{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kMfZv_2FGjO"
      },
      "source": [
        "# 📦 Notebook 1 — Real-World Data Extraction & Synthetic Enrichment\n",
        "### Project: E-Commerce Customer Intelligence — Food & Grocery Products\n",
        "**Business Question:** *How can an online food retailer reduce customer churn and optimize product positioning by analyzing review sentiment (qualitative) and rating/helpfulness patterns (quantitative)?*\n",
        "\n",
        "**Dataset:** Amazon Fine Food Reviews (Kaggle) — 568,000+ real customer reviews\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHzEidhsFGjP"
      },
      "source": [
        "---\n",
        "## 📥 Step 0 — How to get the Kaggle dataset\n",
        "1. Go to: **https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews**\n",
        "2. Click **Download** (free Kaggle account required)\n",
        "3. Extract the ZIP → you get **`Reviews.csv`**\n",
        "4. Upload `Reviews.csv` to Colab (folder icon → upload)\n",
        "5. Leave `CSV_FILE_PATH = \"Reviews.csv\"` as is\n",
        "\n",
        "> 💡 No Kaggle account? The built-in food sample loads automatically as fallback."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_7KefJ2FGjQ"
      },
      "source": [
        "---\n",
        "## ⚙️ Step 1 — Install & Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fp_U4-syFGjQ",
        "outputId": "24676d4a-9a89-416d-d213-a355fab148ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "!pip install pandas matplotlib seaborn textblob -q\n",
        "import nltk\n",
        "nltk.download('punkt', quiet=True)\n",
        "nltk.download('averaged_perceptron_tagger', quiet=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdBlUGWZFGjQ",
        "outputId": "ac8f917f-3337-4b60-971e-61a26da480ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ All libraries imported successfully\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import random\n",
        "import os\n",
        "from textblob import TextBlob\n",
        "\n",
        "pd.set_option('display.max_colwidth', 120)\n",
        "plt.style.use('seaborn-v0_8-whitegrid')\n",
        "random.seed(42)\n",
        "np.random.seed(42)\n",
        "\n",
        "print('✅ All libraries imported successfully')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1xsy8-atFGjR"
      },
      "source": [
        "---\n",
        "## 🔑 Step 2 — Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fwdd5VqeFGjR",
        "outputId": "6eae4756-fe27-4a6b-833e-6f191c7359b7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Config set — will sample 500 reviews for analysis\n"
          ]
        }
      ],
      "source": [
        "CSV_FILE_PATH   = \"Reviews.csv\"   # Kaggle Amazon Fine Food Reviews\n",
        "N_SAMPLE        = 500             # reviews to keep for analysis (adjust up to 2000 if you want more)\n",
        "RANDOM_SEED     = 42\n",
        "\n",
        "print(f'✅ Config set — will sample {N_SAMPLE} reviews for analysis')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xl06KX7IFGjR"
      },
      "source": [
        "---\n",
        "## 📂 Step 3 — Load Real-World Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "BtNjKFlgFGjR",
        "outputId": "61efa873-1899-49ef-d972-f06794ee35e0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⚠️  No CSV found — using built-in food reviews sample...\n",
            "✅ Built-in food sample loaded: 500 rows\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  ProductId            product_name       category  Score  \\\n",
              "0      F001       Organic Green Tea      Beverages      5   \n",
              "1      F001       Organic Green Tea      Beverages      1   \n",
              "2      F002  Dark Chocolate Bar 70%  Confectionery      5   \n",
              "\n",
              "                        Summary  \\\n",
              "0      Best tea I have ever had   \n",
              "1   Arrived stale and tasteless   \n",
              "2  Incredible quality chocolate   \n",
              "\n",
              "                                                                                                                      Text  \\\n",
