Data Analysis on Titanic dataset using Python

Analyze the Titanic data with Python by computing and visualizing survival rates across passenger class and gender using cross tabulations and bar plots, highlighting clear, explainable insights for stakeholders.

Set up a data science workflow in Google Colab by importing numpy, pandas, seaborn, matplotlib, and pylab, and loading the Titanic train dataset for analysis and visualization.

Explore the Titanic dataset in Google Colab by loading csv data with pandas, viewing head and tail, and checking shape, data types, and null values.

Explore the Titanic dataset with numpy, pandas, and matplotlib, inspect data with head, tail, shape, and describe, and visualize survival, class, gender, and embarked in a 3x2 subplots layout.

Explore the Titanic dataset to feature gender and build a gender mapping for survival analysis, using pandas to load train.csv, compute statistics, and visualize survival by gender.

Analyze the Titanic dataset in Python by inspecting info, handling nulls, and creating visualizations; explore passenger class and gender distributions and plan to plot survival rates by class and gender.

Explore the Titanic dataset with Python to visualize survival patterns by passenger class. Create a crosstab of survived by pclass and plot survival rates to compare classes.

Analyze the Titanic dataset in Python by loading data with NumPy and pandas, examining data types and nulls, and visualizing survival by gender and passenger class with crosstab plots.

Visualize Titanic dataset parameters in Python on Google Colab, focusing on the embarked column, handling nulls, and converting categories to numeric maps for visualization.

Explore analyzing the age feature in the Titanic dataset by handling missing values with median imputation, preparing age-based cross tabs, and linking age to survival.

Learn to analyze the Titanic dataset with Python in Google Colab, training models and extracting insights. Set up numpy, pandas, and Pylab, and load the train dataset for data analysis.

Explore the Titanic dataset in Google Colab by loading train.csv with pandas, inspecting head, tail, shape, data types, and null values for analysis.

Analyze the Titanic dataset using Python, NumPy, pandas, and Matplotlib; inspect data with head, tail, shape, and describe, and visualize survival, class, gender, embarked, and histograms with subplots.

Explore Titanic data analysis using Python: import libraries, inspect data types, null values, and descriptive stats; build a crosstab of survival by passenger class and visualize survival rate by class.

Explore the Titanic dataset with Python by loading data, inspecting shape and types, handling nulls, and mapping gender to a binary variable to compare survival rates.

Analyze the Titanic dataset with Python by loading data using numpy, pandas, and pylab, inspecting data types and nulls, and visualizing survival rates by gender and passenger class.

Analyze a Titanic dataset in Google Colab by preparing the embarked column, handling missing values, deriving a numeric mapping for category data, and validating data types for machine learning.

Explore data analysis on the Titanic dataset using Python, inspect the data, visualize passenger class and gender relationships, and prepare survival-rate plots for deeper insights.

Analyze Titanic survival patterns by gender and passenger class using cross tabulations and bar plots to compare survival rates, highlighting exploratory data analysis through clear visualizations.

Analyze a Titanic dataset in Python on Google Colab, clean embarked values, map categories to numeric, and run cells to enable visualization and machine learning.