Upload facebook_metrics_index.py

facebook_metrics_index.py

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+# -*- coding: utf-8 -*-
+"""Facebook Metrics Index
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1oGTWA0ohvfmgTOB8V4K7xTlqjwuHHCJR
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.metrics import mean_squared_error, accuracy_score
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+
+sns.set(style="whitegrid")
+plt.rcParams['figure.figsize'] = (10, 6)
+
+data = pd.read_csv('/content/Facebook Metrics of Cosmetic Brand.csv')
+data.head()
+
+!pip install pingouin
+!pip install simpy
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pingouin as pg
+import simpy
+import random
+import joblib
+from scipy import stats
+from scipy.stats import shapiro, f_oneway, pearsonr, chi2_contingency, ttest_ind
+from scipy.fft import fft
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+from sklearn.metrics import mean_squared_error, r2_score, accuracy_score, confusion_matrix, classification_report
+from sklearn.linear_model import LogisticRegression, LinearRegression
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor, VotingRegressor
+from sklearn.utils import resample
+from sklearn.impute import SimpleImputer
+from sklearn.inspection import PartialDependenceDisplay
+from statsmodels.tsa.seasonal import seasonal_decompose
+from statsmodels.tsa.arima.model import ARIMA
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+from statsmodels.tsa.stattools import ccf
+
+from pandas.plotting import autocorrelation_plot, lag_plot
+import warnings
+
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+warnings.filterwarnings('ignore', category=RuntimeWarning)
+warnings.filterwarnings('ignore', category=DeprecationWarning)
+warnings.filterwarnings('ignore', category=ImportWarning)
+warnings.filterwarnings('ignore', category=SyntaxWarning)
+warnings.filterwarnings('ignore', category=PendingDeprecationWarning)
+warnings.filterwarnings('ignore', category=ResourceWarning)
+
+sns.set(style='whitegrid')
+plt.rcParams['figure.figsize'] = (12, 8)
+
+data = pd.read_csv('/content/Facebook Metrics of Cosmetic Brand.csv')
+
+print("Sample of dataset:")
+display(data.head())
+
+print(f"Dataset shape: {data.shape}")
+
+print(f"Columns in the dataset: {data.columns.tolist()}")
+
+print("\nDataset Information:")
+data.info()
+
+print("\nSummary Statistics:")
+display(data.describe())
+
+print("\nSummary Statistics for Categorical Columns:")
+categorical_columns = data.select_dtypes(include=['object']).columns
+display(data[categorical_columns].describe())
+
+print("\nSummary Statistics for Cetegorical Columns:")
+categorical_columns = data.select_dtypes(include=['object']).columns
+display(data[categorical_columns].describe())
+
+duplicate_rows = data.duplicated().sum()
+print(f"\nNumber of duplicate rows: {duplicate_rows}")
+
+print("\nUnique values in each column:")
+for column in data.columns:
+  unique_values = data[column].nunique()
+  print(f"{column}: {unique_values} unique values")
+
+print("\nDistribution of uniquye values in categorical columns:")
+for column in categorical_columns:
+  value_counts = data[column].value_counts()
+  print(f"\n{column} distribution")
+  print(value_counts)
+
+print("\nSkewness of numerical columns:")
+numerical_columns = data.select_dtypes(include=[np.number]).columns
+skewness = data[numerical_columns].skew()
+print(skewness)
+
+print("\nKutosis of numerical columns:")
+kurtosis = data[numerical_columns].kurtosis()
+print(kurtosis)
+
+print("\nPairwise correlatoin of numerical features:")
+pairwise_corr = data[numerical_columns].corr()
+display(pairwise_corr)
+
+print("\nHighly correlated feature pairs:")
+threshold = 0.8
+high_corr_pairs = [(i, j, pairwise_corr.loc[i, j]) for i in pairwise_corr.columns for j in pairwise_corr.columns if i != j and abs(pairwise_corr.loc[i, j]) > threshold]
+for i, j, corr_value in high_corr_pairs:
+  print(f"Correlation between {i} and {j}: {corr_value:.2f}")
+
+print("\nVariance Inflation Factor (VIF) analysis for multicollinearity:")
+vif_data = pd.DataFrame()
+vif_data["features"] = numerical_columns
+vif_data["VIF"] = [variance_inflation_factor(data[numerical_columns].fillna(0).values, i) for i in range(len(numerical_columns))]
+display(vif_data)
+
+print("\nShapiro-Wilk test for normality of numerical columns:")
+for col in numerical_columns:
+  stat, p = shapiro(data[col].dropna())
+  print(f"Shapiro-Wilk test for {col}: Statistics={stat:.3f}, p={p:.3f}")
+  if p > 0.05:
+        print(f"The {col} distribution looks normal (fail to reject H0)\n")
+  else:
+        print(f"The {col} distribution does not look normal (reject H0)\n")
+
+print("\nANOVA test for interaction between categorical and numerical features:")
+for cat_col in categorical_columns:
+  for num_col in numerical_columns:
+    groups = [data[num_col][data[cat_col] == cat]
+  for cat in data[cat_col].unique()]
+  f_stat, p_val = f_oneway(*groups)
+  print(f"ANOVA test for interaction between {cat_col} and {num_col}: F-statistic={f_stat:.3f}, p-value={p_val:.3f}")
