analyse_titanic

senghok510 / analyse_titanic

analyse the titanic data

Thinking

Clarifying dataset needs

I need to identify which dataset the user is referring to. It looks like they might want to analyze data similar to the Titanic dataset from seaborn, but the current notebook doesn't have it loaded. I should confirm what specific data file they intend to use. I can load the seaborn dataset if there’s internet access, but it seems I need a file path or clarification from the user first. Let's sort that out!

i have already put the titatnic dataset in the working directory in the mljar studio same path as the notebook @analys_titanic.iipynb

Thinking

Clarifying file issues

I need to check for available files. There's a mention of a path being unavailable, and there's a conventional titanic.csv, but I don't have the exact filename. It seems I should clarify that. I could try to list files programmatically in the working directory. The dataset isn’t loaded yet, and I'd typically load the dataset first, but I can’t do that without the right file name. So, discovery is in order!

I’ll identify the available Titanic data file in the notebook’s working directory.

</>List data files
from pathlib import Path

files = [p.name for p in Path(".").iterdir() if p.is_file()]
[name for name in files if "titanic" in name.lower() or name.lower().endswith((".csv", ".xlsx", ".xls", ".parquet"))]
['analyse_titanic.ipynb', 'Titanic-Dataset.csv']

The working directory contains Titanic-Dataset.csv, alongside the notebook. The next step is to load it and verify its schema before analyzing survival patterns.

</>Load Titanic data
import pandas as pd

df = pd.read_csv("Titanic-Dataset.csv")
print(f"Shape: {df.shape}")
columns = df.columns.tolist()
print(f"Columns ({len(columns)}):", columns if len(columns) <= 100 else columns[:100] + ["..."])
df.head()
Shape: (891, 12)
Columns (12): ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

The file is in the same working directory as the notebook and has been read into df. It contains 891 passenger records with the standard Titanic fields, including the survival target (Survived), demographics (Sex, Age), travel class (Pclass), family counts, fare, cabin, and embarkation port.

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