DS Exp 8

GamerBhai02

May 7th, 2025 (edited)

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from numpy import isnan
from pandas import read_csv
from sklearn.impute import SimpleImputer
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/refs/heads/master/horse-colic.csv"
df = read_csv(url, header=None, na_values='?')
data = df.values
for i in range(df.shape[1]):
n_miss = df[[i]].isnull().sum()
perc = n_miss / df.shape[0] * 100
print('> %d, Missing: %d (%.1f%%)' % (i, n_miss, perc))
imputer = SimpleImputer(strategy='mean')
imputer.fit(data)
Xtrans = imputer.transform(data)
print('Missing: %d' % sum(isnan(Xtrans).flatten()))
from numpy import isnan
from pandas import read_csv
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/refs/heads/master/horse-colic.csv"
df = read_csv(url, header=None, na_values='?')
data = df.values
for i in range(df.shape[1]):
n_miss = df[[i]].isnull().sum()
perc = n_miss / df.shape[0] * 100
print('> %d, Missing: %d (%.1f%%)' % (i, n_miss, perc))
imputer = IterativeImputer()
imputer.fit(data)
Xtrans = imputer.transform(data)
print('Missing: %d' % sum(isnan(Xtrans).flatten()))
from numpy import isnan
from pandas import read_csv
from sklearn.impute import KNNImputer
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/refs/heads/master/horse-colic.csv"
df = read_csv(url, header=None, na_values='?')
data = df.values
for i in range(df.shape[1]):
n_miss = df[[i]].isnull().sum()
perc = n_miss / df.shape[0] * 100
print('> %d, Missing: %d (%.1f%%)' % (i, n_miss, perc))
imputer = KNNImputer()
imputer.fit(data)
Xtrans = imputer.transform(data)
print('Missing: %d' % sum(isnan(Xtrans).flatten()))
#ASSIGNMENT 8
from numpy import isnan
from pandas import read_csv
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/refs/heads/master/pima-indians-diabetes.data.csv"
df = read_csv(url, header=None, na_values='?')
data = df.values
for i in range(df.shape[1]):
n_miss = df[[i]].isnull().sum()
perc = n_miss / df.shape[0] * 100
print('> %d, Missing: %d (%.1f%%)' % (i, n_miss, perc))
imputer = SimpleImputer(strategy='mean')
imputer.fit(data)
Xtrans = imputer.transform(data)
print('Missing: %d' % sum(isnan(Xtrans).flatten()))
imputer = IterativeImputer()
imputer.fit(data)
Xtrans = imputer.transform(data)
print('Missing: %d' % sum(isnan(Xtrans).flatten()))
imputer = KNNImputer()
imputer.fit(data)
Xtrans = imputer.transform(data)
print('Missing: %d' % sum(isnan(Xtrans).flatten()))

Tags: Exp 8

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