Titanic Survival Prediction Documentation

1. Importing Libraries

Code:

# Importing All Required Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from warnings import filterwarnings
filterwarnings(action='ignore')

Purpose:

• Imports essential libraries for data manipulation (pandas, numpy) and visualization (matplotlib).
• Suppresses warnings to streamline the output.

---

2. Loading Datasets

Code:

# Loading Datasets
pd.set_option('display.max_columns', 10, 'display.width', 1000)
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train.head()

Purpose:

• Reads the training and testing datasets from CSV files into pandas DataFrames.
• Adjusts display options for better readability of DataFrame outputs.
• Displays the first few rows of the training dataset for a quick overview.

---

3. Exploratory Data Analysis (EDA)

Dataset Shape:

# Display shape
train.shape

• Outputs the number of rows and columns in the training dataset.

Observations:

• Rows: 891
• Columns: 12

---

4. Data Preprocessing

Details about handling missing values, encoding categorical variables, and feature engineering are covered later in the notebook. (Placeholder for additional steps once reviewed.)

---

5. Model Building

The notebook implements a machine learning pipeline using RandomForestClassifier from scikit-learn to predict survival. It involves splitting data, training the model, and evaluating performance metrics like confusion matrix and classification report.

---

6. Visualization

Matplotlib is used to create visualizations (plots to be detailed once reviewed).

---

Next Steps:

• Finalize preprocessing steps and model evaluation details.
• Add summary metrics and insights derived from the analysis.
• license