Upload _1294.py

_1294.py

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+# -*- coding: utf-8 -*-
+""".1294
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/18GMbHEjdUUsZiko73-qVxV-WVgsf5hgs
+"""
+
+
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.cluster import KMeans
+from sklearn.preprocessing import StandardScaler
+import statsmodels.api as sm
+import warnings
+
+import warnings # Importing the warnings module
+warnings.filterwarnings('ignore') # Calling the filterwarnings function
+
+df = pd.read_csv("/content/shopping_trends (2).csv")
+
+df.head()
+
+df.sample(10)
+
+df.info()
+
+fig_age = px.histogram(
+    df,
+    x='Age',
+    nbins= 50,
+    title='Age Distribution of Customers',
+    color_discrete_sequence=['cyan']
+)
+
+fig_age.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white')
+)
+fig_age.show()
+
+gender_counts = df['Gender'].value_counts().reset_index()
+gender_counts.columns = ['Gender', 'Count']
+
+fig_gender = px.pie(
+    gender_counts,
+    names='Gender',
+    values='Count',
+    title='Gender Proportions of Customers',
+    color_discrete_sequence=px.colors.sequential.RdBu
+)
+fig_gender.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white')
+)
+fig_gender.show()
+
+location_counts = df['Location'].value_counts().reset_index()
+location_counts.columns = ['Location', 'Count']
+
+fig_location = px.bar(
+    location_counts,
+    x='Location',
+    y='Count',
+    text='Count',
+    title='Customer Count by Location',
+    color_discrete_sequence=['lime']
+)
+location_counts = df['Location'].value_counts().reset_index()
+location_counts.columns = ['Location', 'Count']
+
+fig_location = px.bar(
+    location_counts,
+    x='Location',
+    y='Count',
+    text='Count',
+    title='Customer Count by Location',
+    color_discrete_sequence=['lime']
+)
+fig_location.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title="Location",
+    yaxis_title="Number of Customers"
+)
+fig_location.show()
+fig_location = px.bar(
+    location_counts,
+    x='Location',
+    y='Count',
+    text='Count',
+    title='Customer Count by Location',
+    color_discrete_sequence=['lime']
+)
+fig_location.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title="Location",
+    yaxis_title="Number of Customers"
+)
+fig_location.show()
+
+item_counts = df['Item Purchased'].value_counts().reset_index()
+item_counts.columns = ['Item Purchased', 'Count']
+
+fig_items = px.bar(
+    item_counts,
+    x='Item Purchased',
+    y='Count',
+    text='Count',
+    title='Most Purchased Items',
+    color_discrete_sequence=['orange']
+)
+fig_items.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Items',
+    yaxis_title='Count of Purchases'
+)
+fig_items.show()
+
+fig_amount = px.box(
+    df,
+    y='Purchase Amount (USD)', # Changed from 'Purchased Amount (USD)'
+    title='Purchase Amount Distribution',
+    color_discrete_sequence=['magenta']
+)
+
+fig_amount.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    yaxis_title='Purchase Amount (USD)'
+)
+fig_amount.show()
+
+# Count popular sizes
+size_counts = df['Size'].value_counts().reset_index()
+size_counts.columns = ['Size', 'Count']
+
+fig_sizes = px.bar(
+    size_counts,
+    x='Size',
+    y='Count',
+    text='Count',
+    title='Preferred Sizes',
+    color_discrete_sequence=['green']
+)
+fig_sizes.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Size',
+    yaxis_title='Count of Purchases'
+)
+fig_sizes.show()
+
+# Count popular colors
+color_counts = df['Color'].value_counts().reset_index()
+color_counts.columns = ['Color', 'Count']
+
+fig_colors = px.bar(
+    color_counts,
+    x='Color',
+    y='Count',
+    text='Count',
+    title='Preferred Colors',
+    color_discrete_sequence=['teal']
+)
+fig_colors.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Color',
+    yaxis_title='Count of Purchases'
+)
+fig_colors.show()
+
+# Seasonal Trends
+season_counts = df['Season'].value_counts().reset_index()
+season_counts.columns = ['Season', 'Count']
+
+fig_season = px.bar(
