|
|
|
|
|
""" |
|
|
Created on Sat Jan 14 17:50:42 2023 |
|
|
|
|
|
@author: Lenovo |
|
|
""" |
|
|
import pandas as pd |
|
|
import plotly.express as px |
|
|
import streamlit as st |
|
|
|
|
|
|
|
|
def load_data(): |
|
|
new_df = pd.read_csv(r"C:\Users\Lenovo\Downloads\sales.csv") |
|
|
return new_df |
|
|
|
|
|
new_df = load_data() |
|
|
def correlation(): |
|
|
st.header("Finding Correlation between variables") |
|
|
new_df = pd.read_csv(r"C:\Users\Lenovo\Downloads\sales.csv") |
|
|
contingency_table =pd.crosstab(new_df['Category'],new_df['Sub Category'],margins =False) |
|
|
fig = px.imshow(contingency_table) |
|
|
st.subheader("Correlation between sub-category and category") |
|
|
st.plotly_chart(fig) |
|
|
st.markdown("*Inference*") |
|
|
st.write("- *Bevarages has highest correlation with Health Drinks with 719 frequencies and with soft drinks 619 frequencies* ") |
|
|
st.write("- *Sub-category like health drinks and soft drinks has highest correlation with bevarage category* ") |
|
|
|
|
|
contingency_table2=pd.crosstab(new_df['Category'],new_df['City'],margins =False) |
|
|
fig1 = px.imshow(contingency_table2) |
|
|
st.subheader("Correlation between category and city") |
|
|
st.plotly_chart(fig1) |
|
|
st.markdown(" *Inference*" ) |
|
|
st.write (" - *for bakery cities like krishnagiri and salem has highest frequencies*") |
|
|
st.write("- *for Beverages cities like Madurai and perambalur has highest frequencies of sales* ") |
|
|
st.write("- *for Egg-meat and fish cities like karur,kanyakumari krishnagri, cumbum have highest frequencies* ") |
|
|
st.write("- *for foodgrains cities like bodi,kanyakumari ,krishnagri, ooty have highest frequencies* ") |
|
|
st.write("- *for Fruits and veggies cities like bodi, coimbatore, nagercoil, pudukottia,tirunelveli , virudhu have highest frequencies* ") |
|
|
st.write("- *for oil and masala cities like kanyakumari ,salem and pudukottia have highest frequencies* ") |
|
|
st.write("- *for snacks cities like kanyakumari ,perambalur ,tirunelveli and virudhuhave highest frequencies* ") |
|
|
|
|
|
contingency_table4 = pd.crosstab(new_df['Category'],new_df['Region'],margins=False) |
|
|
fig2 = px.imshow(contingency_table4) |
|
|
st.subheader("Correlation between category and region") |
|
|
st.plotly_chart(fig2) |
|
|
st.write("Inference") |
|
|
st.write("- *West hase highest correlation with every category* ") |
|
|
|
|
|
return correlation() |
|
|
|
