Project 5 - K-Means Clustering - Duplicate

# import libraries and K-Means function import numpy as np import matplotlib.pyplot as plt import pandas as pd from pandas import DataFrame, Series import seaborn as sns # from sklearn.cluster import KMeans

# Create dataframe for data file='cereal.csv' cereal = pd.read_csv(file)

# drop features we aren't using or create a new dataframe with only the features we want # relevant column names are name, mfr, carbo, sugars, and rating cereal = cereal.drop( ['type', 'calories', 'protein', 'fat', 'sodium', 'fiber', 'potass', 'vitamins', 'shelf', 'weight', 'cups'], axis=1) # checking length (cereal & cereal.head() were bad for formatting when exporting to pdf) len(cereal)

# Check to see if any instances have NaN for entries using .info cereal.info()

# Check to see if any cereals have negative values for sugars or carbohydrates; if 2 or less cereals, drop those # data instances; otherwise replace negative values with 0 cereal[cereal.sugars < 0] cereal[cereal.carbo < 0]

# Address negative values cereal = cereal.drop([57]) # testing to see if negative index/row was dropped len(cereal)

# testing to check if negative values are still present cereal[cereal.sugars < 0] cereal[cereal.carbo < 0]

# set background grid for plots sns.set_style( 'whitegrid')

# Plot number of products per manufacturer mfr = sns.countplot (x='mfr', data = cereal )

# There is only one cereal from American Home Foods Company so we drop that data sample # # Type df[df.mfr == 'A'] and it will give you all data from this manufacturer. # Make sure you set df = drop command; use command to make sure it was deleted # cereal[cereal.mfr == 'A'] cereal = cereal.drop([43]) mfr = sns.countplot (x='mfr', data = cereal )

# For plots we would like the name of manufacturer instead of just "N" or "Q" # Use .map or .apply # # Then drop 'mfr' column # cereal['mfr_name'] = cereal['mfr'].map ({ \ 'A': 'American Home Food Products', \ 'G':'General Mills', \ 'K': 'Kelloggs', \ 'N': 'Nabisco', \ 'P': 'Post', \ 'Q': 'Quaker Oats', \ 'R': 'Ralston Purina' }) cereal = cereal.drop(columns=['mfr'])

# Plot to see which manufacturer has highest rated cereals sns.catplot(x='mfr_name', y='rating' , data=cereal ) plt.xticks(rotation=30)

# Which cereal is rated highest? cereal[cereal.rating == cereal.rating.max()]

# Look at sugars per brand by plotting mfr = sns.catplot(x='mfr_name',y='sugars' , data = cereal ) plt.xticks(rotation=30)

# Get data for clustering X = cereal.sugars.values X = np.reshape( X, (len(X), 1) ) km = KMeans( n_clusters=3, init='random', random_state=0 ) y_km = km.fit(X)

# Form model, fit data and print out cluster centers print ("Sugar cluster centers\n") print (f"{km.cluster_centers_}")

# Add column to dataframe for this clusters, say sugar_cluster cereal['sugar_cluster'] = km.labels_ cereal.tail()

# Plot clusters sns.catplot(x= 'sugar_cluster', y='sugars', data = cereal)

# Determine which cluster number corresponds to lowest, middle and highest level and create a new # column in dataframe using .map # cereal['sugar_level'] = cereal['sugar_cluster'].map ({ \ 0: 'low', \ 1:'medium', \ 2: 'high' }) cereal.tail()

# How are cereals distributed among the 3 levels? sns.catplot(data=cereal, x="sugar_level", y="mfr_name", order=['low', 'medium', 'high'])

# Which cereals have the highest sugar levels # cereal[(cereal.sugar_level == 'high')]

# Which cereals have the lowest sugar levels # cereal[(cereal.sugar_level == 'low')]

# If you eat a particular cereal like Apple Jacks, Froot Loops, etc. what cluster is it in? my_cereal = 'Apple Jacks' my_cereal_index = cereal[cereal.name == my_cereal].index.values my_cereal_cluster = cereal[cereal.name == my_cereal].sugar_cluster.values my_cereal_level = cereal[cereal.name == my_cereal].sugar_level.values print (f"The data instance for {my_cereal} is {my_cereal_index}") print (f"The sugar cluster for {my_cereal} is {my_cereal_cluster}") print (f"The sugar level for {my_cereal} is {my_cereal_level}")

# Look at carbs per brand by plotting mfr = sns.catplot(x='mfr_name',y='carbo' , data = cereal ) plt.xticks(rotation=30)

# Get data for clustering X = cereal.carbo.values X = np.reshape( X, (len(X), 1) ) km = KMeans( n_clusters=3, init='random', random_state=0 ) y_km = km.fit(X)

# Form model, fit data and print out cluster centers print ("carb cluster centers") print() print (km.cluster_centers_ )

# Add column to dataframe for this clusters, say carb_cluster cereal['carb_cluster'] = km.labels_ cereal.tail()

# Plot clusters sns.catplot(x= 'carb_cluster', y='carbo', data = cereal)

# Determine which cluster number corresponds to lowest, middle and highest level and create a new # column in dataframe using .map # cereal['carb_level'] = cereal['carb_cluster'].map ({ \ 0: 'low', \ 1:'medium', \ 2: 'high' }) cereal.tail()

# How are cereals distributed among the 3 levels? sns.catplot(data=cereal, x="carb_level", y="mfr_name", order=['low', 'medium', 'high'])

# Which cereals have the highest carb levels # cereal[(cereal.carb_level == 'high')]

# Which cereals have the lowest carb levels # cereal[(cereal.carb_level == 'low')]

# If you eat a particular cereal like Apple Jacks, Froot Loops, etc. what cluster is it in? my_cereal = 'Apple Jacks' my_cereal_index = cereal[cereal.name == my_cereal].index.values my_cereal_cluster = cereal[cereal.name == my_cereal].carb_cluster.values my_cereal_level = cereal[cereal.name == my_cereal].carb_level.values print (f"The data instance for {my_cereal} is {my_cereal_index}") print (f"The carb cluster for {my_cereal} is {my_cereal_cluster}") print (f"The carb level for {my_cereal} is {my_cereal_level}")

cereal[ (cereal.carb_level == 'high') & (cereal.sugar_level == 'low') ]

# What cereals are low carbs and low sugar? cereal[ (cereal.carb_level == 'low') & (cereal.sugar_level == 'low') ]