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how_to_do_math_for_deep_learning

This is the code for "How to Do Math Easily - Intro to Deep Learning #4' by Siraj Raval on YouTube

keras-test-drive-on-kobe-dataset

The purpose of this notebook is to experiment with Keras on the Kobe dataset from Kaggle. In this notebook the preprocessing is absolutely minimal. The aim of this notebook is not to achieve maximum accuracy; the point is to explore how the Keras Sequential Model can be applied to this dataset.

kobe-shots-home-vs-away

I will try to establish whether or not playing at home vs playing away from home will influence Kobe's success rate in making the shots.

kobe_preprocessing

In this notebook I have done some data preparation, cleaning and preprocessing for the Kobe dataset. I also did some simple train/test split testing accuracy estimates. To do: Analyze and solve potential problems up to this point Feature Selection Variance Threshold Most important features Recursive Feature Elimination PCA Visualization Final predictions Because this is a work in progress and potentially contains errors and bugs, I will not include and comments or explanations at this point.

moviepy

Video editing with Python

pandas-functions

This is a repository for Python files containing a variety of different Pandas functions.

pandas-groups-vs-get_group

In this notebook we will explore .groups and .get_group. We will show examples to explain the difference.

pandas-renaming-categorical-data

How to convert a column with discrete valued names to discrete valued numbers and vice versa in a Pandas DataFrame.

pandas-time-series-data-basics

The Pandas library provides simple, but powerful tools to perform any data tasks, especially when it comes to time series data. In this notebook I will be covering 3 basic functionalities for manipulating time series data: Date Ranges, DatetimeIndex & Timestamps and Resampling.

titanic-data-set-exploration

In this Notebook I explored the Titanic data set, using Pandas. The purpose of this exploration is to gain a better understanding of the data while preparing it for machine learning.

u12-football-data

Simple performance analytics for U12 youth football team.