# Imports
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import os
import plotly.graph_objects as go
from datetime import datetime
from prophet import Prophet
import plotly.io as pio

# set Plotly renderer so charts embed as interactive HTML instead of static images
pio.renderers.default = "notebook_connected"

# Settings for better visualizations
pd.set_option('display.max_columns', None)             # Show all columns
pd.set_option('display.max_rows', 100)                 # Show more rows
pd.set_option('display.float_format', '{:2f}'.format)  # Number formatting

sns.set_style("whitegrid")                             # Plot style
plt.rcParams['figure.figsize'] = (10, 6)               # Default figure size

print("Execution environment initialized successfully.")
print(f"• Pandas version: {pd.__version__}")

Execution environment initialized successfully.
• Pandas version: 2.3.3

# Base path to the data folder
data_path = './data/'

# Load key tables
orders = pd.read_csv(data_path + 'raw/olist_orders_dataset.csv')
order_items = pd.read_csv(data_path + 'raw/olist_order_items_dataset.csv')
customers = pd.read_csv(data_path + 'raw/olist_customers_dataset.csv')
payments = pd.read_csv(data_path + 'raw/olist_order_payments_dataset.csv')
reviews = pd.read_csv(data_path + 'raw/olist_order_reviews_dataset.csv')

# Quick look at each one
def quick_overview(df, name):
    print(f"\n=== {name.upper()} ===")
    print(f"Rows: {df.shape[0]:,}")
    print(f"Columns: {df.shape[1]}")
    print("\nData types and missing values:")
    print(df.info(verbose=False))
    print("\nFirst three rows:")
    display(df.head(3))  # display works better than print in Jupyter

quick_overview(orders, "Orders")
quick_overview(order_items, "Order Items")
quick_overview(customers, "Customers")
quick_overview(payments, "Payments")
quick_overview(reviews, "Reviews")

=== ORDERS ===
Rows: 99,441
Columns: 8

Data types and missing values:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99441 entries, 0 to 99440
Columns: 8 entries, order_id to order_estimated_delivery_date
dtypes: object(8)
memory usage: 6.1+ MB
None

First three rows:

=== ORDER ITEMS ===
Rows: 112,650
Columns: 7

Data types and missing values:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 112650 entries, 0 to 112649
Columns: 7 entries, order_id to freight_value
dtypes: float64(2), int64(1), object(4)
memory usage: 6.0+ MB
None

First three rows:

=== CUSTOMERS ===
Rows: 99,441
Columns: 5

Data types and missing values:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99441 entries, 0 to 99440
Columns: 5 entries, customer_id to customer_state
dtypes: int64(1), object(4)
memory usage: 3.8+ MB
None

First three rows:

=== PAYMENTS ===
Rows: 103,886
Columns: 5

Data types and missing values:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 103886 entries, 0 to 103885
Columns: 5 entries, order_id to payment_value
dtypes: float64(1), int64(2), object(2)
memory usage: 4.0+ MB
None

First three rows:

=== REVIEWS ===
Rows: 99,224
Columns: 7

Data types and missing values:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99224 entries, 0 to 99223
Columns: 7 entries, review_id to review_answer_timestamp
dtypes: int64(1), object(6)
memory usage: 5.3+ MB
None

First three rows:

# 3.1.1. Convert date columns to datetime

date_columns = [
    'order_purchase_timestamp',
    'order_approved_at',
    'order_delivered_carrier_date',
    'order_delivered_customer_date',
    'order_estimated_delivery_date'
]

for col in date_columns:
    orders[col] = pd.to_datetime(orders[col], errors='coerce')

print("Date columns converted:")
print(orders[date_columns].dtypes)

Date columns converted:
order_purchase_timestamp         datetime64[ns]
order_approved_at                datetime64[ns]
order_delivered_carrier_date     datetime64[ns]
order_delivered_customer_date    datetime64[ns]
order_estimated_delivery_date    datetime64[ns]
dtype: object

# 3.1.2. Check duplicates in key tables

print("\nDuplicates:")
print("orders order_id duplicated:", orders['order_id'].duplicated().sum())
print("customers customer_id duplicated:", customers['customer_id'].duplicated().sum())
print("order_items (should be 0):", order_items.duplicated().sum())

Duplicates:
orders order_id duplicated: 0
customers customer_id duplicated: 0
order_items (should be 0): 0

# 3.1.3. Missing values summary (focus on critical columns)

print("\nMissing values in orders:")
print(orders.isnull().sum()[orders.isnull().sum() > 0])

print("\nMissing values in reviews (expected high in comments):")
print(reviews.isnull().sum()[reviews.isnull().sum() > 0])

Missing values in orders:
order_approved_at                 160
order_delivered_carrier_date     1783
order_delivered_customer_date    2965
dtype: int64

Missing values in reviews (expected high in comments):
review_comment_title      87656
review_comment_message    58247
dtype: int64

# 3.2.1. Fill / handle missing timestamps in a business-friendly way

# Create flags (is_delivered, is_approved) for filtering later (e.g., only analyze delivered orders for delivery performance)
orders['is_delivered'] = orders['order_delivered_customer_date'].notna()
orders['is_approved'] = orders['order_approved_at'].notna()

print("Order status breakdown after cleaning:")
print(orders['order_status'].value_counts(normalize=True).round(3) * 100) # Use proportions (fractions that sum to 1), round to 3 d.p., and convert to %

Order status breakdown after cleaning:
order_status
delivered     97.000000
shipped        1.100000
canceled       0.600000
unavailable    0.600000
invoiced       0.300000
processing     0.300000
created        0.000000
approved       0.000000
Name: proportion, dtype: float64

# 3.2.2. Calculate actual delivery time (from purchase to delivered) | how long the customer actually waited (purchase → delivered)
orders['actual_delivery_time_days'] = (
    orders['order_delivered_customer_date'] - orders['order_purchase_timestamp']
).dt.days

# 3.2.3. Calculate estimated vs actual delivery time difference | → positive = arrived late, negative = arrived early
orders['actual_minus_estimated_delivery_days'] = (
    orders['order_delivered_customer_date'] - orders['order_estimated_delivery_date']
).dt.days

# Filter extreme outliers

reasonable_delivery = orders['actual_delivery_time_days'].between(0, 60)  # realistic range
print("\n% of orders with reasonable delivery time (0–60 days):", reasonable_delivery.mean().round(3) * 100)

% of orders with reasonable delivery time (0–60 days): 96.7

# Quick summary of new features

print("\nDelivery time stats (only delivered orders):")  
delivered_orders = orders[orders['is_delivered']]             # Only consider delivered orders: Use mask 'orders['is_delivered']' to keep only rows where mask == True
print(delivered_orders[['actual_delivery_time_days', 'actual_minus_estimated_delivery_days']].describe())

Delivery time stats (only delivered orders):
       actual_delivery_time_days  actual_minus_estimated_delivery_days
count               96476.000000                          96476.000000
mean                   12.094086                            -11.876881
std                     9.551746                             10.183854
min                     0.000000                           -147.000000
25%                     6.000000                            -17.000000
50%                    10.000000                            -12.000000
75%                    15.000000                             -7.000000
max                   209.000000                            188.000000

