added EDA

README.md

@@ -1,8 +1,59 @@
-# Machine Learning School
+## Penguin Classification
+
+Main aim of this project to is implement end-to-end ML pipelines on AWS sagemaker :target:.
+
+
+## 1: Training Pipeline
+
+- In this session we’ll run Exploratory Data Analysis on the [Penguins dataset](https://www.kaggle.com/datasets/parulpandey/palmer-archipelago-antarctica-penguin-data) and we’ll build a simple [SageMaker Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html) with one step to split and transform the data.
+
+<p align="left">
+<img src="program/images/training.png"/>
+</p>
+
+- We’ll use a Scikit-Learn Pipeline for the transformations, and a Processing Step with a SKLearnProcessor to execute a preprocessing script. Check the SageMaker Pipelines Overview for an introduction to the fundamental components of a SageMaker Pipeline.
+
+### Step 1: EDA
+
+- Let’s run Exploratory Data Analysis on the dataset. The goal of this section is to understand the data and the problem we are trying to solve.
+
+- Let’s load the Penguins dataset:
+
+```
+import pandas as pd
+import numpy as np
+
+penguins = pd.read_csv(DATA_FILEPATH)
+penguins.head()
+```
+<p align="left">
+<img src="program/images/eda1.PNG"/>
+</p>
+
+- We can see the dataset contains the following columns:
+
+    - `species`: The species of a penguin. This is the column we want to predict.
+    - `island`: The island where the penguin was found
+    - `culmen_length_mm`: The length of the penguin’s culmen (bill) in millimeters
+    - `culmen_depth_mm`: The depth of the penguin’s culmen in millimeters
+    - `flipper_length_mm`: The length of the penguin’s flipper in millimeters
+    - `body_mass_g`: The body mass of the penguin in grams
+    - `sex`: The sex of the penguin
+
+- If you are curious, here is the description of a penguin’s culmen:
+
+<p align="left">
+<img src="program/images/culmen.jpeg"/>
+</p>
+
+
+
+
+
+
+
 
-This repository contains the source code of the [Machine Learning School](https://www.ml.school) program.
 
-If you find any problems with the code or have any ideas on improving it, please open an issue and share your recommendations.
 
 ## Running the code