Project 5 - K-Means Clustering

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

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

# drop features we aren't using or create a new dataframe with only the features we want df_mod = df.drop(columns = ['name', 'type', 'calories', 'protein', 'fat', 'sodium', 'fiber', 'potass', 'vitamins', 'shelf', 'weight', 'cups']) df_mod.head()

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

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 77 entries, 0 to 76
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   mfr     77 non-null     object 
 1   carbo   77 non-null     float64
 2   sugars  77 non-null     int64  
 3   rating  77 non-null     float64
dtypes: float64(2), int64(1), object(1)
memory usage: 2.5+ KB

# 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 print(df_mod[ ( df_mod.sugars<0) & (df_mod.carbo < 0)]) df_mod= df_mod.drop([57]) df_mod.head(58)

   mfr  carbo  sugars     rating
57   Q   -1.0      -1  50.828392

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

# Plot number of products per manufacturer sns.countplot (x='mfr', data = df_mod) plt.xticks(rotation=-45)

# There is only one cereal from American Home Foods Company so we drop that data sample # print(df_mod[(df_mod.mfr=='A')]) # 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 # df_mod = df_mod.drop([43]) df_mod.head()

   mfr  carbo  sugars     rating
43   A   16.0       3  54.850917

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

0     N
1     Q
2     K
3     K
4     R
     ..
72    G
73    G
74    R
75    G
76    G
Name: mfr, Length: 75, dtype: object

# Plot to see which manufacturer has highest rated cereals sns.countplot (x='mfr_name', data = df_mod) plt.xticks(rotation=-45)

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

   carbo  sugars     rating  mfr_name
3    8.0       0  93.704912  Kelloggs

# Look at sugars per brand by plotting sns.catplot(x='mfr_name', y='sugars', data = df_mod) plt.xticks(rotation=-45)

# Get data for clustering df_mod['target'] = df_mod['sugars'] # add a column with the target species df_mod['sugars'] = df_mod['target'].map( \ {0:df_mod.sugar_lvls[0], \ 1:df_mod.sugar_lvls[1], \ 2:df_mod.sugar_lvls[2] } )

AttributeError: 'DataFrame' object has no attribute 'sugar_lvls'

# Form model, fit data and print out cluster centers

# Add column to dataframe for this clusters, say sugars_clusters

# Plot clusters

# Determine which cluster number corresponds to lowest, middle and highest level and create a new # column in dataframe using .map # #

# How are cereals distributed among the 3 levels?

# Which cereals have the highest sugar levels #

# Which cereals have the lowest sugar levels #

# If you eat a particular cereal like Apple Jacks, Froot Loops, etc. what cluster is it in? my_cereal = 'Apple Jacks' print (f"The data instance and sugar cluster for {my_cereal} is ", \ df[df.name == my_cereal].sugars_clusters )

AttributeError: 'DataFrame' object has no attribute 'sugars_clusters'