# 4.1 Saving Cleaned Data

# Create 'processed' subfolder if it doesn't exist
processed_dir = './data/processed/'
os.makedirs(processed_dir, exist_ok=True)

# Save the enriched orders table
orders.to_csv(processed_dir + 'cleaned_orders_with_features.csv', index=False)

print("Cleaned & enriched orders saved to:")
print(processed_dir + 'cleaned_orders_with_features.csv')

Cleaned & enriched orders saved to:
./data/processed/cleaned_orders_with_features.csv

# 4.2 First Merge – Creating a Base Working Table

# Step 1: Aggregate payments per order (some orders have multiple payments)
# I want total payment value per order_id
payments_total = payments.groupby('order_id')['payment_value'].sum().reset_index()
payments_total = payments_total.rename(columns={'payment_value': 'total_order_value'})

# Step 2: Merge orders + customers (get state and unique customer id)
base_df = orders.merge(
    customers[['customer_id', 'customer_unique_id', 'customer_state', 'customer_city']],
    on='customer_id',
    how='left'
)

# Step 3: Add total payment value
base_df = base_df.merge(
    payments_total,
    on='order_id',
    how='left'
)

# Quality check after merge
print("Shape before merges:", orders.shape)
print("Shape after merges:", base_df.shape)
print("\nMissing total_order_value after merge:", base_df['total_order_value'].isnull().sum())

# Show first few rows of our new base table
print("\nFirst 3 rows of base working table:")
display(base_df[['order_id', 'customer_unique_id', 'customer_state', 'order_status', 
                 'actual_delivery_time_days', 'total_order_value']].head(3))

Shape before merges: (99441, 12)
Shape after merges: (99441, 16)

Missing total_order_value after merge: 1

First 3 rows of base working table:

# 4.3 First Business Insight: Revenue & Delivery Performance by State

# Filter only delivered orders (to make delivery metrics meaningful)
delivered_base = base_df[base_df['is_delivered']]

# Aggregate key metrics by state
state_summary = delivered_base.groupby('customer_state').agg(
    total_revenue=('total_order_value', 'sum'),
    avg_delivery_days=('actual_delivery_time_days', 'mean'),
    median_delivery_days=('actual_delivery_time_days', 'median'),
    order_count=('order_id', 'count'),
    avg_delay_days=('actual_minus_estimated_delivery_days', 'mean')
).reset_index()

# Format numbers for readability
state_summary['total_revenue'] = state_summary['total_revenue'].round(2)
state_summary['avg_delivery_days'] = state_summary['avg_delivery_days'].round(1)
state_summary['median_delivery_days'] = state_summary['median_delivery_days'].round(1)
state_summary['avg_delay_days'] = state_summary['avg_delay_days'].round(1)

# Add the average revenue per order for each state
state_summary['avg_revenue_per_order'] = (state_summary['total_revenue'] / state_summary['order_count']).round(2)

# Sort by total revenue descending
state_summary = state_summary.sort_values('total_revenue', ascending=False)

# Show top 10 states
print("Top 10 states by total revenue (delivered orders only):")
display(
    state_summary.head(10).style.format({
        'total_revenue': '{:,.2f}',
        'avg_delivery_days': '{:.1f}',
        'median_delivery_days': '{:.1f}',
        'order_count': '{:,}',
        'avg_delay_days': '{:.1f}',
        'avg_revenue_per_order': '{:,.2f}',
    })
)

Top 10 states by total revenue (delivered orders only):

# Interactive bar chart with Plotly

fig = px.bar(
    state_summary.head(10),
    x='customer_state',
    y='total_revenue',
    color='avg_delivery_days',
    color_continuous_scale='RdYlGn_r',   # Red = late, Green = early
    title='Top 10 States by Revenue (color = avg actual delivery days)',
    labels={
        'customer_state': 'State',
        'total_revenue': 'Total Revenue (BRL)',
        'avg_delivery_days': 'Avg Delivery Time (days)'
    },
    hover_data=['order_count', 'median_delivery_days', 'avg_delay_days']
)

fig.update_layout(
    xaxis_title="State (UF)",
    yaxis_title="Total Revenue (BRL)",
    coloraxis_colorbar_title="Avg Delivery (days)"
)

fig.show()

# 4.3.1 Saving State-Level Summary

# Reuse the same processed directory I created earlier
processed_dir = './data/processed/'
os.makedirs(processed_dir, exist_ok=True) 

# Save the summary
state_summary.to_csv(
    processed_dir + 'state_performance_summary.csv',
    index=False
)

print("State summary saved successfully to:")
print(processed_dir + 'state_performance_summary.csv')

State summary saved successfully to:
./data/processed/state_performance_summary.csv

# 5.1 Loading Additional Tables

# Load the two new tables
products = pd.read_csv(data_path + 'raw/olist_products_dataset.csv')
category_translation = pd.read_csv(data_path + 'raw/product_category_name_translation.csv')

# Quick check
print("order_items shape:", order_items.shape)
print("products shape:", products.shape)
print("category_translation shape:", category_translation.shape)

# Preview both tables
quick_overview(products, "Products")
quick_overview(category_translation, "Category Translation")

order_items shape: (112650, 7)
products shape: (32951, 9)
category_translation shape: (71, 2)

=== PRODUCTS ===
Rows: 32,951
Columns: 9

Data types and missing values:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 32951 entries, 0 to 32950
Columns: 9 entries, product_id to product_width_cm
dtypes: float64(7), object(2)
memory usage: 2.3+ MB
None

First three rows:

=== CATEGORY TRANSLATION ===
Rows: 71
Columns: 2

Data types and missing values:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 71 entries, 0 to 70
Columns: 2 entries, product_category_name to product_category_name_english
dtypes: object(2)
memory usage: 1.2+ KB
None

First three rows:

# 5.2 Merging Product Information

# Step 1: Translate category names (left join to keep all products)
products_en = products.merge(
    category_translation,
    on='product_category_name',
    how='left'
)

# Step 2: Merge order_items with products_en (add category, price, etc.)
items_with_category = order_items.merge(
    products_en[['product_id', 'product_category_name_english', 'product_weight_g']],
    on='product_id',
    how='left'
)

# Handle missing English category names
items_with_category['product_category_name_english'] = items_with_category['product_category_name_english'].fillna('uncategorized')

# Quick check for missing English names (should be very few)
print("Missing English category names:", 
      items_with_category['product_category_name_english'].isnull().sum())

# Step 3: Aggregate by category (revenue, count, avg price, etc.)
category_performance = items_with_category.groupby('product_category_name_english').agg(
    total_revenue=('price', 'sum'),
    total_freight=('freight_value', 'sum'),
    order_items_count=('order_id', 'count'),
    unique_orders=('order_id', 'nunique'),
    avg_price=('price', 'mean'),
    avg_weight_g=('product_weight_g', 'mean')
).reset_index()

# Add revenue share %
total_revenue_all = category_performance['total_revenue'].sum()
category_performance['revenue_share_pct'] = (
    category_performance['total_revenue'] / total_revenue_all * 100
).round(2)

# Sort by revenue descending
category_performance = category_performance.sort_values('total_revenue', ascending=False)

# Show top 15 categories
print("\nTop 15 categories by total revenue:")
display(
    category_performance.head(15).style.format({
        'total_revenue': '{:,.2f}',
        'total_freight': '{:,.2f}',
        'avg_price': '{:,.2f}',
        'avg_weight_g': '{:.0f}',
        'revenue_share_pct': '{:.2f}%'
    })
)

Missing English category names: 0

Top 15 categories by total revenue:

# Top 10 categories for clean visualization
top_categories = category_performance.head(10)

# Bar chart: Revenue by category
fig_bar = px.bar(
    top_categories,
    x='product_category_name_english',
    y='total_revenue',
    color='avg_price',
    color_continuous_scale='YlOrRd',
    title='Top 10 Product Categories by Total Revenue',
    labels={
        'product_category_name_english': 'Category',
        'total_revenue': 'Total Revenue (BRL)',
        'avg_price': 'Average Unit Price (BRL)'
    },
    hover_data=['order_items_count', 'unique_orders', 'total_freight', 'revenue_share_pct']
)

fig_bar.update_layout(
    xaxis_title="Category",
    yaxis_title="Total Revenue (BRL)",
    xaxis_tickangle=-45,
    coloraxis_colorbar_title="Avg Price"
)

fig_bar.show()

# Pie chart: Revenue share (top 10 + others)
others_revenue = category_performance.iloc[10:]['total_revenue'].sum()

pie_data = pd.concat([
    top_categories[['product_category_name_english', 'total_revenue']],
    pd.DataFrame({'product_category_name_english': ['Others'], 'total_revenue': [others_revenue]})
])

fig_pie = px.pie(
    pie_data,
    values='total_revenue',
    names='product_category_name_english',
    title='Revenue Share - Top 10 Categories vs Others',
    hole=0.4  # donut style
)

fig_pie.show()

# 5.4 Category Performance by State

# Step 1: Keep only delivered orders for meaningful metrics
delivered_base = base_df[base_df['is_delivered']].copy()

# Step 2: Add category info to delivered orders
# First, aggregate items by order_id (one row per order with main category)
# For simplicity, I take the category of the most expensive item per order (common approach)
order_main_category = (
    items_with_category.loc[
        items_with_category.groupby('order_id')['price'].idxmax()
    ]
    [['order_id', 'product_category_name_english']]
    .reset_index(drop=True)
)

# Merge main category into delivered_base
delivered_with_category = delivered_base.merge(
    order_main_category,
    on='order_id',
    how='left'
)

# Fill any missing categories (rare after previous fix)
delivered_with_category['product_category_name_english'] = delivered_with_category['product_category_name_english'].fillna('uncategorized')

# Step 3: Aggregate: revenue & order count per state + category
state_category_summary = delivered_with_category.groupby(
    ['customer_state', 'product_category_name_english']
).agg(
    total_revenue=('total_order_value', 'sum'),
    order_count=('order_id', 'nunique'),
    avg_ticket=('total_order_value', 'mean')
).reset_index()

# Add revenue share within each state
state_category_summary['state_total_revenue'] = state_category_summary.groupby('customer_state')['total_revenue'].transform('sum')
state_category_summary['revenue_share_in_state_pct'] = (
    state_category_summary['total_revenue'] / state_category_summary['state_total_revenue'] * 100
).round(2)

# Sort by state and then by revenue descending within state
state_category_summary = state_category_summary.sort_values(
    ['customer_state', 'total_revenue'], ascending=[True, False]
)

# Show top categories for the top 5 revenue states (SP, RJ, MG, RS, PR)
top_states = ['SP', 'RJ', 'MG', 'RS', 'PR']
for state in top_states:
    print(f"\nTop 10 categories in {state} by revenue:")
    display(
        state_category_summary[state_category_summary['customer_state'] == state]
        .head(10)
        .style.format({
            'total_revenue': '{:,.2f}',
            'avg_ticket': '{:,.2f}',
            'revenue_share_in_state_pct': '{:.2f}%',
            'state_total_revenue': '{:.2f}'
        })
    )

Top 10 categories in SP by revenue:

Top 10 categories in RJ by revenue:

Top 10 categories in MG by revenue:

Top 10 categories in RS by revenue:

Top 10 categories in PR by revenue:

# 5.4.1 Visualization: Heatmap of Revenue Share by State and Category

# Filter to top 5 states and top 10 categories for readability
top_states = ['SP', 'RJ', 'MG', 'RS', 'PR']
top_cats = category_performance.head(10)['product_category_name_english'].tolist()

heatmap_df = state_category_summary[
    (state_category_summary['customer_state'].isin(top_states)) &
    (state_category_summary['product_category_name_english'].isin(top_cats))
]

# Pivot: states as rows, categories as columns, revenue share as values
pivot_table = heatmap_df.pivot(
    index='customer_state',      # rows
    columns='product_category_name_english',
    values='revenue_share_in_state_pct'
).fillna(0)

# Plot heatmap
plt.figure(figsize=(14, 6))

hm = sns.heatmap(
        pivot_table,
        annot=True,                # show numbers in cells
        fmt=".1f",
        annot_kws={"size": 12},    # font size of the numbers inside cells
        cmap="YlGnBu",
        linewidths=0.5,
        cbar_kws={'label': 'Revenue Share in State (%)'}
)

# Style the colorbar label
cbar = hm.collections[0].colorbar
cbar.set_label(
    'Revenue Share in State (%)',
    fontsize=16,               # ← adjust this number (16 is noticeably larger)
    fontweight='bold',
    labelpad=15
)
cbar.ax.tick_params(labelsize=12) # larger ticks on colorbar

plt.title('Revenue Share (%) – Top Categories in Top 5 States', fontsize=20, fontweight='bold', pad=25)
plt.xlabel('Category', fontsize=16, fontweight='bold', labelpad=10)
plt.ylabel('State (UF)', fontsize=16, fontweight='bold', labelpad=25)
plt.xticks(rotation=45, ha='right', fontsize=14)
plt.yticks(fontsize=14)
plt.tight_layout()
plt.show()

# 6.1 RFM Calculation

# Use only delivered orders for accurate purchase behavior
rfm_base = base_df[base_df['is_delivered']].copy()

# Current reference date (use the max purchase date + 1 day for realism)
reference_date = rfm_base['order_purchase_timestamp'].max() + pd.Timedelta(days=1)

# Aggregate per customer_unique_id
rfm = rfm_base.groupby('customer_unique_id').agg(
    recency=('order_purchase_timestamp', lambda x: (reference_date - x.max()).days), # Days since last purchase (lower number = more recent/active customer)
    frequency=('order_id', 'nunique'), # Number of distinct orders (higher = more loyal/repeat buyer)
    monetary=('total_order_value', 'sum') # Total BRL spent (higher = higher lifetime value)
).reset_index()

# Quick overview
print("RFM table shape:", rfm.shape)
print("\nRFM descriptive stats:")
print(rfm[['recency', 'frequency', 'monetary']].describe().round(2))

# Show top 10 customers by monetary value (VIP preview)
print("\nTop 10 customers by total spend:")
display(rfm.sort_values('monetary', ascending=False).head(10).style.format({
    'monetary': '{:,.2f}',
    'recency': '{:,}'
}))

RFM table shape: (93356, 4)

