How I Solved The Kaggle Titanic Competition Question using Pandas, Numpy and Matplotlib

How I Solved The Kaggle Titanic Competition Question using Pandas, Numpy and Matplotlib

How I Solved The Kaggle Titanic Competition Question using Pandas, Numpy and Matplotlib

I have been studying data science and working on competition questions for quite some time now, following the correct procedures and using the most appropriate functions from sklearn and sometimes statsmodels. Even though I followed the rules and used the best models according to the appropriate Python libraries, I nevertheless was failing to make a lot of progression in achieving high accuracies in the algorithms I created. I decided therefore that perhaps the reason why I am not achieving high scores in competition questions was because I was using off the shelf models. I wanted therefore to see if I could achieve a higher a accuracy if I wrote a solution to a competition question entirely in numpy and pandas (and matplotlib so I can make a graph). The question I decided to use was Kaggle’s Titanic competition question because the datasets used to answer this question are small. On a separate note, I read that the highest a person can legitimately achieve on this competition question is in the 80 percentile and if anyone scores higher than that it is likely he researched the survivors and programmed these into the solution. With the knowledge in hand that I was unlikely to achieve a very high accuracy using only standard programming practices that did not involve training the test set, I decided to try to write my Python program using only numpy and pandas, with matplotlib so I could make a chart.

numpy matplotlib python pandas kaggle

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