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my_app_assignment.py

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+# my_app.py
+
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load dataset
+# Load dataset from CSV
+df = pd.read_csv("tips.csv")
+
+# App title
+st.title("Restaurant Tip Insights")
+st.markdown("Explore tip percentages by day, gender, and smoker status.")
+
+# Sidebar filters
+day = st.sidebar.selectbox("Select a day", df['day'].unique())
+gender = st.sidebar.multiselect("Select gender(s)", df['sex'].unique(), default=df['sex'].unique())
+smoker = st.sidebar.multiselect("Select smoker status", df['smoker'].unique(), default=df['smoker'].unique())
+
+# Filter data
+filtered = df[(df['day'] == day) & (df['sex'].isin(gender)) & (df['smoker'].isin(smoker))]
+
+# KPI
+avg_tip = filtered['tip_pct'].mean()
+st.metric(label=f"Average Tip % on {day}", value=f"{avg_tip:.2f}%")
+
+# Plot 1 — Average Tip Percentage by Day
+avg_by_day = df.groupby("day")["tip_pct"].mean()
+fig1, ax1 = plt.subplots(figsize=(6,4))
+avg_by_day.plot(kind="bar", color="skyblue", ax=ax1)
+ax1.set_title("Average Tip Percentage by Day")
+ax1.set_xlabel("Day of Week")
+ax1.set_ylabel("Average Tip Percentage (%)")
+ax1.grid(axis="y", linestyle="--", alpha=0.7)
+st.pyplot(fig1)
+st.markdown("**Interpretation:** This bar chart shows the average tip percentage for each day of the week present in the dataset (Thursday, Friday, Saturday, and Sunday)." \
+" Friday has the highest average tip percentage, while Saturday has the lowest." \
+" Thursday and Sunday have similar average tip percentages, falling between Friday and Saturday.")
+
+# Plot 2 — Tip Percentage vs Total Bill by Sex
+fig2, ax2 = plt.subplots(figsize=(8,6))
+sns.scatterplot(x="total_bill", y="tip_pct", hue="sex", data=filtered, alpha=0.6, ax=ax2)
+ax2.set_title("Tip Percentage vs Total Bill by Sex")
+ax2.set_xlabel("Total Bill")
+ax2.set_ylabel("Tip Percentage (%)")
+ax2.grid(True, linestyle="--", alpha=0.5)
+st.pyplot(fig2)
+st.markdown("**Interpretation:** The scatter plot shows that as the total bill goes up, the tip percentage generally goes down." \
+" This means people(both male and female) tend to tip a smaller percentage on larger bills. " \
+"There are a few cases with very high tip percentages, especially on smaller bills.")
+
+# Plot 3 — Tip Percentage by Smoking Status
+fig3, ax3 = plt.subplots(figsize=(6,4))  # match Colab size
+sns.set_style("whitegrid")  # match Colab style
+
+# Use filtered data from sidebar
+sns.boxplot(x="smoker", y="tip_pct", data=filtered, palette="Set2", ax=ax3)
+
+# Titles and labels
+ax3.set_title("Tip Percentage by Smoking Status")
+ax3.set_xlabel("Smoker Status")
+ax3.set_ylabel("Tip Percentage (%)")
+ax3.grid(axis="y", linestyle="--", alpha=0.7)
+
+# Display plot in Streamlit
+st.pyplot(fig3)
+
+# --- Calculate medians dynamically ---
+median_smokers = filtered[filtered["smoker"]=="Yes"]["tip_pct"].median()
+median_non_smokers = filtered[filtered["smoker"]=="No"]["tip_pct"].median()
+
+# --- Display interpretation dynamically ---
+st.markdown(
+    f"**Interpretation:** The median tip percentage for smokers is {median_smokers:.2f}%, "
+    f"and for non-smokers it is {median_non_smokers:.2f}%. "
+    "The box plot suggests that smoking status does not have a large impact on the average tip percentage. "
+    "However, there are some instances where smokers gave significantly higher tips."
+)

requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+