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pythonanalysis.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "85361b58",
+      "metadata": {
+        "id": "85361b58"
+      },
+      "source": [
+        "# Step 2 — Python Analysis / Modeling\n",
+        "\n",
+        "Clean version for the Hugging Face SE21 app template. It creates dashboard artifacts."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2.1 Environment setup & modeling"
+      ],
+      "metadata": {
+        "id": "WP6akOm_IupL"
+      },
+      "id": "WP6akOm_IupL"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "c88b847c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c88b847c",
+        "outputId": "ee60c93f-29b8-4dcc-9fc4-a5b6aa4b9398"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Environment ready.\n",
+            "BASE_PATH: /content\n",
+            "CSV files found:\n",
+            "- /content/Womens Clothing E-Commerce Reviews.csv\n",
+            "- /content/ecommerce_returns_cleaned.csv\n",
+            "Using reviews file: /content/Womens Clothing E-Commerce Reviews.csv\n",
+            "Using returns file: /content/ecommerce_returns_cleaned.csv\n",
+            "Reviews shape: (23486, 10)\n",
+            "Returns shape: (113314, 29)\n",
+            "Reviews columns: ['Clothing ID', 'Age', 'Title', 'Review Text', 'Rating', 'Recommended IND', 'Positive Feedback Count', 'Division Name', 'Department Name', 'Class Name']\n",
+            "Returns columns: ['order_id', 'order_item_id', 'product_id', 'seller_id', 'customer_id', 'order_status', 'order_purchase_timestamp', 'order_delivered_customer_date', 'order_estimated_delivery_date', 'review_score', 'review_comment_title', 'review_comment_message', 'price', 'freight_value', 'total_cost', 'product_category_name', 'product_name_lenght', 'product_description_lenght', 'product_photos_qty', 'product_weight_g', 'product_length_cm', 'product_height_cm', 'product_width_cm', 'has_review_text', 'review_text_length', 'delivery_delay_days', 'negative_keyword_flag', 'synthetic_return_risk', 'likely_return']\n",
+            "Data loaded and cleaned.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ==================================================\n",
+        "# STEP 2: UNIVERSAL ANALYSIS SETUP\n",
+        "# Works in BOTH Hugging Face Spaces and Google Colab\n",
+        "# ==================================================\n",
+        "\n",
+        "import os\n",
+        "import json\n",
+        "import random\n",
+        "import warnings\n",
+        "from pathlib import Path\n",
+        "\n",
+        "os.environ.setdefault(\"MPLCONFIGDIR\", \"/tmp/matplotlib\")\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "random.seed(42)\n",
+        "np.random.seed(42)\n",
+        "\n",
+        "# Pick the correct runtime folder automatically.\n",
+        "# Hugging Face Space uses /app. Colab uses /content.\n",
+        "candidate_roots = [Path(\"/app\"), Path(\"/content\"), Path.cwd(), Path(\"/mnt/data\")]\n",
+        "BASE_PATH = None\n",
+        "\n",
+        "for root in candidate_roots:\n",
+        "    if root.exists():\n",
+        "        csvs = []\n",
+        "        for p in root.rglob(\"*.csv\"):\n",
+        "            parts = {part.lower() for part in p.parts}\n",
+        "            if \"sample_data\" in parts:\n",
+        "                continue\n",
+        "            if \"outputs\" in parts or \"figures\" in parts or \"tables\" in parts or \"artifacts\" in parts:\n",
+        "                continue\n",
+        "            csvs.append(p)\n",
+        "        if csvs:\n",
+        "            BASE_PATH = root\n",
+        "            break\n",
+        "\n",
+        "if BASE_PATH is None:\n",
+        "    if Path(\"/app\").exists():\n",
+        "        BASE_PATH = Path(\"/app\")\n",
+        "    elif Path(\"/content\").exists():\n",
+        "        BASE_PATH = Path(\"/content\")\n",
+        "    else:\n",
+        "        BASE_PATH = Path.cwd()\n",
+        "\n",
+        "DATA_PROCESSED = BASE_PATH / \"data_processed\"\n",
+        "\n",
+        "OUTPUTS = BASE_PATH / \"outputs\"\n",
+        "FIGURES = BASE_PATH / \"figures\"\n",
+        "TABLES = BASE_PATH / \"tables\"\n",
+        "ARTIFACTS = BASE_PATH / \"artifacts\"\n",
+        "\n",
+        "# Extra folders because different templates check different places\n",
+        "OUTPUT_FIGURES = OUTPUTS / \"figures\"\n",
+        "OUTPUT_TABLES = OUTPUTS / \"tables\"\n",
+        "ARTIFACT_FIGURES = ARTIFACTS / \"figures\"\n",
+        "ARTIFACT_TABLES = ARTIFACTS / \"tables\"\n",
+        "\n",
+        "ALL_OUTPUT_DIRS = [\n",
+        "    DATA_PROCESSED,\n",
+        "    OUTPUTS,\n",
+        "    FIGURES,\n",
+        "    TABLES,\n",
+        "    ARTIFACTS,\n",
+        "    OUTPUT_FIGURES,\n",
+        "    OUTPUT_TABLES,\n",
+        "    ARTIFACT_FIGURES,\n",
+        "    ARTIFACT_TABLES,\n",
+        "]\n",
+        "\n",
+        "for folder in ALL_OUTPUT_DIRS:\n",
+        "    folder.mkdir(parents=True, exist_ok=True)\n",
+        "\n",
+        "print(\"Environment ready.\")\n",
+        "print(\"BASE_PATH:\", BASE_PATH)\n",
+        "\n",
+        "# Load data created by Step 1 if available.\n",
+        "csv_paths = []\n",
+        "for p in BASE_PATH.rglob(\"*.csv\"):\n",
+        "    parts = {part.lower() for part in p.parts}\n",
+        "    if \"sample_data\" in parts:\n",
+        "        continue\n",
+        "    if \"outputs\" in parts or \"figures\" in parts or \"tables\" in parts or \"artifacts\" in parts:\n",
+        "        continue\n",
+        "    csv_paths.append(p)\n",
+        "\n",
+        "print(\"CSV files found:\")\n",
+        "for p in csv_paths:\n",
+        "    print(\"-\", p)\n",
+        "\n",
+        "def first_existing(paths):\n",
+        "    for p in paths:\n",
+        "        if Path(p).exists():\n",
+        "            return Path(p)\n",
+        "    return None\n",
+        "\n",
+        "reviews_path = first_existing([\n",
+        "    DATA_PROCESSED / \"reviews_cleaned.csv\",\n",
+        "    DATA_PROCESSED / \"womens_reviews_cleaned.csv\",\n",
+        "    BASE_PATH / \"Womens Clothing E-Commerce Reviews.csv\",\n",
+        "])\n",
+        "\n",
+        "returns_path = first_existing([\n",
+        "    DATA_PROCESSED / \"returns_input.csv\",\n",
+        "    DATA_PROCESSED / \"returns_cleaned.csv\",\n",
+        "    BASE_PATH / \"ecommerce_returns_cleaned.csv\",\n",
+        "    DATA_PROCESSED / \"synthetic_return_risk.csv\",\n",
+        "])\n",
+        "\n",
+        "# Fallback search.\n",
+        "if reviews_path is None:\n",
+        "    review_matches = [\n",
+        "        p for p in csv_paths\n",
+        "        if (\"clothing\" in p.name.lower()) or (\"review\" in p.name.lower() and \"return\" not in p.name.lower())\n",
+        "    ]\n",
+        "    reviews_path = review_matches[0] if review_matches else None\n",
+        "\n",
+        "if returns_path is None:\n",
+        "    return_matches = [\n",
+        "        p for p in csv_paths\n",
+        "        if \"return\" in p.name.lower()\n",
+        "    ]\n",
+        "    returns_path = return_matches[0] if return_matches else None\n",
+        "\n",
+        "\n",
+        "if returns_path is None:\n",
+        "    raise FileNotFoundError(\"Step 2 could not find the ecommerce returns CSV.\")\n",
+        "\n",
+        "print(\"Using reviews file:\", reviews_path)\n",
