ksv-muralidhar

import pandas as pd
from sklearn.model_selection import train_test_split,StratifiedKFold,cross_validate
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

ksv-muralidhar

import numpy as np
import pandas as pd
from apyori import apriori
from termcolor import colored

data = pd.read_csv("../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv",header=1)

ksv-muralidhar

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import cross_validate, train_test_split
from sklearn.metrics import mean_squared_error

ksv-muralidhar

import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator,TransformerMixin
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import FunctionTransformer
from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression

ksv-muralidhar

import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator,TransformerMixin
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import FunctionTransformer
from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression

ksv-muralidhar

import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, GridSearchCV, StratifiedKFold
from sklearn.preprocessing import MinMaxScaler
from imblearn.over_sampling import SMOTE
from imblearn.pipeline import Pipeline as imbpipeline
from sklearn.pipeline import Pipeline
from sklearn.datasets import make_classification, load_breast_cancer

ksv-muralidhar

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.feature_selection import mutual_info_classif, SelectKBest
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import MinMaxScaler
import joblib

ksv-muralidhar

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import streamlit as st

st.title("Visualizing Central Limit Theorem")

POP_MIN, POP_MAX = st.sidebar.slider('Select the population range (Example: range of age is 0-100)',0, 10000, (0, 1000))

ksv-muralidhar

df.melt()

ksv-muralidhar

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

nifty = pd.read_csv("Nifty.csv",
                     usecols=['Date', "Close"],
                     parse_dates=['Date'])

nifty.set_index("Date", inplace=True) #setting "Date" as index