bagging-with-kaggle

General Machine Learning Pipeline with Bagging Classifier

This project implements a machine learning pipeline to analyze and predict survival on the Titanic dataset. It leverages a Bagging Classifier with decision trees to enhance model robustness. The project includes data preprocessing, model training, evaluation, and saving the results.

Features

• Interactive column selection for preprocessing.
• Handles missing values and encodes categorical variables.
• Implements Bagging Classifier with decision trees.
• Exports the processed dataset to .csv and Google Sheets.

Prerequisites

Before running the code, ensure you have the following:

• Python 3.8+
• Kaggle API credentials for downloading the dataset.
• Necessary Python libraries (see requirements.txt).
• Access to Google Sheets API (if using the csv_to_sheets function).

Instalation

1. Clone this repository

git clone https://github.com/<your-username>/<repository-name>.git 
cd <repository-name>

2. Intall required Python libraries

pip install -r requirements.txt

3. Set up the Kaggle API:

• Download your kaggle.json file from Kaggle API.
• Place it in the appropriate directory (~/.kaggle on Unix or %USERPROFILE%.kaggle on Windows).

4. Configure Google Sheets API:

• Follow Google Sheets API documentation to set up credentials.
• Place the credentials in the project directory.

Usage

1. Run the main script:

python bagging.py

Fetch a dataset from Kaggle:

• When prompted, enter a search term to find datasets on Kaggle (e.g., "Titanic", "Housing Prices").
• A list of datasets matching your search will be displayed. For example:

• Enter the number corresponding to the dataset you want to download.

3. Specify a folder to save the dataset:

• Enter a name for a new folder where the dataset will be downloaded and unzipped.

4. Dataset selection:

• If the downloaded dataset contains multiple .csv files, the script will load the first .csv file by default.
• The dataset is automatically loaded into a Pandas DataFrame.

5. Follow the prompts in bagging.py:

• Interactively select columns for analysis.
• Handle missing values automatically.
• Specify the target column (dependent variable).

6. Model Training and Evaluation:

• The script splits the data into training and testing sets.
• Trains a Bagging Classifier using decision trees.
• Accuracy on the test set is displayed in the console.

7. Save Results:

• Processed data is saved as a .csv file in the save/ directory.

- Optionally, upload the dataset to Google Sheets using the *google_sheets_utils.py* script.

8. Create a Looker Studio with googlesheets:

• Looker Studio: Example - Titanic Survival Rate

Acknowledgments

• Kaggle datasets: Kaggle Datasets.
• Scikit-learn: Scikit-learn Documentation.
• Pandas: Pandas Documentation.
• Curses: Curses Documentation.