Objective 🎯 - To analyze the depth in tragic loss of people during the crash and analyse factors that had correlations.
Dataset from Kaggle
Python Jupyter Notebook contains all analysis and visualization charts.
- Data cleaning
- Data wrangling
- Data Analysis
- Only 38 % people survived the crash.
- A group of older people of age above 45 didnt make it from the crash.
- Women and kids left early before the accident, huge percentage of men had lost their lives.
- Created linear regression model and fitted data. Train and test data into 80/20 split.Target was to predict the chances of survival (0-died,1-survived) from features (age,sex,fare).Included fare as feature since rooms positions differed according to their ticket classes and room of third class ticket was way down for a person to escape during accident.
- Regression model had accuracy of 77 % prediction when validated with real data.