## Keras and Tensorflow Basics in Python – Part II

Another simple example of utilizing Keras to predict a multi-classification problem. Details in the Jupyter notebook below. ...

## Natural Language Processing in Python – Sentiment using VADER

Another little bit of NLP showing how to do a quick and dirty sentiment analysis utilizing VADER within NLTK. It doesn't give the best accuracy on all datasets, but it removes complexity. Details below in the Jupyter Notebook. ...

## Natural Language Processing in Python – LDA vs. NMF

Another little bit of NLP comparing Topic clustering in an unsupervised problem. Details below in the Jupyter Notebook. ...

## Kaggle Submission: Mobile Phone Price Classification

A recent "submission" I completed based upon a Kaggle dataset. I use quotations as this particular dataset didn't include the ability to submit your final dataset. However, I had fun doing it and is a good example of multi-classification. ...

## Project: Social Media Sentiment

A recent project I completed related to Natural Language Processing - embedded below as a Jupyter Notebook. ...

## Frequency Table – Python

A small script I wrote just because I happened to need it. Generates a quick and more "easy-to-read" frequency table for ranges specified in the bins. Simple Frequency Table CodeTakes a list (sample), separates them by bins, and gives a frequency table with histogram In [1]: #Imports import pandas as pd import seaborn as sns In [2]: #Give list sample = [10, 15, 12, 17, 22, 14, 23, 8, 15, 11, 17, 12, 16, 26, 12, 11, 9, 16, 15, 24, 12, 17, 16, 14, 19, 13, 10, 15, 19, 20, 10, 25, 14, 15, 12, 22, 7, 28, 16, 9] #Put list into df df = pd.DataFrame(sample, columns=['nums']) In [3]: #Set bin sizes bins = [5, 9, 13, 17, 21, 25, 29] In [4]: #Put into dataframe newdf = pd.DataFrame(pd.cut(df['nums'], bins=bins).value_counts()).sort_index() newdf.reset_index(inplace=True) #Convert to String newdf['index'] = newdf['index'].astype(str) In [5]: #Set 'easy-to-read' names for bins left = newdf['index'].str.split(',').str[0].str.split('(').str[1].astype('int32') + 1 right = newdf['index'].str.split(',').str[1].str.split(']').str[0] fullname = left.astype(str) + ' -' + right newdf['index'] = fullname In [6]: #cummulative frequency newdf['cumfreq'] = newdf['nums'].cumsum() #relative frequency newdf['relfreq'] = newdf['nums'] / newdf['nums'].sum() #cummulative relative frequency newdf['cumrelfreq'] = newdf['relfreq'].cumsum() #Add column names newdf.columns =['Class Interval', 'Frequency', 'Cummulative Frequency', 'Relative Frequency', 'Cumulative Relative Frequency'] In [7]: #Show frequency table newdf Out[7]: Class...

## Keras and Tensorflow Pt III – Classification Example

This is a good simple example of a classification problem utilizing Keras and Tensorflow. In addition, I'm utilizing early stopping in an attempt to avoid overfitting in the model. You'll notice this take effect as the model stops training well before the 600 set epochs. Keras / Tensorflow Classification - ExampleHere we're going to attempt to utilize Keras/Tensorflow to predict the whether or not an individual has cancer. The data being used can be seen on my github below: https://github.com/kaledev/PythonSnippets/blob/master/Datasets/Keras/cancer_classification.csv Data Imports and EDA In [1]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns In [2]: df = pd.read_csv('DATA/cancer_classification.csv') Here we can see the dataset is fairly well balanced in terms of classification of the labels, if the dataset was unbalanced then we might see issues with overfitting. In [3]: sns.countplot(x='benign_0__mal_1',data=df) Out[3]: <AxesSubplot:xlabel='benign_0__mal_1', ylabel='count'> Create Models and Predict In [4]: #Set X/y X = df.drop('benign_0__mal_1', axis=1).values y = df['benign_0__mal_1'].values In [5]: from sklearn.model_selection import train_test_split In [6]: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42) We need to scale the data so all features are in sync In [7]: from sklearn.preprocessing import MinMaxScaler In [8]: scaler = MinMaxScaler() In [9]: X_train =...