Final Hackathon

Data Loading: Load the training and testing datasets into pandas DataFrames. Data Exploration: Perform exploratory data analysis to understand the dataset's structure, check for missing values, examine data distributions, and identify any patterns or insights. Data Preprocessing: Clean the data by handling missing values, encoding categorical variables if necessary, and performing any required feature engineering or transformations. Split the training data into features (X) and the target variable (y). Model Selection: Choose an appropriate machine learning model for the classification task. In this project, the CatBoost classifier, LightGBM, Neural Networks, and HistGradientBoostingClassifier models were used as examples. Model Training: Fit the selected model on the training data. Use techniques like cross-validation to estimate the model's performance and tune hyperparameters if needed. Model Evaluation: Evaluate the trained model's performance using suitable evaluation metrics such as accuracy, precision, recall, and F1-score. Use cross-validation or a separate validation set to get an unbiased estimate of the model's performance. Model Prediction: Make predictions on the testing dataset using the trained model. Submission: Create a submission file containing the predictions and submit it for evaluation. Model Improvement: Explore techniques to improve the model's performance, such as hyperparameter tuning, feature engineering, ensemble methods, or different algorithms. Iterate on the steps above to improve the model's accuracy