korkridake · March 5, 2025 14:57
diff --git a/dsagent-walmart-stock-data-2025.py b/dsagent-walmart-stock-data-2025.py
 # -*- coding: utf-8 -*-
 """DS Agent - Walmart Stocks Data 2025 (Unfiltered).ipynb

 Automatically generated by Colab.

 Original file is located at
    https://colab.research.google.com/drive/1xQPflL75hlDCGRRntyEBdSt_DgegxlTi

 # Task
 Hey Data Science Agent, can you help with the data analysis on this dataset? It includes EDA, descriptive stats, and some data visualizations. If you can, could you also suggest some time-series modeling?

 Here is all the data you need:
 "walmart_stock_prices.csv"

 ## Data loading

 ### Subtask:
 Load the Walmart stock price data from the provided CSV file.

 **Reasoning**:
 Load the Walmart stock price data from the provided CSV file into a pandas DataFrame and display the first few rows.
 """

 import pandas as pd

 try:
    df = pd.read_csv('walmart_stock_prices.csv')
    display(df.head())
 except FileNotFoundError:
    print("Error: 'walmart_stock_prices.csv' not found.")
    df = None  # Set df to None to indicate failure
 except pd.errors.ParserError:
    print("Error: Could not parse the CSV file.")
    df = None
 except Exception as e:
    print(f"An unexpected error occurred: {e}")
    df = None

 """## Data exploration

 ### Subtask:
 Explore the loaded Walmart stock price dataset.

 **Reasoning**:
 I need to explore the data by checking its shape, data types, missing values, first/last few rows, and descriptive statistics.
 """

 # Check the shape of the DataFrame
 print("Shape of the DataFrame:", df.shape)

 # Examine the data types of each column
 print("\nData types of each column:\n", df.dtypes)

 # Identify the number of missing values in each column
 print("\nNumber of missing values in each column:\n", df.isnull().sum())

 # Print the first 5 rows
 print("\nFirst 5 rows:\n", df.head())

 # Print the last 5 rows
 print("\nLast 5 rows:\n", df.tail())

 # Calculate basic descriptive statistics for numerical features
 print("\nDescriptive statistics for numerical features:\n", df.describe())

 """## Data visualization

 ### Subtask:
 Visualize the distribution of key numerical features and the time series of stock prices.

 **Reasoning**:
 Convert the 'Date' column to datetime objects and create the visualizations as requested.
 """

 import matplotlib.pyplot as plt

 # Convert the 'Date' column to datetime objects
 df['Date'] = pd.to_datetime(df['Date'])

 # Create histograms for 'Open', 'High', 'Low', and 'Close' prices
 plt.figure(figsize=(12, 8))
 plt.subplot(2, 2, 1)
 plt.hist(df['Open'], bins=30, color='skyblue', edgecolor='black')
 plt.xlabel('Open Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of Open Prices')

 plt.subplot(2, 2, 2)
 plt.hist(df['High'], bins=30, color='lightcoral', edgecolor='black')
 plt.xlabel('High Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of High Prices')

 plt.subplot(2, 2, 3)
 plt.hist(df['Low'], bins=30, color='lightgreen', edgecolor='black')
 plt.xlabel('Low Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of Low Prices')

 plt.subplot(2, 2, 4)
 plt.hist(df['Close'], bins=30, color='gold', edgecolor='black')
 plt.xlabel('Close Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of Close Prices')

 plt.tight_layout()
 plt.show()

 # Create box plots for 'Open', 'High', 'Low', and 'Close' prices
 plt.figure(figsize=(12, 6))
 plt.boxplot([df['Open'], df['High'], df['Low'], df['Close']], labels=['Open', 'High', 'Low', 'Close'], patch_artist=True,
            boxprops=dict(facecolor='lightblue'), medianprops=dict(color='black'))
 plt.ylabel('Price')
 plt.title('Box Plots of Stock Prices')
 plt.show()


 # Plot the time series of the 'Close' price
 plt.figure(figsize=(12, 6))
 plt.plot(df['Date'], df['Close'], color='blue')
 plt.xlabel('Date')
 plt.ylabel('Close Price')
 plt.title('Time Series of Walmart Stock Close Price')
 plt.xticks(rotation=45)
 plt.tight_layout()
 plt.show()

 """**Reasoning**:
 The previous code block execution generated warnings and the code needs to be fixed. The `FutureWarning` related to mixed timezones in the `to_datetime` function needs to be addressed by specifying `utc=True`. Also, the `MatplotlibDeprecationWarning` about the `labels` parameter in `boxplot` should be fixed by changing it to `tick_labels`. Finally, I will re-execute the code to ensure that the plots are generated correctly.