RFM descriptive stats:
           recency    frequency     monetary
count 93356.000000 93356.000000 93356.000000
mean    237.970000     1.030000   165.190000
std     152.620000     0.210000   226.320000
min       1.000000     1.000000     0.000000
25%     114.000000     1.000000    63.050000
50%     219.000000     1.000000   107.780000
75%     346.000000     1.000000   182.540000
max     714.000000    15.000000 13664.080000

Top 10 customers by total spend:

# 6.2 RFM Scoring & Customer Segmentation

# 6.2.1. Create scores (quartiles)

# Recency: use qcut (works fine because recency is more spread out)
rfm['R_score'] = pd.qcut(rfm['recency'], q=4, labels=[4, 3, 2, 1])  # 4 = most recent

# Monetary: use qcut (also spread out)
rfm['M_score'] = pd.qcut(rfm['monetary'], q=4, labels=[1, 2, 3, 4])  # 4 = highest spend

# Frequency: custom thresholds because of heavy skew
def frequency_score(f):
    if f == 1:
        return 1
    elif f == 2:
        return 2
    elif 3 <= f <= 4:
        return 3
    else:  # f >= 5
        return 4

rfm['F_score'] = rfm['frequency'].apply(frequency_score)

# Combine scores
rfm['RFM_score'] = rfm['R_score'].astype(str) + rfm['F_score'].astype(str) + rfm['M_score'].astype(str)

print("Frequency score distribution:")
print(rfm['F_score'].value_counts(normalize=True).sort_index() * 100)

# 6.2.2. Define meaningful business segments
def get_segment(row):
    r = int(row['R_score'])
    f = int(row['F_score'])
    m = int(row['M_score'])
    
    if r >= 3 and f >= 3 and m >= 3:
        return 'Champions'                  # Best customers – recent, frequent, high value
    elif r >= 3 and f >= 2:
        return 'Loyal Customers'            # Recent & frequent/loyal
    elif r >= 4 and f == 1 and m >= 2:
        return 'Recent High-Value New'      # New but spent a lot recently
    elif r <= 2 and f >= 3:
        return 'At Risk'                    # Good past behavior, but inactive now
    elif r <= 2 and f <= 2 and m <= 2:
        return 'Lost / Hibernating'         # Inactive, low value
    else:
        return 'Other / Potential'          # Middle ground

rfm['segment'] = rfm.apply(get_segment, axis=1)

# 6.2.3. Summary table by segment
segment_summary = rfm.groupby('segment').agg(
    customer_count=('customer_unique_id', 'count'),
    avg_recency_days=('recency', 'mean'),
    avg_frequency=('frequency', 'mean'),
    avg_monetary=('monetary', 'mean'),
    total_revenue=('monetary', 'sum')
).reset_index()

# Add percentage columns
total_customers = segment_summary['customer_count'].sum()
total_revenue_all = segment_summary['total_revenue'].sum()

segment_summary['customer_share_pct'] = (segment_summary['customer_count'] / total_customers * 100).round(2)
segment_summary['revenue_share_pct'] = (segment_summary['total_revenue'] / total_revenue_all * 100).round(2)

# Sort by revenue descending
segment_summary = segment_summary.sort_values('total_revenue', ascending=False)

print("\nRFM Segments Summary:")
display(
    segment_summary.style.format({
        'avg_recency_days': '{:.1f}',
        'avg_frequency': '{:.2f}',
        'avg_monetary': '{:,.2f}',
        'total_revenue': '{:,.2f}',
        'customer_share_pct': '{:.2f}%',
        'revenue_share_pct': '{:.2f}%'
    })
)

Frequency score distribution:
F_score
1   96.999657
2    2.756116
3    0.223874
4    0.020352
Name: proportion, dtype: float64

RFM Segments Summary:

# 6.3 RFM Segment Visualization

# 1. Bar chart: Customer count & Revenue share by segment
fig_bar = px.bar(
    segment_summary,
    x='segment',
    y=['customer_share_pct', 'revenue_share_pct'],
    barmode='group',
    title='<b>Customer Share vs Revenue Share by RFM Segment</b>',
    labels={
        'value': 'Percentage (%)',
        'variable': 'Metric',
        'segment': 'Customer Segment'
    },
    color_discrete_sequence=['#636EFA', '#EF553B']  # blue for customers, red for revenue
)

fig_bar.update_layout(
    title={'font': {'size': 18, 'color': 'black'}},
    xaxis_title="Segment",
    yaxis_title="Percentage (%)",
    legend_title="Metric",
    xaxis_tickangle=-45,
    height=500
)

fig_bar.show()

# 2. Pie chart: Revenue contribution by segment (focus on who drives the money)
fig_pie = px.pie(
    segment_summary,
    values='total_revenue',
    names='segment',
    title='Total Revenue Contribution by RFM Segment',
    hole=0.4,  # donut style
    color_discrete_sequence=px.colors.qualitative.Pastel
)

fig_pie.update_traces(textposition='inside', textinfo='percent+label')
fig_pie.update_layout(
    height=500,
    title={'font': {'size': 18, 'color': 'black'}}
)

fig_pie.show()

# 6.4 Exporting RFM Results

# Ensure processed folder exists
processed_dir = './data/processed/'
os.makedirs(processed_dir, exist_ok=True)

# 1. Save full RFM table (per customer)
rfm.to_csv(
    processed_dir + 'rfm_customers_with_segments.csv',
    index=False
)
print("Full RFM table saved to:")
print(processed_dir + 'rfm_customers_with_segments.csv')

# 2. Save segment summary (aggregated)
segment_summary.to_csv(
    processed_dir + 'rfm_segment_summary.csv',
    index=False
)
print("\nSegment summary saved to:")
print(processed_dir + 'rfm_segment_summary.csv')

# Save a formatted version (with rounded numbers for easy Excel/Power BI use)
segment_summary_formatted = segment_summary.copy()
segment_summary_formatted['avg_recency_days'] = segment_summary_formatted['avg_recency_days'].round(1)
segment_summary_formatted['avg_frequency'] = segment_summary_formatted['avg_frequency'].round(2)
segment_summary_formatted['avg_monetary'] = segment_summary_formatted['avg_monetary'].round(2)
segment_summary_formatted['total_revenue'] = segment_summary_formatted['total_revenue'].round(2)

segment_summary_formatted.to_csv(
    processed_dir + 'rfm_segment_summary_formatted.csv',
    index=False
)
print("\nFormatted segment summary also saved (ready for Power BI/Excel):")
print(processed_dir + 'rfm_segment_summary_formatted.csv')

Full RFM table saved to:
./data/processed/rfm_customers_with_segments.csv

Segment summary saved to:
./data/processed/rfm_segment_summary.csv

Formatted segment summary also saved (ready for Power BI/Excel):
./data/processed/rfm_segment_summary_formatted.csv

# Use only delivered orders for accurate purchase behavior
cohort_base = base_df[base_df['is_delivered']].copy()

# 7.1.1. Convert purchase timestamp to period (monthly)
cohort_base['purchase_month'] = cohort_base['order_purchase_timestamp'].dt.to_period('M')

# 7.1.2. Find first purchase month per customer
first_purchase = cohort_base.groupby('customer_unique_id')['purchase_month'].min().reset_index()
first_purchase = first_purchase.rename(columns={'purchase_month': 'cohort_month'})

# 7.1.3. Merge cohort month back to main table
cohort_base = cohort_base.merge(
    first_purchase,
    on='customer_unique_id',
    how='left'
)

# 7.1.4. Create cohort index: months since first purchase
cohort_base['cohort_index'] = (cohort_base['purchase_month'] - cohort_base['cohort_month']).apply(lambda x: x.n)

# 7.1.5. Build cohort table: count unique customers per cohort + period
cohort_table = cohort_base.groupby(['cohort_month', 'cohort_index']).agg(
    customer_count=('customer_unique_id', 'nunique')
).reset_index()

# 7.1.6. Pivot to get retention matrix
cohort_pivot = cohort_table.pivot(
    index='cohort_month',
    columns='cohort_index',
    values='customer_count'
)

# 7.1.7. Calculate retention rates (% of original cohort still buying)
cohort_size = cohort_pivot.iloc[:, 0]  # customers in month 0 (first purchase)
retention_matrix = cohort_pivot.divide(cohort_size, axis=0) * 100  # as percentage

# 7.2.1. Filter cohorts with at least 50 customers (a common threshold)
valid_cohorts = cohort_size[cohort_size >= 50].index
retention_matrix_filtered = retention_matrix.loc[valid_cohorts]

# Round for display
retention_matrix = retention_matrix.round(1)

# 7.2.2. Heatmap visualization
plt.figure(figsize=(14, 10))

sns.heatmap(
    retention_matrix_filtered,
    annot=True,
    fmt='.1f',
    cmap='YlGnBu',
    linewidths=0.5,
    cbar_kws={'label': 'Retention Rate (%)'}
)

# Get the current axes and colorbar
ax = plt.gca()
cbar = ax.collections[0].colorbar  # the colorbar object

# Make the colour bar label larger and bolder
cbar.set_label(
    'Retention Rate (%)',           
    fontsize=16,                    
    fontweight='bold',              
    labelpad=15                     
)

# make the percentage numbers/ticks on the colorbar larger
cbar.ax.tick_params(labelsize=12)   # controls the 0, 20, 40... numbers

plt.title('Monthly Cohort Retention Rates (%)', fontsize=20, fontweight='bold', pad=25)
plt.xlabel('Months Since First Purchase', fontsize=16, fontweight='bold' , labelpad=25)
plt.ylabel('Cohort Month (First Purchase)', fontsize=16, fontweight='bold' , labelpad=25)
plt.xticks(rotation=0, fontsize=16)
plt.yticks(rotation=0, fontsize=12)

plt.tight_layout()
plt.show()

# 7.2.3. Print retention table for reference (first 12 months)
print("Cohort Retention Rates (%) - First 12 months:")
display(retention_matrix.iloc[:, :13].style.format('{:.1f}'))

Cohort Retention Rates (%) - First 12 months:

# Average retention per month (across all cohorts)
avg_retention = retention_matrix.mean().dropna()  # ignore NaN in later months

plt.figure(figsize=(10, 6))
plt.plot(avg_retention.index, avg_retention.values, marker='o', color='#1f77b4')
plt.title('Average Cohort Retention Over Time', fontsize=14, fontweight='bold')
plt.xlabel('Months Since First Purchase')
plt.ylabel('Average Retention Rate (%)')
plt.grid(True, linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

# 8.1 & 8.2 Basic Forecasting with Prophet

# 8.1.1. Prepare time series data: monthly total revenue

# Use base_df (only delivered orders) for consistency
monthly_revenue = base_df[base_df['is_delivered']].copy()

# Group by month
monthly_revenue['ds'] = monthly_revenue['order_purchase_timestamp'].dt.to_period('M').dt.to_timestamp()

# Aggregate revenue per month
monthly_revenue = monthly_revenue.groupby('ds')['total_order_value'].sum().reset_index()

# Rename for Prophet (Prophet requires 'ds' (date) and 'y' (value))
monthly_revenue = monthly_revenue.rename(columns={'total_order_value': 'y'})

# Quick look at the data
print("Monthly revenue data shape:", monthly_revenue.shape)
print(monthly_revenue.head())

# 8.1.2. Create and fit model with logistic growth
model = Prophet(
    yearly_seasonality=True,
    weekly_seasonality=False,   # daily data not needed
    daily_seasonality=False,
    seasonality_mode='additive'  # better for revenue with growth
)

model.fit(monthly_revenue)

# 8.1.3. Create future dataframe and add the same cap/floor (forecast next 12 months)
future = model.make_future_dataframe(periods=12, freq='MS')  # MS = month start

# 8.1.4. Predict
forecast = model.predict(future)

# Clip any remaining negatives (extra safety at post-processing)
forecast['yhat'] = forecast['yhat'].clip(lower=0)
forecast['yhat_lower'] = forecast['yhat_lower'].clip(lower=0)
forecast['yhat_upper'] = forecast['yhat_upper'].clip(lower=0)

# 8.1.5. Plot forecast with Plotly (interactive)
fig = go.Figure()

# Uncertainty interval (two traces to create the shaded area)
fig.add_trace(go.Scatter(  # Upper bound (invisible line)
    x=forecast['ds'],
    y=forecast['yhat_upper'],
    mode='lines',
    line=dict(color='#27ae60', width=1, dash='dot'),
        name='Upper Uncertainty Bound'
))

fig.add_trace(go.Scatter(  # Lower bound + fill between upper & lower
    x=forecast['ds'],
    y=forecast['yhat_lower'],
    mode='lines',
    #fill='tonexty',         # fill area to the previous trace (upper bound)
    line=dict(color='#27ae60', width=1, dash='dot'),
    name='Lower Uncertainty Bound'
))

# Forecast
fig.add_trace(go.Scatter(      # Adds the forecast line
    x=forecast['ds'],
    y=forecast['yhat'],        # predicted values
    mode='lines',
    name='Forecast',
    line=dict(color='#ff7f0e', dash='dash', width=3.5) # orange + dashed
))

# Historical data
fig.add_trace(go.Scatter(      # Adds the historical line
    x=monthly_revenue['ds'],   # dates on x-axis
    y=monthly_revenue['y'],    # actual revenue values
    mode='lines+markers',      # line with dots at each point
    name='Historical Revenue', # legend label
    line=dict(color='#1f77b4', width=3.5) # blue color
))

# Final styling
fig.update_layout(          
    title='<b>Monthly Revenue Forecast (Next 12 Months)</b>',
    title_x=0.5,
    title_font_size=18,
    xaxis_title='Date',
    yaxis_title='Total Revenue (BRL)',
    hovermode='x unified',  # shows all values when hovering on a date
    height=600,
    template='plotly_white' # clean white background
)

fig.show()

# 6. Print forecast for next 6 months
print("\nForecast for next 6 months:")
future_forecast = forecast[forecast['ds'] > monthly_revenue['ds'].max()][['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head(6)
future_forecast['yhat'] = future_forecast['yhat'].round(2)
future_forecast['yhat_lower'] = future_forecast['yhat_lower'].round(2)
future_forecast['yhat_upper'] = future_forecast['yhat_upper'].round(2)
display(future_forecast)

