madhurdevkota / catboost

Catboost model implementation

Harnessing the Power of CatBoost: A Comprehensive Guide to Efficient Classification and Regression in Data Science

1. Classification using CatBoost 1.1 Loading and preparing the Titanic dataset - Importing necessary libraries - Loading training and test datasets using CatBoost's built-in datasets - Handling missing data by dropping rows with NaN values - Selecting relevant features for modeling - Splitting data into training and test sets 1.2 Training a CatBoost classifier - Specifying categorical features - Setting CatBoost parameters (iterations, learning rate, evaluation metric, etc.) - Fitting the model on training data with early stopping based on test set performance - Visualizing the training process 1.3 Evaluating feature importance - Extracting feature importance scores from the trained model - Creating a DataFrame with features and their importance - Visualizing feature importance using a bar plot

2. Regression using CatBoost
   2.1 Loading and exploring the Boston Housing dataset - Loading the built-in Boston Housing dataset from scikit-learn - Converting the dataset to a pandas DataFrame - Adding the target variable (house prices) to the DataFrame - Generating a comprehensive data report using pandas-profiling

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Short Professional Summary:

In this cutting-edge data science project, the powerful CatBoost library was leveraged to tackle both classification and regression tasks. The Titanic dataset was used to demonstrate binary classification, with data preprocessing, model training, and evaluation of feature importance. For regression, the Boston Housing dataset was employed to predict house prices. The project showcases best practices in data handling, model training with early stopping, and insightful visualizations. The results highlight the effectiveness of CatBoost in delivering accurate predictions and its ability to provide interpretable feature importance scores.