 """

 import matplotlib.pyplot as plt

 # Convert the 'Date' column to datetime objects, handling mixed time zones
 df['Date'] = pd.to_datetime(df['Date'], utc=True)

 # Create histograms for 'Open', 'High', 'Low', and 'Close' prices
 plt.figure(figsize=(12, 8))
 plt.subplot(2, 2, 1)
 plt.hist(df['Open'], bins=30, color='skyblue', edgecolor='black')
 plt.xlabel('Open Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of Open Prices')

 plt.subplot(2, 2, 2)
 plt.hist(df['High'], bins=30, color='lightcoral', edgecolor='black')
 plt.xlabel('High Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of High Prices')

 plt.subplot(2, 2, 3)
 plt.hist(df['Low'], bins=30, color='lightgreen', edgecolor='black')
 plt.xlabel('Low Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of Low Prices')

 plt.subplot(2, 2, 4)
 plt.hist(df['Close'], bins=30, color='gold', edgecolor='black')
 plt.xlabel('Close Price')
 plt.ylabel('Frequency')
 plt.title('Distribution of Close Prices')

 plt.tight_layout()
 plt.show()

 # Create box plots for 'Open', 'High', 'Low', and 'Close' prices
 plt.figure(figsize=(12, 6))
 plt.boxplot([df['Open'], df['High'], df['Low'], df['Close']],
            tick_labels=['Open', 'High', 'Low', 'Close'], patch_artist=True,
            boxprops=dict(facecolor='lightblue'), medianprops=dict(color='black'))
 plt.ylabel('Price')
 plt.title('Box Plots of Stock Prices')
 plt.show()


 # Plot the time series of the 'Close' price
 plt.figure(figsize=(12, 6))
 plt.plot(df['Date'], df['Close'], color='blue')
 plt.xlabel('Date')
 plt.ylabel('Close Price')
 plt.title('Time Series of Walmart Stock Close Price')
 plt.xticks(rotation=45)
 plt.tight_layout()
 plt.show()

 """## Data visualization

 ### Subtask:
 Visualize the correlations between different numerical features in the dataset.

 **Reasoning**:
 Calculate the correlation matrix and visualize it as a heatmap.
 """

 import matplotlib.pyplot as plt
 import seaborn as sns

 # Calculate the correlation matrix
 correlation_matrix = df.corr()

 # Create the heatmap
 plt.figure(figsize=(10, 8))
 sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
 plt.title('Correlation Matrix of Numerical Features')
 plt.show()

 """## Data preparation

 ### Subtask:
 Prepare the data for time series modeling.

 **Reasoning**:
 Convert the 'Date' column to datetime objects, set it as the index, select the 'Close' price, difference the series, create lagged features, and split the data into training and testing sets.
 """

 import pandas as pd
 from sklearn.preprocessing import MinMaxScaler

 # Convert 'Date' to datetime and set as index
 df['Date'] = pd.to_datetime(df['Date'])
 df = df.set_index('Date')

 # Create a new DataFrame for the target variable
 close_prices = pd.DataFrame(df['Close'])

 # Differencing
 close_prices['Close_diff'] = close_prices['Close'].diff()

 # Lagged features
 close_prices['Close_lag1'] = close_prices['Close'].shift(1)

 # Scaling (optional, but demonstrating the process)
 scaler = MinMaxScaler()
 close_prices[['Close_scaled']] = scaler.fit_transform(close_prices[['Close']])

 # Splitting the data
 train_size = int(len(close_prices) * 0.8)
 train_data = close_prices[:train_size]
 test_data = close_prices[train_size:]

 display(train_data.head())
 display(test_data.head())

 """## Model training

 ### Subtask:
 Train an ARIMA model on the prepared training data.

 **Reasoning**:
 Train an ARIMA model on the training data and generate predictions for the test data.
 """

 from statsmodels.tsa.arima.model import ARIMA

 # Instantiate and fit the ARIMA model
 try:
    model = ARIMA(train_data['Close'], order=(5, 1, 0))
    model_fit = model.fit()

    # Generate predictions for the test data
    predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

    # Store predictions in the test data
    test_data['ARIMA_Predictions'] = predictions
    display(test_data.head())
 except Exception as e:
    print(f"An error occurred during model training or prediction: {e}")

 """**Reasoning**:
 The previous code block generated a `SettingWithCopyWarning`.  I will modify the code to use `.loc` to avoid this warning and address the ValueWarnings related to the date index. I will also investigate the NaN values in the 'ARIMA_Predictions' column.


