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Introduction

Importing required libraries

# Basic Data Science libraries
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set()


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Predicting the customers that will leave the bank

Exploring The Dataset



Image source: http://www.busitelce.com/data-visualisation/30-word-cloud-of-big-data

Word Clouds

“Word clouds (also known as text clouds or tag clouds) work in a simple way: the more a specific word appears in a source of textual data (such as a speech, blog post, or database), the bigger and bolder it appears in the word cloud.”

import numpy as np  # useful for many scientific computing in Python
import pandas as pd
# primary data structure library
from PIL import Image
# converting images into arrays
import matplotlib.pyplot as plt
# for visualizing the data


Table of Contents

  1. Introduction
  2. Feature Extraction and Dimensionality Reduction
  3. Autoencoder Structure
  4. Performance
  5. Code

1. Introduction


Choropleth Maps


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Maps with Markers

df_incidents = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Police_Department_Incidents_-_Previous_Year__2016_.csv')print('Dataset downloaded and read into a pandas dataframe!')
df_incidents.head()


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Introduction

Table of contents

Samyak Kala

A Machine Learning enthusiast, a python developer, focusing on Deep Learning and NLP

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