NoCommentsElder
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9986d62
Delete app.py
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app.py
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# Importing necessary libraries
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import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.linear_model import LinearRegression
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# Ensure seaborn is set up correctly
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sns.set(color_codes=True)
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# Load the dataset
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df = pd.read_csv('US_Accidents_March23.csv')
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# Convert 'Start_Time' to datetime format
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df['Start_Time'] = pd.to_datetime(df['Start_Time'], format='mixed', errors='coerce')
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# Analysis of hourly accidents distribution
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def plot_hourly_accidents(df):
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# Create a 4x2 subplot grid
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fig, axes = plt.subplots(4, 2, figsize=(18, 10))
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plt.subplots_adjust(hspace=0.5)
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# Gradient blue color palette
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blue_palette = sns.light_palette("blue", n_colors=8, reverse=True)
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for i in range(8):
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ax = axes[i//2, i%2]
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if i == 0:
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sns.histplot(df['Start_Time'].dt.hour, bins=24, ax=ax, color=blue_palette[i])
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ax.set_title("Overall Hourly Accident Distribution")
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else:
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day_data = df[df['Start_Time'].dt.dayofweek == i-1]
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sns.histplot(day_data['Start_Time'].dt.hour, bins=24, ax=ax, color=blue_palette[i])
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ax.set_title(f"Hourly Distribution: {day_data['Start_Time'].dt.day_name().iloc[0]}")
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ax.set_xlabel("Hour")
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ax.set_ylabel("Accidents")
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plt.tight_layout()
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plt.show()
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plot_hourly_accidents(df)
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# Analysis of weather conditions
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def plot_weather_conditions(df):
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weather = df['Weather_Condition'].value_counts().head(15)
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plt.figure(figsize=(30, 10))
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sns.barplot(x=weather.index, y=weather.values, palette="Reds_r")
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plt.xticks(rotation=45, fontsize=15)
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plt.yticks(fontsize=15)
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plt.xlabel("Weather Condition", fontsize=20)
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plt.ylabel("Count", fontsize=20)
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plt.title("Weather Condition vs Accidents", fontsize=30)
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plt.show()
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plot_weather_conditions(df)
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# Additional Plots (as per your original code)
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# ... (include other plot functions as needed)
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# Prophet Model for Accident Prediction
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def prophet_model(df):
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from prophet import Prophet
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# Resampling data to get yearly count
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df_yearly = df.resample('Y', on='Start_Time').size().reset_index(name='counts')
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df_prophet = df_yearly.rename(columns={'Start_Time': 'ds', 'counts': 'y'})
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model = Prophet()
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model.fit(df_prophet)
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future = model.make_future_dataframe(periods=5, freq='Y')
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forecast = model.predict(future)
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fig = model.plot(forecast)
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plt.xlabel("Year")
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plt.ylabel("Accidents")
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plt.show()
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# Run the Prophet model function only if the Prophet package is installed
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try:
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prophet_model(df)
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except ImportError:
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print("Prophet package is not installed. Skipping the Prophet model prediction.")
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