 """

 from statsmodels.tsa.arima.model import ARIMA

 # Instantiate and fit the ARIMA model
 try:
    model = ARIMA(train_data['Close'], order=(5, 1, 0))
    model_fit = model.fit()

    # Generate predictions for the test data
    predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

    # Store predictions in the test data using .loc to avoid SettingWithCopyWarning
    test_data.loc[:, 'ARIMA_Predictions'] = predictions
    display(test_data.head())
 except Exception as e:
    print(f"An error occurred during model training or prediction: {e}")

 """**Reasoning**:
 The ARIMA model predictions are all NaN.  I suspect this is due to the date index still being present, despite the warnings. I will try removing the date index from both training and testing data before fitting and predicting, and then try again.


 """

 from statsmodels.tsa.arima.model import ARIMA

 # Remove the date index from train_data and test_data
 train_data_no_index = train_data.reset_index(drop=True)
 test_data_no_index = test_data.reset_index(drop=True)


 # Instantiate and fit the ARIMA model
 try:
    model = ARIMA(train_data_no_index['Close'], order=(5, 1, 0))
    model_fit = model.fit()

    # Generate predictions for the test data
    predictions = model_fit.predict(start=len(train_data_no_index), end=len(train_data_no_index) + len(test_data_no_index) - 1)

    # Store predictions in the test data
    test_data_no_index['ARIMA_Predictions'] = predictions

    display(test_data_no_index.head())
 except Exception as e:
    print(f"An error occurred during model training or prediction: {e}")

 """**Reasoning**:
 The ARIMA predictions are still NaN.  I will try a different (p,d,q) order for the ARIMA model.  I'll also double-check that the data is in the correct format and that there are no remaining issues with the date index.  It might be useful to print the shape and info of the data to help diagnose the problem.


 """

 from statsmodels.tsa.arima.model import ARIMA

 # Remove the date index from train_data and test_data
 train_data_no_index = train_data.reset_index(drop=True)
 test_data_no_index = test_data.reset_index(drop=True)

 print("Train data shape:", train_data_no_index.shape)
 print("Train data info:\n", train_data_no_index.info())
 print("\nTest data shape:", test_data_no_index.shape)
 print("Test data info:\n", test_data_no_index.info())

 # Try a different (p,d,q) order for ARIMA
 try:
    model = ARIMA(train_data_no_index['Close'], order=(1, 1, 1)) # Trying (1,1,1)
    model_fit = model.fit()

    # Generate predictions for the test data
    predictions = model_fit.predict(start=len(train_data_no_index), end=len(train_data_no_index) + len(test_data_no_index) - 1)

    test_data_no_index['ARIMA_Predictions'] = predictions
    display(test_data_no_index.head())

 except Exception as e:
    print(f"An error occurred: {e}")

 """## Model training

 ### Subtask:
 Train an ARIMA model on the prepared training data.

 **Reasoning**:
 The previous attempts to train the ARIMA model resulted in NaN predictions.  The issue might stem from the differencing and scaling applied to the data. Let's try training the model directly on the 'Close' column of the training data without any transformations, and try different (p, d, q) orders.  The index of the train data is a DateTimeIndex.
 """

 from statsmodels.tsa.arima.model import ARIMA

 # Try different (p, d, q) orders for ARIMA
 for order in [(1, 1, 1), (5, 1, 0), (0, 1, 1), (1, 0, 0)]:
    try:
        print(f"Trying order: {order}")
        model = ARIMA(train_data['Close'], order=order)
        model_fit = model.fit()

        # Generate predictions for the test data
        predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

        # Store predictions in the test data
        test_data['ARIMA_Predictions'] = predictions
        display(test_data.head())
        break # Exit the loop if successful

    except Exception as e:
        print(f"An error occurred with order {order}: {e}")

 """**Reasoning**:
 The previous attempts still resulted in NaN predictions.  Let's try setting the frequency of the DateTimeIndex and see if that helps. Then, try fitting the model again.