+        "print(\"Using returns file:\", returns_path)\n",
+        "\n",
+        "reviews_df = pd.read_csv(reviews_path).drop(columns=[\"Unnamed: 0\"], errors=\"ignore\")\n",
+        "returns_df = pd.read_csv(returns_path).drop(columns=[\"Unnamed: 0\"], errors=\"ignore\")\n",
+        "\n",
+        "print(\"Reviews shape:\", reviews_df.shape)\n",
+        "print(\"Returns shape:\", returns_df.shape)\n",
+        "print(\"Reviews columns:\", reviews_df.columns.tolist())\n",
+        "print(\"Returns columns:\", returns_df.columns.tolist())\n",
+        "\n",
+        "# Basic cleanup / type safety\n",
+        "for col in [\"Age\", \"Rating\", \"Recommended IND\", \"Positive Feedback Count\"]:\n",
+        "    if col in reviews_df.columns:\n",
+        "        reviews_df[col] = pd.to_numeric(reviews_df[col], errors=\"coerce\")\n",
+        "\n",
+        "if \"Review Text\" in reviews_df.columns:\n",
+        "    reviews_df[\"Review Text\"] = reviews_df[\"Review Text\"].fillna(\"\").astype(str)\n",
+        "\n",
+        "if \"Class Name\" in reviews_df.columns:\n",
+        "    reviews_df[\"Class Name\"] = reviews_df[\"Class Name\"].fillna(\"Unknown\").astype(str)\n",
+        "\n",
+        "for col in [\"review_score\", \"likely_return\", \"price\", \"freight_value\", \"delivery_delay_days\", \"synthetic_return_risk\"]:\n",
+        "    if col in returns_df.columns:\n",
+        "        returns_df[col] = pd.to_numeric(returns_df[col], errors=\"coerce\")\n",
+        "\n",
+        "print(\"Data loaded and cleaned.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2.2 Dashboard visualization"
+      ],
+      "metadata": {
+        "id": "KAj-RspZI2EI"
+      },
+      "id": "KAj-RspZI2EI"
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "f9eb3801",
+      "metadata": {
+        "id": "f9eb3801"
+      },
+      "outputs": [],
+      "source": [
+        "# ==================================================\n",
+        "# HELPERS: save artifacts where the app can find them\n",
+        "# ==================================================\n",
+        "# ==================================================\n",
+        "# HELPERS: save artifacts everywhere the app may check\n",
+        "# ==================================================\n",
+        "\n",
+        "def safe_write_csv(df, path):\n",
+        "    try:\n",
+        "        df.to_csv(path)\n",
+        "        return True\n",
+        "    except Exception as e:\n",
+        "        print(f\"Could not save {path}: {e}\")\n",
+        "        return False\n",
+        "\n",
+        "\n",
+        "def safe_savefig(path):\n",
+        "    try:\n",
+        "        plt.savefig(path, dpi=150, bbox_inches=\"tight\")\n",
+        "        return True\n",
+        "    except Exception as e:\n",
+        "        print(f\"Could not save {path}: {e}\")\n",
+        "        return False\n",
+        "\n",
+        "\n",
+        "def safe_write_text(text, path):\n",
+        "    try:\n",
+        "        path.write_text(text, encoding=\"utf-8\")\n",
+        "        return True\n",
+        "    except Exception as e:\n",
+        "        print(f\"Could not save {path}: {e}\")\n",
+        "        return False\n",
+        "\n",
+        "\n",
+        "def save_table(df, name):\n",
+        "    if isinstance(df, pd.Series):\n",
+        "        df = df.to_frame()\n",
+        "\n",
+        "    table_folders = [\n",
+        "        TABLES,\n",
+        "        OUTPUT_TABLES,\n",
+        "        OUTPUTS,\n",
+        "        ARTIFACT_TABLES,\n",
+        "        ARTIFACTS,\n",
+        "    ]\n",
+        "\n",
+        "    saved_anywhere = False\n",
+        "\n",
+        "    for folder in table_folders:\n",
+        "        folder.mkdir(parents=True, exist_ok=True)\n",
+        "        path = folder / f\"{name}.csv\"\n",
+        "        saved_anywhere = safe_write_csv(df, path) or saved_anywhere\n",
+        "\n",
+        "    if saved_anywhere:\n",
+        "        print(f\"Saved table everywhere: {name}.csv\")\n",
+        "    else:\n",
+        "        raise RuntimeError(f\"Could not save table {name}.csv\")\n",
+        "\n",
+        "\n",
+        "def save_figure(name):\n",
+        "    figure_folders = [\n",
+        "        FIGURES,\n",
+        "        OUTPUT_FIGURES,\n",
+        "        OUTPUTS,\n",
+        "        ARTIFACT_FIGURES,\n",
+        "        ARTIFACTS,\n",
+        "    ]\n",
+        "\n",
+        "    saved_anywhere = False\n",
+        "\n",
+        "    for folder in figure_folders:\n",
+        "        folder.mkdir(parents=True, exist_ok=True)\n",
+        "        path = folder / f\"{name}.png\"\n",
+        "        saved_anywhere = safe_savefig(path) or saved_anywhere\n",
+        "\n",
+        "    if saved_anywhere:\n",
+        "        print(f\"Saved figure everywhere: {name}.png\")\n",
+        "    else:\n",
+        "        raise RuntimeError(f\"Could not save figure {name}.png\")\n",
+        "\n",
+        "\n",
+        "def save_text(text, name):\n",
+        "    text_folders = [\n",
+        "        TABLES,\n",
+        "        OUTPUT_TABLES,\n",
+        "        OUTPUTS,\n",
+        "        ARTIFACT_TABLES,\n",
+        "        ARTIFACTS,\n",
+        "    ]\n",
+        "\n",
+        "    saved_anywhere = False\n",
+        "\n",
+        "    for folder in text_folders:\n",
+        "        folder.mkdir(parents=True, exist_ok=True)\n",
+        "        path = folder / f\"{name}.txt\"\n",
+        "        saved_anywhere = safe_write_text(text, path) or saved_anywhere\n",
+        "\n",
+        "    if saved_anywhere:\n",
+        "        print(f\"Saved text everywhere: {name}.txt\")\n",
+        "    else:\n",
+        "        raise RuntimeError(f\"Could not save text {name}.txt\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "a99949ac",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a99949ac",
+        "outputId": "012f8a9b-e72b-4288-d471-de548e9d35e0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved table everywhere: rating_distribution.csv\n",
+            "Saved figure everywhere: rating_distribution.png\n",
+            "Saved table everywhere: recommendation_by_class.csv\n",
+            "Saved figure everywhere: recommendation_by_class.png\n",
+            "Saved table everywhere: average_rating_by_age.csv\n",
+            "Saved figure everywhere: average_rating_by_age.png\n",
+            "Saved table everywhere: negative_keyword_counts.csv\n",
+            "Saved figure everywhere: negative_keyword_counts.png\n",
+            "Saved table everywhere: category_return_rate.csv\n",
+            "Saved figure everywhere: category_return_rate.png\n",
+            "Saved table everywhere: monthly_return_rate.csv\n",
+            "Saved figure everywhere: monthly_return_rate.png\n",
+            "Saved table everywhere: feature_importance.csv\n",
+            "Saved figure everywhere: feature_importance.png\n",
+            "Saved text everywhere: classification_report.txt\n",
+            "Artifact creation section finished.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ==================================================\n",
+        "# CREATE DASHBOARD ARTIFACTS\n",
+        "# ==================================================\n",
+        "\n",
+        "created_figures = []\n",
+        "created_tables = []\n",
+        "\n",