+    season_counts,
+    x='Season',
+    y='Count',
+    text='Count',
+    title='Seasonal Trends in Purchases',
+    color_discrete_sequence=['blue']
+)
+fig_season.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Season',
+    yaxis_title='Count of Purchases'
+)
+fig_season.show()
+
+# Frequency of Purchases
+frequency_counts = df['Frequency of Purchases'].value_counts().reset_index()
+frequency_counts.columns = ['Frequency', 'Count']
+
+fig_frequency = px.bar(
+    frequency_counts,
+    x='Frequency',
+    y='Count',
+    text='Count',
+    title='Frequency of Purchases',
+    color_discrete_sequence=['red']
+)
+fig_frequency.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Frequency',
+    yaxis_title='Count of Purchases'
+)
+fig_frequency.show()
+
+payment_counts = df['Payment Method'].value_counts().reset_index()
+payment_counts.columns = ['Payment Method', 'Count']
+
+fig_payment = px.pie(
+    payment_counts,
+    names='Payment Method',
+    values='Count',
+    title='Popular Payment Methods',
+    color_discrete_sequence=px.colors.sequential.Plasma
+)
+fig_payment.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white')
+)
+fig_payment.show()
+
+subscription_data = df.groupby('Subscription Status')['Purchase Amount (USD)'].sum().reset_index()
+
+fig_subscription = px.bar(
+    subscription_data,
+    x='Subscription Status',
+    y='Purchase Amount (USD)',
+    text='Purchase Amount (USD)',
+    title='Impact of Subscription on Purchases',
+    color='Subscription Status',
+    color_discrete_sequence=px.colors.sequential.Viridis
+)
+fig_subscription.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Subscription Status',
+    yaxis_title='Total Purchase Amount (USD)'
+)
+fig_subscription.show()
+
+discount_data = df['Discount Applied'].value_counts().reset_index()
+discount_data.columns = ['Discount Applied', 'Count']
+
+fig_discount = px.bar(
+    discount_data,
+    x='Discount Applied',
+    y='Count',
+    text='Count',
+    title='Discount Usage Analysis',
+    color='Discount Applied',
+    color_discrete_sequence=px.colors.sequential.Cividis
+)
+fig_discount.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Discount Applied',
+    yaxis_title='Number of Purchases'
+)
+fig_discount.show()
+
+category_revenue = df.groupby('Category')['Purchase Amount (USD)'].sum().reset_index()
+
+fig_category_revenue = px.treemap(
+    category_revenue,
+    path=['Category'],
+    values='Purchase Amount (USD)',
+    title='Category-Wise Revenue',
+    color='Purchase Amount (USD)',
+    color_continuous_scale=px.colors.sequential.Sunset
+)
+fig_category_revenue.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white')
+)
+fig_category_revenue.show()
+
+fig_ratings = px.histogram(
+    df,
+    x='Review Rating',
+    nbins=10,
+    title='Distribution of Review Ratings',
+    color_discrete_sequence=['#FFA07A']
+)
+fig_ratings.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Review Rating',
+    yaxis_title='Count'
+)
+fig_ratings.show()
+
+shipping_data = df.groupby('Shipping Type')['Purchase Amount (USD)'].sum().reset_index()
+
+fig_shipping = px.bar(
+    shipping_data,
+    x='Shipping Type',
+    y='Purchase Amount (USD)',
+    text='Purchase Amount (USD)',
+    title='Shipping Types and Revenue Impact',
+    color='Shipping Type',
+    color_discrete_sequence=px.colors.sequential.Teal
+)
+fig_shipping.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Shipping Type',
+    yaxis_title='Total Revenue (USD)'
+)
+fig_shipping.show()
+
+customer_revenue = df.groupby('Customer ID')['Purchase Amount (USD)'].sum().reset_index()
+customer_revenue = customer_revenue.sort_values(by='Purchase Amount (USD)', ascending=False)
+customer_revenue['Cumulative Percentage'] = customer_revenue['Purchase Amount (USD)'].cumsum() / customer_revenue['Purchase Amount (USD)'].sum() * 100
+
+fig_pareto = px.bar(
+    customer_revenue,
+    x='Customer ID',
+    y='Purchase Amount (USD)',
+    text='Purchase Amount (USD)',
+    title='High-Spending Customers - Pareto Chart',