 """

 from statsmodels.tsa.arima.model import ARIMA

 # Set the frequency of the DateTimeIndex
 train_data = train_data.asfreq('D')
 test_data = test_data.asfreq('D')


 # Try different (p, d, q) orders for ARIMA
 for order in [(1, 1, 1), (5, 1, 0), (0, 1, 1), (1, 0, 0)]:
    try:
        print(f"Trying order: {order}")
        model = ARIMA(train_data['Close'], order=order)
        model_fit = model.fit()

        # Generate predictions for the test data
        predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

        # Store predictions in the test data
        test_data['ARIMA_Predictions'] = predictions
        display(test_data.head())
        break  # Exit the loop if successful

    except Exception as e:
        print(f"An error occurred with order {order}: {e}")

 """**Reasoning**:
 The ARIMA model is now producing predictions, but there are still NaN values in the 'Close' column of the test data. We need to handle these NaN values and evaluate the model.


 """

 # Fill NaN values in 'Close' column of test_data using forward fill
 test_data['Close'] = test_data['Close'].fillna(method='ffill')

 # Evaluate the model (example using RMSE)
 from sklearn.metrics import mean_squared_error
 import numpy as np

 rmse = np.sqrt(mean_squared_error(test_data['Close'], test_data['ARIMA_Predictions']))
 print(f"RMSE: {rmse}")

 display(test_data.head())

 """## Model evaluation

 ### Subtask:
 Evaluate the performance of the ARIMA model using RMSE, MAE, and MAPE.

 **Reasoning**:
 Evaluate the performance of the ARIMA model using RMSE, MAE, and MAPE. Handle potential division-by-zero errors. Print the results.
 """

 from sklearn.metrics import mean_squared_error, mean_absolute_error
 import numpy as np

 # Calculate RMSE
 rmse = np.sqrt(mean_squared_error(test_data['Close'], test_data['ARIMA_Predictions']))

 # Calculate MAE
 mae = mean_absolute_error(test_data['Close'], test_data['ARIMA_Predictions'])

 # Calculate MAPE
 # Avoid division by zero by adding a small constant to the denominator
 mape = np.mean(np.abs((test_data['Close'] - test_data['ARIMA_Predictions']) / (test_data['Close'] + 1e-8))) * 100

 print(f"RMSE: {rmse}")
 print(f"MAE: {mae}")
 print(f"MAPE: {mape}")

 """## Summary:

 ### 1. Q&A
 * **Can you suggest some time-series modeling?**  Yes, the analysis explored using an ARIMA model.  However, the initial attempts to fit the ARIMA model resulted in NaN predictions.  Setting the frequency of the DateTimeIndex to daily ('D') improved the prediction generation but still required forward filling of NaN values in the 'Close' column. The final ARIMA model had an RMSE of approximately 23.51, MAE of 17.42, and MAPE of 37.57%, suggesting the need for further model tuning or alternative model exploration.

 ### 2. Data Analysis Key Findings
 * **Data Characteristics:** The dataset contains 13,233 rows and 8 columns with no missing values. The 'Date' column, initially an object, was converted to datetime, and the 'Volume' column has a mean of ~22.4 million, with a maximum exceeding 395 million.  'Dividends' and 'Stock Splits' are mostly zero.
 * **Price Distributions:** Histograms and box plots were generated to visualize the distributions of 'Open', 'High', 'Low', and 'Close' prices.  Box plots helped to identify potential outliers in the stock prices.
 * **Time Series Analysis:** The time series of the 'Close' price was plotted over time showing trends and seasonality.
 * **Feature Correlation:** A heatmap of the correlation matrix revealed the correlation coefficients between numerical features.
 * **ARIMA Model Performance:** The ARIMA model, with various (p,d,q) order parameters, was trained and evaluated using RMSE, MAE, and MAPE.  The final model yielded an RMSE of approximately 23.51, MAE of 17.42, and MAPE of 37.57%, indicating a need for improvement.
 * **Data Preparation for Time Series:** The data was preprocessed using differencing, lagged features, and scaling, preparing it for time series modeling.  The data was split into 80% training and 20% testing sets.

 ### 3. Insights or Next Steps
 * **Improve ARIMA Model:**  The current ARIMA model's performance is not satisfactory.  Explore different (p,d,q) combinations more systematically, using techniques like ACF and PACF plots to determine optimal values. Consider alternative models (e.g., Prophet, SARIMAX) that might better capture the underlying time series dynamics.
 * **Feature Engineering:** Investigate potential for creating additional features (e.g., rolling averages, moving standard deviations, external economic indicators) that might enhance the model's predictive power.

 """
	# -- coding: utf-8 --
	"""DS Agent - Walmart Stocks Data 2025 (Unfiltered).ipynb

	Automatically generated by Colab.

	Original file is located at
	https://colab.research.google.com/drive/1xQPflL75hlDCGRRntyEBdSt_DgegxlTi

	# Task
	Hey Data Science Agent, can you help with the data analysis on this dataset? It includes EDA, descriptive stats, and some data visualizations. If you can, could you also suggest some time-series modeling?

	Here is all the data you need:
	"walmart_stock_prices.csv"

	## Data loading

	### Subtask:
	Load the Walmart stock price data from the provided CSV file.

	Reasoning:
	Load the Walmart stock price data from the provided CSV file into a pandas DataFrame and display the first few rows.
	"""

	import pandas as pd

	try:
	df = pd.read_csv('walmart_stock_prices.csv')
	display(df.head())
	except FileNotFoundError:
	print("Error: 'walmart_stock_prices.csv' not found.")
	df = None # Set df to None to indicate failure
	except pd.errors.ParserError:
	print("Error: Could not parse the CSV file.")
	df = None
	except Exception as e:
	print(f"An unexpected error occurred: {e}")
	df = None

	"""## Data exploration

	### Subtask:
	Explore the loaded Walmart stock price dataset.

	Reasoning:
	I need to explore the data by checking its shape, data types, missing values, first/last few rows, and descriptive statistics.
	"""

	# Check the shape of the DataFrame
	print("Shape of the DataFrame:", df.shape)

	# Examine the data types of each column
	print("\nData types of each column:\n", df.dtypes)

	# Identify the number of missing values in each column
	print("\nNumber of missing values in each column:\n", df.isnull().sum())

	# Print the first 5 rows
	print("\nFirst 5 rows:\n", df.head())

	# Print the last 5 rows
	print("\nLast 5 rows:\n", df.tail())

	# Calculate basic descriptive statistics for numerical features
	print("\nDescriptive statistics for numerical features:\n", df.describe())

	"""## Data visualization

	### Subtask:
	Visualize the distribution of key numerical features and the time series of stock prices.

	Reasoning:
	Convert the 'Date' column to datetime objects and create the visualizations as requested.
	"""

	import matplotlib.pyplot as plt

	# Convert the 'Date' column to datetime objects
	df['Date'] = pd.to_datetime(df['Date'])

	# Create histograms for 'Open', 'High', 'Low', and 'Close' prices
	plt.figure(figsize=(12, 8))
	plt.subplot(2, 2, 1)
	plt.hist(df['Open'], bins=30, color='skyblue', edgecolor='black')
	plt.xlabel('Open Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of Open Prices')

	plt.subplot(2, 2, 2)
	plt.hist(df['High'], bins=30, color='lightcoral', edgecolor='black')
	plt.xlabel('High Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of High Prices')

	plt.subplot(2, 2, 3)
	plt.hist(df['Low'], bins=30, color='lightgreen', edgecolor='black')
	plt.xlabel('Low Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of Low Prices')

	plt.subplot(2, 2, 4)
	plt.hist(df['Close'], bins=30, color='gold', edgecolor='black')
	plt.xlabel('Close Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of Close Prices')

	plt.tight_layout()
	plt.show()

	# Create box plots for 'Open', 'High', 'Low', and 'Close' prices
	plt.figure(figsize=(12, 6))
	plt.boxplot([df['Open'], df['High'], df['Low'], df['Close']], labels=['Open', 'High', 'Low', 'Close'], patch_artist=True,
	boxprops=dict(facecolor='lightblue'), medianprops=dict(color='black'))
	plt.ylabel('Price')
	plt.title('Box Plots of Stock Prices')
	plt.show()


	# Plot the time series of the 'Close' price
	plt.figure(figsize=(12, 6))
	plt.plot(df['Date'], df['Close'], color='blue')
	plt.xlabel('Date')
	plt.ylabel('Close Price')
	plt.title('Time Series of Walmart Stock Close Price')
	plt.xticks(rotation=45)
	plt.tight_layout()
	plt.show()

	"""Reasoning:
	The previous code block execution generated warnings and the code needs to be fixed. The `FutureWarning` related to mixed timezones in the `to_datetime` function needs to be addressed by specifying `utc=True`. Also, the `MatplotlibDeprecationWarning` about the `labels` parameter in `boxplot` should be fixed by changing it to `tick_labels`. Finally, I will re-execute the code to ensure that the plots are generated correctly.


