saimadhu-polamuri

from scipy.stats import jarque_bera
import numpy as np

# generate two sample datasets
data1 = np.random.normal(0, 1, size=100)
data2 = np.random.uniform(size=100)

# perform Jarque-Bera test on each dataset
jb_stat1, jb_p1 = jarque_bera(data1)
jb_stat2, jb_p2 = jarque_bera(data2)

saimadhu-polamuri

from scipy.stats import jarque_bera
import numpy as np

# generate a sample dataset
data = np.random.normal(0, 1, 1000)

# calculate the test statistic and p-value
jb_stat, jb_p = jarque_bera(data)

# print the results

saimadhu-polamuri

from sklearn.datasets import load_diabetes
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import numpy as np

# Load the diabetes dataset
diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target

saimadhu-polamuri

from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target

# Introduce some missing values in X
missing_mask = np.random.rand(*X.shape) < 0.1
X_missing = X.copy()

saimadhu-polamuri

from sklearn.impute import KNNImputer
from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target

# Introduce some missing values in X
missing_mask = np.random.rand(*X.shape) < 0.1
X_missing = X.copy()
X_missing[missing_mask] = np.nan

saimadhu-polamuri

from sklearn.impute import KNNImputer
from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target

# Introduce some missing values in X
missing_mask = np.random.rand(*X.shape) < 0.1
X_missing = X.copy()
X_missing[missing_mask] = np.nan

saimadhu-polamuri

import numpy as np
from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target

X_imputed = X.copy()
for i in range(X.shape[1]):
    missing_mask = np.isnan(X[:, i])
    if np.any(missing_mask):

saimadhu-polamuri

import numpy as np
from sklearn.datasets import load_diabetes

diabetes = load_diabetes()
X, y = diabetes.data, diabetes.target

X_imputed = X.copy()
for i in range(X.shape[1]):
    missing_mask = np.isnan(X[:, i])
    if np.any(missing_mask):

saimadhu-polamuri

import pandas as pd

# Sample data
data = {'value': [1, 2, None, 4, 5, None, 7]}
df = pd.DataFrame(data)

# Replace with Maximum Value
df['max_fill'] = df['value'].fillna(df['value'].max())

# Replace with Minimum Value

saimadhu-polamuri

import pandas as pd

# Sample data
data = {'value': [1, 2, None, 4, 5, None, 7]}
df = pd.DataFrame(data)

# Forward Fill
df['forward_fill'] = df['value'].fillna(method='ffill')

# Backward Fill