TITANIC-solution

Titanic Survival Prediction

Welcome to the Titanic Survival Prediction project! This repository contains a Jupyter notebook that demonstrates a comprehensive data analysis process and machine learning techniques to predict the survival of passengers on the Titanic. This project is intended for beginners who are looking to get started with data analysis and machine learning.

Table of Contents

Introduction

Dataset

Exploratory Data Analysis (EDA)

Data Preprocessing

Machine Learning Models

Results

Conclusion

1.Introduction

The Titanic disaster is one of the most infamous shipwrecks in history. In this project, we use data from the Titanic passengers to predict who would survive the disaster. This project covers the entire data analysis and machine learning pipeline, from data cleaning and exploratory data analysis (EDA) to model training and evaluation.

2. Dataset

The dataset used in this project is the Titanic dataset provided by Kaggle. It contains information about the passengers on the Titanic, including whether they survived, their age, fare, class, and other attributes.

You can download the dataset from Kaggle Titanic Competition.

3. Installation

To run this notebook, you need to have Python installed along with the following libraries:

pandas

numpy

matplotlib

seaborn

scikit-learn

Jupyter Notebook You can install the required libraries using pip:

4. Exploratory Data Analysis (EDA)

In the EDA section, we perform the following steps:

Load the dataset and understand its structure.

Handle missing values.

Visualize the data to find patterns and correlations.

Summarize the insights from the visualizations.

5. Data Preprocessing

Data preprocessing involves:

Handling missing values.

Encoding categorical variables.

Feature scaling.

Splitting the data into training and testing sets.

Machine Learning Models We explore and compare several machine learning models, including:

Logistic Regression

Decision Tree

Random Forest

k-Nearest Neighbors (k-NN)

5. Results

The results section includes:

Model evaluation metrics (accuracy, precision, recall, F1-score). Comparison of model performance.