	"""

	import matplotlib.pyplot as plt

	# Convert the 'Date' column to datetime objects, handling mixed time zones
	df['Date'] = pd.to_datetime(df['Date'], utc=True)

	# Create histograms for 'Open', 'High', 'Low', and 'Close' prices
	plt.figure(figsize=(12, 8))
	plt.subplot(2, 2, 1)
	plt.hist(df['Open'], bins=30, color='skyblue', edgecolor='black')
	plt.xlabel('Open Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of Open Prices')

	plt.subplot(2, 2, 2)
	plt.hist(df['High'], bins=30, color='lightcoral', edgecolor='black')
	plt.xlabel('High Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of High Prices')

	plt.subplot(2, 2, 3)
	plt.hist(df['Low'], bins=30, color='lightgreen', edgecolor='black')
	plt.xlabel('Low Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of Low Prices')

	plt.subplot(2, 2, 4)
	plt.hist(df['Close'], bins=30, color='gold', edgecolor='black')
	plt.xlabel('Close Price')
	plt.ylabel('Frequency')
	plt.title('Distribution of Close Prices')

	plt.tight_layout()
	plt.show()

	# Create box plots for 'Open', 'High', 'Low', and 'Close' prices
	plt.figure(figsize=(12, 6))
	plt.boxplot([df['Open'], df['High'], df['Low'], df['Close']],
	tick_labels=['Open', 'High', 'Low', 'Close'], patch_artist=True,
	boxprops=dict(facecolor='lightblue'), medianprops=dict(color='black'))
	plt.ylabel('Price')
	plt.title('Box Plots of Stock Prices')
	plt.show()


	# Plot the time series of the 'Close' price
	plt.figure(figsize=(12, 6))
	plt.plot(df['Date'], df['Close'], color='blue')
	plt.xlabel('Date')
	plt.ylabel('Close Price')
	plt.title('Time Series of Walmart Stock Close Price')
	plt.xticks(rotation=45)
	plt.tight_layout()
	plt.show()

	"""## Data visualization

	### Subtask:
	Visualize the correlations between different numerical features in the dataset.

	Reasoning:
	Calculate the correlation matrix and visualize it as a heatmap.
	"""

	import matplotlib.pyplot as plt
	import seaborn as sns

	# Calculate the correlation matrix
	correlation_matrix = df.corr()

	# Create the heatmap
	plt.figure(figsize=(10, 8))
	sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
	plt.title('Correlation Matrix of Numerical Features')
	plt.show()

	"""## Data preparation

	### Subtask:
	Prepare the data for time series modeling.

	Reasoning:
	Convert the 'Date' column to datetime objects, set it as the index, select the 'Close' price, difference the series, create lagged features, and split the data into training and testing sets.
	"""

	import pandas as pd
	from sklearn.preprocessing import MinMaxScaler

	# Convert 'Date' to datetime and set as index
	df['Date'] = pd.to_datetime(df['Date'])
	df = df.set_index('Date')

	# Create a new DataFrame for the target variable
	close_prices = pd.DataFrame(df['Close'])

	# Differencing
	close_prices['Close_diff'] = close_prices['Close'].diff()

	# Lagged features
	close_prices['Close_lag1'] = close_prices['Close'].shift(1)

	# Scaling (optional, but demonstrating the process)
	scaler = MinMaxScaler()
	close_prices[['Close_scaled']] = scaler.fit_transform(close_prices[['Close']])

	# Splitting the data
	train_size = int(len(close_prices) * 0.8)
	train_data = close_prices[:train_size]
	test_data = close_prices[train_size:]

	display(train_data.head())
	display(test_data.head())

	"""## Model training

	### Subtask:
	Train an ARIMA model on the prepared training data.

	Reasoning:
	Train an ARIMA model on the training data and generate predictions for the test data.
	"""

	from statsmodels.tsa.arima.model import ARIMA

	# Instantiate and fit the ARIMA model
	try:
	model = ARIMA(train_data['Close'], order=(5, 1, 0))
	model_fit = model.fit()

	# Generate predictions for the test data
	predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

	# Store predictions in the test data
	test_data['ARIMA_Predictions'] = predictions
	display(test_data.head())
	except Exception as e:
	print(f"An error occurred during model training or prediction: {e}")

	"""Reasoning:
	The previous code block generated a `SettingWithCopyWarning`. I will modify the code to use `.loc` to avoid this warning and address the ValueWarnings related to the date index. I will also investigate the NaN values in the 'ARIMA_Predictions' column.


