (0.4.3)\n",
+      "Requirement already satisfied: appdirs in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from black->lineapy) (1.4.4)\n",
+      "Requirement already satisfied: pathspec<1,>=0.8.1 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from black->lineapy) (0.9.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas->lineapy) (2.8.1)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas->lineapy) (2022.1)\n",
+      "Requirement already satisfied: numpy>=1.17.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas->lineapy) (1.18.5)\n",
+      "Requirement already satisfied: idna<3,>=2.5 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from requests->lineapy) (2.10)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from requests->lineapy) (2020.6.20)\n",
+      "Requirement already satisfied: chardet<5,>=3.0.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from requests->lineapy) (3.0.4)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from requests->lineapy) (1.25.11)\n",
+      "Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from rich->lineapy) (0.9.1)\n",
+      "Requirement already satisfied: parso<0.8.0,>=0.7.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from jedi>=0.10->IPython>=7.0.0->lineapy) (0.7.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: attrs>=17.4.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat->lineapy) (20.3.0)\n",
+      "Requirement already satisfied: pyrsistent>=0.14.0 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat->lineapy) (0.17.3)\n",
+      "Requirement already satisfied: jupyter-client>=6.1.5 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert<7.0.0->lineapy) (6.1.7)\n",
+      "Requirement already satisfied: async-generator in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert<7.0.0->lineapy) (1.10)\n",
+      "Requirement already satisfied: nest-asyncio in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from nbclient<0.6.0,>=0.5.0->nbconvert<7.0.0->lineapy) (1.5.1)\n",
+      "Requirement already satisfied: ptyprocess>=0.5 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pexpect>4.3->IPython>=7.0.0->lineapy) (0.6.0)\n",
+      "Requirement already satisfied: wcwidth in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->IPython>=7.0.0->lineapy) (0.2.5)\n",
+      "Requirement already satisfied: webencodings in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from bleach->nbconvert<7.0.0->lineapy) (0.5.1)\n",
+      "Requirement already satisfied: packaging in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from bleach->nbconvert<7.0.0->lineapy) (20.4)\n",
+      "Requirement already satisfied: zipp>=0.5 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from importlib-metadata->alembic==1.8.0->lineapy) (3.4.0)\n",
+      "Requirement already satisfied: tornado>=4.1 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from jupyter-client>=6.1.5->nbclient<0.6.0,>=0.5.0->nbconvert<7.0.0->lineapy) (6.1)\n",
+      "Requirement already satisfied: pyzmq>=13 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from jupyter-client>=6.1.5->nbclient<0.6.0,>=0.5.0->nbconvert<7.0.0->lineapy) (19.0.2)\n",
+      "Requirement already satisfied: pyparsing>=2.0.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from packaging->bleach->nbconvert<7.0.0->lineapy) (2.4.7)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\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;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "! pip install lineapy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pandas==1.3.2 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (1.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas==1.3.2) (2.8.1)\n",
+      "Requirement already satisfied: numpy>=1.17.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas==1.3.2) (1.18.5)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from pandas==1.3.2) (2022.1)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenif/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas==1.3.2) (1.15.0)\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\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;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "! python -m pip install pandas==1.3.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext lineapy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "lineapy_config(home_dir=PosixPath('/Users/jenif/.lineapy'), database_url='sqlite:////Users/jenif/.lineapy/db.sqlite', artifact_storage_dir=PosixPath('/Users/jenif/.lineapy/linea_pickles'), customized_annotation_folder=PosixPath('/Users/jenif/.lineapy/custom-annotations'), do_not_track=False, logging_level='INFO', logging_file=PosixPath('/Users/jenif/.lineapy/lineapy.log'), storage_options=None, mlflow_registry_uri=None, mlflow_tracking_uri=None, default_ml_models_storage_backend=None)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lineapy.options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:17:31.460557Z",
+     "start_time": "2019-06-16T21:17:29.395297Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import lineapy\n",
+    "import joblib\n",
+    "import json\n",
+    "import itertools\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import confusion_matrix, f1_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:17:31.485189Z",
+     "start_time": "2019-06-16T21:17:31.473720Z"
+    }
+   },
+   "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>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "3                4.6               3.1                1.5               0.2   \n",
+       "4                5.0               3.6                1.4               0.2   \n",
+       "\n",
+       "   target  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get data \n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "data = load_iris(as_frame=True)\n",
+    "dataset = data.frame\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0: setosa\n",
+      "1: versicolor\n",
+      "2: virginica\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[None, None, None]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print labels for target values \n",
+    "\n",
+    "[print(f'{target}: {label}') for target, label in zip(data.target.unique(), data.target_names)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:17:32.328046Z",
+     "start_time": "2019-06-16T21:17:32.323611Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# feature names\n",
+    "\n",
+    "dataset.columns = [colname.strip(' (cm)').replace(' ', '_') for colname in dataset.columns.tolist()]\n",
+    "\n",
+    "feature_names = dataset.columns.tolist()[:4]\n",
+    "feature_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#save raw data as artifact\n",
+    "dataset_csv = './data/raw/iris.csv'\n",
+    "dataset.to_csv(dataset_csv, index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     sepal_length  sepal_width  petal_length  petal_width  target\n",
+       "0             5.1          3.5           1.4          0.2       0\n",
+       "1             4.9          3.0           1.4          0.2       0\n",
+       "2             4.7          3.2           1.3          0.2       0\n",
+       "3             4.6          3.1           1.5          0.2       0\n",
+       "4             5.0          3.6           1.4          0.2       0\n",
+       "..            ...          ...           ...          ...     ...\n",
+       "145           6.7          3.0           5.2          2.3       2\n",
+       "146           6.3          2.5           5.0          1.9       2\n",
+       "147           6.5          3.0           5.2          2.0       2\n",
+       "148           6.2          3.4           5.4          2.3       2\n",
+       "149           5.9          3.0           5.1          1.8       2\n",
+       "\n",
+       "[150 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.3.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(pd.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='iris-raw', _version=4)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#save raw data as artifact to lineapy\n",
+    "lineapy.save(dataset, \"iris-raw\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Features engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:02.150708Z",
+     "start_time": "2019-06-16T21:21:02.144518Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset['sepal_length_to_sepal_width'] = dataset['sepal_length'] / dataset['sepal_width']\n",
+    "dataset['petal_length_to_petal_width'] = dataset['petal_length'] / dataset['petal_width']\n",
+    "\n",
+    "dataset = dataset[[\n",
+    "    'sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
+    "#     'sepal_length_in_square', 'sepal_width_in_square', 'petal_length_in_square', 'petal_width_in_square',\n",
+    "    'sepal_length_to_sepal_width', 'petal_length_to_petal_width',\n",
+    "    'target'\n",
+    "]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:02.987144Z",
+     "start_time": "2019-06-16T21:21:02.976092Z"
+    }
+   },
+   "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>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>sepal_length_to_sepal_width</th>\n",
+       "      <th>petal_length_to_petal_width</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.457143</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.633333</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.468750</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.483871</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.388889</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width  \\\n",
+       "0           5.1          3.5           1.4          0.2   \n",
+       "1           4.9          3.0           1.4          0.2   \n",
+       "2           4.7          3.2           1.3          0.2   \n",
+       "3           4.6          3.1           1.5          0.2   \n",
+       "4           5.0          3.6           1.4          0.2   \n",
