import streamlit as st

def rec():
    st.sidebar.markdown("""---""")
    st.title("Conclusion and Recommendation")

    st.write('## Conclusion')
    st.write("- We have created a linear regression model based on a number of "
             "factors in order to predict delivery days taken for an order.")
    st.write("- We increased our Explained Variance value by reducing outliers and introducing more variables "
             "like distance between sellers and buyers. Using the linear regression model, we calculated the "
             "estimated days taken using the model and added it to our dataframe.")
    st.write("- We trained our data to follow different Machine Learning models, and analysed which model was most "
             "apt in representing and predicting our data.")
    st.write("- We checked the correlation between actual number of days for delivery, estimated number of days and "
             "estimated days from our model.")
    st.write("- We found that the values from our model actually was closer to the actual delivery days, ")
    st.write("- In conclusion, this tool explore the O-list data set in a interactive setting")
    st.write("- The user is able to explore different variables, and control which variables are used in the analysis")


    st.write('## Recommendation')
    st.write('### 1) Use machine learning to better gauge estimated delivery duration.')
    st.write("- We were able to predict the delivery days taken much better compared to Olist.")
    st.write("- Olist should improve their estimated number os days for delivery.")
    st.write("- More realistic data gives more transparency between O-List and the consumer allowing for more customer "
             "satisfaction.")

    st.write('### 2) Increase distribution network.')
    st.write("- Distance has highest correlation with delivery days.")
    st.write("- When the buyer and seller are far apart the delivery takes longer.")
    st.write("- O-List could increasing their distribution network.")
    st.write("- They could tap into local distribution networks across the cities of Brazil, and not limit itself to "
             "delivering from cities like Rio de Janeiro and Sao Paulo.")

    st.write('### 3) Create more warehouses in certain locations.')
    st.write("- From the geospatial analysis it is apparent that there are various pockets in brazil, that have high "
             "demand for O-list products")
    st.write("- When deliery duration is long, the review scores are low.")
    st.write("- O-list could look into creating more warehouses in locations like: West of Floresta da Tijura, Saito, "
             "Ribeirao Preto, Salvador.")
    st.write("- This would allow more deliveries to more consumers, with shorter number of delivery days.")
aac9a7a9efcfac4c8a24fd4113b3824eed61ff86
<mask token>

<mask token>


def rec():
    st.sidebar.markdown('---')
    st.title('Conclusion and Recommendation')
    st.write('## Conclusion')
    st.write(
        '- We have created a linear regression model based on a number of factors in order to predict delivery days taken for an order.'
        )
    st.write(
        '- We increased our Explained Variance value by reducing outliers and introducing more variables like distance between sellers and buyers. Using the linear regression model, we calculated the estimated days taken using the model and added it to our dataframe.'
        )
    st.write(
        '- We trained our data to follow different Machine Learning models, and analysed which model was most apt in representing and predicting our data.'
        )
    st.write(
        '- We checked the correlation between actual number of days for delivery, estimated number of days and estimated days from our model.'
        )
    st.write(
        '- We found that the values from our model actually was closer to the actual delivery days, '
        )
    st.write(
        '- In conclusion, this tool explore the O-list data set in a interactive setting'
        )
    st.write(
        '- The user is able to explore different variables, and control which variables are used in the analysis'
        )
    st.write('## Recommendation')
    st.write(
        '### 1) Use machine learning to better gauge estimated delivery duration.'
        )
    st.write(
        '- We were able to predict the delivery days taken much better compared to Olist.'
        )
    st.write(
        '- Olist should improve their estimated number os days for delivery.')
    st.write(
        '- More realistic data gives more transparency between O-List and the consumer allowing for more customer satisfaction.'
        )
    st.write('### 2) Increase distribution network.')
    st.write('- Distance has highest correlation with delivery days.')
    st.write(
        '- When the buyer and seller are far apart the delivery takes longer.')
    st.write('- O-List could increasing their distribution network.')
    st.write(
        '- They could tap into local distribution networks across the cities of Brazil, and not limit itself to delivering from cities like Rio de Janeiro and Sao Paulo.'
        )
    st.write('### 3) Create more warehouses in certain locations.')
    st.write(
        '- From the geospatial analysis it is apparent that there are various pockets in brazil, that have high demand for O-list products'
        )
    st.write('- When deliery duration is long, the review scores are low.')
    st.write(
        '- O-list could look into creating more warehouses in locations like: West of Floresta da Tijura, Saito, Ribeirao Preto, Salvador.'
        )
    st.write(
        '- This would allow more deliveries to more consumers, with shorter number of delivery days.'
        )