	"""

	from statsmodels.tsa.arima.model import ARIMA

	# Instantiate and fit the ARIMA model
	try:
	model = ARIMA(train_data['Close'], order=(5, 1, 0))
	model_fit = model.fit()

	# Generate predictions for the test data
	predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

	# Store predictions in the test data using .loc to avoid SettingWithCopyWarning
	test_data.loc[:, 'ARIMA_Predictions'] = predictions
	display(test_data.head())
	except Exception as e:
	print(f"An error occurred during model training or prediction: {e}")

	"""Reasoning:
	The ARIMA model predictions are all NaN. I suspect this is due to the date index still being present, despite the warnings. I will try removing the date index from both training and testing data before fitting and predicting, and then try again.


	"""

	from statsmodels.tsa.arima.model import ARIMA

	# Remove the date index from train_data and test_data
	train_data_no_index = train_data.reset_index(drop=True)
	test_data_no_index = test_data.reset_index(drop=True)


	# Instantiate and fit the ARIMA model
	try:
	model = ARIMA(train_data_no_index['Close'], order=(5, 1, 0))
	model_fit = model.fit()

	# Generate predictions for the test data
	predictions = model_fit.predict(start=len(train_data_no_index), end=len(train_data_no_index) + len(test_data_no_index) - 1)

	# Store predictions in the test data
	test_data_no_index['ARIMA_Predictions'] = predictions

	display(test_data_no_index.head())
	except Exception as e:
	print(f"An error occurred during model training or prediction: {e}")

	"""Reasoning:
	The ARIMA predictions are still NaN. I will try a different (p,d,q) order for the ARIMA model. I'll also double-check that the data is in the correct format and that there are no remaining issues with the date index. It might be useful to print the shape and info of the data to help diagnose the problem.


	"""

	from statsmodels.tsa.arima.model import ARIMA

	# Remove the date index from train_data and test_data
	train_data_no_index = train_data.reset_index(drop=True)
	test_data_no_index = test_data.reset_index(drop=True)

	print("Train data shape:", train_data_no_index.shape)
	print("Train data info:\n", train_data_no_index.info())
	print("\nTest data shape:", test_data_no_index.shape)
	print("Test data info:\n", test_data_no_index.info())

	# Try a different (p,d,q) order for ARIMA
	try:
	model = ARIMA(train_data_no_index['Close'], order=(1, 1, 1)) # Trying (1,1,1)
	model_fit = model.fit()

	# Generate predictions for the test data
	predictions = model_fit.predict(start=len(train_data_no_index), end=len(train_data_no_index) + len(test_data_no_index) - 1)

	test_data_no_index['ARIMA_Predictions'] = predictions
	display(test_data_no_index.head())

	except Exception as e:
	print(f"An error occurred: {e}")

	"""## Model training

	### Subtask:
	Train an ARIMA model on the prepared training data.

	Reasoning:
	The previous attempts to train the ARIMA model resulted in NaN predictions. The issue might stem from the differencing and scaling applied to the data. Let's try training the model directly on the 'Close' column of the training data without any transformations, and try different (p, d, q) orders. The index of the train data is a DateTimeIndex.
	"""

	from statsmodels.tsa.arima.model import ARIMA

	# Try different (p, d, q) orders for ARIMA
	for order in [(1, 1, 1), (5, 1, 0), (0, 1, 1), (1, 0, 0)]:
	try:
	print(f"Trying order: {order}")
	model = ARIMA(train_data['Close'], order=order)
	model_fit = model.fit()

	# Generate predictions for the test data
	predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

	# Store predictions in the test data
	test_data['ARIMA_Predictions'] = predictions
	display(test_data.head())
	break # Exit the loop if successful

	except Exception as e:
	print(f"An error occurred with order {order}: {e}")

	"""Reasoning:
	The previous attempts still resulted in NaN predictions. Let's try setting the frequency of the DateTimeIndex and see if that helps. Then, try fitting the model again.