              "0  This organic green tea is absolutely wonderful. The flavour is delicate and fresh, nothing like the bitter supermark...   \n",
              "1  Extremely disappointed. The tea had no aroma whatsoever and tasted like hot water. The bags were clearly old stock. ...   \n",
              "2  This is hands down the best dark chocolate I have tasted outside of a specialist chocolatier. The bitterness is perf...   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator  \n",
              "0                    35                      36  \n",
              "1                    26                      30  \n",
              "2                    59                      54  "
            ],
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              "      <td>F001</td>\n",
              "      <td>Organic Green Tea</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>5</td>\n",
              "      <td>Best tea I have ever had</td>\n",
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              "      <td>F001</td>\n",
              "      <td>Organic Green Tea</td>\n",
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              "      <td>1</td>\n",
              "      <td>Arrived stale and tasteless</td>\n",
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              "      <td>F002</td>\n",
              "      <td>Dark Chocolate Bar 70%</td>\n",
              "      <td>Confectionery</td>\n",
              "      <td>5</td>\n",
              "      <td>Incredible quality chocolate</td>\n",
              "      <td>This is hands down the best dark chocolate I have tasted outside of a specialist chocolatier. The bitterness is perf...</td>\n",
              "      <td>59</td>\n",
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              "summary": "{\n  \"name\": \"df_raw\",\n  \"rows\": 500,\n  \"fields\": [\n    {\n      \"column\": \"ProductId\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"F009\",\n          \"F002\",\n          \"F006\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Protein Granola\",\n          \"Dark Chocolate Bar 70%\",\n          \"Instant Ramen Variety Pack\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Confectionery\",\n          \"Noodles\",\n          \"Beverages\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Summary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Best tea I have ever had\",\n          \"Artificial sweetener aftertaste ruins it\",\n          \"Oil separation is extreme and annoying\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"This organic green tea is absolutely wonderful. The flavour is delicate and fresh, nothing like the bitter supermarket brands. I have been drinking it every morning for 3 months and will never switch.\",\n          \"The macros look great on paper but the stevia aftertaste is so strong it makes the whole bowl unpleasant. I pushed through two bags hoping to adjust but never did. The texture is good but that aftertaste is a dealbreaker.\",\n          \"The product itself is fine but the oil separation is the worst I have experienced in any natural nut butter. You need to stir for several minutes before every use and it still does not mix properly. Very frustrating.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessNumerator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 4,\n        \"max\": 78,\n        \"num_unique_values\": 74,\n        \"samples\": [\n          43,\n          63,\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessDenominator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 12,\n        \"max\": 73,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          36,\n          30,\n          65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "def load_fallback_sample():\n",
        "    \"\"\"Built-in Food & Grocery sample — loads if no CSV found.\"\"\"\n",
        "    print('⚠️  No CSV found — using built-in food reviews sample...')\n",
        "    records = [\n",
        "        {'ProductId':'F001','product_name':'Organic Green Tea','category':'Beverages','Score':5,\n",
        "         'Summary':'Best tea I have ever had',\n",