16:23:21 - cmdstanpy - INFO - Chain [1] start processing
16:23:21 - cmdstanpy - INFO - Chain [1] done processing

Monthly revenue data shape: (23, 2)
          ds             y
0 2016-09-01      0.000000
1 2016-10-01  47271.200000
2 2016-12-01     19.620000
3 2017-01-01 127545.670000
4 2017-02-01 271298.650000

Forecast for next 6 months:

# 9.1 Exporting Key Tables

processed_dir = './data/processed/'
os.makedirs(processed_dir, exist_ok=True)

# 9.1.1. Cleaned orders with features
orders.to_csv(processed_dir + 'cleaned_orders_with_features.csv', index=False)

# 9.1.2. Base working table (orders + customers + payments)
base_df.to_csv(processed_dir + 'base_working_table.csv', index=False)

# 9.1.3. State performance summary
state_summary.to_csv(processed_dir + 'state_performance_summary.csv', index=False)

# 9.1.4. Category performance
category_performance.to_csv(processed_dir + 'category_performance.csv', index=False)

# 9.1.5. State-category breakdown
state_category_summary.to_csv(processed_dir + 'state_category_summary.csv', index=False)

# 9.1.6. RFM customers with scores & segments
rfm.to_csv(processed_dir + 'rfm_customers_with_segments.csv', index=False)

# 9.1.7. RFM segment summary
segment_summary.to_csv(processed_dir + 'rfm_segment_summary.csv', index=False)

# 9.1.8. Cohort retention matrix
retention_matrix.to_csv(processed_dir + 'cohort_retention_matrix.csv')

# 9.1.9. Monthly revenue + forecast
monthly_revenue.to_csv(processed_dir + 'monthly_historical_revenue.csv', index=False)
forecast.to_csv(processed_dir + 'monthly_revenue_forecast.csv', index=False)

print("All key tables exported successfully to:")
print(processed_dir)
print("Ready for Power BI import!")

All key tables exported successfully to:
./data/processed/
Ready for Power BI import!

	order_id	customer_id	order_status	order_purchase_timestamp	order_approved_at	order_delivered_carrier_date	order_delivered_customer_date	order_estimated_delivery_date
0	e481f51cbdc54678b7cc49136f2d6af7	9ef432eb6251297304e76186b10a928d	delivered	2017-10-02 10:56:33	2017-10-02 11:07:15	2017-10-04 19:55:00	2017-10-10 21:25:13	2017-10-18 00:00:00
1	53cdb2fc8bc7dce0b6741e2150273451	b0830fb4747a6c6d20dea0b8c802d7ef	delivered	2018-07-24 20:41:37	2018-07-26 03:24:27	2018-07-26 14:31:00	2018-08-07 15:27:45	2018-08-13 00:00:00
2	47770eb9100c2d0c44946d9cf07ec65d	41ce2a54c0b03bf3443c3d931a367089	delivered	2018-08-08 08:38:49	2018-08-08 08:55:23	2018-08-08 13:50:00	2018-08-17 18:06:29	2018-09-04 00:00:00

	order_id	order_item_id	product_id	seller_id	shipping_limit_date	price	freight_value
0	00010242fe8c5a6d1ba2dd792cb16214	1	4244733e06e7ecb4970a6e2683c13e61	48436dade18ac8b2bce089ec2a041202	2017-09-19 09:45:35	58.900000	13.290000
1	00018f77f2f0320c557190d7a144bdd3	1	e5f2d52b802189ee658865ca93d83a8f	dd7ddc04e1b6c2c614352b383efe2d36	2017-05-03 11:05:13	239.900000	19.930000
2	000229ec398224ef6ca0657da4fc703e	1	c777355d18b72b67abbeef9df44fd0fd	5b51032eddd242adc84c38acab88f23d	2018-01-18 14:48:30	199.000000	17.870000

	customer_id	customer_unique_id	customer_zip_code_prefix	customer_city	customer_state
0	06b8999e2fba1a1fbc88172c00ba8bc7	861eff4711a542e4b93843c6dd7febb0	14409	franca	SP
1	18955e83d337fd6b2def6b18a428ac77	290c77bc529b7ac935b93aa66c333dc3	9790	sao bernardo do campo	SP
2	4e7b3e00288586ebd08712fdd0374a03	060e732b5b29e8181a18229c7b0b2b5e	1151	sao paulo	SP

	order_id	payment_sequential	payment_type	payment_installments	payment_value
0	b81ef226f3fe1789b1e8b2acac839d17	1	credit_card	8	99.330000
1	a9810da82917af2d9aefd1278f1dcfa0	1	credit_card	1	24.390000
2	25e8ea4e93396b6fa0d3dd708e76c1bd	1	credit_card	1	65.710000

	review_id	order_id	review_score	review_comment_title	review_comment_message	review_creation_date	review_answer_timestamp
0	7bc2406110b926393aa56f80a40eba40	73fc7af87114b39712e6da79b0a377eb	4	NaN	NaN	2018-01-18 00:00:00	2018-01-18 21:46:59
1	80e641a11e56f04c1ad469d5645fdfde	a548910a1c6147796b98fdf73dbeba33	5	NaN	NaN	2018-03-10 00:00:00	2018-03-11 03:05:13
2	228ce5500dc1d8e020d8d1322874b6f0	f9e4b658b201a9f2ecdecbb34bed034b	5	NaN	NaN	2018-02-17 00:00:00	2018-02-18 14:36:24

	order_id	customer_unique_id	customer_state	order_status	actual_delivery_time_days	total_order_value
0	e481f51cbdc54678b7cc49136f2d6af7	7c396fd4830fd04220f754e42b4e5bff	SP	delivered	8.000000	38.710000
1	53cdb2fc8bc7dce0b6741e2150273451	af07308b275d755c9edb36a90c618231	BA	delivered	13.000000	141.460000
2	47770eb9100c2d0c44946d9cf07ec65d	3a653a41f6f9fc3d2a113cf8398680e8	GO	delivered	9.000000	179.120000

	customer_state	total_revenue	avg_delivery_days	median_delivery_days	order_count	avg_delay_days	avg_revenue_per_order
25	SP	5,769,221.49	8.3	7.0	40,495	-11.1	142.47
18	RJ	2,056,101.21	14.8	12.0	12,353	-11.8	166.45
10	MG	1,819,321.70	11.5	10.0	11,355	-13.2	160.22
22	RS	861,608.40	14.8	13.0	5,344	-13.9	161.23
17	PR	781,919.55	11.5	10.0	4,923	-13.3	158.83
23	SC	595,361.91	14.5	13.0	3,547	-11.5	167.85
4	BA	591,270.60	18.9	16.0	3,256	-10.8	181.59
6	DF	346,146.17	12.5	11.0	2,080	-12.0	166.42
8	GO	334,294.22	15.2	13.0	1,957	-12.2	170.82
7	ES	317,682.65	15.3	13.0	1,995	-10.5	159.24

