astrojuanlu
commented
3 months ago Output from ChatGPT 4:
### Step-by-Step Tutorial: Creating a Data Preprocessing Pipeline with Kedro
In this tutorial, we will walk through the process of setting up a data preprocessing pipeline using Kedro, a development workflow framework that helps structure reproducible, scalable, and maintainable data science code. We will use a telecom company churn rate dataset from Kaggle.
#### Prerequisites:
- Python installed on your system
- Kedro version 0.18.6
- Access to the dataset from Kaggle
#### Step 1: Install Kedro
First, ensure you have Kedro installed. You can install Kedro using pip:
```bash
pip install kedro==0.18.6
```
#### Step 2: Create a New Kedro Project
Create a new directory for your project and navigate into it:
```bash
mkdir kedro_telecom
cd kedro_telecom
```
Now, create a new Kedro project:
```bash
kedro new --starter=pandas-iris
```
Follow the prompts to set up the project. For this tutorial, you can name the project "Telecom Churn Analysis".
#### Step 3: Set Up Your `requirements.txt`
Create a `requirements.txt` file in the root of your project directory and add the following dependencies:
```
kedro==0.18.6
pandas
scikit-learn
```
Install the requirements:
```bash
pip install -r requirements.txt
```
#### Step 4: Download and Configure the Dataset
Download the dataset from Kaggle and place it in the `data/01_raw` directory of your Kedro project.
#### Step 5: Create a Pipeline
Use Kedro CLI to create a new pipeline for data preprocessing:
```bash
kedro pipeline create data_preprocessing
```
This command creates a new pipeline in `src/telecom_churn_analysis/pipelines/data_preprocessing`.
#### Step 6: Define Nodes in the Pipeline
Navigate to `src/telecom_churn_analysis/pipelines/data_preprocessing/nodes.py` and define the functions for your data preprocessing steps. For example:
```python
import pandas as pd
from sklearn.preprocessing import StandardScaler
def load_data(data_path: str) -> pd.DataFrame:
"""Loads data from the raw data directory."""
return pd.read_csv(data_path)
def clean_data(df: pd.DataFrame) -> pd.DataFrame:
"""Cleans the DataFrame by handling missing values, etc."""
df.dropna(inplace=True)
return df
def scale_features(df: pd.DataFrame) -> pd.DataFrame:
"""Scales numerical features using StandardScaler."""
scaler = StandardScaler()
numerical_cols = df.select_dtypes(include=['float64', 'int64']).columns
df[numerical_cols] = scaler.fit_transform(df[numerical_cols])
return df
```
#### Step 7: Build the Pipeline
In `src/telecom_churn_analysis/pipelines/data_preprocessing/pipeline.py`, import the nodes and build the pipeline:
```python
from kedro.pipeline import Pipeline, node
from .nodes import load_data, clean_data, scale_features
def create_pipeline(**kwargs):
return Pipeline(
[
node(
func=load_data,
inputs="params:raw_data_path",
outputs="preprocessed_data",
name="load_data_node"
),
node(
func=clean_data,
inputs="preprocessed_data",
outputs="cleaned_data",
name="clean_data_node"
),
node(
func=scale_features,
inputs="cleaned_data",
outputs="scaled_data",
name="scale_features_node"
),
]
)
```
#### Step 8: Configure Data Paths and Parameters
In `conf/base/parameters.yml`, specify the path to your raw data:
```yaml
raw_data_path: data/01_raw/your_dataset.csv
```
#### Step 9: Run the Pipeline
Navigate back to the root of your Kedro project and run the pipeline:
```bash
kedro run
```
#### Step 10: Review the Results
Check the outputs in the `data` directory under the appropriate processing folder to see the results of your pipeline.
This tutorial outlines the basic setup and steps to get started with a data preprocessing pipeline using Kedro. You can expand this pipeline by adding more complex data transformations, integrating different data sources, or creating additional pipelines for data analysis and modeling.
Looks like, to this day, ChatGPT still doesn't understand Kedro very well.
Description
This task documents a blog post that we could explore around using ChatGPT to generate a Kedro pipeline for a Kaggle Dataset. Here is a prompt I've used to do this: