| import streamlit as st
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| import pandas as pd
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| import matplotlib.pyplot as plt
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| import matplotlib.image as mpimg
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| import seaborn as sns
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| import plotly.express as px
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|
|
| def run():
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| st.title("F1 Podium Prediction App")
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|
|
| st.subheader("This page consists of Exploratory Data Analysis (EDA) of F1 dataset")
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|
|
| data = mpimg.imread('./src/f1.jpg')
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| st.image(data, caption = "Formula 1")
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|
|
| df = pd.read_csv('./src/f1df.csv')
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| st.dataframe(df)
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|
|
|
|
| st.write('### Barplot of Podium Probability by Grid Position')
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| grid_podium = (df.groupby('grid_position')['podium'].agg(['mean', 'count']).reset_index()
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| .rename(columns={'mean': 'podium_rate', 'count': 'races'}))
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|
|
| fig, ax = plt.subplots(figsize = (9, 4))
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| ax.bar(grid_podium['grid_position'], grid_podium['podium_rate'] * 100)
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| ax.set_xlabel("Starting Grid Position")
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| ax.set_ylabel("Podium Probability (%)")
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| ax.set_title("Podium Probability by Grid Position")
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| plt.tight_layout()
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| st.pyplot(fig)
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|
|
| st.write('### Driver age and Podiums')
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|
|
| fig, ax = plt.subplots(figsize = (9, 4))
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| colors = {0: '#FF0000', 1: "#2BFF00"}
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| for label in [0, 1]:
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| ax.hist(df[df['podium'] == label]['driver_age'].dropna(),bins = 40,label = f'Podium={label}', color = colors[label], edgecolor = 'black')
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| ax.set_xlabel('Driver Age')
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| ax.set_ylabel('Count')
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| ax.set_title('Driver Age Distribution: Podium vs Not Podium')
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| ax.legend()
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| plt.tight_layout()
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| st.pyplot(fig)
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|
|
| st.write('### Correlation heatmap of numerical columns')
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| 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']
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| corrnum = df[num_cols].corr()
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| fig, ax = plt.subplots(figsize = (12, 9))
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| sns.heatmap(corrnum, annot = True, fmt = '.2f',cmap = 'YlOrRd', ax = ax, vmin = -1, vmax = 1)
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| ax.set_title("Correlation Heatmap of Numeric Features")
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| plt.tight_layout()
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| st.pyplot(fig)
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|
|
| if __name__ == "__main__":
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| run() |