+        "# 1) Rating distribution\n",
+        "if \"Rating\" in reviews_df.columns:\n",
+        "    rating_distribution = reviews_df[\"Rating\"].dropna().value_counts().sort_index().to_frame(\"count\")\n",
+        "    save_table(rating_distribution, \"rating_distribution\")\n",
+        "    created_tables.append(\"rating_distribution.csv\")\n",
+        "\n",
+        "    plt.figure(figsize=(7, 4))\n",
+        "    plt.bar(rating_distribution.index.astype(str), rating_distribution[\"count\"])\n",
+        "    plt.title(\"Distribution of Customer Ratings\")\n",
+        "    plt.xlabel(\"Rating\")\n",
+        "    plt.ylabel(\"Number of Reviews\")\n",
+        "    plt.tight_layout()\n",
+        "    save_figure(\"rating_distribution\")\n",
+        "    created_figures.append(\"rating_distribution.png\")\n",
+        "    plt.close()\n",
+        "\n",
+        "# 2) Recommendation rate by clothing class\n",
+        "if {\"Class Name\", \"Recommended IND\"}.issubset(reviews_df.columns):\n",
+        "    recommendation_by_class = (\n",
+        "        reviews_df.groupby(\"Class Name\")[\"Recommended IND\"]\n",
+        "        .mean()\n",
+        "        .sort_values(ascending=False)\n",
+        "        .head(10)\n",
+        "        .to_frame(\"recommendation_rate\")\n",
+        "    )\n",
+        "    save_table(recommendation_by_class, \"recommendation_by_class\")\n",
+        "    created_tables.append(\"recommendation_by_class.csv\")\n",
+        "\n",
+        "    plt.figure(figsize=(10, 5))\n",
+        "    plt.bar(recommendation_by_class.index.astype(str), recommendation_by_class[\"recommendation_rate\"])\n",
+        "    plt.title(\"Top 10 Most Recommended Clothing Classes\")\n",
+        "    plt.xlabel(\"Class Name\")\n",
+        "    plt.ylabel(\"Recommendation Rate\")\n",
+        "    plt.xticks(rotation=75)\n",
+        "    plt.tight_layout()\n",
+        "    save_figure(\"recommendation_by_class\")\n",
+        "    created_figures.append(\"recommendation_by_class.png\")\n",
+        "    plt.close()\n",
+        "\n",
+        "# 3) Average rating by age\n",
+        "if {\"Age\", \"Rating\"}.issubset(reviews_df.columns):\n",
+        "    average_rating_by_age = (\n",
+        "        reviews_df.groupby(\"Age\")[\"Rating\"]\n",
+        "        .mean()\n",
+        "        .dropna()\n",
+        "        .to_frame(\"average_rating\")\n",
+        "    )\n",
+        "    save_table(average_rating_by_age, \"average_rating_by_age\")\n",
+        "    created_tables.append(\"average_rating_by_age.csv\")\n",
+        "\n",
+        "    plt.figure(figsize=(10, 4))\n",
+        "    plt.plot(average_rating_by_age.index, average_rating_by_age[\"average_rating\"])\n",
+        "    plt.title(\"Average Rating by Customer Age\")\n",
+        "    plt.xlabel(\"Age\")\n",
+        "    plt.ylabel(\"Average Rating\")\n",
+        "    plt.tight_layout()\n",
+        "    save_figure(\"average_rating_by_age\")\n",
+        "    created_figures.append(\"average_rating_by_age.png\")\n",
+        "    plt.close()\n",
+        "\n",
+        "# 4) Complaint / return-risk keyword counts\n",
+        "review_text_column = None\n",
+        "for candidate in [\"Review Text\", \"review_text\", \"review_comment_message\"]:\n",
+        "    if candidate in reviews_df.columns:\n",
+        "        review_text_column = candidate\n",
+        "        break\n",
+        "\n",
+        "if review_text_column is not None:\n",
+        "    keywords = [\n",
+        "        \"bad\", \"poor\", \"cheap\", \"small\", \"large\", \"tight\", \"loose\",\n",
+        "        \"scratchy\", \"thin\", \"return\", \"returned\", \"disappointed\",\n",
+        "        \"quality\", \"fit\", \"sizing\", \"fabric\", \"uncomfortable\"\n",
+        "    ]\n",
+        "    text_series = reviews_df[review_text_column].fillna(\"\").astype(str).str.lower()\n",
+        "    keyword_counts = {}\n",
+        "    for word in keywords:\n",
+        "        keyword_counts[word] = int(text_series.str.contains(word, regex=False).sum())\n",
+        "\n",
+        "    negative_keyword_counts = (\n",
+        "        pd.DataFrame(keyword_counts.items(), columns=[\"keyword\", \"review_count\"])\n",
+        "        .sort_values(\"review_count\", ascending=False)\n",
+        "        .set_index(\"keyword\")\n",
+        "    )\n",
+        "    save_table(negative_keyword_counts, \"negative_keyword_counts\")\n",
+        "    created_tables.append(\"negative_keyword_counts.csv\")\n",
+        "\n",
+        "    top_keywords = negative_keyword_counts.head(10)\n",
+        "    plt.figure(figsize=(9, 4))\n",
+        "    plt.bar(top_keywords.index.astype(str), top_keywords[\"review_count\"])\n",
+        "    plt.title(\"Most Common Return-Risk Keywords in Reviews\")\n",
+        "    plt.xlabel(\"Keyword\")\n",
+        "    plt.ylabel(\"Number of Reviews\")\n",
+        "    plt.xticks(rotation=45)\n",
+        "    plt.tight_layout()\n",
+        "    save_figure(\"negative_keyword_counts\")\n",
+        "    created_figures.append(\"negative_keyword_counts.png\")\n",
+        "    plt.close()\n",
+        "\n",
+        "# 5) Product category return rate\n",
+        "if {\"product_category_name\", \"likely_return\"}.issubset(returns_df.columns):\n",
+        "    category_return_rate = (\n",
+        "        returns_df.groupby(\"product_category_name\")[\"likely_return\"]\n",
+        "        .mean()\n",
+        "        .sort_values(ascending=False)\n",
+        "        .head(15)\n",
+        "        .to_frame(\"return_rate\")\n",
+        "    )\n",
+        "    save_table(category_return_rate, \"category_return_rate\")\n",
+        "    created_tables.append(\"category_return_rate.csv\")\n",
+        "\n",
+        "    plt.figure(figsize=(11, 5))\n",
+        "    plt.bar(category_return_rate.index.astype(str), category_return_rate[\"return_rate\"])\n",
+        "    plt.title(\"Top Product Categories by Estimated Return Rate\")\n",
+        "    plt.xlabel(\"Product Category\")\n",
+        "    plt.ylabel(\"Return Rate\")\n",
+        "    plt.xticks(rotation=75)\n",
+        "    plt.tight_layout()\n",
+        "    save_figure(\"category_return_rate\")\n",
+        "    created_figures.append(\"category_return_rate.png\")\n",
+        "    plt.close()\n",
+        "\n",
+        "# 6) Monthly return rate\n",
+        "if {\"order_purchase_timestamp\", \"likely_return\"}.issubset(returns_df.columns):\n",
+        "    monthly_df = returns_df.copy()\n",
+        "    monthly_df[\"order_purchase_timestamp\"] = pd.to_datetime(monthly_df[\"order_purchase_timestamp\"], errors=\"coerce\")\n",
+        "    monthly_df = monthly_df.dropna(subset=[\"order_purchase_timestamp\"])\n",
+        "\n",
+        "    if len(monthly_df) > 0:\n",
+        "        monthly_return_rate = (\n",
+        "            monthly_df.set_index(\"order_purchase_timestamp\")\n",
+        "            .resample(\"M\")[\"likely_return\"]\n",
+        "            .mean()\n",
+        "            .dropna()\n",
+        "            .to_frame(\"return_rate\")\n",
+        "        )\n",
+        "        save_table(monthly_return_rate, \"monthly_return_rate\")\n",
+        "        created_tables.append(\"monthly_return_rate.csv\")\n",
+        "\n",
+        "        plt.figure(figsize=(10, 4))\n",
+        "        plt.plot(monthly_return_rate.index, monthly_return_rate[\"return_rate\"])\n",
