ML_APP.py

import streamlit as st
import numpy as np
import pandas as pd
import seaborn as sns

import matplotlib
import matplotlib.pyplot as plt
#from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn import model_selection
#from sklearn.preprocessing import LabelEncoder
matplotlib.use('Agg')

from PIL import Image

#Set title

st.title('JL DATA EXPRESSION')
image = Image.open("C:\\Users\\Hp\\OneDrive\\Pictures\\logo-color.png")
st.image(image,use_column_width=True)

def main():
	activities=('EDA','Visualization','model','About us')
	option=st.sidebar.selectbox('Select option:',activities)


#DEALING WITH THE EDA PART

	if option=='EDA':
		st.subheader('Exploratory Data Analysis')

		data=st.file_uploader('Upload dataset:',type=['csv','xlsx','txt','json'])
		st.success('Data successfully loaded')
		if data is not None:
			df=pd.read_csv(data)
			st.dataframe(df.head(50))

			if st.checkbox('Display shape'):
				st.write(df.shape)
			if st.checkbox('Display columns'):
				st.write(df.columns)
			if st.checkbox('Select multiple columns'):
				selected_columns=st.multiselect('Select preferred columns:',df.columns)
				df1=df[selected_columns]
				st.dataframe(df1)

			if st.checkbox('Display summary'):
				st.write(df1.describe().T) 

			if st.checkbox('Display Null Values'):
				st.write(df.isnull().sum())

			if st.checkbox('Display rhe data types'):
				st.write(df.dtypes)
			if st.checkbox('Display correlation of data various columns'):
				st.write(df.corr())



			

#DEALING WITH THE VISUALIZATION PART


	elif option=='Visualization':
		st.subheader("Data Visualization")

		data=st.file_uploader('Upload dataset:',type=['csv','xlsx','txt','json'])
		st.success('Data successfully loaded')
		if data is not None:
			df=pd.read_csv(data)
			st.dataframe(df.head(50))

			if st.checkbox('Select multiple columns to plot'):
				selected_columns=st.multiselect('Select your preferred columns',df.columns)
				df1=df[selected_columns]
				st.dataframe(df1)

			if st.checkbox('Display Heatmap'):
				st.write(sns.heatmap(df1.corr(),vmax=1,square=True,annot=True,cmap='viridis'))
				st.pyplot()
			if st.checkbox('Display Pairplot'):
				st.write(sns.pairplot(df1,diag_kind='kde'))
				st.pyplot()
			if st.checkbox('Display Pie Chart'):
				all_columns=df.columns.to_list()
				pie_columns=st.selectbox("select column to display",all_columns)
				pieChart=df[pie_columns].value_counts().plot.pie(autopct="%1.1f%%")
				st.set_option('deprecation.showPyplotGlobalUse', False)
				st.write(pieChart)
				st.pyplot()




	#DEALING WITH THE MODEL BUILDING PART

	elif option=='model':
		st.subheader("Model Building")

		data=st.file_uploader('Upload dataset:',type=['csv','xlsx','txt','json'])
		st.success('Data successfully loaded')
		if data is not None:
			df=pd.read_csv(data)
			st.dataframe(df.head(50))

			if st.checkbox('Select Multiple columns'):
				new_data=st.multiselect('Select your preferred columns',df.columns)
				df1=df[new_data]
				st.dataframe(df1)

			
			#Dividing my data into x and y variables

			X=df1.iloc[:,0:-1]
			y=df1.iloc[:,-1]

		seed=st.sidebar.slider('Seed',1,200)

		classifier_name=st.sidebar.selectbox('Select your preferred classifier:',('KNN','SVM','LR','naive_bayes','decision tree'))

		def add_parameter(name_of_clf):
			param=dict()
			if name_of_clf=='SVM':
				C=st.sidebar.slider('C',0.01,15.0)
				param['C']=C
			if name_of_clf=='KNN':
				K=st.sidebar.slider('K',1,15)
				param['K']=K
				return param

		#calling the function

		#params=add_parameter(classifier_name)


		#define a function for our classifier

		#def get_classifier(name_of_clf,params):