+       "\n",
+       "   sepal_length_to_sepal_width  petal_length_to_petal_width  target  \n",
+       "0                     1.457143                          7.0       0  \n",
+       "1                     1.633333                          7.0       0  \n",
+       "2                     1.468750                          6.5       0  \n",
+       "3                     1.483871                          7.5       0  \n",
+       "4                     1.388889                          7.0       0  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save features\n",
+    "features_path = './data/processed/featured_iris.csv'\n",
+    "dataset.to_csv(features_path, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='iris-preprocessed', _version=4)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#save features to lineapy\n",
+    "lineapy.save(dataset, \"iris-preprocessed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Split dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:06.361378Z",
+     "start_time": "2019-06-16T21:21:06.358647Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "test_size=0.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Splittail train/test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:07.438133Z",
+     "start_time": "2019-06-16T21:21:07.431649Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((120, 7), (30, 7))"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_dataset, test_dataset = train_test_split(dataset, test_size=test_size, random_state=42)\n",
+    "train_dataset.shape, test_dataset.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save train and test sets\n",
+    "trainset_path = './data/processed/train_iris.csv'\n",
+    "testset_path = './data/processed/test_iris.csv'\n",
+    "\n",
+    "train_dataset.to_csv(trainset_path)\n",
+    "test_dataset.to_csv(testset_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='test-dataset', _version=4)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#save train and test sets to lineapy\n",
+    "lineapy.save(train_dataset, \"train-dataset\")\n",
+    "lineapy.save(test_dataset, \"test-dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:10.932148Z",
+     "start_time": "2019-06-16T21:21:10.927844Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Get X and Y\n",
+    "\n",
+    "y_train = train_dataset.loc[:, 'target'].values.astype('int32')\n",
+    "X_train = train_dataset.drop('target', axis=1).values.astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:55.427365Z",
+     "start_time": "2019-06-16T21:21:55.416431Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=0.001, multi_class='multinomial')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an instance of Logistic Regression Classifier CV and fit the data\n",
+    "\n",
+    "logreg = LogisticRegression(C=0.001, solver='lbfgs', multi_class='multinomial', max_iter=100)\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['./models/model.joblib']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_path= './models/model.joblib'\n",
+    "joblib.dump(logreg, model_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='logreg-model', _version=3)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#save model to lineapy\n",
+    "lineapy.save(model_path, \"logreg-model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:55.875303Z",
+     "start_time": "2019-06-16T21:21:55.864724Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def plot_confusion_matrix(cm,\n",
+    "                          target_names,\n",
+    "                          title='Confusion matrix',\n",
+    "                          cmap=None,\n",
+    "                          normalize=True):\n",
+    "    \"\"\"\n",
+    "    given a sklearn confusion matrix (cm), make a nice plot\n",
+    "\n",
+    "    Arguments\n",
+    "    ---------\n",
+    "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
+    "\n",
+    "    target_names: given classification classes such as [0, 1, 2]\n",
+    "                  the class names, for example: ['high', 'medium', 'low']\n",
+    "\n",
+    "    title:        the text to display at the top of the matrix\n",
+    "\n",
+    "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
+    "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
+    "                  plt.get_cmap('jet') or plt.cm.Blues\n",
+    "\n",
+    "    normalize:    If False, plot the raw numbers\n",
+    "                  If True, plot the proportions\n",
+    "\n",
+    "    Usage\n",
+    "    -----\n",
+    "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
+    "                                                              # sklearn.metrics.confusion_matrix\n",
+    "                          normalize    = True,                # show proportions\n",
+    "                          target_names = y_labels_vals,       # list of names of the classes\n",
+    "                          title        = best_estimator_name) # title of graph\n",
+    "\n",
+    "    Citiation\n",
+    "    ---------\n",
+    "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    accuracy = np.trace(cm) / float(np.sum(cm))\n",
+    "    misclass = 1 - accuracy\n",
+    "\n",
+    "    if cmap is None:\n",
+    "        cmap = plt.get_cmap('Blues')\n",
+    "\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
+    "    plt.title(title)\n",
+    "    plt.colorbar()\n",
+    "\n",
+    "    if target_names is not None:\n",
+    "        tick_marks = np.arange(len(target_names))\n",
+    "        plt.xticks(tick_marks, target_names, rotation=45)\n",
+    "        plt.yticks(tick_marks, target_names)\n",
+    "\n",
+    "    if normalize:\n",
+    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
+    "\n",
+    "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
+    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
+    "        if normalize:\n",
+    "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
+    "                     horizontalalignment=\"center\",\n",
+    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "        else:\n",
+    "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
+    "                     horizontalalignment=\"center\",\n",
+    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.ylabel('True label')\n",
+    "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n",
+    "    \n",
+    "    return plt.gcf()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:56.090756Z",
+     "start_time": "2019-06-16T21:21:56.086966Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Get X and Y\n",
+    "\n",
+    "y_test = test_dataset.loc[:, 'target'].values.astype('int32')\n",
+    "X_test = test_dataset.drop('target', axis=1).values.astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:56.270245Z",
+     "start_time": "2019-06-16T21:21:56.265054Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "prediction = logreg.predict(X_test)\n",
+    "cm = confusion_matrix(prediction, y_test)\n",
+    "f1 = f1_score(y_true = y_test, y_pred = prediction, average='macro')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:56.493617Z",
+     "start_time": "2019-06-16T21:21:56.489929Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9305555555555555"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# f1 score value\n",
+    "f1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save metrics\n",
+    "metrics_file = './reports/metrics.json'\n",
+    "\n",
+    "metrics = {\n",
+    "    'f1': f1\n",
+    "}\n",
+    "\n",
+    "with open(metrics_file, 'w') as mf:\n",
+    "    json.dump(\n",
+    "        obj=metrics,\n",
+    "        fp=mf,\n",
+    "        indent=4\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 576x432 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm_plot = plot_confusion_matrix(cm, data.target_names, normalize=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save confusion matrix image\n",
+    "confusion_matrix_image = './reports/confusion_matrix.png'\n",
+    "cm_plot.savefig(confusion_matrix_image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LineaArtifact(name='plot-confusion-matrix', _version=1)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#save confusion matrix to lineapy\n",
+    "lineapy.save(plot_confusion_matrix, \"plot-confusion-matrix\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#commenting for change\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/step-0-prototype.ipynb b/notebooks/step-0-prototype.ipynb
index 3f7fee6c..608a10f2 100644
--- a/notebooks/step-0-prototype.ipynb
+++ b/notebooks/step-0-prototype.ipynb
@@ -383,7 +383,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/step-1-organize-ml-project.ipynb b/notebooks/step-1-organize-ml-project.ipynb
index 3a115fea..dcbbae38 100644
--- a/notebooks/step-1-organize-ml-project.ipynb
+++ b/notebooks/step-1-organize-ml-project.ipynb
@@ -1,5 +1,25 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test code\n",
+    "\n",
+    "def add(a,b):\n",
+    "    added = a + b\n",
+    "    retur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -459,7 +479,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/step-2-create-config-file.ipynb b/notebooks/step-2-create-config-file.ipynb
index a95d4434..364049ef 100644
--- a/notebooks/step-2-create-config-file.ipynb
+++ b/notebooks/step-2-create-config-file.ipynb
@@ -480,7 +480,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/notebooks/step-3-reusable-code.ipynb b/notebooks/step-3-reusable-code.ipynb
new file mode 100644
index 00000000..abe9652c
--- /dev/null
+++ b/notebooks/step-3-reusable-code.ipynb
@@ -0,0 +1,786 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:17:31.460557Z",
+     "start_time": "2019-06-16T21:17:29.395297Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import joblib\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import confusion_matrix, f1_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenif/course-ds-base\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Go to project root folder\n",