+        "        plt.title(\"Monthly Estimated Return Rate\")\n",
+        "        plt.xlabel(\"Month\")\n",
+        "        plt.ylabel(\"Return Rate\")\n",
+        "        plt.tight_layout()\n",
+        "        save_figure(\"monthly_return_rate\")\n",
+        "        created_figures.append(\"monthly_return_rate.png\")\n",
+        "        plt.close()\n",
+        "\n",
+        "# 7) Simple feature importance if sklearn is available\n",
+        "try:\n",
+        "    from sklearn.ensemble import RandomForestClassifier\n",
+        "    from sklearn.model_selection import train_test_split\n",
+        "    from sklearn.metrics import accuracy_score, classification_report\n",
+        "\n",
+        "    feature_columns = [c for c in [\"Age\", \"Rating\", \"Positive Feedback Count\"] if c in reviews_df.columns]\n",
+        "    if \"Recommended IND\" in reviews_df.columns and len(feature_columns) > 0:\n",
+        "        model_df = reviews_df[feature_columns + [\"Recommended IND\"]].dropna().copy()\n",
+        "        if model_df[\"Recommended IND\"].nunique() >= 2:\n",
+        "            X = model_df[feature_columns]\n",
+        "            y = model_df[\"Recommended IND\"].astype(int)\n",
+        "            X_train, X_test, y_train, y_test = train_test_split(\n",
+        "                X, y, test_size=0.2, random_state=42, stratify=y\n",
+        "            )\n",
+        "\n",
+        "            clf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+        "            clf.fit(X_train, y_train)\n",
+        "            predictions = clf.predict(X_test)\n",
+        "            accuracy = accuracy_score(y_test, predictions)\n",
+        "\n",
+        "            feature_importance = (\n",
+        "                pd.Series(clf.feature_importances_, index=feature_columns)\n",
+        "                .sort_values(ascending=False)\n",
+        "                .to_frame(\"importance\")\n",
+        "            )\n",
+        "            save_table(feature_importance, \"feature_importance\")\n",
+        "            created_tables.append(\"feature_importance.csv\")\n",
+        "\n",
+        "            plt.figure(figsize=(7, 4))\n",
+        "            plt.bar(feature_importance.index.astype(str), feature_importance[\"importance\"])\n",
+        "            plt.title(\"Feature Importance for Recommendation Prediction\")\n",
+        "            plt.xlabel(\"Feature\")\n",
+        "            plt.ylabel(\"Importance\")\n",
+        "            plt.tight_layout()\n",
+        "            save_figure(\"feature_importance\")\n",
+        "            created_figures.append(\"feature_importance.png\")\n",
+        "            plt.close()\n",
+        "\n",
+        "            report = \"Model accuracy: {:.4f}\\n\\n{}\".format(\n",
+        "                accuracy,\n",
+        "                classification_report(y_test, predictions)\n",
+        "            )\n",
+        "            save_text(report, \"classification_report\")\n",
+        "except Exception as e:\n",
+        "    print(\"ML section skipped:\", repr(e))\n",
+        "\n",
+        "print(\"Artifact creation section finished.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "c4bbc916",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c4bbc916",
+        "outputId": "1b70021e-6125-457a-bcd8-be0c896dfa58"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved table everywhere: dashboard_summary.csv\n",
+            "Saved text everywhere: business_insights_report.txt\n",
+            "STEP 2 COMPLETE.\n",
+            "Figures: ['average_rating_by_age.png', 'category_return_rate.png', 'feature_importance.png', 'monthly_return_rate.png', 'negative_keyword_counts.png', 'rating_distribution.png', 'recommendation_by_class.png']\n",
+            "Tables: ['average_rating_by_age.csv', 'category_return_rate.csv', 'dashboard_summary.csv', 'feature_importance.csv', 'monthly_return_rate.csv', 'negative_keyword_counts.csv', 'rating_distribution.csv', 'recommendation_by_class.csv']\n",
+            "Outputs: ['average_rating_by_age.csv', 'average_rating_by_age.png', 'business_insights_report.txt', 'category_return_rate.csv', 'category_return_rate.png', 'classification_report.txt', 'dashboard_summary.csv', 'feature_importance.csv', 'feature_importance.png', 'monthly_return_rate.csv', 'monthly_return_rate.png', 'negative_keyword_counts.csv', 'negative_keyword_counts.png', 'rating_distribution.csv', 'rating_distribution.png', 'recommendation_by_class.csv', 'recommendation_by_class.png']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ==================================================\n",
+        "# FINAL REPORT + MANIFEST\n",
+        "# ==================================================\n",
+        "\n",
+        "summary_rows = [\n",
+        "    {\"metric\": \"reviews_rows\", \"value\": int(len(reviews_df))},\n",
+        "    {\"metric\": \"returns_rows\", \"value\": int(len(returns_df))},\n",
+        "    {\"metric\": \"figures_created\", \"value\": int(len(list(FIGURES.glob(\"*.png\"))))},\n",
+        "    {\"metric\": \"tables_created\", \"value\": int(len(list(TABLES.glob(\"*.csv\"))))},\n",
+        "]\n",
+        "\n",
+        "summary_df = pd.DataFrame(summary_rows).set_index(\"metric\")\n",
+        "save_table(summary_df, \"dashboard_summary\")\n",
+        "\n",
+        "insights = \"\"\"\n",
+        "FINAL BUSINESS INSIGHTS\n",
+        "=======================\n",
+        "\n",
+        "This analysis supports an e-commerce return prediction and review intelligence assistant.\n",
+        "\n",
+        "Main findings:\n",
+        "- Customer ratings and recommendation behavior are useful signals for product satisfaction.\n",
+        "- Review text reveals return-risk themes such as fit, sizing, fabric, quality, and discomfort.\n",
+        "- Product categories with higher estimated return rates should be prioritized for improvement.\n",
+        "- Monthly return-rate tracking can help the business monitor operational or seasonal changes.\n",
+        "\n",
+        "Recommended automations:\n",
+        "1. Automatically scan new reviews for return-risk keywords.\n",
+        "2. Automatically rank products and categories by estimated return risk.\n",
+        "3. Automatically generate business recommendations for product pages, sizing guidance, and quality control.\n",
+        "\"\"\"\n",
+        "\n",
+        "save_text(insights, \"business_insights_report\")\n",
+        "\n",
+        "manifest = {\n",
+        "    \"base_path\": str(BASE_PATH),\n",
+        "    \"figures\": sorted([p.name for p in FIGURES.glob(\"*.png\")]),\n",
+        "    \"tables\": sorted([p.name for p in TABLES.glob(\"*.csv\")]),\n",
+        "    \"outputs\": sorted([p.name for p in OUTPUTS.iterdir() if p.is_file()]),\n",
+        "}\n",
+        "\n",
+        "for folder in [OUTPUTS, ARTIFACTS, TABLES]:\n",
+        "    try:\n",
+        "        with open(folder / \"artifacts_manifest.json\", \"w\", encoding=\"utf-8\") as f:\n",
+        "            json.dump(manifest, f, indent=2)\n",
+        "    except Exception as e:\n",
+        "        print(f\"Could not save manifest in {folder}: {e}\")\n",
+        "\n",
+        "print(\"STEP 2 COMPLETE.\")\n",
+        "print(\"Figures:\", manifest[\"figures\"])\n",
+        "print(\"Tables:\", manifest[\"tables\"])\n",
+        "print(\"Outputs:\", manifest[\"outputs\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"\\nFINAL ARTIFACT CHECK\")\n",
