Titanic_Analysis_Model

Classification model on Titanic: Tragic shipwreck

Titanic Survival Prediction Project

This project aims to analyze the famous Titanic dataset from Kaggle, which provides information about passengers on board the ill-fated Titanic. The objective is to develop a predictive model that can accurately determine whether a passenger survived or not based on various features such as age, gender, passenger class, and more.

Key Steps Involved:

1. Exploratory Data Analysis: Understanding the structure and patterns in the data through visualization and statistical analysis.

2. Data Preprocessing: Handling missing values, converting categorical variables, and performing feature engineering to extract relevant information. This includes creating new features, such as family size and title from name, that can improve the predictive power of the model.

3. Model Development: Building and training machine learning models, such as logistic regression, decision trees, random forests, or support vector machines, to predict survival based on the selected features. Multiple models will be developed to compare their performance and choose the best one.

4. Model Comparison: Evaluating and comparing the performance of different models using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score. This helps in selecting the model with the highest predictive accuracy.

5. Hyperparameter Tuning: Optimizing the model's parameters to improve its accuracy and generalization ability using techniques like grid search or random search.

6. Final Prediction: Applying the best-performing model to make predictions on the test dataset and the model developed as part of this project has achieved an accuracy score of ~0.78 on the Kaggle evaluation. This score indicates the model's ability to correctly predict passenger survival based on the provided features.