Kaggle Competition Notebooks

Welcome to my collection of Kaggle competition notebooks! In this repository, I share my solutions, insights, and techniques for tackling various real-world data science challenges through Kaggle competitions.

Overview

This repository contains notebooks for several Kaggle competitions I've participated in. The notebooks include detailed explanations, step-by-step approaches, and code implementations that showcase different machine learning techniques, data wrangling methods, and model optimization strategies.

Key Areas of Focus:

• Predictive Modeling: Building models to predict outcomes based on historical data.
• Data Preprocessing: Cleaning and transforming data for optimal model performance.
• Feature Engineering: Creating new features to improve model accuracy.
• Model Optimization: Tuning models to achieve competitive results.
• Visualization: Using data visualizations to uncover patterns and insights.

Notebooks

You can explore the following notebooks within this repository:

1. Dog Vs Cat Classification - Dog_Vs_Cat.ipynb

   • Description: A basic deep learning model using TensorFlow-Keras to classify images of dogs and cats.
   • Techniques used: Convolutional Neural Networks (CNN), data augmentation, and transfer learning.

2. Mental Health Prediction using H2O.ai - mental-health-data-using-h2o-ai.ipynb

   • Description: Predicting mental health outcomes using a dataset and H2O.ai's machine learning capabilities.
   • Techniques used: H2O.ai AutoML, data preprocessing, and model evaluation.

3. Child Mind Institute Data - child-mind-institute-l-gbm-h2o-ai.ipynb

   • Description: A model for predicting outcomes related to child mental health using LightGBM and H2O.ai.
   • Techniques used: Gradient Boosting Machine (GBM), feature selection, and hyperparameter tuning.

4. CatBoost for Classification - cibmtr-catboost.ipynb

   • Description: A classification model using CatBoost for predicting outcomes based on categorical features.
   • Techniques used: CatBoost, feature engineering, and model interpretation.

5. Deep Learning for Classification - classification-using-dl-basic.ipynb

   • Description: A simple deep learning model to classify data with a basic architecture.
   • Techniques used: Deep Learning (DL), activation functions, and backpropagation.

6. Prediction with H2O.ai - prediction-using-h2o-ai (1).ipynb

   • Description: A predictive modeling approach using H2O.ai, focusing on automating the machine learning pipeline.
   • Techniques used: H2O.ai AutoML, model stacking, and model evaluation.

Installation

To run these notebooks, you'll need to set up the required Python environment. You can use the following steps:

1. Clone this repository:

   git clone https://github.com/SHRISH01/Kaggle-Competition-Notebooks.git
   cd Kaggle-Competition-Notebooks