+    "%cd .."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'base': {'random_state': 42},\n",
+      " 'data': {'dataset_csv': 'data/raw/iris.csv',\n",
+      "          'features_path': 'data/processed/featured_iris.csv',\n",
+      "          'test_size': 0.2,\n",
+      "          'testset_path': 'data/processed/test_iris.csv',\n",
+      "          'trainset_path': 'data/processed/train_iris.csv'},\n",
+      " 'reports': {'confusion_matrix_image': 'reports/confusion_matrix.png',\n",
+      "             'metrics_file': 'reports/metrics.json'},\n",
+      " 'train': {'clf_params': {'C': 0.001,\n",
+      "                          'max_iter': 100,\n",
+      "                          'multi_class': 'multinomial',\n",
+      "                          'solver': 'lbfgs'},\n",
+      "           'model_path': 'models/model.joblib'}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Read config\n",
+    "import pprint\n",
+    "\n",
+    "with open('params.yaml') as conf_file:\n",
+    "    config = yaml.safe_load(conf_file)\n",
+    "\n",
+    "pprint.pprint(config)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:17:31.485189Z",
+     "start_time": "2019-06-16T21:17:31.473720Z"
+    }
+   },
+   "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>sepal length (cm)</th>\n",
+       "      <th>sepal width (cm)</th>\n",
+       "      <th>petal length (cm)</th>\n",
+       "      <th>petal width (cm)</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
+       "0                5.1               3.5                1.4               0.2   \n",
+       "1                4.9               3.0                1.4               0.2   \n",
+       "2                4.7               3.2                1.3               0.2   \n",
+       "3                4.6               3.1                1.5               0.2   \n",
+       "4                5.0               3.6                1.4               0.2   \n",
+       "\n",
+       "   target  \n",
+       "0       0  \n",
+       "1       0  \n",
+       "2       0  \n",
+       "3       0  \n",
+       "4       0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get data \n",
+    "\n",
+    "import pandas as pd\n",
+    "from sklearn.datasets import load_iris\n",
+    "\n",
+    "data = load_iris(as_frame=True)\n",
+    "dataset = data.frame\n",
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0: setosa\n",
+      "1: versicolor\n",
+      "2: virginica\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[None, None, None]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print labels for target values \n",
+    "\n",
+    "[print(f'{target}: {label}') for target, label in zip(data.target.unique(), data.target_names)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:17:32.328046Z",
+     "start_time": "2019-06-16T21:17:32.323611Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['sepal_length', 'sepal_width', 'petal_length', 'petal_width']"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# feature names\n",
+    "\n",
+    "dataset.columns = [colname.strip(' (cm)').replace(' ', '_') for colname in dataset.columns.tolist()]\n",
+    "\n",
+    "feature_names = dataset.columns.tolist()[:4]\n",
+    "feature_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save raw data\n",
+    "dataset.to_csv(config['data']['dataset_csv'], index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Features engineering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:02.150708Z",
+     "start_time": "2019-06-16T21:21:02.144518Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset['sepal_length_to_sepal_width'] = dataset['sepal_length'] / dataset['sepal_width']\n",
+    "dataset['petal_length_to_petal_width'] = dataset['petal_length'] / dataset['petal_width']\n",
+    "\n",
+    "dataset = dataset[[\n",
+    "    'sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n",
+    "#     'sepal_length_in_square', 'sepal_width_in_square', 'petal_length_in_square', 'petal_width_in_square',\n",
+    "    'sepal_length_to_sepal_width', 'petal_length_to_petal_width',\n",
+    "    'target'\n",
+    "]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:02.987144Z",
+     "start_time": "2019-06-16T21:21:02.976092Z"
+    }
+   },
+   "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>sepal_length</th>\n",
+       "      <th>sepal_width</th>\n",
+       "      <th>petal_length</th>\n",
+       "      <th>petal_width</th>\n",
+       "      <th>sepal_length_to_sepal_width</th>\n",
+       "      <th>petal_length_to_petal_width</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.457143</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.633333</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.468750</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.483871</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.388889</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sepal_length  sepal_width  petal_length  petal_width  \\\n",
+       "0           5.1          3.5           1.4          0.2   \n",
+       "1           4.9          3.0           1.4          0.2   \n",
+       "2           4.7          3.2           1.3          0.2   \n",
+       "3           4.6          3.1           1.5          0.2   \n",
+       "4           5.0          3.6           1.4          0.2   \n",
+       "\n",
+       "   sepal_length_to_sepal_width  petal_length_to_petal_width  target  \n",
+       "0                     1.457143                          7.0       0  \n",
+       "1                     1.633333                          7.0       0  \n",
+       "2                     1.468750                          6.5       0  \n",
+       "3                     1.483871                          7.5       0  \n",
+       "4                     1.388889                          7.0       0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save features\n",
+    "dataset.to_csv(config['data']['features_path'], index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Split dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:07.438133Z",
+     "start_time": "2019-06-16T21:21:07.431649Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((120, 7), (30, 7))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_dataset, test_dataset = train_test_split(\n",
+    "    dataset, test_size=config['data']['test_size'],\n",
+    "    random_state=config['base']['random_state']\n",
+    ")\n",
+    "train_dataset.shape, test_dataset.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save train and test sets\n",
+    "train_dataset.to_csv(config['data']['trainset_path'])\n",
+    "test_dataset.to_csv(config['data']['testset_path'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:10.932148Z",
+     "start_time": "2019-06-16T21:21:10.927844Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Get X and Y\n",
+    "\n",
+    "y_train = train_dataset.loc[:, 'target'].values.astype('int32')\n",
+    "X_train = train_dataset.drop('target', axis=1).values.astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:55.427365Z",
+     "start_time": "2019-06-16T21:21:55.416431Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression(C=0.001, multi_class='multinomial', random_state=42)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create an instance of Logistic Regression Classifier CV and fit the data\n",
+    "\n",
+    "logreg = LogisticRegression(\n",
+    "    **config['train']['clf_params'],\n",
+    "    random_state=config['base']['random_state']\n",
+    ")\n",
+    "logreg.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['models/model.joblib']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "joblib.dump(logreg, config['train']['model_path'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:55.875303Z",
+     "start_time": "2019-06-16T21:21:55.864724Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from src.report.visualization import plot_confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:56.090756Z",
+     "start_time": "2019-06-16T21:21:56.086966Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Get X and Y\n",
+    "\n",
+    "y_test = test_dataset.loc[:, 'target'].values.astype('int32')\n",
+    "X_test = test_dataset.drop('target', axis=1).values.astype('float32')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:56.270245Z",
+     "start_time": "2019-06-16T21:21:56.265054Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "prediction = logreg.predict(X_test)\n",
+    "cm = confusion_matrix(prediction, y_test)\n",
+    "f1 = f1_score(y_true = y_test, y_pred = prediction, average='macro')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:56.493617Z",
+     "start_time": "2019-06-16T21:21:56.489929Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9305555555555555"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# f1 score value\n",
+    "f1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save metrics\n",
+    "metrics = {\n",
+    "    'f1': f1\n",
+    "}\n",
+    "\n",
+    "with open(config['reports']['metrics_file'], 'w') as mf:\n",
+    "    json.dump(\n",
+    "        obj=metrics,\n",
+    "        fp=mf,\n",
+    "        indent=4\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:21:56.966279Z",
