Delete app.py

app.py

@@ -1,45 +0,0 @@
-import pandas as pd
-!pip install seaborn
-import seaborn as sns
-sns.set(color_codes=True)
-from sklearn.tree import DecisionTreeRegressor
-import matplotlib.pyplot as plt
-
-df = pd.read_csv('/input/us-accidents/US_Accidents_March23.csv')
-from sklearn.linear_model import LinearRegression
-
-import matplotlib.pyplot as plt
-import seaborn as sns
-import pandas as pd
-
-# Convert 'Start_Time' to datetime format if not already
-df['Start_Time'] = pd.to_datetime(df['Start_Time'], format='mixed')
-
-
-# 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  # Number of colors needed
-blue_palette = sns.light_palette("Red", n_colors=n_colors, reverse=True)
-
-# Plot overall distribution and for each day of the week
-for i in range(8):
-    ax = axes[i//2, i%2]  # Determine the position of the subplot
-    
-    if i == 0:
-        # Overall hourly accident distribution
-        sns.histplot(df['Start_Time'].dt.hour, bins=24, ax=ax, color=blue_palette[i])
-        ax.set_title("Overall Hourly Accident Distribution")
-    else:
-        # Hourly distribution for each day of the week
-        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_data['Start_Time'].dt.day_name().iloc[0]}")
-
-    # Set x and y labels
-    ax.set_xlabel(f"Hour of {['Day', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'][i]}")
-    ax.set_ylabel("No. of Accidents")
-
-plt.tight_layout()