# 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 Kaggleâ€™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.

## Python for Data Science - Learn Python, Pandas, NumPy, Matplotlib

Python tutorial for Data Science - Learn Python, Pandas, NumPy, Matplotlib, will take you from knowing nothing about Python to coding and analyzing data with Python using tools like Pandas, NumPy, and matplotlib. This is a hands-on course and you will practice everything you learn step-by-step.

## Data Visualization using Pandas, NumPy and Matplotlib Python Libraries

Data Visualization using Pandas, NumPy, and Matplotlib Python Libraries. Numpy is a library for scientific computing in Python and also a basis for pandas.

## How to install NumPy SciPy Pandas Matplotlib libraries for Python in Windows 10

In this video tutorial you will learn How to install numpy scipy pandas matplotlib libraries for Python in Windows 10 operating system. NumPy adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

## Python for Analytics Full Course for Beginners | Numpy, Pandas, Matplotlib, Seaborn

Learn Python for analytics with Numpy, Pandas, Matplotlib and Seaborn! Python programming language is extensively used for the purpose of data analytics, and what makes it extremely versatile to work with is the multitudes of libraries it presents, which is exactly why it is a supremely popular programming language with huge amounts of people learning it every day.

## Coding with Python, Pandas, Numpy & Matplotlib: Become a Data Scientist in 3 Hours | Part 2

Welcome to Part 2 of Become a Data Scientist! Today we go over the Python programming language, along with its central data science libraries: Pandas, NumPy, and Matplotlib. We describe Pandas Dataframes, NumPy n-dimensional arrays, and show off how to perform graphing both in Pandas and Matplotlib. We use Google Colab Notebooks, an interactive environment for writing Python code where we don't have to worry about any system dependencies!