        "         'Text':'This organic green tea is absolutely wonderful. The flavour is delicate and fresh, nothing like the bitter supermarket brands. I have been drinking it every morning for 3 months and will never switch.','HelpfulnessNumerator':34,'HelpfulnessDenominator':36},\n",
        "        {'ProductId':'F001','product_name':'Organic Green Tea','category':'Beverages','Score':1,\n",
        "         'Summary':'Arrived stale and tasteless',\n",
        "         'Text':'Extremely disappointed. The tea had no aroma whatsoever and tasted like hot water. The bags were clearly old stock. I have tried to contact the seller with no response. Do not buy.','HelpfulnessNumerator':28,'HelpfulnessDenominator':30},\n",
        "        {'ProductId':'F002','product_name':'Dark Chocolate Bar 70%','category':'Confectionery','Score':5,\n",
        "         'Summary':'Incredible quality chocolate',\n",
        "         'Text':'This is hands down the best dark chocolate I have tasted outside of a specialist chocolatier. The bitterness is perfectly balanced with a hint of fruit. Melts beautifully. I order 6 bars at a time now.','HelpfulnessNumerator':52,'HelpfulnessDenominator':54},\n",
        "        {'ProductId':'F002','product_name':'Dark Chocolate Bar 70%','category':'Confectionery','Score':2,\n",
        "         'Summary':'Way too bitter, packaging damaged',\n",
        "         'Text':'The chocolate arrived with the wrapper partially open and one bar was broken into pieces. Even aside from that, 70% is too bitter for my taste. The description should be clearer about the intensity.','HelpfulnessNumerator':15,'HelpfulnessDenominator':18},\n",
        "        {'ProductId':'F003','product_name':'Extra Virgin Olive Oil','category':'Oils & Condiments','Score':5,\n",
        "         'Summary':'Authentic and fragrant',\n",
        "         'Text':'Finally found an olive oil that actually tastes like olive oil should. Peppery finish, grassy aroma, golden colour. I use it exclusively for salad dressings now. The quality is obvious from the first pour.','HelpfulnessNumerator':41,'HelpfulnessDenominator':43},\n",
        "        {'ProductId':'F003','product_name':'Extra Virgin Olive Oil','category':'Oils & Condiments','Score':3,\n",
        "         'Summary':'Decent but nothing special',\n",
        "         'Text':'It is a reasonable olive oil for everyday cooking but I would not call it extra virgin by taste. The aroma is mild and the flavour is neutral. Good value for the price but not premium quality.','HelpfulnessNumerator':9,'HelpfulnessDenominator':12},\n",
        "        {'ProductId':'F004','product_name':'Sourdough Crackers','category':'Snacks','Score':4,\n",
        "         'Summary':'Great with cheese, slightly salty',\n",
        "         'Text':'These crackers are excellent with cheese and charcuterie. The sourdough flavour is authentic and the texture is perfectly crispy. Only minor complaint is they are a bit salty on their own. Would buy again.','HelpfulnessNumerator':22,'HelpfulnessDenominator':23},\n",
        "        {'ProductId':'F004','product_name':'Sourdough Crackers','category':'Snacks','Score':1,\n",
        "         'Summary':'Half the box was broken crumbs',\n",
        "         'Text':'Terrible packaging. More than half the crackers arrived as crumbs because there is no internal protection in the box. The flavour was fine but completely unusable. I expect better for the price charged.','HelpfulnessNumerator':38,'HelpfulnessDenominator':40},\n",
        "        {'ProductId':'F005','product_name':'Manuka Honey 250g','category':'Spreads','Score':5,\n",
        "         'Summary':'Worth every penny for the health benefits',\n",
        "         'Text':'I have been taking a teaspoon of this every morning for my immune system and have noticed a real difference. The flavour is rich and distinctive. Authentic UMF rating, good traceability. Highly recommended.','HelpfulnessNumerator':63,'HelpfulnessDenominator':65},\n",
        "        {'ProductId':'F005','product_name':'Manuka Honey 250g','category':'Spreads','Score':2,\n",
        "         'Summary':'Not convinced it is genuine Manuka',\n",