+        "\n",
+        "check_dirs = {\n",
+        "    \"FIGURES\": FIGURES,\n",
+        "    \"TABLES\": TABLES,\n",
+        "    \"OUTPUTS\": OUTPUTS,\n",
+        "    \"OUTPUT_FIGURES\": OUTPUT_FIGURES,\n",
+        "    \"OUTPUT_TABLES\": OUTPUT_TABLES,\n",
+        "    \"ARTIFACTS\": ARTIFACTS,\n",
+        "    \"ARTIFACT_FIGURES\": ARTIFACT_FIGURES,\n",
+        "    \"ARTIFACT_TABLES\": ARTIFACT_TABLES,\n",
+        "}\n",
+        "\n",
+        "for label, folder in check_dirs.items():\n",
+        "    files = sorted([p.name for p in folder.iterdir() if p.is_file()])\n",
+        "    print(label, \"=\", files)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fexa62gDM2c7",
+        "outputId": "92a75649-5c5b-4202-ba1c-839682d28eee"
+      },
+      "id": "fexa62gDM2c7",
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "FINAL ARTIFACT CHECK\n",
+            "FIGURES = ['average_rating_by_age.png', 'category_return_rate.png', 'feature_importance.png', 'monthly_return_rate.png', 'negative_keyword_counts.png', 'rating_distribution.png', 'recommendation_by_class.png']\n",
+            "TABLES = ['artifacts_manifest.json', 'average_rating_by_age.csv', 'business_insights_report.txt', 'category_return_rate.csv', 'classification_report.txt', 'dashboard_summary.csv', 'feature_importance.csv', 'monthly_return_rate.csv', 'negative_keyword_counts.csv', 'rating_distribution.csv', 'recommendation_by_class.csv']\n",
+            "OUTPUTS = ['artifacts_manifest.json', 'average_rating_by_age.csv', 'average_rating_by_age.png', 'business_insights_report.txt', 'category_return_rate.csv', 'category_return_rate.png', 'classification_report.txt', 'dashboard_summary.csv', 'feature_importance.csv', 'feature_importance.png', 'monthly_return_rate.csv', 'monthly_return_rate.png', 'negative_keyword_counts.csv', 'negative_keyword_counts.png', 'rating_distribution.csv', 'rating_distribution.png', 'recommendation_by_class.csv', 'recommendation_by_class.png']\n",
+            "OUTPUT_FIGURES = ['average_rating_by_age.png', 'category_return_rate.png', 'feature_importance.png', 'monthly_return_rate.png', 'negative_keyword_counts.png', 'rating_distribution.png', 'recommendation_by_class.png']\n",
+            "OUTPUT_TABLES = ['average_rating_by_age.csv', 'business_insights_report.txt', 'category_return_rate.csv', 'classification_report.txt', 'dashboard_summary.csv', 'feature_importance.csv', 'monthly_return_rate.csv', 'negative_keyword_counts.csv', 'rating_distribution.csv', 'recommendation_by_class.csv']\n",
+            "ARTIFACTS = ['artifacts_manifest.json', 'average_rating_by_age.csv', 'average_rating_by_age.png', 'business_insights_report.txt', 'category_return_rate.csv', 'category_return_rate.png', 'classification_report.txt', 'dashboard_summary.csv', 'feature_importance.csv', 'feature_importance.png', 'monthly_return_rate.csv', 'monthly_return_rate.png', 'negative_keyword_counts.csv', 'negative_keyword_counts.png', 'rating_distribution.csv', 'rating_distribution.png', 'recommendation_by_class.csv', 'recommendation_by_class.png']\n",
+            "ARTIFACT_FIGURES = ['average_rating_by_age.png', 'category_return_rate.png', 'feature_importance.png', 'monthly_return_rate.png', 'negative_keyword_counts.png', 'rating_distribution.png', 'recommendation_by_class.png']\n",
+            "ARTIFACT_TABLES = ['average_rating_by_age.csv', 'business_insights_report.txt', 'category_return_rate.csv', 'classification_report.txt', 'dashboard_summary.csv', 'feature_importance.csv', 'monthly_return_rate.csv', 'negative_keyword_counts.csv', 'rating_distribution.csv', 'recommendation_by_class.csv']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ==================================================\n",
+        "# FORCE DASHBOARD ARTIFACTS FOR SE21 HUGGING FACE APP\n",
+        "# Put this as the VERY LAST CELL of pythonanalysis.ipynb\n",
+        "# ==================================================\n",
+        "\n",
+        "import os\n",
+        "import json\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "import matplotlib\n",
+        "matplotlib.use(\"Agg\")\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Detect runtime\n",
+        "if Path(\"/app\").exists():\n",
+        "    BASE_PATH = Path(\"/app\")\n",
+        "elif Path(\"/content\").exists():\n",
+        "    BASE_PATH = Path(\"/content\")\n",
+        "else:\n",
+        "    BASE_PATH = Path.cwd()\n",
+        "\n",
+        "# THESE ARE THE EXACT FOLDERS app.py READS\n",
+        "PY_FIG_DIR = BASE_PATH / \"artifacts\" / \"py\" / \"figures\"\n",
+        "PY_TAB_DIR = BASE_PATH / \"artifacts\" / \"py\" / \"tables\"\n",
+        "\n",
+        "PY_FIG_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "PY_TAB_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "\n",
+        "print(\"Saving dashboard artifacts to:\")\n",
+        "print(\"Figures:\", PY_FIG_DIR)\n",
+        "print(\"Tables:\", PY_TAB_DIR)\n",
+        "\n",
+        "# Find CSV files\n",
+        "csv_paths = [\n",
+        "    p for p in BASE_PATH.rglob(\"*.csv\")\n",
+        "    if \"sample_data\" not in str(p)\n",
+        "    and \"artifacts\" not in str(p)\n",
+        "    and \"outputs\" not in str(p)\n",
+        "    and \"figures\" not in str(p)\n",
+        "    and \"tables\" not in str(p)\n",
+        "]\n",
+        "\n",
+        "print(\"CSV files found:\")\n",
+        "for p in csv_paths:\n",
+        "    print(\"-\", p)\n",
+        "\n",
+        "# Find reviews dataset\n",
+        "reviews_candidates = [\n",
+        "    BASE_PATH / \"data_processed\" / \"reviews_cleaned.csv\",\n",
+        "    BASE_PATH / \"Womens Clothing E-Commerce Reviews.csv\",\n",
+        "]\n",
+        "\n",
+        "reviews_path = next((p for p in reviews_candidates if p.exists()), None)\n",
+        "\n",
+        "if reviews_path is None:\n",
+        "    matches = [\n",
+        "        p for p in csv_paths\n",
+        "        if \"clothing\" in p.name.lower() or \"review\" in p.name.lower()\n",
+        "    ]\n",
+        "    reviews_path = matches[0] if matches else None\n",
+        "\n",
+        "# Find returns dataset\n",
+        "returns_candidates = [\n",
+        "    BASE_PATH / \"data_processed\" / \"returns_input.csv\",\n",
+        "    BASE_PATH / \"data_processed\" / \"returns_cleaned.csv\",\n",
+        "    BASE_PATH / \"ecommerce_returns_cleaned.csv\",\n",
+        "    BASE_PATH / \"data_processed\" / \"synthetic_return_risk.csv\",\n",
+        "]\n",
+        "\n",
+        "returns_path = next((p for p in returns_candidates if p.exists()), None)\n",
+        "\n",
+        "if returns_path is None:\n",
+        "    matches = [\n",
+        "        p for p in csv_paths\n",
+        "        if \"return\" in p.name.lower()\n",
+        "    ]\n",
+        "    returns_path = matches[0] if matches else None\n",
+        "\n",
+        "if reviews_path is None:\n",
+        "    raise FileNotFoundError(\"Could not find reviews CSV.\")\n",
+        "\n",
+        "if returns_path is None:\n",
+        "    raise FileNotFoundError(\"Could not find returns CSV.\")\n",
+        "\n",
+        "print(\"Using reviews:\", reviews_path)\n",
+        "print(\"Using returns:\", returns_path)\n",
+        "\n",
+        "reviews_df = pd.read_csv(reviews_path).drop(columns=[\"Unnamed: 0\"], errors=\"ignore\")\n",
