Create app.py

app.py

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+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.linear_model import LinearRegression
+
+# Ensure seaborn is installed in your environment
+sns.set(color_codes=True)
+
+# Load the dataset
+df = pd.read_csv('/input/us-accidents/US_Accidents_March23.csv')
+
+# Convert 'Start_Time' to datetime format
+df['Start_Time'] = pd.to_datetime(df['Start_Time'])
+
+# Create a 4x2 subplot grid
+fig, axes = plt.subplots(4, 2, figsize=(18, 10))
+plt.subplots_adjust(hspace=0.5)  # Adjust horizontal space between plots
+
+# Create a gradient blue color palette
+n_colors = 8
+blue_palette = sns.light_palette("blue", n_colors=n_colors, reverse=True)
+
+# Day names for labeling
+day_names = ['Overall'] + [pd.Timestamp('2023-01-0' + str(i)).day_name() for i in range(1, 8)]
+
+# Plot overall distribution and for each day of the week
+for i in range(7):
+    ax = axes[i//2, i%2]  # Determine the position of the subplot
+    
+    if i == 0:
+        sns.histplot(df['Start_Time'].dt.hour, bins=24, ax=ax, color=blue_palette[i])
+        ax.set_title("Overall Hourly Accident Distribution")
+    else:
+        day_data = df[df['Start_Time'].dt.dayofweek == i-1]
+        sns.histplot(day_data['Start_Time'].dt.hour, bins=24, ax=ax, color=blue_palette[i])
+        ax.set_title(f"Hourly Accident Distribution: {day_names[i+1]}")
+
+    ax.set_xlabel(f"Hour of {day_names[i]}")
+    ax.set_ylabel("No. of Accidents")
+
+plt.tight_layout()