https://github.com/mayank341/datascienceproject_titanicdataanalysis

Exploring the Titanic dataset with data visualization and ML models to predict survival — classic Kaggle project.
https://github.com/mayank341/datascienceproject_titanicdataanalysis

aiml datascience-machinelearning

Last synced: 4 months ago
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Exploring the Titanic dataset with data visualization and ML models to predict survival — classic Kaggle project.

• Host: GitHub
• URL: https://github.com/mayank341/datascienceproject_titanicdataanalysis
• Owner: mayank341
• Created: 2025-04-14T07:32:29.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-04-26T20:09:54.000Z (9 months ago)
• Last Synced: 2025-05-31T02:26:28.059Z (8 months ago)
• Topics: aiml, datascience-machinelearning
• Language: Jupyter Notebook
• Homepage:
• Size: 433 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# DataScienceproject_titanicdataanalysis

# 🛳️ Titanic Survival Prediction - Data Science Project

This repository contains a Jupyter Notebook (`_titanic.ipynb`) for a classic machine learning and data science project based on the Titanic dataset. The goal is to predict passenger survival using various machine learning techniques.

---

## 📁 Project Structure

## 📊 Dataset Information

The dataset used is from [Kaggle’s Titanic: Machine Learning from Disaster](https://www.kaggle.com/c/titanic). It includes details about the passengers such as:

- PassengerId

- Survived (Target variable)

- Pclass (Ticket class)

- Name

- Sex

- Age

- SibSp (Siblings/Spouses aboard)

- Parch (Parents/Children aboard)

- Ticket

- Fare

- Cabin

- Embarked (Port of Embarkation)

---

## 🧪 Project Workflow

The notebook covers the following steps:

1. **Importing Libraries**  

   Basic data analysis and ML libraries like `pandas`, `numpy`, `matplotlib`, `seaborn`, `sklearn`.

2. **Data Loading & Exploration**  

   Load CSV data, explore structure, identify missing values, and visualize key features.

3. **Data Cleaning & Feature Engineering**

   - Handling missing data (e.g., Age, Embarked, Cabin)

   - Encoding categorical variables (Sex, Embarked)

   - Creating new features (e.g., FamilySize, IsAlone)

4. **Exploratory Data Analysis (EDA)**  

   - Correlation heatmap

   - Survival rate comparisons by class, sex, age

   - Visualizations using `seaborn` & `matplotlib`

5. **Model Building**

   - Train/Test split

   - Algorithms: Logistic Regression, Decision Trees, Random Forest, KNN, SVM

   - Model evaluation using accuracy, confusion matrix, cross-validation

6. **Prediction**

   - Predict on test data (if available)

   - Export results for submission

   - 

## 📈 Results

Accuracy measures how often the model correctly predicts whether a passenger survived or not. It is calculated as:

Accuracy = (Number of Correct Predictions) / (Total Predictions)

For example, if the model predicts correctly for 82 out of 100 passengers, the accuracy is 82%.

The notebook includes model evaluation and comparison. The best-performing model can be selected for final predictions based on accuracy or cross-validation scores.

---

## 🔧 Installation

To run the notebook locally:

1. Clone this repository  

   ```bash

https://github.com/mayank341/DataScienceproject_titanicdataanalysis/edit/main/README.md

   cd DataScience_Titanic

   #

# 📘 Explanation of Each Section:

1. **Project Title & Overview**

   - A catchy title (`🛳️ Titanic Survival Prediction`) and a brief intro describing what the repo is about.

2. **Project Structure**

   - Shows how your repo is organized, which is helpful for new contributors.

3. **Dataset Info**

   - Describes the data source and variables, crucial for understanding what you're working with.

4. **Workflow**

   - Detailed step-by-step outline of what your notebook does—makes your work reproducible and clear to readers.

5. **Results**

   - Mentions model evaluations. You can also add charts or accuracy metrics here if desired.

6. **Installation**

   - Instructions on how to run the notebook on someone else's system. This ensures anyone can use it easily.

7. **Learn More**

   - Resources for further reading.

8. **Contributing**

   - Invites collaboration and bug reports.

9. **License**

   - Defines how others can use your code. Default is MIT, but you can change it.