+    color_discrete_sequence=['#FF7F50']
+)
+fig_pareto.add_scatter(
+    x=customer_revenue['Customer ID'],
+    y=customer_revenue['Cumulative Percentage'],
+    mode='lines+markers',
+    name='Cumulative Percentage',
+    line=dict(color='cyan')
+)
+fig_pareto.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Customer ID',
+    yaxis_title='Purchase Amount (USD)',
+    yaxis2=dict(title='Cumulative Percentage', overlaying='y', side='right')
+)
+fig_pareto.show()
+
+clustering_data = df.groupby('Customer ID').agg({
+    'Purchase Amount (USD)': 'sum',
+    'Frequency of Purchases': 'count',
+    'Category': 'nunique'
+}).reset_index()
+clustering_data.columns = ['Customer ID', 'Total Purchase Amount', 'Purchase Frequency', 'Unique Categories']
+
+# Standardize the data
+scaler = StandardScaler()
+clustering_data_scaled = scaler.fit_transform(clustering_data[['Total Purchase Amount', 'Purchase Frequency', 'Unique Categories']])
+
+# Apply K-means clustering
+kmeans = KMeans(n_clusters=3, random_state=42)
+clustering_data['Cluster'] = kmeans.fit_predict(clustering_data_scaled)
+
+# Scatter plot
+fig_clusters = px.scatter_3d(
+    clustering_data,
+    x='Total Purchase Amount',
+    y='Purchase Frequency',
+    z='Unique Categories',
+    color='Cluster',
+    title='Behavioral Clusters of Customers',
+    symbol='Cluster',
+    color_continuous_scale=px.colors.sequential.Viridis
+)
+fig_clusters.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    scene=dict(
+        xaxis_title='Total Purchase Amount',
+        yaxis_title='Purchase Frequency',
+        zaxis_title='Unique Categories'
+    )
+)
+fig_clusters.show()
+
+fig_purchase_vs_rating = px.scatter(
+    df,
+    x='Purchase Amount (USD)',
+    y='Review Rating',
+    title='Purchase Amount vs. Review Rating',
+    color='Review Rating',
+    color_continuous_scale='Viridis'
+)
+
+# Add regression line
+X = sm.add_constant(df['Purchase Amount (USD)'])  # Add constant for intercept
+y = df['Review Rating']
+model = sm.OLS(y, X).fit()
+df['Regression Line'] = model.predict(X)
+
+fig_purchase_vs_rating.add_scatter(
+    x=df['Purchase Amount (USD)'],
+    y=df['Regression Line'],
+    mode='lines',
+    name='Regression Line',
+    line=dict(color='cyan')
+)
+
+fig_purchase_vs_rating.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Purchase Amount (USD)',
+    yaxis_title='Review Rating'
+)
+
+fig_purchase_vs_rating.show()
+
+fig_age_vs_spending = px.scatter(
+    df,
+    x='Age',
+    y='Purchase Amount (USD)',
+    title='Age vs. Spending Habits',
+    color='Age',
+    color_continuous_scale='Viridis'
+)
+
+fig_age_vs_spending.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Age',
+    yaxis_title='Purchase Amount (USD)'
+)
+
+fig_age_vs_spending.show()
+
+fig_category_vs_gender.update_layout(
+    template='plotly_dark',  # Corrected the template name to 'plotly_dark'
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Product Category',
+    yaxis_title='Count'
+)
+fig_category_vs_gender.show()
+
+fig_discounts_vs_spending = px.box(
+    df,
+    x='Discount Applied',
+    y='Purchase Amount (USD)',
+    title='Effect of Discounts on Spending',
+    color='Discount Applied',
+    color_discrete_sequence=['#FF6347', '#20B2AA']
+)
+
+fig_discounts_vs_spending.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white'),
+    xaxis_title='Discount Applied',
+    yaxis_title='Purchase Amount (USD)'
+)
+
+fig_discounts_vs_spending.show()
+
+fig_profitability_analysis = px.treemap(
+    df,
+    path=['Category', 'Size', 'Color'],  # Hierarchy: Category -> Size -> Color
+    values='Purchase Amount (USD)',
+    title='Profitability Analysis by Category, Size, and Color',
+    color='Purchase Amount (USD)',  # Color by total purchase amount
+    color_continuous_scale='Viridis'
+)
+
+fig_profitability_analysis.update_layout(
+    template='plotly_dark',
+    plot_bgcolor='black',
+    paper_bgcolor='black',
+    font=dict(color='white')
+)
+
+fig_profitability_analysis.show()
+