        "         'Text':'For the price I expected more. The taste is sweeter than authentic Manuka should be and the UMF certification seems dubious. I have bought certified Manuka before and this does not compare. Very suspicious.','HelpfulnessNumerator':44,'HelpfulnessDenominator':48},\n",
        "        {'ProductId':'F006','product_name':'Instant Ramen Variety Pack','category':'Noodles','Score':4,\n",
        "         'Summary':'Great variety, solid flavour all round',\n",
        "         'Text':'Good selection of flavours in the pack. The spicy chicken is our household favourite. Quick to prepare and genuinely tasty for an instant product. The portion size is generous. Will subscribe for monthly delivery.','HelpfulnessNumerator':17,'HelpfulnessDenominator':18},\n",
        "        {'ProductId':'F006','product_name':'Instant Ramen Variety Pack','category':'Noodles','Score':2,\n",
        "         'Summary':'Far too much sodium, bland broth',\n",
        "         'Text':'The sodium content in these is absolutely alarming at over 1700mg per serving. The broth tastes more like salt water than anything else. I tried three flavours and they were all disappointing. Not worth it.','HelpfulnessNumerator':29,'HelpfulnessDenominator':31},\n",
        "        {'ProductId':'F007','product_name':'Cold Brew Coffee Concentrate','category':'Beverages','Score':5,\n",
        "         'Summary':'Smooth, rich and incredibly convenient',\n",
        "         'Text':'This cold brew concentrate has completely replaced my morning coffee shop habit. I just dilute 1:3 with milk and it is better than anything I can buy locally. Smooth, zero bitterness, great crema. Obsessed.','HelpfulnessNumerator':71,'HelpfulnessDenominator':73},\n",
        "        {'ProductId':'F007','product_name':'Cold Brew Coffee Concentrate','category':'Beverages','Score':1,\n",
        "         'Summary':'Leaked all over the box, appalling',\n",
        "         'Text':'The bottle seal failed in transit and the entire contents leaked destroying everything else in the delivery. There was no protective packaging around the bottle at all. This is basic logistics. Absolutely unacceptable.','HelpfulnessNumerator':55,'HelpfulnessDenominator':57},\n",
        "        {'ProductId':'F008','product_name':'Almond Butter Smooth','category':'Spreads','Score':4,\n",
        "         'Summary':'Clean ingredients, tastes great',\n",
        "         'Text':'Just almonds and a pinch of salt. No palm oil, no added sugar. The flavour is nutty and rich and it spreads well even straight from the fridge. Slightly expensive but the quality justifies it. Solid product.','HelpfulnessNumerator':26,'HelpfulnessDenominator':27},\n",
        "        {'ProductId':'F008','product_name':'Almond Butter Smooth','category':'Spreads','Score':3,\n",
        "         'Summary':'Oil separation is extreme and annoying',\n",
        "         'Text':'The product itself is fine but the oil separation is the worst I have experienced in any natural nut butter. You need to stir for several minutes before every use and it still does not mix properly. Very frustrating.','HelpfulnessNumerator':12,'HelpfulnessDenominator':14},\n",
        "        {'ProductId':'F009','product_name':'Protein Granola','category':'Breakfast','Score':5,\n",
        "         'Summary':'High protein and actually delicious',\n",
        "         'Text':'I have tried many protein granolas and they are usually chalky or overly sweet. This one is genuinely good. Crunchy clusters, real nuts, 20g protein per serving. Goes perfectly with Greek yoghurt. Reordering today.','HelpfulnessNumerator':33,'HelpfulnessDenominator':35},\n",
        "        {'ProductId':'F009','product_name':'Protein Granola','category':'Breakfast','Score':2,\n",
        "         'Summary':'Artificial sweetener aftertaste ruins it',\n",
        "         'Text':'The macros look great on paper but the stevia aftertaste is so strong it makes the whole bowl unpleasant. I pushed through two bags hoping to adjust but never did. The texture is good but that aftertaste is a dealbreaker.','HelpfulnessNumerator':21,'HelpfulnessDenominator':23},\n",
        "        {'ProductId':'F010','product_name':'Pasta Sauce Arrabbiata','category':'Sauces','Score':5,\n",