+        "returns_df = pd.read_csv(returns_path).drop(columns=[\"Unnamed: 0\"], errors=\"ignore\")\n",
+        "\n",
+        "print(\"Reviews shape:\", reviews_df.shape)\n",
+        "print(\"Returns shape:\", returns_df.shape)\n",
+        "\n",
+        "# --------------------------------------------------\n",
+        "# 1. Rating distribution\n",
+        "# --------------------------------------------------\n",
+        "if \"Rating\" in reviews_df.columns:\n",
+        "    rating_distribution = (\n",
+        "        reviews_df[\"Rating\"]\n",
+        "        .dropna()\n",
+        "        .value_counts()\n",
+        "        .sort_index()\n",
+        "        .reset_index()\n",
+        "    )\n",
+        "    rating_distribution.columns = [\"rating\", \"count\"]\n",
+        "\n",
+        "    rating_distribution.to_csv(PY_TAB_DIR / \"rating_distribution.csv\", index=False)\n",
+        "\n",
+        "    plt.figure(figsize=(7, 4))\n",
+        "    plt.bar(rating_distribution[\"rating\"].astype(str), rating_distribution[\"count\"])\n",
+        "    plt.title(\"Distribution of Customer Ratings\")\n",
+        "    plt.xlabel(\"Rating\")\n",
+        "    plt.ylabel(\"Number of Reviews\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.savefig(PY_FIG_DIR / \"rating_distribution.png\", dpi=150, bbox_inches=\"tight\")\n",
+        "    plt.close()\n",
+        "\n",
+        "# --------------------------------------------------\n",
+        "# 2. Sentiment counts for app's sentiment chart\n",
+        "# The app specifically looks for sentiment_counts_sampled.csv\n",
+        "# --------------------------------------------------\n",
+        "if \"Rating\" in reviews_df.columns:\n",
+        "    temp = reviews_df.copy()\n",
+        "\n",
+        "    def rating_to_sentiment(r):\n",
+        "        try:\n",
+        "            r = float(r)\n",
+        "            if r <= 2:\n",
+        "                return \"negative\"\n",
+        "            elif r == 3:\n",
+        "                return \"neutral\"\n",
+        "            else:\n",
+        "                return \"positive\"\n",
+        "        except:\n",
+        "            return \"neutral\"\n",
+        "\n",
+        "    temp[\"sentiment\"] = temp[\"Rating\"].apply(rating_to_sentiment)\n",
+        "\n",
+        "    group_col = \"Class Name\" if \"Class Name\" in temp.columns else None\n",
+        "\n",
+        "    if group_col:\n",
+        "        sentiment_counts = (\n",
+        "            temp.groupby([group_col, \"sentiment\"])\n",
+        "            .size()\n",
+        "            .unstack(fill_value=0)\n",
+        "            .reset_index()\n",
+        "            .head(15)\n",
+        "        )\n",
+        "        sentiment_counts = sentiment_counts.rename(columns={group_col: \"title\"})\n",
+        "    else:\n",
+        "        sentiment_counts = (\n",
+        "            temp[\"sentiment\"]\n",
+        "            .value_counts()\n",
+        "            .to_frame()\n",
+        "            .T\n",
+        "            .reset_index(drop=True)\n",
+        "        )\n",
+        "        sentiment_counts.insert(0, \"title\", \"All Reviews\")\n",
+        "\n",
+        "    for col in [\"negative\", \"neutral\", \"positive\"]:\n",
+        "        if col not in sentiment_counts.columns:\n",
+        "            sentiment_counts[col] = 0\n",
+        "\n",
+        "    sentiment_counts[[\"title\", \"negative\", \"neutral\", \"positive\"]].to_csv(\n",
+        "        PY_TAB_DIR / \"sentiment_counts_sampled.csv\",\n",
+        "        index=False\n",
+        "    )\n",
+        "\n",
+        "    # Also save a normal figure\n",
+        "    sentiment_total = temp[\"sentiment\"].value_counts().reindex(\n",
+        "        [\"negative\", \"neutral\", \"positive\"],\n",
+        "        fill_value=0\n",
+        "    )\n",
+        "\n",
+        "    plt.figure(figsize=(7, 4))\n",
+        "    plt.bar(sentiment_total.index, sentiment_total.values)\n",
+        "    plt.title(\"Review Sentiment Distribution\")\n",
+        "    plt.xlabel(\"Sentiment\")\n",
+        "    plt.ylabel(\"Number of Reviews\")\n",
+        "    plt.tight_layout()\n",
+        "    plt.savefig(PY_FIG_DIR / \"sentiment_distribution.png\", dpi=150, bbox_inches=\"tight\")\n",
+        "    plt.close()\n",
+        "\n",
+        "# --------------------------------------------------\n",
+        "# 3. Category return rate\n",
+        "# --------------------------------------------------\n",
+        "return_col = None\n",
+        "for candidate in [\"likely_return\", \"synthetic_return_risk\", \"returned\", \"return_flag\"]:\n",
+        "    if candidate in returns_df.columns:\n",
+        "        return_col = candidate\n",
+        "        break\n",
+        "\n",
+        "category_col = None\n",
+        "for candidate in [\"product_category_name\", \"category\", \"Class Name\", \"product_id\"]:\n",
+        "    if candidate in returns_df.columns:\n",
+        "        category_col = candidate\n",
+        "        break\n",
+        "\n",
+        "if return_col is not None:\n",
+        "    returns_df[return_col] = pd.to_numeric(returns_df[return_col], errors=\"coerce\")\n",
+        "\n",
+        "if return_col is not None and category_col is not None:\n",
+        "    category_return_rate = (\n",
+        "        returns_df.groupby(category_col)[return_col]\n",
+        "        .mean()\n",
+        "        .sort_values(ascending=False)\n",
+        "        .head(15)\n",
+        "        .reset_index()\n",
+        "    )\n",
+        "    category_return_rate.columns = [\"category\", \"return_rate\"]\n",
+        "\n",
+        "    category_return_rate.to_csv(PY_TAB_DIR / \"category_return_rate.csv\", index=False)\n",
+        "\n",
+        "    plt.figure(figsize=(11, 5))\n",
+        "    plt.bar(category_return_rate[\"category\"].astype(str), category_return_rate[\"return_rate\"])\n",
+        "    plt.title(\"Highest Return-Rate Categories\")\n",
+        "    plt.xlabel(\"Category\")\n",
+        "    plt.ylabel(\"Return Rate\")\n",
+        "    plt.xticks(rotation=75)\n",
+        "    plt.tight_layout()\n",
+        "    plt.savefig(PY_FIG_DIR / \"category_return_rate.png\", dpi=150, bbox_inches=\"tight\")\n",
+        "    plt.close()\n",
+        "\n",
+        "    # The template's AI fallback weirdly expects this filename for \"top\" questions.\n",
+        "    # We reuse it to show highest return-risk categories.\n",
+        "    top_titles_by_units_sold = category_return_rate.copy()\n",
+        "    top_titles_by_units_sold.columns = [\"title\", \"units_sold\"]\n",
+        "    top_titles_by_units_sold.to_csv(PY_TAB_DIR / \"top_titles_by_units_sold.csv\", index=False)\n",
+        "\n",
+        "# --------------------------------------------------\n",
+        "# 4. Dashboard time-series file\n",
+        "# The app's dashboard chart specifically looks for df_dashboard.csv\n",
+        "# --------------------------------------------------\n",
+        "if \"order_purchase_timestamp\" in returns_df.columns and return_col is not None:\n",
+        "    ts = returns_df.copy()\n",
+        "    ts[\"order_purchase_timestamp\"] = pd.to_datetime(\n",
+        "        ts[\"order_purchase_timestamp\"],\n",
+        "        errors=\"coerce\"\n",
+        "    )\n",
+        "    ts = ts.dropna(subset=[\"order_purchase_timestamp\"])\n",
+        "\n",