+     "start_time": "2019-06-16T21:21:56.726149Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'np' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m--------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                    Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-29-0b496aa9e4b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcm_plot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/course-ds-base/src/report/visualization.py\u001b[0m in \u001b[0;36mplot_confusion_matrix\u001b[0;34m(cm, target_names, title, cmap, normalize)\u001b[0m\n\u001b[1;32m     39\u001b[0m     \"\"\"\n\u001b[1;32m     40\u001b[0m     \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mmisclass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcmap\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "cm_plot = plot_confusion_matrix(cm, data.target_names, normalize=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'cm_plot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m--------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                    Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-27-79d7e70a9d4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Save confusion matrix image\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcm_plot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'reports'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'confusion_matrix_image'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'cm_plot' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# Save confusion matrix image\n",
+    "cm_plot.savefig(config['reports']['confusion_matrix_image'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/step-4-build-ml-pipeline.ipynb b/notebooks/step-4-build-ml-pipeline.ipynb
new file mode 100644
index 00000000..230d2b08
--- /dev/null
+++ b/notebooks/step-4-build-ml-pipeline.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2019-06-16T21:17:31.460557Z",
+     "start_time": "2019-06-16T21:17:29.395297Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Assist with the automatic loading of the Python module in this jupyter notebook\n",
+    "%load_ext autoreload \n",
+    "%autoreload 2\n",
+    "\n",
+    "import itertools\n",
+    "import joblib\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import confusion_matrix, f1_score\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import yaml\n",
+    "\n",
+    "from src.report.visualization import plot_confusion_matrix "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/mnt/c/Users/MR-BEST/course-ds-base-root/course-ds-base\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mr-best/.local/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
+      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Go to project root folder\n",
+    "\n",
+    "%cd ..\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data load completed successfully\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the function to load raw data\n",
+    "\n",
+    "from src.stages.data_load import data_load\n",
+    "\n",
+    "# Call function\n",
+    "data_load(config_file = 'params.yaml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "data load completed successfully\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Shell prompt for running \"load_data\" function\n",
+    "\n",
+    "!python3 src/stages/data_load.py --config=params.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "iris.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash \n",
+    "\n",
+    "# View the Raw Iris dataset saved \n",
+    "\n",
+    "ls data/raw"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract feautures using python module at src/stages/featurize.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 09:30:00,157 — FEATURIZE — INFO — Load the raw data\n",
+      "2024-01-15 09:30:00,166 — FEATURIZE — INFO — Curate by extraction of features from the dataset\n",
+      "2024-01-15 09:30:00,169 — FEATURIZE — INFO — Save features\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load,curate and save features (x1,...xn) the function to load raw data\n",
+    "\n",
+    "from src.stages.featurize import featurize\n",
+    "\n",
+    "# Call function\n",
+    "featurize(config_path = 'params.yaml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 09:30:21,699 — FEATURIZE — INFO — Load the raw data\n",
+      "2024-01-15 09:30:21,706 — FEATURIZE — INFO — Curate by extraction of features from the dataset\n",
+      "2024-01-15 09:30:21,708 — FEATURIZE — INFO — Save features\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Shell prompt for running \"load_data\" function. Load,curate and save features (x1,...xn)\n",
+    "\n",
+    "!python src/stages/featurize.py --config=params.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Split dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 09:31:43,126 — DATA_SPLIT — INFO — Load features\n",
+      "2024-01-15 09:31:43,137 — DATA_SPLIT — INFO — Split features into train and test sets\n",
+      "2024-01-15 09:31:43,140 — DATA_SPLIT — INFO — Save features for training and testing models\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Call the Split module by loading saved features from local memory, splitting into train and test sets; and saving completion\n",
+    "\n",
+    "from src.stages.data_split import data_split\n",
+    "\n",
+    "# Call function\n",
+    "data_split(config_path = 'params.yaml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 09:31:51,795 — DATA_SPLIT — INFO — Load features\n",
+      "2024-01-15 09:31:51,804 — DATA_SPLIT — INFO — Split features into train and test sets\n",
+      "2024-01-15 09:31:51,805 — DATA_SPLIT — INFO — Save features for training and testing models\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Shell prompt for running \"data split\" function\n",
+    "\n",
+    "!python3 src/stages/data_split.py --config=params.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 10:22:31,528 — TRAIN — INFO — Get model/estimator name\n",
+      "2024-01-15 10:22:31,529 — TRAIN — INFO — The name of Model/Estimator: logreg\n",
+      "2024-01-15 10:22:31,530 — TRAIN — INFO — Load train dataset\n",
+      "2024-01-15 10:22:31,544 — TRAIN — INFO — Train model/estimator\n",
+      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n",
+      "2024-01-15 10:22:31,577 — TRAIN — INFO — Best score: 0.857564307288572\n",
+      "2024-01-15 10:22:31,578 — TRAIN — INFO — Trained Model Saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Name model,Load, train and save model/estimator\n",
+    "\n",
+    "from src.stages.train import train_model\n",
+    "\n",
+    "# Call function\n",
+    "train_model(config_path = 'params.yaml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 10:22:21,429 — TRAIN — INFO — Get model/estimator name\n",
+      "2024-01-15 10:22:21,429 — TRAIN — INFO — The name of Model/Estimator: logreg\n",
+      "2024-01-15 10:22:21,429 — TRAIN — INFO — Load train dataset\n",
+      "2024-01-15 10:22:21,440 — TRAIN — INFO — Train model/estimator\n",
+      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n",
+      "2024-01-15 10:22:21,463 — TRAIN — INFO — Best score: 0.857564307288572\n",
+      "2024-01-15 10:22:21,463 — TRAIN — INFO — Trained Model Saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Shell prompt for running \"train model\" function\n",
+    "\n",
+    "!python3 src/stages/train.py --config=params.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.  EVALUATE MODEL : load dataset,test, Evaluate with F1 and CM and save model/estimator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 10:51:16,267 — EVALUATE — INFO — Load model\n",
+      "2024-01-15 10:51:16,276 — EVALUATE — INFO — Load test dataset\n",
+      "2024-01-15 10:51:16,288 — EVALUATE — INFO — Evaluate (build report)\n",
+      "2024-01-15 10:51:16,295 — EVALUATE — INFO — Save metrics\n",
+      "2024-01-15 10:51:16,302 — EVALUATE — INFO — F1 metrics file saved to : reports/metrics.json\n",
+      "2024-01-15 10:51:16,303 — EVALUATE — INFO — Save confusion matrix\n",
+      "2024-01-15 10:51:16,474 — EVALUATE — INFO — Confusion matrix saved to : reports/confusion_matrix.png\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "#  Load dataset,test, Evaluate with F1 and CM and save model/estimator\n",
+    "\n",
+    "from src.stages.evaluate import evaluate_model\n",
+    "\n",
+    "\n",
+    "# Call function\n",
+    "evaluate_model(config_path = 'params.yaml')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 10:51:24,829 — EVALUATE — INFO — Load model\n",
+      "2024-01-15 10:51:24,866 — EVALUATE — INFO — Load test dataset\n",
+      "2024-01-15 10:51:24,877 — EVALUATE — INFO — Evaluate (build report)\n",
+      "2024-01-15 10:51:24,880 — EVALUATE — INFO — Save metrics\n",
+      "2024-01-15 10:51:24,884 — EVALUATE — INFO — F1 metrics file saved to : reports/metrics.json\n",
+      "2024-01-15 10:51:24,884 — EVALUATE — INFO — Save confusion matrix\n",
+      "2024-01-15 10:51:25,005 — EVALUATE — INFO — Confusion matrix saved to : reports/confusion_matrix.png\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Shell prompt for running \"EVALUATE MODEL\" function. oad dataset,test, Evaluate with F1 and CM and save model/estimator\n",
+    "\n",
+    "!python3 src/stages/evaluate.py --config=params.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/step-5-automate-ml-pipeline-bee.ipynb b/notebooks/step-5-automate-ml-pipeline-bee.ipynb
new file mode 100644
index 00000000..d5c924fc
--- /dev/null
+++ b/notebooks/step-5-automate-ml-pipeline-bee.ipynb
@@ -0,0 +1,356 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.0 Change working Directory to Root Directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/mnt/c/Users/MR-BEST/course-ds-base-root/course-ds-base\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/mr-best/.local/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.\n",
+      "  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set the repository root as a working directory \n",
+    "%cd .."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 0.1 Init DVC repository\n",
+    " 2.1 Init DVC repository and setup DVC remote storage\n",
+    "\n",
+    "dvc init\n",
+    "\n",
+    "2.2 Add DVC repository under git control\n",
+    "\n",
+    "git add .\n",
+    "git commit -m \"Init DVC repo\"\n",
+    "\n",
+    "# 0.2 View config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "#### Initial state before the update\n",
+      "\n",
+      "# base:\n",
+      "#   random_state: 42\n",
+      "#   log_level: INFO\n",
+      "\n",
+      "# data:\n",
+      "  # dataset_csv: 'data/raw/iris.csv'\n",
+      "#   features_path: 'data/processed/featured_iris.csv'\n",
+      "#   test_size: 0.2\n",
+      "#   trainset_path: 'data/processed/train_iris.csv'\n",
+      "#   testset_path: 'data/processed/test_iris.csv'\n",
+      "\n",
+      "\n",
+      "# train:\n",
+      "#   clf_params:\n",
+      "#     'C': 0.001\n",
+      "#     'solver': 'lbfgs'\n",
+      "#     'multi_class': 'multinomial'\n",
+      "#     'max_iter': 100\n",
+      "#   model_path: 'models/model.joblib'\n",
+      "\n",
+      "# reports:\n",
+      "#   metrics_file: 'reports/metrics.json'\n",
+      "#   confusion_matrix_image: 'reports/confusion_matrix.png'\n",
+      "\n",
+      "  #### Newer Versions of Metrics\n",
+      "\n",
+      "  \n",
+      "base:\n",
+      "  random_state: 42\n",
+      "  log_level: INFO\n",
+      "\n",
+      "\n",
+      "data_load:\n",
+      "  dataset_csv: 'data/raw/iris.csv'\n",
+      "\n",
+      "\n",
+      "featurize:\n",
+      "  features_path: 'data/processed/featured_iris.csv'\n",
+      "  target_column: target\n",
+      "\n",
+      "\n",
+      "data_split:\n",
+      "  test_size: 0.2\n",
+      "  trainset_path: 'data/processed/train_iris.csv'\n",
+      "  testset_path: 'data/processed/test_iris.csv'\n",
+      "\n",
+      "\n",
+      "train:\n",
+      "\n",
+      "  cv: 3\n",
+      "  estimator_name: logreg\n",
+      "  estimators:\n",
+      "    logreg: # sklearn.linear_model.LogisticRegression\n",
+      "      param_grid: # params of GridSearchCV constructor\n",
+      "        C: [0.001]\n",
+      "        max_iter: [100]\n",
+      "        solver: ['lbfgs']\n",
+      "        multi_class: ['multinomial']\n",
+      "    svm: # sklearn.svm.SVC\n",
+      "      param_grid:\n",
+      "        C: [0.1, 1.0]\n",
+      "        kernel: ['rbf', 'linear']\n",
+      "        gamma: ['scale']\n",
+      "        degree: [3, 5]\n",
+      "  model_path: models/model.joblib\n",
+      "\n",
+      "\n",
+      "evaluate:\n",
+      "  reports_dir: reports\n",
+      "  metrics_file: 'metrics.json'\n",
+      "  confusion_matrix_image: 'confusion_matrix.png'"
+     ]
+    }
+   ],
+   "source": [
+    "# Look on stages config \n",
+    "!cat params.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Create and run stages for a DVC pipeline\n",
+    "\n",
+    "##  First Stage of ML Pipeline : Extract and Raw Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "dvc stage add -n data_load \\\n",
+    "    -d src/stages/data_load.py \\\n",
+    "    -o data/raw/iris.csv \\\n",
+    "    -p base,data_load \\\n",
+    "    python src/stages/data_load.py --config=params.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Featurization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "dvc stage add -n featurize \\\n",
+    "    -d src/stages/featurize.py \\\n",
+    "    -d data/raw/iris.csv \\\n",
+    "    -o data/processed/featured_iris.csv \\\n",
+    "    -p base,featurize \\\n",
+    "    python src/stages/featurize.py --config=params.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3 Split dataset into train/test edited directly into the dvc.yaml config file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "data_split:\n",
+    "    cmd: python src/stages/data_split.py --config=params.yaml\n",
+    "    deps:\n",
+    "    - data/processed/featured_iris.csv\n",
+    "    - src/stages/data_split.py\n",
+    "    params:\n",
+    "    - base\n",
+    "    - data_split\n",
+    "    - featurize\n",
+    "    outs:\n",
+    "    - data/processed/test_iris.csv\n",
+    "    - data/processed/train_iris.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4 Train Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "train:\n",
+    "    cmd: python src/stages/train.py --config=params.yaml\n",
+    "    deps:\n",
+    "    - data/processed/test_iris.csv\n",
+    "    - data/processed/train_iris.csv\n",
+    "    - src/stages/train.py\n",
+    "    params:\n",
+    "    - base\n",
+    "    - train\n",
+    "    outs:\n",
+    "    - models/model.joblib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5 Evaluate Model with Test Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-01-15 10:22:31,528 — TRAIN — INFO — Get model/estimator name\n",
+      "2024-01-15 10:22:31,529 — TRAIN — INFO — The name of Model/Estimator: logreg\n",
+      "2024-01-15 10:22:31,530 — TRAIN — INFO — Load train dataset\n",
+      "2024-01-15 10:22:31,544 — TRAIN — INFO — Train model/estimator\n",
+      "Fitting 3 folds for each of 1 candidates, totalling 3 fits\n",
+      "2024-01-15 10:22:31,577 — TRAIN — INFO — Best score: 0.857564307288572\n",
+      "2024-01-15 10:22:31,578 — TRAIN — INFO — Trained Model Saved\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluate:\n",
+    "    cmd: python src/stages/evaluate.py --config=params.yaml\n",
+    "    deps:\n",
+    "    - models/model.joblib\n",
+    "    - data/processed/test_iris.csv\n",
+    "\n",
+    "    params:\n",
+    "    - base\n",
+    "    - train\n",
+    "    - data_split\n",
+    "    - featurize\n",
+    "    - evaluate\n",
+    "    outs:\n",
+    "    - metrics.json\n",
+    "    - confusion_matrix.png"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#  MAnual entry on bash shell\n",
+    "\n",
+    "dvc stage add -n evaluate \\\n",
+    "    -d models/model.joblib \\\n",
+    "    -d data/processed/test_iris.csv\\\n",
+    "    -o metrics.json \\\n",
+    "    -o confusion_matrix.png\\\n",
+    "    -p base, evaluate \\\n",
+    "    python src/stages/evaluate.py --config=params.yaml"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/params.yaml b/params.yaml
index a48a0538..161c5f7c 100644
--- a/params.yaml
+++ b/params.yaml
@@ -1,22 +1,45 @@
+  
 base:
   random_state: 42
+  log_level: INFO
 
-data:
+
+data_load:
   dataset_csv: 'data/raw/iris.csv'
+
+
+featurize:
   features_path: 'data/processed/featured_iris.csv'
-  test_size: 0.2
+  target_column: target
+
+
+data_split:
+  test_size: 0.21
   trainset_path: 'data/processed/train_iris.csv'
   testset_path: 'data/processed/test_iris.csv'
 
 
 train:
-  clf_params:
-    'C': 0.001
-    'solver': 'lbfgs'
-    'multi_class': 'multinomial'
-    'max_iter': 100
-  model_path: 'models/model.joblib'
-
-reports:
-  metrics_file: 'reports/metrics.json'
-  confusion_matrix_image: 'reports/confusion_matrix.png'
\ No newline at end of file
+
+  cv: 3
+  estimator_name: logreg
+  estimators:
+    logreg: # sklearn.linear_model.LogisticRegression
+      param_grid: # params of GridSearchCV constructor
+        C: [0.001]
+        max_iter: [100]
+        solver: ['lbfgs']
+        multi_class: ['multinomial']
+    svm: # sklearn.svm.SVC
+      param_grid:
+        C: [0.1, 1.0]
+        kernel: ['rbf', 'linear']
+        gamma: ['scale']
+        degree: [3, 5]
+  model_path: models/model.joblib
+
+
+evaluate:
+  reports_dir: reports
+  metrics_file: 'metrics.json'
+  confusion_matrix_image: 'confusion_matrix.png'
\ No newline at end of file
diff --git a/reports/.gitignore b/reports/.gitignore
deleted file mode 100644
index b722e9e1..00000000
--- a/reports/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-!.gitignore
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index d5b4910e..572cf417 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
-dvc==2.6.4
+
+dvc==2.8.3
 joblib==1.0.1
 jupyter==1.0.0
 jupyter_contrib_nbextensions==0.5.1
@@ -10,4 +11,6 @@ python-box==5.4.1
 pyyaml==5.4.1
 scikit-learn==0.24.2
 scipy==1.7.1
-tqdm==4.62.2
\ No newline at end of file
+tqdm==4.62.2
+
+
diff --git a/src/evaluate.py b/src/evaluate.py
new file mode 100644
index 00000000..599d73d5
--- /dev/null
+++ b/src/evaluate.py
@@ -0,0 +1,112 @@
+import json
+import math
+import os
+import pickle
+import sys
+
+import pandas as pd
+from sklearn import metrics
+from sklearn import tree
+from dvclive import Live
+from matplotlib import pyplot as plt
+
+
+def evaluate(model, matrix, split, live, save_path):
+    """
+    Dump all evaluation metrics and plots for given datasets.
+
+    Args:
+        model (sklearn.ensemble.RandomForestClassifier): Trained classifier.
+        matrix (scipy.sparse.csr_matrix): Input matrix.
+        split (str): Dataset name.
+        live (dvclive.Live): Dvclive instance.
+        save_path (str): Path to save the metrics.
+    """
+    labels = matrix[:, 1].toarray().astype(int)
+    x = matrix[:, 2:]
+
+    predictions_by_class = model.predict_proba(x)
+    predictions = predictions_by_class[:, 1]
+
+    # Use dvclive to log a few simple metrics...
+    avg_prec = metrics.average_precision_score(labels, predictions)
+    roc_auc = metrics.roc_auc_score(labels, predictions)
+    if not live.summary:
+        live.summary = {"avg_prec": {}, "roc_auc": {}}
+    live.summary["avg_prec"][split] = avg_prec
+    live.summary["roc_auc"][split] = roc_auc
+
+    # ... and plots...
+    # ... like an roc plot...
+    live.log_sklearn_plot("roc", labels, predictions, name=f"roc/{split}")
+    # ... and precision recall plot...
+    # ... which passes `drop_intermediate=True` to the sklearn method...
+    live.log_sklearn_plot(
+        "precision_recall",
+        labels,
+        predictions,
+        name=f"prc/{split}",
+        drop_intermediate=True,
+    )
+    # ... and confusion matrix plot
+    live.log_sklearn_plot(
+        "confusion_matrix",
+        labels.squeeze(),
+        predictions_by_class.argmax(-1),
+        name=f"cm/{split}",
+    )
+
+
+def save_importance_plot(live, model, feature_names):
+    """
+    Save feature importance plot.
+
+    Args:
+        live (dvclive.Live): DVCLive instance.
+        model (sklearn.ensemble.RandomForestClassifier): Trained classifier.
+        feature_names (list): List of feature names.