import streamlit as st


def rec():
    st.sidebar.markdown('---')
    st.title('Conclusion and Recommendation')
    st.write('## Conclusion')
    st.write(
        '- We have created a linear regression model based on a number of factors in order to predict delivery days taken for an order.'
        )
    st.write(
        '- We increased our Explained Variance value by reducing outliers and introducing more variables like distance between sellers and buyers. Using the linear regression model, we calculated the estimated days taken using the model and added it to our dataframe.'
        )
    st.write(
        '- We trained our data to follow different Machine Learning models, and analysed which model was most apt in representing and predicting our data.'
        )
    st.write(
        '- We checked the correlation between actual number of days for delivery, estimated number of days and estimated days from our model.'
        )
    st.write(
        '- We found that the values from our model actually was closer to the actual delivery days, '
        )
    st.write(
        '- In conclusion, this tool explore the O-list data set in a interactive setting'
        )
    st.write(
        '- The user is able to explore different variables, and control which variables are used in the analysis'
        )
    st.write('## Recommendation')
    st.write(
        '### 1) Use machine learning to better gauge estimated delivery duration.'
        )
    st.write(
        '- We were able to predict the delivery days taken much better compared to Olist.'
        )
    st.write(
        '- Olist should improve their estimated number os days for delivery.')
    st.write(
        '- More realistic data gives more transparency between O-List and the consumer allowing for more customer satisfaction.'
        )
    st.write('### 2) Increase distribution network.')
    st.write('- Distance has highest correlation with delivery days.')
    st.write(
        '- When the buyer and seller are far apart the delivery takes longer.')
    st.write('- O-List could increasing their distribution network.')
    st.write(
        '- They could tap into local distribution networks across the cities of Brazil, and not limit itself to delivering from cities like Rio de Janeiro and Sao Paulo.'
        )
    st.write('### 3) Create more warehouses in certain locations.')
    st.write(
        '- From the geospatial analysis it is apparent that there are various pockets in brazil, that have high demand for O-list products'
        )
    st.write('- When deliery duration is long, the review scores are low.')
    st.write(
        '- O-list could look into creating more warehouses in locations like: West of Floresta da Tijura, Saito, Ribeirao Preto, Salvador.'
        )
    st.write(
        '- This would allow more deliveries to more consumers, with shorter number of delivery days.'
        )

import streamlit as st

def rec():
    st.sidebar.markdown("""---""")
    st.title("Conclusion and Recommendation")

    st.write('## Conclusion')
    st.write("- We have created a linear regression model based on a number of "
             "factors in order to predict delivery days taken for an order.")
    st.write("- We increased our Explained Variance value by reducing outliers and introducing more variables "
             "like distance between sellers and buyers. Using the linear regression model, we calculated the "
             "estimated days taken using the model and added it to our dataframe.")
    st.write("- We trained our data to follow different Machine Learning models, and analysed which model was most "
             "apt in representing and predicting our data.")
    st.write("- We checked the correlation between actual number of days for delivery, estimated number of days and "
             "estimated days from our model.")
    st.write("- We found that the values from our model actually was closer to the actual delivery days, ")
    st.write("- In conclusion, this tool explore the O-list data set in a interactive setting")
    st.write("- The user is able to explore different variables, and control which variables are used in the analysis")


    st.write('## Recommendation')
    st.write('### 1) Use machine learning to better gauge estimated delivery duration.')
    st.write("- We were able to predict the delivery days taken much better compared to Olist.")
    st.write("- Olist should improve their estimated number os days for delivery.")
    st.write("- More realistic data gives more transparency between O-List and the consumer allowing for more customer "
             "satisfaction.")

    st.write('### 2) Increase distribution network.')
    st.write("- Distance has highest correlation with delivery days.")
    st.write("- When the buyer and seller are far apart the delivery takes longer.")
    st.write("- O-List could increasing their distribution network.")
    st.write("- They could tap into local distribution networks across the cities of Brazil, and not limit itself to "
             "delivering from cities like Rio de Janeiro and Sao Paulo.")

    st.write('### 3) Create more warehouses in certain locations.')
    st.write("- From the geospatial analysis it is apparent that there are various pockets in brazil, that have high "
             "demand for O-list products")
    st.write("- When deliery duration is long, the review scores are low.")
    st.write("- O-list could look into creating more warehouses in locations like: West of Floresta da Tijura, Saito, "
             "Ribeirao Preto, Salvador.")
    st.write("- This would allow more deliveries to more consumers, with shorter number of delivery days.")