Titanic-Survival-Prediction

The aim is to make predictions on the survival outcome of passengers. Since this is a binary classification, logistic regression can be used to build the model.

Data Source : Kaggle.com (https://www.kaggle.com/c/titanic/data)

Python Libraries used

1. NumPy
2. Pandas
3. Matplotib
4. Seaborn
5. from sklearn.model_selection import train_test_split
6. from sklearn import metrics

Pre-processing operations

1. Checking for missing values in dataset
2. Dropping unnecessary columns
3. Creating categorical variable for traveling alone
4. Label Encoding

Exploratory Data Analysis

1. Dataset shape
2. Dataset info
3. Dataset description
4. Analysis of all the features in the dataset

Model Building : Logistic Regression

Model Evaluation

1. Confusion matrix
2. Classification report