	product_id	product_category_name	product_name_lenght	product_description_lenght	product_photos_qty	product_weight_g	product_length_cm	product_height_cm	product_width_cm
0	1e9e8ef04dbcff4541ed26657ea517e5	perfumaria	40.000000	287.000000	1.000000	225.000000	16.000000	10.000000	14.000000
1	3aa071139cb16b67ca9e5dea641aaa2f	artes	44.000000	276.000000	1.000000	1000.000000	30.000000	18.000000	20.000000
2	96bd76ec8810374ed1b65e291975717f	esporte_lazer	46.000000	250.000000	1.000000	154.000000	18.000000	9.000000	15.000000

	product_category_name	product_category_name_english
0	beleza_saude	health_beauty
1	informatica_acessorios	computers_accessories
2	automotivo	auto

	product_category_name_english	total_revenue	total_freight	order_items_count	unique_orders	avg_price	avg_weight_g	revenue_share_pct
43	health_beauty	1,258,681.34	182,566.73	9670	8836	130.16	1049	9.26%
71	watches_gifts	1,205,005.68	100,535.93	5991	5624	201.14	581	8.87%
7	bed_bath_table	1,036,988.68	204,693.04	11115	9417	93.30	2117	7.63%
65	sports_leisure	988,048.97	168,607.51	8641	7720	114.34	1743	7.27%
15	computers_accessories	911,954.32	147,318.08	7827	6689	116.51	903	6.71%
39	furniture_decor	729,762.49	172,749.30	8334	6449	87.56	2653	5.37%
20	cool_stuff	635,290.85	84,039.10	3796	3632	167.36	2549	4.67%
49	housewares	632,248.66	146,149.11	6964	5884	90.79	3215	4.65%
5	auto	592,720.11	92,664.21	4235	3897	139.96	2594	4.36%
42	garden_tools	485,256.46	98,962.75	4347	3518	111.63	2824	3.57%
69	toys	483,946.60	77,425.95	4117	3886	117.55	1857	3.56%
6	baby	411,764.89	68,353.11	3065	2885	134.34	3273	3.03%
59	perfumery	399,124.87	54,213.84	3419	3162	116.74	480	2.94%
68	telephony	323,667.53	71,215.79	4545	4199	71.21	262	2.38%
57	office_furniture	273,960.70	68,571.95	1691	1273	162.01	11390	2.02%

	customer_state	product_category_name_english	total_revenue	order_count	avg_ticket	state_total_revenue	revenue_share_in_state_pct
1272	SP	bed_bath_table	549,408.91	4307	127.56	5769221.49	9.52%
1308	SP	health_beauty	509,859.18	3693	138.10	5769221.49	8.84%
1335	SP	watches_gifts	449,135.06	2083	215.62	5769221.49	7.79%
1329	SP	sports_leisure	427,734.06	3203	133.54	5769221.49	7.41%
1280	SP	computers_accessories	386,706.97	2609	148.22	5769221.49	6.70%
1304	SP	furniture_decor	331,287.25	2618	126.54	5769221.49	5.74%
1314	SP	housewares	323,729.96	2693	120.21	5769221.49	5.61%
1270	SP	auto	235,440.59	1579	149.11	5769221.49	4.08%
1285	SP	cool_stuff	230,410.92	1279	180.15	5769221.49	3.99%
1333	SP	toys	205,513.18	1568	131.07	5769221.49	3.56%

	customer_state	product_category_name_english	total_revenue	order_count	avg_ticket	state_total_revenue	revenue_share_in_state_pct
986	RJ	watches_gifts	188,485.58	784	240.42	2056101.21	9.17%
924	RJ	bed_bath_table	175,594.31	1342	130.85	2056101.21	8.54%
960	RJ	health_beauty	159,174.66	935	170.24	2056101.21	7.74%
980	RJ	sports_leisure	140,578.56	889	158.13	2056101.21	6.84%
932	RJ	computers_accessories	138,232.02	832	166.14	2056101.21	6.72%
956	RJ	furniture_decor	118,425.00	809	146.38	2056101.21	5.76%
966	RJ	housewares	93,000.20	709	131.17	2056101.21	4.52%
937	RJ	cool_stuff	91,488.42	478	191.40	2056101.21	4.45%
959	RJ	garden_tools	85,899.27	522	164.56	2056101.21	4.18%
984	RJ	toys	83,263.44	539	154.48	2056101.21	4.05%

	customer_state	product_category_name_english	total_revenue	order_count	avg_ticket	state_total_revenue	revenue_share_in_state_pct
514	MG	health_beauty	175,305.23	987	177.61	1819321.70	9.64%
480	MG	bed_bath_table	155,527.65	1108	140.37	1819321.70	8.55%
541	MG	watches_gifts	132,117.43	598	220.93	1819321.70	7.26%
535	MG	sports_leisure	130,027.02	845	153.88	1819321.70	7.15%
487	MG	computers_accessories	126,693.85	857	147.83	1819321.70	6.96%
510	MG	furniture_decor	97,409.77	701	138.96	1819321.70	5.35%
520	MG	housewares	92,826.66	676	137.32	1819321.70	5.10%
478	MG	auto	82,521.85	458	180.18	1819321.70	4.54%
492	MG	cool_stuff	79,890.81	422	189.31	1819321.70	4.39%
513	MG	garden_tools	72,488.16	478	151.65	1819321.70	3.98%

	customer_state	product_category_name_english	total_revenue	order_count	avg_ticket	state_total_revenue	revenue_share_in_state_pct
1098	RS	bed_bath_table	73,416.22	532	138.00	861608.40	8.52%
1127	RS	furniture_decor	65,638.11	425	154.44	861608.40	7.62%
1106	RS	computers_accessories	61,275.72	385	159.16	861608.40	7.11%
1151	RS	sports_leisure	60,578.49	411	147.39	861608.40	7.03%
1130	RS	health_beauty	59,453.00	388	153.23	861608.40	6.90%
1157	RS	watches_gifts	51,874.17	222	233.67	861608.40	6.02%
1111	RS	cool_stuff	49,747.79	251	198.20	861608.40	5.77%
1136	RS	housewares	48,260.70	340	141.94	861608.40	5.60%
1129	RS	garden_tools	38,871.63	219	177.50	861608.40	4.51%
1155	RS	toys	33,347.80	200	166.74	861608.40	3.87%

	customer_state	product_category_name_english	total_revenue	order_count	avg_ticket	state_total_revenue	revenue_share_in_state_pct
911	PR	sports_leisure	66,731.65	419	159.26	781919.55	8.53%
917	PR	watches_gifts	62,263.22	265	234.96	781919.55	7.96%
891	PR	health_beauty	61,366.50	375	163.64	781919.55	7.85%
888	PR	furniture_decor	60,326.83	382	157.92	781919.55	7.72%
859	PR	bed_bath_table	55,499.30	395	140.50	781919.55	7.10%
866	PR	computers_accessories	50,583.03	333	151.90	781919.55	6.47%
871	PR	cool_stuff	41,963.34	201	208.77	781919.55	5.37%
897	PR	housewares	38,546.88	279	138.16	781919.55	4.93%
857	PR	auto	32,421.59	202	160.50	781919.55	4.15%
915	PR	toys	27,313.06	198	137.94	781919.55	3.49%