+    """
+    fig, axes = plt.subplots(dpi=100)
+    fig.subplots_adjust(bottom=0.2, top=0.95)
+    axes.set_ylabel("Mean decrease in impurity")
+
+    importances = model.feature_importances_
+    forest_importances = pd.Series(importances, index=feature_names).nlargest(n=30)
+    forest_importances.plot.bar(ax=axes)
+
+    live.log_image("importance.png", fig)
+
+
+def main():
+    EVAL_PATH = "eval"
+
+    if len(sys.argv) != 3:
+        sys.stderr.write("Arguments error. Usage:\n")
+        sys.stderr.write("\tpython evaluate.py model features\n")
+        sys.exit(1)
+
+    model_file = sys.argv[1]
+    train_file = os.path.join(sys.argv[2], "train.pkl")
+    test_file = os.path.join(sys.argv[2], "test.pkl")
+
+    # Load model and data.
+    with open(model_file, "rb") as fd:
+        model = pickle.load(fd)
+
+    with open(train_file, "rb") as fd:
+        train, feature_names = pickle.load(fd)
+
+    with open(test_file, "rb") as fd:
+        test, _ = pickle.load(fd)
+
+    # Evaluate train and test datasets.
+    with Live(EVAL_PATH, dvcyaml=False) as live:
+        evaluate(model, train, "train", live, save_path=EVAL_PATH)
+        evaluate(model, test, "test", live, save_path=EVAL_PATH)
+
+        # Dump feature importance plot.
+        save_importance_plot(live, model, feature_names)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/featurization.py b/src/featurization.py
new file mode 100644
index 00000000..9f493049
--- /dev/null
+++ b/src/featurization.py
@@ -0,0 +1,136 @@
+import os
+import pickle
+import sys
+
+import numpy as np
+import pandas as pd
+import scipy.sparse as sparse
+import yaml
+from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
+
+
+def get_df(data):
+    """Read the input data file and return a data frame."""
+    df = pd.read_csv(
+        data,
+        encoding="utf-8",
+        header=None,
+        delimiter="\t",
+        names=["id", "label", "text"],
+    )
+    sys.stderr.write(f"The input data frame {data} size is {df.shape}\n")
+    return df
+
+
+def save_matrix(df, matrix, names, output):
+    """
+    Save the matrix to a pickle file.
+
+    Args:
+        df (pandas.DataFrame): Input data frame.
+        matrix (scipy.sparse.csr_matrix): Input matrix.
+        names (list): List of feature names.
+        output (str): Output file name.
+    """
+    id_matrix = sparse.csr_matrix(df.id.astype(np.int64)).T
+    label_matrix = sparse.csr_matrix(df.label.astype(np.int64)).T
+
+    result = sparse.hstack([id_matrix, label_matrix, matrix], format="csr")
+
+    msg = "The output matrix {} size is {} and data type is {}\n"
+    sys.stderr.write(msg.format(output, result.shape, result.dtype))
+
+    with open(output, "wb") as fd:
+        pickle.dump((result, names), fd)
+    pass
+
+
+def generate_and_save_train_features(train_input, train_output, bag_of_words, tfidf):
+    """
+    Generate train feature matrix.
+
+    Args:
+        train_input (str): Train input file name.
+        train_output (str): Train output file name.
+        bag_of_words (sklearn.feature_extraction.text.CountVectorizer): Bag of words.
+        tfidf (sklearn.feature_extraction.text.TfidfTransformer): TF-IDF transformer.
+    """
+    df_train = get_df(train_input)
+    train_words = np.array(df_train.text.str.lower().values)
+
+    bag_of_words.fit(train_words)
+
+    train_words_binary_matrix = bag_of_words.transform(train_words)
+    feature_names = bag_of_words.get_feature_names_out()
+
+    tfidf.fit(train_words_binary_matrix)
+    train_words_tfidf_matrix = tfidf.transform(train_words_binary_matrix)
+
+    save_matrix(df_train, train_words_tfidf_matrix, feature_names, train_output)
+
+
+def generate_and_save_test_features(test_input, test_output, bag_of_words, tfidf):
+    """
+    Generate test feature matrix.
+
+    Args:
+        test_input (str): Test input file name.
+        test_output (str): Test output file name.
+        bag_of_words (sklearn.feature_extraction.text.CountVectorizer): Bag of words.
+        tfidf (sklearn.feature_extraction.text.TfidfTransformer): TF-IDF transformer.
+    """
+    df_test = get_df(test_input)
+    test_words = np.array(df_test.text.str.lower().values)
+
+    test_words_binary_matrix = bag_of_words.transform(test_words)
+    test_words_tfidf_matrix = tfidf.transform(test_words_binary_matrix)
+    feature_names = bag_of_words.get_feature_names_out()
+
+    save_matrix(df_test, test_words_tfidf_matrix, feature_names, test_output)
+
+
+def main():
+    params = yaml.safe_load(open("params.yaml"))["featurize"]
+
+    np.set_printoptions(suppress=True)
+
+    if len(sys.argv) != 3 and len(sys.argv) != 5:
+        sys.stderr.write("Arguments error. Usage:\n")
+        sys.stderr.write("\tpython featurization.py data-dir-path features-dir-path\n")
+        sys.exit(1)
+
+    in_path = sys.argv[1]
+    out_path = sys.argv[2]
+
+    train_input = os.path.join(in_path, "train.tsv")
+    test_input = os.path.join(in_path, "test.tsv")
+    train_output = os.path.join(out_path, "train.pkl")
+    test_output = os.path.join(out_path, "test.pkl")
+
+    max_features = params["max_features"]
+    ngrams = params["ngrams"]
+
+    os.makedirs(out_path, exist_ok=True)
+
+    bag_of_words = CountVectorizer(
+        stop_words="english", max_features=max_features, ngram_range=(1, ngrams)
+    )
+    tfidf = TfidfTransformer(smooth_idf=False)
+
+    generate_and_save_train_features(
+        train_input=train_input,
+        train_output=train_output,
+        bag_of_words=bag_of_words,
+        tfidf=tfidf,
+    )
+
+    generate_and_save_test_features(
+        test_input=test_input,
+        test_output=test_output,
+        bag_of_words=bag_of_words,
+        tfidf=tfidf,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/prepare.py b/src/prepare.py
new file mode 100644
index 00000000..e6b3a2c0
--- /dev/null
+++ b/src/prepare.py
@@ -0,0 +1,78 @@
+import os
+import random
+import re
+import sys
+import xml.etree.ElementTree
+
+import yaml
+
+
+def process_posts(input_lines, fd_out_train, fd_out_test, target_tag, split):
+    """
+    Process the input lines and write the output to the output files.
+
+    Args:
+        input_lines (list): List of input lines.
+        fd_out_train (file): Output file for the training data set.
+        fd_out_test (file): Output file for the test data set.
+        target_tag (str): Target tag.
+        split (float): Test data set split ratio.