+        "    if not ts.empty:\n",
+        "        dashboard_df = (\n",
+        "            ts.set_index(\"order_purchase_timestamp\")\n",
+        "            .resample(\"M\")\n",
+        "            .agg(\n",
+        "                return_rate=(return_col, \"mean\"),\n",
+        "                orders=(return_col, \"count\")\n",
+        "            )\n",
+        "            .reset_index()\n",
+        "        )\n",
+        "        dashboard_df = dashboard_df.rename(columns={\"order_purchase_timestamp\": \"month\"})\n",
+        "    else:\n",
+        "        dashboard_df = pd.DataFrame({\n",
+        "            \"month\": pd.date_range(\"2024-01-01\", periods=3, freq=\"M\"),\n",
+        "            \"return_rate\": [0, 0, 0],\n",
+        "            \"orders\": [0, 0, 0],\n",
+        "        })\n",
+        "else:\n",
+        "    dashboard_df = pd.DataFrame({\n",
+        "        \"month\": pd.date_range(\"2024-01-01\", periods=3, freq=\"M\"),\n",
+        "        \"return_rate\": [0, 0, 0],\n",
+        "        \"orders\": [0, 0, 0],\n",
+        "    })\n",
+        "\n",
+        "dashboard_df.to_csv(PY_TAB_DIR / \"df_dashboard.csv\", index=False)\n",
+        "\n",
+        "plt.figure(figsize=(9, 4))\n",
+        "plt.plot(pd.to_datetime(dashboard_df[\"month\"]), dashboard_df[\"return_rate\"], marker=\"o\")\n",
+        "plt.title(\"Monthly Estimated Return Rate\")\n",
+        "plt.xlabel(\"Month\")\n",
+        "plt.ylabel(\"Return Rate\")\n",
+        "plt.tight_layout()\n",
+        "plt.savefig(PY_FIG_DIR / \"monthly_return_rate.png\", dpi=150, bbox_inches=\"tight\")\n",
+        "plt.close()\n",
+        "\n",
+        "# --------------------------------------------------\n",
+        "# 5. KPIs\n",
+        "# --------------------------------------------------\n",
+        "kpis = {\n",
+        "    \"reviews_rows\": int(len(reviews_df)),\n",
+        "    \"returns_rows\": int(len(returns_df)),\n",
+        "    \"n_titles\": int(reviews_df[\"Clothing ID\"].nunique()) if \"Clothing ID\" in reviews_df.columns else int(len(reviews_df)),\n",
+        "    \"n_months\": int(len(dashboard_df)),\n",
+        "    \"total_units_sold\": int(len(returns_df)),\n",
+        "    \"estimated_return_rate\": float(returns_df[return_col].mean()) if return_col is not None else None,\n",
+        "}\n",
+        "\n",
+        "with open(PY_TAB_DIR / \"kpis.json\", \"w\", encoding=\"utf-8\") as f:\n",
+        "    json.dump(kpis, f, indent=2)\n",
+        "\n",
+        "# --------------------------------------------------\n",
+        "# Final verification\n",
+        "# --------------------------------------------------\n",
+        "print(\"\\nFORCE ARTIFACT CELL RAN SUCCESSFULLY\")\n",
+        "print(\"Figures now in app-readable folder:\")\n",
+        "print(sorted([p.name for p in PY_FIG_DIR.glob(\"*\")]))\n",
+        "\n",
+        "print(\"Tables now in app-readable folder:\")\n",
+        "print(sorted([p.name for p in PY_TAB_DIR.glob(\"*\")]))"
+      ],
+      "metadata": {
+        "id": "G-jXRriWP1TW",
+        "outputId": "69ac490c-cb7b-479f-b5b9-d85e02b0ba1d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "id": "G-jXRriWP1TW",
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving dashboard artifacts to:\n",
+            "Figures: /content/artifacts/py/figures\n",
+            "Tables: /content/artifacts/py/tables\n",
+            "CSV files found:\n",
+            "- /content/Womens Clothing E-Commerce Reviews.csv\n",
+            "- /content/ecommerce_returns_cleaned.csv\n",
+            "Using reviews: /content/Womens Clothing E-Commerce Reviews.csv\n",
+            "Using returns: /content/ecommerce_returns_cleaned.csv\n",
+            "Reviews shape: (23486, 10)\n",
+            "Returns shape: (113314, 29)\n",
+            "\n",
+            "FORCE ARTIFACT CELL RAN SUCCESSFULLY\n",
+            "Figures now in app-readable folder:\n",
+            "['category_return_rate.png', 'monthly_return_rate.png', 'rating_distribution.png', 'sentiment_distribution.png']\n",
+            "Tables now in app-readable folder:\n",
+            "['category_return_rate.csv', 'df_dashboard.csv', 'kpis.json', 'rating_distribution.csv', 'sentiment_counts_sampled.csv', 'top_titles_by_units_sold.csv']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2.3 ARIMA Pricfe Forecasting"
+      ],
+      "metadata": {
+        "id": "WztEFgicJAfE"
+      },
+      "id": "WztEFgicJAfE"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ==================================================\n",
+        "# ARIMA PRICE FORECASTING\n",
+        "# ==================================================\n",
+        "\n",
+        "!pip install statsmodels --quiet\n",
+        "\n",
+        "from statsmodels.tsa.arima.model import ARIMA\n",
+        "from statsmodels.tsa.stattools import adfuller\n",
+        "import matplotlib.pyplot as plt\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "# Build a monthly average price time series from returns data\n",
+        "if \"price\" in returns_df.columns and \"order_purchase_timestamp\" in returns_df.columns:\n",
+        "    ts_price = returns_df.copy()\n",
+        "    ts_price[\"order_purchase_timestamp\"] = pd.to_datetime(\n",
+        "        ts_price[\"order_purchase_timestamp\"], errors=\"coerce\"\n",
+        "    )\n",
+        "    ts_price = ts_price.dropna(subset=[\"order_purchase_timestamp\", \"price\"])\n",
+        "\n",
+        "    monthly_price = (\n",
+        "        ts_price.set_index(\"order_purchase_timestamp\")\n",
+        "        .resample(\"M\")[\"price\"]\n",
+        "        .mean()\n",
+        "        .dropna()\n",
+        "    )\n",
+        "\n",
+        "    print(\"Monthly price series shape:\", monthly_price.shape)\n",
+        "    print(monthly_price.tail())\n",
+        "\n",
+        "    # --- Stationarity check ---\n",
+        "    adf_result = adfuller(monthly_price)\n",
+        "    print(f\"\\nADF Statistic: {adf_result[0]:.4f}\")\n",
+        "    print(f\"p-value: {adf_result[1]:.4f}\")\n",
+        "    if adf_result[1] < 0.05:\n",
+        "        print(\"Series is stationary — good for ARIMA.\")\n",
+        "    else:\n",
+        "        print(\"Series is NOT stationary — differencing will be applied (d=1).\")\n",
+        "\n",
+        "    # --- Fit ARIMA ---\n",
+        "    # p=2 (autoregression), d=1 (differencing), q=1 (moving average)\n",
+        "    model = ARIMA(monthly_price, order=(2, 1, 1))\n",
+        "    model_fit = model.fit()\n",
+        "    print(\"\\nARIMA Model Summary:\")\n",
+        "    print(model_fit.summary())\n",
+        "\n",
+        "    # --- Forecast next 6 months ---\n",
+        "    forecast_steps = 6\n",
+        "    forecast = model_fit.forecast(steps=forecast_steps)\n",
+        "    forecast_index = pd.date_range(\n",
+        "        start=monthly_price.index[-1] + pd.DateOffset(months=1),\n",
+        "        periods=forecast_steps,\n",
+        "        freq=\"M\"\n",
+        "    )\n",
+        "    forecast_series = pd.Series(forecast.values, index=forecast_index)\n",
+        "\n",
+        "    # --- Plot ---\n",
+        "    plt.figure(figsize=(12, 5))\n",
+        "    plt.plot(monthly_price, label=\"Historical Price\", marker=\"o\")\n",
+        "    plt.plot(forecast_series, label=\"ARIMA Forecast (6 months)\",\n",
+        "             marker=\"o\", linestyle=\"--\", color=\"orange\")\n",
