import pandas as pd
from scipy import stats
import numpy as np
df=pd.read_csv("/content/bmi.csv")
df.head()

df.dropna()

max_vals=np.max(np.abs(df[['Height','Weight']]))
max_vals

from sklearn.preprocessing import StandardScaler
sc=StandardScaler()
df[['Height','Weight']]=sc.fit_transform(df[['Height','Weight']])
df.head(10)

from sklearn.preprocessing import MinMaxScaler
scaler=MinMaxScaler()
df[['Height','Weight']]=scaler.fit_transform(df[['Height','Weight']])
df.head(10)

from sklearn.preprocessing import Normalizer
scaler=Normalizer()
df[['Height','Weight']]=scaler.fit_transform(df[['Height','Weight']])
df

df1=pd.read_csv("/content/bmi.csv")
from sklearn.preprocessing import MaxAbsScaler
scaler=MaxAbsScaler()
df1[['Height','Weight']]=scaler.fit_transform(df1[['Height','Weight']])
df1

df2=pd.read_csv("/content/bmi.csv")
from sklearn.preprocessing import RobustScaler
scaler=RobustScaler()
df2[['Height','Weight']]=scaler.fit_transform(df2[['Height','Weight']])
df2.head()

import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score,confusion_matrix
data=pd.read_csv('/content/income(1) (1).csv',na_values=[" ?"])
data

data.isnull().sum()

missing=data[data.isnull().any(axis=1)]
missing

data2 = data.dropna(axis=0)
data2

sal=data['SalStat']
data2['SalStat']=data2['SalStat'].map({' less than or equal to 50,000':0,' greater than 50,000':1})
print(data2['SalStat'])

sal2=data2['SalStat']
dfs=pd.concat([sal,sal2],axis=1)
dfs

data2

new_data=pd.get_dummies(data2, drop_first=True)
new_data

columns_list=list(new_data.columns)
print(columns_list)

features=list(set(columns_list)-set(['SalStat']))
print(features)

y=new_data['SalStat'].values
print(y)

x = new_data[features].values
print(x)

train_x,test_x,train_y,test_y = train_test_split(x,y,test_size=0.3, random_state=0)
KNN_classifier=KNeighborsClassifier(n_neighbors = 5)
KNN_classifier.fit(train_x,train_y)

prediction = KNN_classifier.predict(test_x)
confusionMmatrix = confusion_matrix(test_y, prediction)
print(confusionMmatrix)

accuracy_score=accuracy_score(test_y, prediction)
print(accuracy_score)

print('Misclassified samples: %d' % (test_y != prediction).sum())

data.shape

import pandas as pd
import numpy as np
from scipy.stats import chi2_contingency
import seaborn as sns
tips=sns.load_dataset('tips')
tips.head()

contingency_table=pd.crosstab(tips['sex'],tips['time'])
print(contingency_table)

chi2, p, _, _ = chi2_contingency(contingency_table)
print(f"Chi-Square Statistic: {chi2}")
print(f"P-value: {p}")

import pandas as pd
from sklearn.feature_selection import SelectKBest, mutual_info_classif, f_classif
data={
    'Feature1':[1,2,3,4,5],
    'Feature2': ['A','B','C','A','B'],
    'Feature3':[0,1,1,0,1],
    'Target' :[0,1,1,0,1]
}
df=pd.DataFrame(data)
X=df[['Feature1','Feature3']]
y=df['Target']
selector=SelectKBest(score_func=mutual_info_classif, k=1)
X_new = selector.fit_transform (X,y)
selected_feature_indices = selector.get_support(indices=True)
selected_features = X.columns[selected_feature_indices]
print("Selected Features:")
print(selected_features)

 To read the given data and perform Feature Scaling and Feature Selection process and save the data to a file is successful.