+    """
+    num = 1
+    for line in input_lines:
+        try:
+            fd_out = fd_out_train if random.random() > split else fd_out_test
+            attr = xml.etree.ElementTree.fromstring(line).attrib
+
+            pid = attr.get("Id", "")
+            label = 1 if target_tag in attr.get("Tags", "") else 0
+            title = re.sub(r"\s+", " ", attr.get("Title", "")).strip()
+            body = re.sub(r"\s+", " ", attr.get("Body", "")).strip()
+            text = title + " " + body
+
+            fd_out.write("{}\t{}\t{}\n".format(pid, label, text))
+
+            num += 1
+        except Exception as ex:
+            sys.stderr.write(f"Skipping the broken line {num}: {ex}\n")
+
+
+def main():
+    params = yaml.safe_load(open("params.yaml"))["prepare"]
+
+    if len(sys.argv) != 2:
+        sys.stderr.write("Arguments error. Usage:\n")
+        sys.stderr.write("\tpython prepare.py data-file\n")
+        sys.exit(1)
+
+    # Test data set split ratio
+    split = params["split"]
+    random.seed(params["seed"])
+
+    input = sys.argv[1]
+    output_train = os.path.join("data", "prepared", "train.tsv")
+    output_test = os.path.join("data", "prepared", "test.tsv")
+
+    os.makedirs(os.path.join("data", "prepared"), exist_ok=True)
+
+    input_lines = []
+    with open(input) as fd_in:
+        input_lines = fd_in.readlines()
+
+    fd_out_train = open(output_train, "w", encoding="utf-8")
+    fd_out_test = open(output_test, "w", encoding="utf-8")
+
+    process_posts(
+        input_lines=input_lines,
+        fd_out_train=fd_out_train,
+        fd_out_test=fd_out_test,
+        target_tag="<r>",
+        split=split,
+    )
+
+    fd_out_train.close()
+    fd_out_test.close()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/report/visualization.py b/src/report/visualization.py
new file mode 100644
index 00000000..32952a43
--- /dev/null
+++ b/src/report/visualization.py
@@ -0,0 +1,74 @@
+import itertools
+import matplotlib.pyplot as plt
+import numpy as np 
+
+def plot_confusion_matrix(cm,
+                          target_names,
+                          title='Confusion matrix',
+                          cmap=None,
+                          normalize=True):
+    """
+    given a sklearn confusion matrix (cm), make a nice plot
+
+    Arguments
+    ---------
+    cm:           confusion matrix from sklearn.metrics.confusion_matrix
+
+    target_names: given classification classes such as [0, 1, 2]
+                  the class names, for example: ['high', 'medium', 'low']
+
+    title:        the text to display at the top of the matrix
+
+    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm
+                  see http://matplotlib.org/examples/color/colormaps_reference.html
+                  plt.get_cmap('jet') or plt.cm.Blues
+
+    normalize:    If False, plot the raw numbers
+                  If True, plot the proportions
+
+    Usage
+    -----
+    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by
+                                                              # sklearn.metrics.confusion_matrix
+                          normalize    = True,                # show proportions
+                          target_names = y_labels_vals,       # list of names of the classes
+                          title        = best_estimator_name) # title of graph
+    Citiation
+    ---------
+    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html
+    """
+    accuracy = np.trace(cm) / float(np.sum(cm))
+    misclass = 1 - accuracy
+
+    if cmap is None:
+      cmap = plt.get_cmap('Blues')
+
+    plt.figure(figsize=(8, 6))
+    plt.imshow(cm, interpolation='nearest', cmap=cmap)
+    plt.title(title)
+    plt.colorbar()
+
+    if target_names is not None:
+        tick_marks = np.arange(len(target_names))
+        plt.xticks(tick_marks, target_names, rotation=45)
+        plt.yticks(tick_marks, target_names)
+
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+
+    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
+    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
+      if normalize:
+            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
+                     horizontalalignment="center",
+                     color="white" if cm[i, j] > thresh else "black")
+      else:
+            plt.text(j, i, "{:,}".format(cm[i, j]),
+                     horizontalalignment="center",
+                     color="white" if cm[i, j] > thresh else "black")
+
+    plt.tight_layout()
+    plt.ylabel('True label')
+    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
+    
+    return plt.gcf()
\ No newline at end of file
diff --git a/src/requirements.txt b/src/requirements.txt
new file mode 100644
index 00000000..e72d1a30
--- /dev/null
+++ b/src/requirements.txt
@@ -0,0 +1,6 @@
+dvclive>=3.0
+pandas
+pyaml
+scikit-learn>=1.3
+scipy
+matplotlib
diff --git a/src/stages/data_load.py b/src/stages/data_load.py
new file mode 100644
index 00000000..90518af8
--- /dev/null
+++ b/src/stages/data_load.py
@@ -0,0 +1,38 @@
+# Import Dependencies
+
+import argparse
+import pandas as pd
+from sklearn.datasets import load_iris
+from typing import Text
+import yaml
+
+
+# Load data functions
+
+def data_load(config_file: Text) -> None:
+    
+    # Load configuration file
+    with open('params.yaml') as conf_file:
+        config = yaml.safe_load(conf_file)
+    
+    # load the raw data functions from sklearn
+    data = load_iris(as_frame=True)
+    dataset = data.frame
+    
+    # feature names curated from dataset
+    dataset.columns = [colname.strip(' (cm)').replace(' ', '_') for colname in dataset.columns.tolist()]
+       
+    # Save raw data to path contained in params.yaml   
+    dataset.to_csv(config['data_load']['dataset_csv'], index=False)
+    
+print ("data load completed successfully")
+
+# Call the argparser api
+
+if __name__ == '__main__':
+    
+    arg_parser = argparse.ArgumentParser()
+    arg_parser.add_argument("--config", dest = 'config',required=True,help="input config file path")
+    args = arg_parser.parse_args()
+    
+    data_load(config_file=args.config)
\ No newline at end of file
diff --git a/src/stages/data_split.py b/src/stages/data_split.py
new file mode 100644
index 00000000..22352633
--- /dev/null
+++ b/src/stages/data_split.py
@@ -0,0 +1,44 @@
+import argparse
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from typing import Text
+import yaml
+
+from src.utils.logs import get_logger
+
+
+def data_split(config_path: Text) -> None:
+    """Split dataset into train/test.
+    Args:
+        config_path {Text}: path to config
+    """
+
+    with open('params.yaml') as conf_file:
+        config = yaml.safe_load(conf_file)
+
+    logger = get_logger('DATA_SPLIT', log_level=config['base']['log_level'])
+
+    logger.info('Load features')
+    dataset = pd.read_csv(config['featurize']['features_path'])
+
+    logger.info('Split features into train and test sets')
+    train_dataset, test_dataset = train_test_split(
+        dataset,
+        test_size=config['data_split']['test_size'],
+        random_state=config['base']['random_state']
+    )
+
+    logger.info('Save features for training and testing models')
+    train_csv_path = config['data_split']['trainset_path']
+    test_csv_path = config['data_split']['testset_path']
+    train_dataset.to_csv(train_csv_path, index=False)
+    test_dataset.to_csv(test_csv_path, index=False)
+
+
+if __name__ == '__main__':
+
+    args_parser = argparse.ArgumentParser()
+    args_parser.add_argument('--config', dest='config', required=True, help='Split dataset into train/test')
+    args = args_parser.parse_args()
+
+    data_split(config_path=args.config)
\ No newline at end of file
diff --git a/src/stages/evaluate.py b/src/stages/evaluate.py
new file mode 100644
index 00000000..3c10a4b3
--- /dev/null
+++ b/src/stages/evaluate.py
@@ -0,0 +1,76 @@
+import argparse
+import joblib
+import json
+import pandas as pd
+from pathlib import Path
+from sklearn.datasets import load_iris
+from sklearn.metrics import confusion_matrix, f1_score
+from typing import Text, Dict
+import yaml
+
+from src.report.visualization import plot_confusion_matrix
+from src.utils.logs import get_logger
+
+
+def evaluate_model(config_path: Text) -> None:
+    """Evaluate model.
+    Args:
+        config_path {Text}: path to config
+    """
+
+    with open('params.yaml') as conf_file:
+        config = yaml.safe_load(conf_file)
+
+    logger = get_logger('EVALUATE', log_level=config['base']['log_level'])
+
+    logger.info('Load model')
+    model_path = config['train']['model_path']
+    model = joblib.load(model_path)
+
+    logger.info('Load test dataset')
+    test_df = pd.read_csv(config['data_split']['testset_path'])
+
+    logger.info('Evaluate (build report)')
+    target_column=config['featurize']['target_column']
+    y_test = test_df.loc[:, target_column].values
+    X_test = test_df.drop(target_column, axis=1).values
+
+    prediction = model.predict(X_test)
+    f1 = f1_score(y_true=y_test, y_pred=prediction, average='macro')
+    cm = confusion_matrix(prediction, y_test)
+    report = {
+        'f1': f1,
+        'cm': cm,
+        'actual': y_test,
+        'predicted': prediction
+    }
+
+    logger.info('Save metrics')
+    # save f1 metrics file
+    reports_folder = Path(config['evaluate']['reports_dir'])
+    metrics_path = reports_folder / config['evaluate']['metrics_file']
+
+    json.dump(
+        obj={'f1_score': report['f1']},
+        fp=open(metrics_path, 'w')
+    )
+
+    logger.info(f'F1 metrics file saved to : {metrics_path}')
+
+    logger.info('Save confusion matrix')
+    # save confusion_matrix.png
+    plt = plot_confusion_matrix(cm=report['cm'],
+                                target_names=load_iris(as_frame=True).target_names.tolist(),
+                                normalize=False)
+    confusion_matrix_png_path = reports_folder / config['evaluate']['confusion_matrix_image']
+    plt.savefig(confusion_matrix_png_path)
+    logger.info(f'Confusion matrix saved to : {confusion_matrix_png_path}')
+
+
+if __name__ == '__main__':
+
+    args_parser = argparse.ArgumentParser()
+    args_parser.add_argument('--config', dest='config', required=True, help="Assist in Evaluation of model using F1 and CM")
+    args = args_parser.parse_args()
+
+    evaluate_model(config_path=args.config)
\ No newline at end of file
diff --git a/src/stages/featurize.py b/src/stages/featurize.py
new file mode 100644
index 00000000..2898b555
--- /dev/null
+++ b/src/stages/featurize.py
@@ -0,0 +1,43 @@
+import argparse
+import pandas as pd
+from typing import Text
+import yaml
+
+from src.utils.logs import get_logger
+
+
+def featurize(config_path: Text) -> None:
+    """Create new features.
