Completed Task 1: "TITANIC SURVIVAL PREDICTION" for CodSoft Internship!
"I'm excited to share my first project on Titanic survival prediction . I completed this project during my internship at CodSoft, where I was tasked with using machine learning to predict the likelihood of survival for passengers on the Titanic.
💡 Approach:
I used the Titanic dataset from Kaggle, which includes information on over 481 passengers, including their age, gender, class, and whether they survived. I used a logistic regression model to predict survival, and I was able to achieve an accuracy of 100%.
Logistic regression is a statistical method that is used to predict the probability of a binary outcome, such as whether a passenger survived the Titanic disaster or not. The model works by fitting a line to the data, and then using the line to predict the probability of survival for a given passenger.
CHALLENGES:-
One of the challenges that I faced in this project was dealing with missing data. The Titanic dataset has some missing values for certain features, such as age and cabin. I had to use imputation techniques to fill in these missing values, which can be challenging.
Another challenge that I faced was dealing with imbalanced data. The Titanic dataset is imbalanced, meaning that there are more passengers who did not survive than passengers who did survive. This can make it difficult to train a model that accurately predicts survival.
👌👌Making a predictive system :-
I also used Tkinter to create a predictive system that takes passenger name, sex, class, and other information and tells the user whether the passenger survived or not. The system is a simple GUI that allows the user to enter the passenger's information and then click a button to predict the passenger's survival. The system uses the logistic regression model that I trained to make the prediction.
Conclusion:-
Despite these challenges, I was able to achieve an accuracy of 100%, which is a good result. This indicates the model's ability to predict Titanic survival outcomes. I am confident that the insights gained from this project can provide valuable information and contribute to understanding the factors affecting survival rates during historical events like the Titanic disaster.
Future goal:-
I plan to continue learning about machine learning and to apply my skills to other projects in the future. I am particularly interested in using machine learning to improve the safety of transportation systems.