        "         'Summary':'Authentic Italian flavour',\n",
        "         'Text':'This sauce tastes like something from a Roman trattoria. The heat level is perfect, the tomatoes are clearly good quality and the whole thing is fragrant and fresh tasting. No unnecessary additives. Simply excellent.','HelpfulnessNumerator':48,'HelpfulnessDenominator':50},\n",
        "        {'ProductId':'F010','product_name':'Pasta Sauce Arrabbiata','category':'Sauces','Score':1,\n",
        "         'Summary':'Jar arrived broken, glass in the sauce',\n",
        "         'Text':'The jar cracked during delivery and when I opened it I could see glass fragments near the top. Clearly the courier handling was rough but there was also no protective padding in the box. Dangerous and unacceptable.','HelpfulnessNumerator':67,'HelpfulnessDenominator':68},\n",
        "    ]\n",
        "    extended = (records * 25)[:500]\n",
        "    df = pd.DataFrame(extended)\n",
        "    df['HelpfulnessNumerator'] = df['HelpfulnessNumerator'].apply(lambda x: max(0, x + np.random.randint(-5,8)))\n",
        "    print(f'✅ Built-in food sample loaded: {len(df)} rows')\n",
        "    return df\n",
        "\n",
        "# Load\n",
        "if os.path.exists(CSV_FILE_PATH):\n",
        "    df_raw = pd.read_csv(CSV_FILE_PATH, on_bad_lines='skip', engine='python', nrows=10000)\n",
        "    print(f'✅ Kaggle CSV loaded: {df_raw.shape}')\n",
        "    print(f'   Columns: {list(df_raw.columns)}')\n",
        "else:\n",
        "    df_raw = load_fallback_sample()\n",
        "\n",
        "df_raw.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_raw.to_csv(\"food_dataset.csv\", index=False)"
      ],
      "metadata": {
        "id": "4SCK_Wqew-pJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PCVcj5JFGjS"
      },
      "source": [
        "---\n",
        "## 🧹 Step 4 — Data Cleaning & Standardisation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kFwPNZK5FGjS",
        "outputId": "42912beb-caea-45c9-cf6f-1ac722c301f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ After cleaning: 500 rows × 8 columns\n",
            "\n",
            "Rating distribution:\n",
            "rating\n",
            "1    100\n",
            "2    100\n",
            "3     50\n",
            "4     75\n",
            "5    175\n"
          ]
        }
      ],
      "source": [
        "def clean_dataframe(df):\n",
        "    # Rename to standard schema (handles Amazon Fine Food columns)\n",
        "    col_map = {\n",
        "        'ProductId'             : 'product_id',\n",
        "        'Score'                 : 'rating',\n",
        "        'Summary'               : 'review_title',\n",
        "        'Text'                  : 'review_text',\n",
        "        'HelpfulnessNumerator'  : 'helpful_votes',\n",
        "        'HelpfulnessDenominator': 'total_votes',\n",
        "        'UserId'                : 'user_id',\n",
        "        'Time'                  : 'timestamp',\n",
        "        'ProfileName'           : 'reviewer_name',\n",
        "    }\n",
        "    df = df.rename(columns={k:v for k,v in col_map.items() if k in df.columns})\n",
        "\n",
        "    essential = ['product_id','rating','review_title','review_text']\n",
        "    optional  = ['product_name','category','helpful_votes','total_votes',\n",
        "                 'user_id','timestamp','reviewer_name']\n",
        "    keep = [c for c in essential + optional if c in df.columns]\n",
        "    df = df[keep].copy()\n",
        "\n",
        "    # Drop missing critical fields\n",
        "    df = df.dropna(subset=['review_text','rating'])\n",
        "    df['rating'] = pd.to_numeric(df['rating'], errors='coerce')\n",
        "    df = df[df['rating'].between(1,5)].copy()\n",
        "    df['rating'] = df['rating'].astype(int)\n",
        "\n",
        "    # Remove very short reviews\n",
        "    df = df[df['review_text'].astype(str).str.len() >= 40].copy()\n",
        "\n",
        "    # Clean text\n",
        "    df['review_text']  = df['review_text'].astype(str).str.replace('<br />', ' ').str.replace('<br/>', ' ').str.strip()\n",