+  if p_val < 0.05:
+    print(f"Significant interaction detected between {cat_col} and {num_col}\n")
+  else:
+    print(f"No significant interaction detected between {cat_col} and {num_col}")
+
+print("\nMissing Values in Each Column:")
+missing_values = data.isnull().sum()
+missing_percentage = data.isnull().mean() * 100
+missing_data = pd.DataFrame({
+    'Missing Values': missing_values,
+    'Percentage': missing_percentage
+})
+display(missing_data)
+
+plt.figure(figsize=(12, 8))
+sns.heatmap(data.isnull(), cbar=False, cmap='viridis')
+plt.title('Missing Data Heatmap')
+plt.show()
+
+threshold = 30
+columns_with_missing_above_threshold = missing_data[missing_data['Percentage'] > threshold].index.tolist()
+print(f"\nColumns with more than {threshold}% missing values:")
+print(columns_with_missing_above_threshold)
+
+data_cleaned = data.drop(columns = columns_with_missing_above_threshold)
+print(f"\nShape of data after dropping columns with > {threshold}% missing values: {data_cleaned.shape}")
+
+numerical_columns = data_cleaned.select_dtypes(include=[np.number]).columns
+data_cleaned[numerical_columns] = data_cleaned[numerical_columns].fillna(data_cleaned[numerical_columns].median())
+
+categorical_columns = data_cleaned.select_dtypes(include=['object']).columns
+for column in categorical_columns:
+  data_cleaned[column].fillna(data_cleaned[column].mode()[0], inplace=True)
+
+print("\nMissing Values After Imputation:")
+display(data_cleaned.isnull().sum())
+
+print("\nDistribution of 'Type' column:")
+type_counts = data['Type'].value_counts()
+display(type_counts)
+
+plt.figure(figsize=(10, 6))
+sns.countplot(x='Type', data=data, palette='Set3')
+plt.title('Distribution of Post Types')
+plt.xlabel('Type of Post')
+plt.ylabel('Count')
+plt.show()
+
+print("\nDistribution of 'Category' column:")
+category_counts = data['Category'].value_counts
+display(category_counts)
+
+plt.figure(figsize=(10, 6))
+sns.countplot(x='Category', data=data, palette='Set2')
+plt.title('Distribution of Post Categories')
+plt.xlabel('Category of Post')
+plt.ylabel('Count')
+plt.show()
+
+print("\nDistribution of 'Paid' column:")
+paid_counts = data['Paid'].value_counts()
+display(paid_counts)
+
+plt.figure(figsize=(10, 6))
+sns.countplot(x='Paid', data=data, palette='Set1')
+plt.title('Distribution of Paid vs Non-Paid Posts')
+plt.xlabel('Paid (1 = Yes, 0 = No)')
+plt.ylabel('Count')
+plt.show()
+
+print("\nCross-tabulation of 'Type' and 'Paid' columns:")
+type_paid_crosstab = pd.crosstab(data['Type'], data['Paid'])
+display(type_paid_crosstab)
+
+type_paid_crosstab.plot(kind='bar', stacked=True, colormap='coolwarm')
+plt.title('Stacked Bar Plot of Post Type vs Paid Status')
+plt.xlabel('Type of Post')
+plt.ylabel('Count')
+plt.legend(title='Paid', loc='upper right')
+plt.show()
+
+print("\nCross-tabulation of 'Category' and 'Paid' columns:")
+category_paid_crosstab = pd.crosstab(data['Category'], data['Paid'])
+display(category_paid_crosstab)
+
+category_paid_crosstab.plot(kind='bar', stacked=True, colormap='viridis')
+plt.title('Stacked Bar Plot of Post Catgory vs Paid Status')
+plt.xlabel('Category of Post')
+plt.ylabel('Count')
+plt.legend(title='Paid', loc='upper right')
+plt.show()
+
+numerical_metrics = ['like', 'comment', 'share']
+
+for metric in numerical_metrics:
+  plt.figure(figsize=(18, 6))
+  plt.subplot(1, 3, 1)
+  sns.boxplot(x='Type', y=metric, data=data, palette='Set3')
+  plt.title(f'Distribution of {metric} by Post Type')
+
+  plt.subplot(1, 3, 2)
+  sns.boxplot(x='Category', y=metric, data=data, palette='Set2')
+  plt.title(f'Distribution of {metric} by Post Category')
+
+  plt.subplot(1, 3, 3)
+  sns.boxplot(x='Paid', y=metric, data=data, palette='Set1')
+  plt.title(f'Distribution of {metric} by Paid Status')
+
+  plt.tight_layout()
+  plt.show()
+
+for metric in numerical_metrics:
+  plt.figure(figsize=(18, 6))
+  plt.subplot(1, 3, 1)
+  sns.violinplot(x='Type', y=metric, data=data, palette='coolwarm', inner='quartile')
+  plt.title(f'Violin Plot of {metric} by Post Type')
+  plt.subplot(1, 3, 2)
+  sns.violinplot(x='Category', y=metric, data=data, palette='viridis', inner='quartile')
+  plt.title(f'Violin Plot of {metric} by Post Category')
+
+  plt.subplot(1, 3, 3)
+  sns.violinplot(x='Paid', y=metric, data=data, palette='magma', inner='quartile')
+  plt.title(f'Violin Plof of {metric} by Paid Status')
+
+  plt.tight_layout()
+  plt.show()
+
+from scipy.stats import chi2_contingency
+
+categorical_pairs = [('Type', 'Paid'), ('Category', 'Paid'), ('Type', 'Category')]
+print("\nChi-Square Test for Independence between Categorical Variables:")
+for pair in categorical_pairs:
+  contingency_table = pd.crosstab(data[pair[0]], data[pair[1]])
+  chi2, p, dof, expected = chi2_contingency(contingency_table)
+
+  print(f"Chi-Square Test between {pair[0]} and {pair[1]}:")
+  print(f"Chi2 = {chi2:.2f}, p-value = {p:.3f}")
+  if p < 0.05:
+    print(f"There is a significant association between {pair[0]} and {pair[1]}.\n")
+  else:
+    print(f"No significant association between {pair[0]} and {pair[1]}.\n")
+