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1e971b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns
import plotly.express as px
def run():
st.title("F1 Podium Prediction App")
st.subheader("This page consists of Exploratory Data Analysis (EDA) of F1 dataset")
data = mpimg.imread('./src/f1.jpg')
st.image(data, caption = "Formula 1")
df = pd.read_csv('./src/f1df.csv')
st.dataframe(df)
# Barplot
st.write('### Barplot of Podium Probability by Grid Position')
grid_podium = (df.groupby('grid_position')['podium'].agg(['mean', 'count']).reset_index()
.rename(columns={'mean': 'podium_rate', 'count': 'races'}))
fig, ax = plt.subplots(figsize = (9, 4))
ax.bar(grid_podium['grid_position'], grid_podium['podium_rate'] * 100)
ax.set_xlabel("Starting Grid Position")
ax.set_ylabel("Podium Probability (%)")
ax.set_title("Podium Probability by Grid Position")
plt.tight_layout()
st.pyplot(fig)
st.write('### Driver age and Podiums')
# Age and Podium Barplot
fig, ax = plt.subplots(figsize = (9, 4))
colors = {0: '#FF0000', 1: "#2BFF00"}
for label in [0, 1]:
ax.hist(df[df['podium'] == label]['driver_age'].dropna(),bins = 40,label = f'Podium={label}', color = colors[label], edgecolor = 'black')
ax.set_xlabel('Driver Age')
ax.set_ylabel('Count')
ax.set_title('Driver Age Distribution: Podium vs Not Podium')
ax.legend()
plt.tight_layout()
st.pyplot(fig)
st.write('### Correlation heatmap of numerical columns')
num_cols = ['grid_position', 'quali_position', 'round', 'year', 'driver_points_before', 'driver_position_before', 'driver_wins_before', 'driver_age', 'constructor_position_before', 'driver_historical_dnf_rate', 'pit_stops', 'podium']
corrnum = df[num_cols].corr()
fig, ax = plt.subplots(figsize = (12, 9))
sns.heatmap(corrnum, annot = True, fmt = '.2f',cmap = 'YlOrRd', ax = ax, vmin = -1, vmax = 1)
ax.set_title("Correlation Heatmap of Numeric Features")
plt.tight_layout()
st.pyplot(fig)
if __name__ == "__main__":
run() |