How amberkakkar01/titanic-survival-prediction works
This is a beginner-oriented Titanic survival prediction project centered on a Jupyter notebook, with train/test CSVs in the repository. The README describes data import, cleaning, visualization, and supervised classification models, including logistic regression, SVM kernels, and random forest. It also mentions plotting results. No notebook source was available, so the stages and their ordering are based on the README; the CSV-to-notebook relationships are not documented.
Main entry points: Titanic Predication.ipynb Data Cleaning
Components 9
Titanic Data
- Training Data
train.csv - Test Data
test.csv
Notebook Analysis
- Data Import
Titanic Predication.ipynb - Data Cleaning
Titanic Predication.ipynb - Data Visualization
Titanic Predication.ipynb
Prediction Models
- Logistic Regression
Titanic Predication.ipynb - SVM Kernels
Titanic Predication.ipynb - Random Forest
Titanic Predication.ipynb - Result Plots
Titanic Predication.ipynb
Connections 7
- Data Import → Data Cleaning: prepares data
- Data Cleaning → Data Visualization: explores data
- Data Cleaning → Logistic Regression: trains model
- Data Cleaning → SVM Kernels: trains model
- Data Cleaning → Random Forest: trains model
- Logistic Regression → Result Plots: plots results
- Random Forest → Result Plots: plots results