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import streamlit as st |
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import streamlit.components.v1 as components |
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from activities.activity import ml |
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from ml.mlmodel import MLDataset |
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import plotly.express as px |
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import pandas as pd |
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import pandas_profiling |
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import os |
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from sklearn.datasets import load_diabetes |
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from streamlit_pandas_profiling import st_profile_report |
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from streamlit_option_menu import option_menu |
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def main(): |
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from PIL import Image |
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st.sidebar.image('logo.png', use_column_width=True) |
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image_loan=Image.open(os.path.join("data analysis.jpg")) |
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rad = st.sidebar.radio("Navigation",["Home","Analysis","Visualize","Machine-Learning"]) |
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with open('style.css') as f: |
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st.markdown(f'<style>{f.read()}</stle>',unsafe_allow_html=True) |
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if rad=='Home': |
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html_temp = """ |
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<div id="container"> |
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<h1>Welcome to Our Website</h1> |
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<p>Our advanced software will help you make sense<br> |
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of your data quickly and easily. With powerful <br> |
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algorithms and customizable dashboards, you'll<br> |
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be able to see patterns and insights that<br> |
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you never knew existed.</p> |
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</div> |
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""" |
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st.markdown(html_temp,unsafe_allow_html=True) |
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if rad == "Visualize": |
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file_upload=st.sidebar.file_uploader(" ",type=["csv"]) |
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st.sidebar.image(image_loan,use_column_width=True) |
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chart_select = st.sidebar.selectbox( |
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label = "select the chart type", |
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options=['ScatterPlots','Lineplots','Histogram','Boxplot'] |
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) |
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html_temp = """ |
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<div style="background-color:red;padding:10px"> |
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<h2 style="color:white;text-align:center;border-radius:30px">Automatic Exploratory Analysis</h2> |
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</div> |
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""" |
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st.markdown(html_temp,unsafe_allow_html=True) |
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st.sidebar.title("Upload Input csv file : ") |
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st.subheader('1.Datasets') |
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st.write(""" |
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# Automatic Exploratory Analysis |
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""") |
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if file_upload is not None: |
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df = pd.read_csv(file_upload) |
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st.write(df) |
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numeric_columns = list(df.select_dtypes(['float','int']).columns) |
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if chart_select == "ScatterPlots": |
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st.sidebar.subheader("ScatterPlot Settings") |
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x_values = st.sidebar.selectbox('X axis',options=numeric_columns) |
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y_values = st.sidebar.selectbox('Y axis',options=numeric_columns) |
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plot = px.scatter(data_frame =df, x=x_values,y=y_values) |
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st.plotly_chart(plot) |
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if chart_select == "Lineplots": |
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st.sidebar.subheader("ScatterPlot Settings") |
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x_values = st.sidebar.selectbox('X axis',options=numeric_columns) |
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y_values = st.sidebar.selectbox('Y axis',options=numeric_columns) |
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plot = px.line(data_frame =df, x=x_values,y=y_values) |
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st.plotly_chart(plot) |
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if chart_select=="Histogram": |
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st.sidebar.subheader("ScatterPlot Settings") |
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x_values = st.sidebar.selectbox('X axis',options=numeric_columns) |
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y_values = st.sidebar.selectbox('Y axis',options=numeric_columns) |
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plot=px.histogram(data_frame=df,x=x_values,y=y_values) |
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st.plotly_chart(plot) |
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if chart_select=="Boxplot": |
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st.sidebar.subheader("ScatterPlot Settings") |
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x_values = st.sidebar.selectbox('X axis',options=numeric_columns) |
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y_values = st.sidebar.selectbox('Y axis',options=numeric_columns) |
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plot=px.box(data_frame=df,x=x_values,y=y_values) |
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st.plotly_chart(plot) |
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else: |
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st.info('Awaiting for CSV file to be uploaded.') |
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if rad == "Analysis": |
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file_upload=st.sidebar.file_uploader(" ",type=["csv"]) |
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st.sidebar.image(image_loan,use_column_width=True) |
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html_temp = """ |
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<div style="background-color:red;padding:10px"> |
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<h2 style="color:white;text-align:center;">Automatic Exploratory Analysis </h2> |
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</div> |
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""" |
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st.markdown(html_temp,unsafe_allow_html=True) |
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st.sidebar.title("Upload Input csv file : ") |
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st.subheader('1.Datasets') |
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st.write(""" |
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# Automated Exploratory Analysis |
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""") |
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if file_upload is not None: |
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df = pd.read_csv(file_upload) |
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st.write(df) |
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df1 = df.dropna() |
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if st.button('Run Modeling'): |
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st.title("Exploratory Data Analysis") |
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profile_df = df.profile_report() |
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st_profile_report(profile_df) |
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if st.button("No. of Missing values"): |
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st.write(df.isna().sum()) |
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if st.button('Drop Missing values'): |
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df1 = df.dropna() |
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st.write(df1) |
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else: |
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st.info('Awaiting for CSV file to be uploaded.') |
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if st.button('Press to use Example Datasets'): |
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diabetes = load_diabetes() |
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X = pd.DataFrame(diabetes.data,columns=diabetes.feature_names) |
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Y = pd.Series(diabetes.target,name='response') |
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df = pd.concat([X,Y],axis=1) |
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st.markdown('The Diabetes dataset is used as the example.') |
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st.write(df.head()) |
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st.markdown("Shape of Diabetes dataset") |
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st.write(df.shape) |
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st.title("Exploratory Data Analysis") |
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profile_df = df.profile_report() |
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st_profile_report(profile_df) |
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st.title("Sum of Null Values") |
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st.write(df.isna().sum()) |
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st.title("Dropping Null Values") |
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df1 = df.dropna() |
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data = df1.to_csv("new.csv") |
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st.write(df1) |
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if rad == "Machine-Learning": |
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file_upload=st.sidebar.file_uploader(" ",type=["csv"]) |
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dataset_name = st.selectbox("Pick a dataset", ["Iris", "Breast Cancer", "Wine Quality", "Mnist Digits", "Boston Houses", 'Diabetes']) |
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dataset = MLDataset(dataset_name) |
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df = dataset.get_dataframe() |
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ml(df) |
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if __name__ == '__main__': |
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main() |
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