	"""

	from statsmodels.tsa.arima.model import ARIMA

	# Set the frequency of the DateTimeIndex
	train_data = train_data.asfreq('D')
	test_data = test_data.asfreq('D')


	# Try different (p, d, q) orders for ARIMA
	for order in [(1, 1, 1), (5, 1, 0), (0, 1, 1), (1, 0, 0)]:
	try:
	print(f"Trying order: {order}")
	model = ARIMA(train_data['Close'], order=order)
	model_fit = model.fit()

	# Generate predictions for the test data
	predictions = model_fit.predict(start=len(train_data), end=len(train_data) + len(test_data) - 1)

	# Store predictions in the test data
	test_data['ARIMA_Predictions'] = predictions
	display(test_data.head())
	break # Exit the loop if successful

	except Exception as e:
	print(f"An error occurred with order {order}: {e}")

	"""Reasoning:
	The ARIMA model is now producing predictions, but there are still NaN values in the 'Close' column of the test data. We need to handle these NaN values and evaluate the model.


	"""

	# Fill NaN values in 'Close' column of test_data using forward fill
	test_data['Close'] = test_data['Close'].fillna(method='ffill')

	# Evaluate the model (example using RMSE)
	from sklearn.metrics import mean_squared_error
	import numpy as np

	rmse = np.sqrt(mean_squared_error(test_data['Close'], test_data['ARIMA_Predictions']))
	print(f"RMSE: {rmse}")

	display(test_data.head())

	"""## Model evaluation

	### Subtask:
	Evaluate the performance of the ARIMA model using RMSE, MAE, and MAPE.

	Reasoning:
	Evaluate the performance of the ARIMA model using RMSE, MAE, and MAPE. Handle potential division-by-zero errors. Print the results.
	"""

	from sklearn.metrics import mean_squared_error, mean_absolute_error
	import numpy as np

	# Calculate RMSE
	rmse = np.sqrt(mean_squared_error(test_data['Close'], test_data['ARIMA_Predictions']))

	# Calculate MAE
	mae = mean_absolute_error(test_data['Close'], test_data['ARIMA_Predictions'])

	# Calculate MAPE
	# Avoid division by zero by adding a small constant to the denominator
	mape = np.mean(np.abs((test_data['Close'] - test_data['ARIMA_Predictions']) / (test_data['Close'] + 1e-8))) * 100

	print(f"RMSE: {rmse}")
	print(f"MAE: {mae}")
	print(f"MAPE: {mape}")

	"""## Summary:

	### 1. Q&A
	* Can you suggest some time-series modeling? Yes, the analysis explored using an ARIMA model. However, the initial attempts to fit the ARIMA model resulted in NaN predictions. Setting the frequency of the DateTimeIndex to daily ('D') improved the prediction generation but still required forward filling of NaN values in the 'Close' column. The final ARIMA model had an RMSE of approximately 23.51, MAE of 17.42, and MAPE of 37.57%, suggesting the need for further model tuning or alternative model exploration.

	### 2. Data Analysis Key Findings
	* Data Characteristics: The dataset contains 13,233 rows and 8 columns with no missing values. The 'Date' column, initially an object, was converted to datetime, and the 'Volume' column has a mean of ~22.4 million, with a maximum exceeding 395 million. 'Dividends' and 'Stock Splits' are mostly zero.
	* Price Distributions: Histograms and box plots were generated to visualize the distributions of 'Open', 'High', 'Low', and 'Close' prices. Box plots helped to identify potential outliers in the stock prices.
	* Time Series Analysis: The time series of the 'Close' price was plotted over time showing trends and seasonality.
	* Feature Correlation: A heatmap of the correlation matrix revealed the correlation coefficients between numerical features.
	* ARIMA Model Performance: The ARIMA model, with various (p,d,q) order parameters, was trained and evaluated using RMSE, MAE, and MAPE. The final model yielded an RMSE of approximately 23.51, MAE of 17.42, and MAPE of 37.57%, indicating a need for improvement.
	* Data Preparation for Time Series: The data was preprocessed using differencing, lagged features, and scaling, preparing it for time series modeling. The data was split into 80% training and 20% testing sets.

	### 3. Insights or Next Steps
	* Improve ARIMA Model: The current ARIMA model's performance is not satisfactory. Explore different (p,d,q) combinations more systematically, using techniques like ACF and PACF plots to determine optimal values. Consider alternative models (e.g., Prophet, SARIMAX) that might better capture the underlying time series dynamics.
	* Feature Engineering: Investigate potential for creating additional features (e.g., rolling averages, moving standard deviations, external economic indicators) that might enhance the model's predictive power.

	"""