itrobotics · September 25, 2024 01:35 · Sep 25, 2024 · Sep 25, 2024 · Sep 25, 2024
diff --git a/eda_trend.py → EDA_trend.py b/eda_trend.py → EDA_trend.py
diff --git a/Analyze the Trend in the data → eda_trend.py b/Analyze the Trend in the data → eda_trend.py
@@ -1,4 +1,11 @@
 
+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import statsmodels.api as sm
+
+
 def process_stock_data(df):
     # Copy the DataFrame to avoid modifying the original
     processed_df = df.copy()

diff --git a/Analyze the Trend in the data b/Analyze the Trend in the data
@@ -0,0 +1,59 @@
+
+def process_stock_data(df):
+    # Copy the DataFrame to avoid modifying the original
+    processed_df = df.copy()
+
+    # Define columns to drop
+    cols_to_drop = ['Open', 'High', 'Low', 'Volume', 'Dividends', 'Stock Splits']
+
+    # Drop specified columns
+    processed_df.drop(cols_to_drop, axis=1, inplace=True)
+
+    # Convert 'Close' column to numeric
+    processed_df['Close'] = pd.to_numeric(processed_df['Close'], errors='coerce')
+
+    # Drop rows with missing 'Close' values, if any
+    processed_df = processed_df.dropna(subset=['Close'])
+
+    return processed_df
+
+
+
+filtered_dataframe = process_stock_data(df)
+filtered_dataframe = filtered_dataframe.groupby('Date')['Close'].sum().reset_index()
+filtered_dataframe.head()
+
+# Data decomposition
+from pylab import rcParams
+rcParams['figure.figsize'] = 18, 8
+
+# Convert 'Date' column to datetime if not already in datetime format
+filtered_dataframe['Date'] = pd.to_datetime(filtered_dataframe['Date'])
+
+# Set 'Date' column as the index
+filtered_dataframe.set_index('Date', inplace=True)
+
+# Now perform seasonal decomposition
+# Now perform seasonal decomposition
+decomposition = sm.tsa.seasonal_decompose(filtered_dataframe['Close'], model='additive', period=30)  # Assuming monthly data
+
+trend = decomposition.trend
+seasonal = decomposition.seasonal
+residual = decomposition.resid
+
+# Plot the decomposed series
+plt.figure(figsize=(18, 8))
+plt.subplot(411)
+plt.plot(filtered_dataframe.index, filtered_dataframe['Close'], label='Original')
+plt.legend(loc='best')
+plt.subplot(412)
+plt.plot(filtered_dataframe.index, trend, label='Trend')
+plt.legend(loc='best')
+plt.subplot(413)
+plt.plot(filtered_dataframe.index, seasonal, label='Seasonal')
+plt.legend(loc='best')
+plt.subplot(414)
+plt.plot(filtered_dataframe.index, residual, label='Residual')
+plt.legend(loc='best')
+plt.tight_layout()
+plt.show()