+    Args:
+        config_path {Text}: path to config
+    """
+
+    with open('params.yaml') as conf_file:
+        config = yaml.safe_load(conf_file)
+
+    logger = get_logger('FEATURIZE', log_level=config['base']['log_level'])
+
+    logger.info('Load the raw data')
+    dataset = pd.read_csv(config['data_load']['dataset_csv'])
+
+    logger.info('Curate by extraction of features from the dataset')
+    dataset['sepal_length_to_sepal_width'] = dataset['sepal_length'] / dataset['sepal_width']
+    dataset['petal_length_to_petal_width'] = dataset['petal_length'] / dataset['petal_width']
+    featured_dataset = dataset[[
+        'sepal_length', 'sepal_width', 'petal_length', 'petal_width',
+        'sepal_length_to_sepal_width', 'petal_length_to_petal_width',
+        'target'
+    ]]
+
+    logger.info('Save features')
+    features_path = config['featurize']['features_path']
+    featured_dataset.to_csv(features_path, index=False)
+
+
+if __name__ == '__main__':
+
+    args_parser = argparse.ArgumentParser()
+    args_parser.add_argument('--config', dest='config', required=True, help="curate dataset")
+    args = args_parser.parse_args()
+
+    featurize(config_path=args.config)
\ No newline at end of file
diff --git a/src/stages/train.py b/src/stages/train.py
new file mode 100644
index 00000000..5764143c
--- /dev/null
+++ b/src/stages/train.py
@@ -0,0 +1,52 @@
+import argparse
+import joblib
+import pandas as pd
+from typing import Text
+import yaml
+
+from src.train.train import train
+from src.utils.logs import get_logger
+
+
+def train_model(config_path: Text) -> None:
+    """Train model.
+    Args:
+        config_path {Text}: path to config
+    """
+
+    with open('params.yaml') as conf_file:
+        config = yaml.safe_load(conf_file)
+
+    logger = get_logger('TRAIN', log_level=config['base']['log_level'])
+
+    logger.info('Get model/estimator name')
+    estimator_name = config['train']['estimator_name']
+    logger.info(f'The name of Model/Estimator: {estimator_name}')
+
+    logger.info('Load train dataset')
+    train_df = pd.read_csv(config['data_split']['trainset_path'])
+
+    logger.info('Train model/estimator')
+    model = train(
+        df=train_df,
+        target_column=config['featurize']['target_column'],
+        estimator_name=estimator_name,
+        param_grid=config['train']['estimators'][estimator_name]['param_grid'],
+        cv=config['train']['cv']
+    )
+    logger.info(f'Best score: {model.best_score_}')
+
+    logger.info('Trained Model Saved')
+    models_path = config['train']['model_path']
+    joblib.dump(model, models_path)
+    
+    print(f'the model trained successfully is : {estimator_name}')
+
+
+if __name__ == '__main__':
+
+    args_parser = argparse.ArgumentParser()
+    args_parser.add_argument('--config', dest='config', required=True, help='Training of model')
+    args = args_parser.parse_args()
+
+    train_model(config_path=args.config)
\ No newline at end of file
diff --git a/src/train.py b/src/train.py
new file mode 100644
index 00000000..3a476458
--- /dev/null
+++ b/src/train.py
@@ -0,0 +1,65 @@
+import os
+import pickle
+import sys
+
+import numpy as np
+import yaml
+from sklearn.ensemble import RandomForestClassifier
+
+
+def train(seed, n_est, min_split, matrix):
+    """
+    Train a random forest classifier.
+
+    Args:
+        seed (int): Random seed.
+        n_est (int): Number of trees in the forest.
+        min_split (int): Minimum number of samples required to split an internal node.
+        matrix (scipy.sparse.csr_matrix): Input matrix.
+
+    Returns:
+        sklearn.ensemble.RandomForestClassifier: Trained classifier.
+    """
+    labels = np.squeeze(matrix[:, 1].toarray())
+    x = matrix[:, 2:]
+
+    sys.stderr.write("Input matrix size {}\n".format(matrix.shape))
+    sys.stderr.write("X matrix size {}\n".format(x.shape))
+    sys.stderr.write("Y matrix size {}\n".format(labels.shape))
+
+    clf = RandomForestClassifier(
+        n_estimators=n_est, min_samples_split=min_split, n_jobs=2, random_state=seed
+    )
+
+    clf.fit(x, labels)
+
+    return clf
+
+
+def main():
+    params = yaml.safe_load(open("params.yaml"))["train"]
+
+    if len(sys.argv) != 3:
+        sys.stderr.write("Arguments error. Usage:\n")
+        sys.stderr.write("\tpython train.py features model\n")
+        sys.exit(1)
+
+    input = sys.argv[1]
+    output = sys.argv[2]
+    seed = params["seed"]
+    n_est = params["n_est"]
+    min_split = params["min_split"]
+
+    # Load the data
+    with open(os.path.join(input, "train.pkl"), "rb") as fd:
+        matrix, _ = pickle.load(fd)
+
+    clf = train(seed=seed, n_est=n_est, min_split=min_split, matrix=matrix)
+
+    # Save the model
+    with open(output, "wb") as fd:
+        pickle.dump(clf, fd)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/train/train.py b/src/train/train.py
new file mode 100644
index 00000000..6327fe68
--- /dev/null
+++ b/src/train/train.py
@@ -0,0 +1,61 @@
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import f1_score, make_scorer
+from typing import Dict, Text
+
+
+class UnsupportedClassifier(Exception):
+
+    def __init__(self, estimator_name):
+
+        self.msg = f'Unsupported estimator {estimator_name}'
+        super().__init__(self.msg)
+
+
+def get_supported_estimator() -> Dict:
+    """
+    Returns:
+        Dict: supported classifiers
+    """
+
+    return {
+        'logreg': LogisticRegression,
+        'svm': SVC,
+        'knn': KNeighborsClassifier
+    }
+
+
+def train(df: pd.DataFrame, target_column: Text,
+          estimator_name: Text, param_grid: Dict,  cv: int):
+    """Train model.
+    Args:
+        df {pandas.DataFrame}: dataset
+        target_column {Text}: target column name
+        estimator_name {Text}: estimator name
+        param_grid {Dict}: grid parameters
+        cv {int}: cross-validation value
+    Returns:
+        trained model
+    """
+
+    estimators = get_supported_estimator()
+
+    if estimator_name not in estimators.keys():
+        raise UnsupportedClassifier(estimator_name)
+
+    estimator = estimators[estimator_name]()
+    f1_scorer = make_scorer(f1_score, average='weighted')
+    clf = GridSearchCV(estimator=estimator,
+                       param_grid=param_grid,
+                       cv=cv,
+                       verbose=1,
+                       scoring=f1_scorer)
+    # Get X and Y
+    y_train = df.loc[:, target_column].values.astype('int32')
+    X_train = df.drop(target_column, axis=1).values.astype('float32')
+    clf.fit(X_train, y_train)
+
+    return clf
\ No newline at end of file
diff --git a/src/utils/logs.py b/src/utils/logs.py
new file mode 100644
index 00000000..f7acc055
--- /dev/null
+++ b/src/utils/logs.py
@@ -0,0 +1,40 @@
+"""Provides functions to create loggers."""
+
+import logging
+from typing import Text, Union
+import sys
+
+
+def get_console_handler() -> logging.StreamHandler:
+    """Get console handler.
+    Returns:
+        logging.StreamHandler which logs into stdout
+    """
+
+    console_handler = logging.StreamHandler(sys.stdout)
+    formatter = logging.Formatter("%(asctime)s — %(name)s — %(levelname)s — %(message)s")
+    console_handler.setFormatter(formatter)
+
+    return console_handler
+
+
+def get_logger(name: Text = __name__, log_level: Union[Text, int] = logging.DEBUG) -> logging.Logger:
+    """Get logger.
+    Args:
+        name {Text}: logger name
+        log_level {Text or int}: logging level; can be string name or integer value
+    Returns:
+        logging.Logger instance
+    """
+
+    logger = logging.getLogger(name)
+    logger.setLevel(log_level)
+
+    # Prevent duplicate outputs in Jypyter Notebook
+    if logger.hasHandlers():
+        logger.handlers.clear()
+
+    logger.addHandler(get_console_handler())
+    logger.propagate = False
+
+    return logger
\ No newline at end of file