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("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"]=data["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)
confusionMatrix=confusion_matrix(test_y, prediction)
print(confusionMatrix)

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

print("Misclassified Samples : %d" % (test_y !=prediction).sum())

data.shape

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)

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()

tips.time.unique()

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

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

  Thus the code to read the given data and perform Feature Scaling and Feature Selection process and save the data to a file is implemented successfully.