Delete app.py

app.py

@@ -1,83 +0,0 @@
-# Importing necessary libraries
-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 set up correctly
-sns.set(color_codes=True)
-
-# Load the dataset
-df = pd.read_csv('US_Accidents_March23.csv')
-
-# Convert 'Start_Time' to datetime format
-df['Start_Time'] = pd.to_datetime(df['Start_Time'], format='mixed', errors='coerce')
-
-# Analysis of hourly accidents distribution
-def plot_hourly_accidents(df):
-    # Create a 4x2 subplot grid
-    fig, axes = plt.subplots(4, 2, figsize=(18, 10))
-    plt.subplots_adjust(hspace=0.5)
-
-    # Gradient blue color palette
-    blue_palette = sns.light_palette("blue", n_colors=8, reverse=True)
-
-    for i in range(8):
-        ax = axes[i//2, i%2]
-        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 Distribution: {day_data['Start_Time'].dt.day_name().iloc[0]}")
-        ax.set_xlabel("Hour")
-        ax.set_ylabel("Accidents")
-
-    plt.tight_layout()
-    plt.show()
-
-plot_hourly_accidents(df)
-
-# Analysis of weather conditions
-def plot_weather_conditions(df):
-    weather = df['Weather_Condition'].value_counts().head(15)
-    plt.figure(figsize=(30, 10))
-    sns.barplot(x=weather.index, y=weather.values, palette="Reds_r")
-    plt.xticks(rotation=45, fontsize=15)
-    plt.yticks(fontsize=15)
-    plt.xlabel("Weather Condition", fontsize=20)
-    plt.ylabel("Count", fontsize=20)
-    plt.title("Weather Condition vs Accidents", fontsize=30)
-    plt.show()
-
-plot_weather_conditions(df)
-
-# Additional Plots (as per your original code)
-# ... (include other plot functions as needed)
-
-# Prophet Model for Accident Prediction
-def prophet_model(df):
-    from prophet import Prophet
-
-    # Resampling data to get yearly count
-    df_yearly = df.resample('Y', on='Start_Time').size().reset_index(name='counts')
-    df_prophet = df_yearly.rename(columns={'Start_Time': 'ds', 'counts': 'y'})
-
-    model = Prophet()
-    model.fit(df_prophet)
-
-    future = model.make_future_dataframe(periods=5, freq='Y')
-    forecast = model.predict(future)
-
-    fig = model.plot(forecast)
-    plt.xlabel("Year")
-    plt.ylabel("Accidents")
-    plt.show()
-
-# Run the Prophet model function only if the Prophet package is installed
-try:
-    prophet_model(df)
-except ImportError:
-    print("Prophet package is not installed. Skipping the Prophet model prediction.")