Titanic Survival Prediction

Overview

This project is focused on building a machine learning model to perform classification tasks. The notebook contains various cells that guide you through the process of data loading, preprocessing, model training, and evaluation.

Cells Explanation

1. Data Loading: This cell is responsible for loading the dataset into the notebook. It uses libraries like pandas to read data from CSV files.
2. Data Preprocessing: This cell handles data cleaning and preprocessing tasks such as handling missing values, encoding categorical variables, and normalizing numerical features.
3. Exploratory Data Analysis (EDA): This cell includes visualizations and statistical analysis to understand the data distribution and relationships between features.
4. Feature Selection: This cell selects the most relevant features for the model using techniques like correlation analysis and feature importance scores.
5. Model Selection: This cell involves choosing the appropriate machine learning model for the classification task. Common models include Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines (SVM).
6. Model Training: This cell trains the selected model using the training dataset. It includes setting hyperparameters and fitting the model.
7. Model Evaluation: This cell evaluates the model's performance using metrics such as accuracy, precision, recall, F1-score, and confusion matrix.
8. Hyperparameter Tuning: This cell optimizes the model's hyperparameters using techniques like Grid Search or Random Search to improve performance.
9. Model Deployment: This cell demonstrates how to save the trained model and load it for making predictions on new data.

Parameters

• Learning Rate: Controls the step size during gradient descent.
• Number of Estimators: The number of trees in ensemble methods like Random Forest.
• Max Depth: The maximum depth of the tree in Decision Trees.
• Kernel: The kernel type to be used in SVM.
• Regularization Parameter: Controls the trade-off between achieving a low training error and a low testing error.

Machine Learning Model

The primary model used in this project is a Random Forest Classifier, which is an ensemble learning method that operates by constructing multiple decision trees during training and outputting the mode of the classes for classification tasks.