	customer_unique_id	recency	frequency	monetary
3724	0a0a92112bd4c708ca5fde585afaa872	334	1	13,664.08
79634	da122df9eeddfedc1dc1f5349a1a690c	515	2	7,571.63
43166	763c8b1c9c68a0229c42c9fc6f662b93	46	1	7,274.88
80461	dc4802a71eae9be1dd28f5d788ceb526	563	1	6,929.31
25432	459bef486812aa25204be022145caa62	35	1	6,922.21
93079	ff4159b92c40ebe40454e3e6a7c35ed6	462	1	6,726.66
23407	4007669dec559734d6f53e029e360987	279	1	6,081.54
87145	eebb5dda148d3893cdaf5b5ca3040ccb	498	1	4,764.34
26636	48e1ac109decbb87765a3eade6854098	69	1	4,681.78
73126	c8460e4251689ba205045f3ea17884a1	22	4	4,655.91

	segment	customer_count	avg_recency_days	avg_frequency	avg_monetary	total_revenue	customer_share_pct	revenue_share_pct
4	Other / Potential	51147	241.7	1.02	193.65	9,904,644.95	54.79%	64.22%
5	Recent High-Value New	16824	57.9	1.00	206.31	3,471,006.78	18.02%	22.51%
2	Lost / Hibernating	23750	365.1	1.01	63.98	1,519,629.32	25.44%	9.85%
3	Loyal Customers	1408	113.7	2.00	291.97	411,089.82	1.51%	2.67%
1	Champions	141	105.7	3.55	542.11	76,437.42	0.15%	0.50%
0	At Risk	86	362.9	3.15	453.76	39,023.14	0.09%	0.25%

cohort_index	0	1	2	3	4	5	6	7	8	9	10	11	12
cohort_month
2016-09	100.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2016-10	100.0	nan	nan	nan	nan	nan	0.4	nan	nan	0.4	nan	0.4	nan
2016-12	100.0	100.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2017-01	100.0	0.3	0.3	0.1	0.4	0.1	0.4	0.1	0.1	nan	0.4	0.1	0.7
2017-02	100.0	0.2	0.3	0.1	0.4	0.1	0.2	0.2	0.1	0.2	0.1	0.3	0.1
2017-03	100.0	0.4	0.4	0.4	0.4	0.2	0.2	0.3	0.3	0.1	0.4	0.1	0.2
2017-04	100.0	0.6	0.2	0.2	0.3	0.3	0.4	0.3	0.3	0.2	0.3	0.1	0.0
2017-05	100.0	0.5	0.5	0.3	0.3	0.3	0.4	0.1	0.3	0.3	0.3	0.3	0.2
2017-06	100.0	0.5	0.4	0.4	0.3	0.4	0.4	0.2	0.1	0.2	0.3	0.4	0.2
2017-07	100.0	0.5	0.3	0.2	0.3	0.2	0.3	0.1	0.2	0.3	0.2	0.3	0.1
2017-08	100.0	0.7	0.3	0.3	0.3	0.5	0.3	0.3	0.1	0.1	0.2	0.2	0.1
2017-09	100.0	0.7	0.5	0.3	0.4	0.2	0.2	0.2	0.3	0.2	0.2	0.1	nan
2017-10	100.0	0.7	0.3	0.1	0.2	0.2	0.2	0.4	0.3	0.2	0.2	nan	nan
2017-11	100.0	0.6	0.4	0.2	0.2	0.2	0.1	0.2	0.1	0.1	nan	nan	nan
2017-12	100.0	0.2	0.3	0.3	0.3	0.2	0.2	0.0	0.2	nan	nan	nan	nan
2018-01	100.0	0.3	0.4	0.3	0.3	0.2	0.2	0.2	nan	nan	nan	nan	nan
2018-02	100.0	0.3	0.4	0.3	0.3	0.2	0.2	nan	nan	nan	nan	nan	nan
2018-03	100.0	0.4	0.3	0.3	0.1	0.1	nan	nan	nan	nan	nan	nan	nan
2018-04	100.0	0.6	0.3	0.2	0.1	nan	nan	nan	nan	nan	nan	nan	nan
2018-05	100.0	0.5	0.3	0.2	nan	nan	nan	nan	nan	nan	nan	nan	nan
2018-06	100.0	0.4	0.3	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2018-07	100.0	0.5	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan
2018-08	100.0	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan

	ds	yhat	yhat_lower	yhat_upper
23	2018-09-01	1589425.680000	1512216.580000	1660038.290000
24	2018-10-01	1553858.560000	1484011.300000	1624259.150000
25	2018-11-01	1279389.770000	1207122.120000	1347560.330000
26	2018-12-01	1578930.870000	1507843.260000	1654009.240000
27	2019-01-01	1829107.500000	1757976.910000	1903398.130000
28	2019-02-01	1397814.710000	1324277.870000	1460586.690000

Análisis de E-Commerce Brasileño – Olist¶

Análisis Retail y de Clientes (2016–2018)¶

Tabla de Contenidos¶

Introducción¶

1. Comprensión del Negocio y Preguntas ¶

2. Carga y Visión General de los Datos ¶

2.1. Carga de Datos ¶

2.2. Visión General de los Datos y Chequeos Iniciales ¶

3. Limpieza y Preparación de Datos ¶

3.1. Limpieza Inicial de Datos ¶

Conversiones de tipo¶

Chequeos de Duplicados¶

Inspección de Valores Faltantes¶

3.2. Ingeniería de Features y Transformaciones de Negocio ¶

Banderas de Estado¶

Cálculos de Tiempo de Entrega¶

Métricas de Retraso y Performance¶

4. Integración de Datos y Primeros Insights de Negocio ¶

4.1. Guardado de Datos Limpios ¶

4.2. Primer Merge – Creación de una Tabla Base de Trabajo ¶

4.3. Primer Insight de Negocio: Ingresos y Performance de Entrega por Estado ¶

Guardado del Resumen por Estado¶

5. Análisis de Productos y Categorías ¶

5.1. Carga de Tablas Adicionales ¶

5.2. Merge de Información de Productos en la Tabla Base ¶

5.3. Visualización de Performance por Categoría e Insights ¶

5.4. Performance por Categoría y Estado ¶

5.5. Visualización: Participación de Ingresos por Categoría y Estado ¶

6. Segmentación de Clientes – Análisis RFM ¶

6.1. Cálculo RFM ¶

6.2. Scoring RFM y Segmentación de Clientes ¶

6.3. Visualización de Segmentos RFM y Recomendaciones Accionables ¶

6.4. Exportación de Resultados RFM ¶

7. Análisis de Cohorts – Retención de Clientes a lo Largo del Tiempo ¶

7.1. Configuración y Cálculo de Cohorts ¶

7.2. Tabla y Heatmap de Retención ¶

7.3. Insights de Cohorts y Recomendaciones de Retención ¶

8. Pronóstico Básico – Predicción de Ingresos Futuros con Prophet ¶

8.1. Pronóstico con Prophet ¶

8.2. Insights de Pronóstico y Recomendaciones de Negocio ¶

9. Finalización y Presentación ¶

9.1. Exportación de Tablas Clave ¶

9.2. Resumen del Proyecto e Insights Clave ¶

9.3. Dashboard en Power BI ¶

9.4. Conclusión del Proyecto y Próximos Pasos ¶

9.5 Reporte Publicado y Descargas ¶