Building the Frontend of Python Web Applications with Streamlit/

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Solution: Build UI for Candy Bar Prediction

Solution: Build UI for Candy Bar Prediction

Learn how to display UI components for the candy bar prediction web application.

We'll cover the following...

Solution for task 1

In task 1, you were required to show the first 10 rows from the candydData.csv file using Streamlit UI.

Let’s run the following code to display the dataset.

# CopyRights : https://www.kaggle.com/code/gcdatkin/candy-bar-prediction
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import streamlit as st
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

data = pd.read_csv("candyData.csv") # Reading dataset
# Solution
st.title("Candy Bar Dataset") # It will add the title for the dataset
# The following line will display the text as sub heading
st.subheader("This model will predict whether the candy bar is a bar or not")
st.table(data.head(11)) # This line displays the dataset from 0-10 rows
Displaying the dataset on Streamlit UI

Explanation

  • Lines 1–8: We import the required Python modules.
  • Line 11: We read the dataset from candyData.csv file.
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