+        "    plt.axvline(x=monthly_price.index[-1], color=\"gray\",\n",
+        "                linestyle=\":\", label=\"Forecast Start\")\n",
+        "    plt.title(\"Monthly Average Price — ARIMA Forecast\")\n",
+        "    plt.xlabel(\"Month\")\n",
+        "    plt.ylabel(\"Average Price (€)\")\n",
+        "    plt.legend()\n",
+        "    plt.tight_layout()\n",
+        "    plt.savefig(PY_FIG_DIR / \"arima_price_forecast.png\", dpi=150, bbox_inches=\"tight\")\n",
+        "    plt.show()\n",
+        "    plt.close()\n",
+        "\n",
+        "    # --- Save forecast table ---\n",
+        "    forecast_df = pd.DataFrame({\n",
+        "        \"month\": forecast_series.index,\n",
+        "        \"forecasted_price\": forecast_series.values.round(2)\n",
+        "    })\n",
+        "    forecast_df.to_csv(PY_TAB_DIR / \"arima_price_forecast.csv\", index=False)\n",
+        "\n",
+        "    print(\"\\nForecast for next 6 months:\")\n",
+        "    display(forecast_df)\n",
+        "    print(\"Saved forecast to arima_price_forecast.csv\")\n",
+        "\n",
+        "else:\n",
+        "    print(\"Required columns 'price' or 'order_purchase_timestamp' not found in returns_df.\")\n",
+        "    print(\"Available columns:\", returns_df.columns.tolist())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "3gv4S_a-Gyt8",
+        "outputId": "63b8278e-772d-41ed-9c05-acfd5b0e3a3c"
+      },
+      "id": "3gv4S_a-Gyt8",
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Monthly price series shape: (24,)\n",
+            "order_purchase_timestamp\n",
+            "2018-05-31    125.654273\n",
+            "2018-06-30    122.241141\n",
+            "2018-07-31    126.141411\n",
+            "2018-08-31    117.897993\n",
+            "2018-09-30    145.000000\n",
+            "Name: price, dtype: float64\n",
+            "\n",
+            "ADF Statistic: -6.4329\n",
+            "p-value: 0.0000\n",
+            "Series is stationary — good for ARIMA.\n",
+            "\n",
+            "ARIMA Model Summary:\n",
+            "                               SARIMAX Results                                \n",
+            "==============================================================================\n",
+            "Dep. Variable:                  price   No. Observations:                   24\n",
+            "Model:                 ARIMA(2, 1, 1)   Log Likelihood                -107.606\n",
+            "Date:                Mon, 27 Apr 2026   AIC                            223.212\n",
+            "Time:                        18:02:47   BIC                            227.754\n",
+            "Sample:                             0   HQIC                           224.354\n",
+            "                                 - 24                                         \n",
+            "Covariance Type:                  opg                                         \n",
+            "==============================================================================\n",
+            "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+            "------------------------------------------------------------------------------\n",
+            "ar.L1         -0.2139      0.785     -0.273      0.785      -1.752       1.324\n",
+            "ar.L2          0.4311      0.684      0.630      0.529      -0.910       1.772\n",
+            "ma.L1         -0.8209      0.849     -0.966      0.334      -2.486       0.844\n",
+            "sigma2       636.5748    198.429      3.208      0.001     247.661    1025.488\n",
+            "===================================================================================\n",
+            "Ljung-Box (L1) (Q):                   0.10   Jarque-Bera (JB):                19.72\n",
+            "Prob(Q):                              0.75   Prob(JB):                         0.00\n",
+            "Heteroskedasticity (H):               0.06   Skew:                             1.13\n",
+            "Prob(H) (two-sided):                  0.00   Kurtosis:                         6.94\n",
+            "===================================================================================\n",
+            "\n",
+            "Warnings:\n",
+            "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
+            "\n",
+            "Forecast for next 6 months:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       month  forecasted_price\n",
+              "0 2018-10-31            118.22\n",
+              "1 2018-11-30            135.63\n",
+              "2 2018-12-31            120.36\n",
+              "3 2019-01-31            131.13\n",
+              "4 2019-02-28            122.25\n",
+              "5 2019-03-31            128.79"
+            ],
+            "text/html": [
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "\n",
+              "    .dataframe thead th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>month</th>\n",
+              "      <th>forecasted_price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2018-10-31</td>\n",
+              "      <td>118.22</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2018-11-30</td>\n",
+              "      <td>135.63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2018-12-31</td>\n",
+              "      <td>120.36</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2019-01-31</td>\n",
+              "      <td>131.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2019-02-28</td>\n",
+              "      <td>122.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2019-03-31</td>\n",
+              "      <td>128.79</td>\n",
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+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a42cb869-67b3-409e-aeab-5052ae9df540')\"\n",
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+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
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+              "\n",
+              "  <div id=\"id_212a5a0a-67cd-4f84-b790-76c21fb20928\">\n",
+              "    <style>\n",
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+              "        cursor: pointer;\n",
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+              "        fill: #174EA6;\n",
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+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
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+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
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+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('forecast_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "forecast_df",
+              "summary": "{\n  \"name\": \"forecast_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2018-10-31 00:00:00\",\n        \"max\": \"2019-03-31 00:00:00\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"2018-10-31 00:00:00\",\n          \"2018-11-30 00:00:00\",\n          \"2019-03-31 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"forecasted_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.829558306850204,\n        \"min\": 118.22,\n        \"max\": 135.63,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          118.22,\n          135.63,\n          128.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved forecast to arima_price_forecast.csv\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.10"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}