        "    df['review_title'] = df['review_title'].fillna('No title').astype(str).str.strip()\n",
        "\n",
        "    # Helpful votes\n",
        "    if 'helpful_votes' in df.columns:\n",
        "        df['helpful_votes'] = pd.to_numeric(df['helpful_votes'], errors='coerce').fillna(0).astype(int)\n",
        "\n",
        "    return df.reset_index(drop=True)\n",
        "\n",
        "df_clean = clean_dataframe(df_raw)\n",
        "\n",
        "print(f'✅ After cleaning: {df_clean.shape[0]:,} rows × {df_clean.shape[1]} columns')\n",
        "print(f'\\nRating distribution:')\n",
        "print(df_clean['rating'].value_counts().sort_index().to_string())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABdG_Rs8FGjS"
      },
      "source": [
        "---\n",
        "## 📊 Step 5 — Sample & Preview Raw Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "gkrV0ZgZFGjS",
        "outputId": "06d5b756-84cb-45b4-a41c-74fdfa88c091"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📋 Working sample: 425 reviews\n",
            "   Rating distribution: {1: 100, 2: 100, 3: 50, 4: 75, 5: 100}\n",
            "   Unique products    : 10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_2503/3507263612.py:35: UserWarning: Glyph 11088 (\\N{WHITE MEDIUM STAR}) missing from font(s) Liberation Sans.\n",
            "  plt.tight_layout()\n",
            "/tmp/ipykernel_2503/3507263612.py:36: UserWarning: Glyph 11088 (\\N{WHITE MEDIUM STAR}) missing from font(s) Liberation Sans.\n",
            "  plt.savefig('01_raw_data_overview.png', dpi=150, bbox_inches='tight')\n",
            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 11088 (\\N{WHITE MEDIUM STAR}) missing from font(s) Liberation Sans.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📊 Saved: 01_raw_data_overview.png\n"
          ]
        }
      ],
      "source": [
        "# Stratified sample — equal reviews per star rating for balanced analysis\n",
        "per_rating = N_SAMPLE // 5\n",
        "df_sample = pd.concat([\n",
        "    df_clean[df_clean['rating']==s].sample(\n",
        "        n=min(per_rating, (df_clean['rating']==s).sum()),\n",
        "        random_state=RANDOM_SEED\n",
        "    )\n",
        "    for s in [1,2,3,4,5]\n",
        "]).sample(frac=1, random_state=RANDOM_SEED).reset_index(drop=True)\n",
        "\n",
        "print(f'📋 Working sample: {len(df_sample)} reviews')\n",
        "print(f'   Rating distribution: {df_sample[\"rating\"].value_counts().sort_index().to_dict()}')\n",
        "print(f'   Unique products    : {df_sample[\"product_id\"].nunique()}')\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(13, 5))\n",
        "fig.suptitle('Raw Data Overview — Amazon Fine Food Reviews', fontsize=14, fontweight='bold')\n",
        "\n",
        "df_sample['rating'].value_counts().sort_index().plot(\n",
        "    kind='bar', ax=axes[0],\n",
        "    color=['#e74c3c','#e67e22','#f1c40f','#2ecc71','#27ae60'], edgecolor='white'\n",
        ")\n",
        "axes[0].set_title('Rating Distribution (Sample)', fontweight='bold')\n",
        "axes[0].set_xticklabels(['1⭐','2⭐','3⭐','4⭐','5⭐'], rotation=0)\n",
        "axes[0].set_ylabel('Number of Reviews')\n",
        "for p in axes[0].patches:\n",
        "    axes[0].annotate(str(int(p.get_height())),\n",
        "                     (p.get_x()+p.get_width()/2, p.get_height()),\n",
        "                     ha='center', va='bottom', fontsize=10)\n",
        "\n",
        "rule_sent = df_sample['rating'].map({1:'Negative',2:'Negative',3:'Neutral',4:'Positive',5:'Positive'}).value_counts()\n",
        "axes[1].pie(rule_sent, labels=rule_sent.index, autopct='%1.1f%%', startangle=90,\n",
        "            colors=[{'Positive':'#27ae60','Neutral':'#f39c12','Negative':'#e74c3c'}.get(s,'grey') for s in rule_sent.index])\n",
        "axes[1].set_title('Baseline Sentiment (from Star Rating)', fontweight='bold')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('01_raw_data_overview.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "print('📊 Saved: 01_raw_data_overview.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_36yXm5AFGjS"
      },
      "source": [
        "---\n",
        "## 🤖 Step 6 — TextBlob Sentiment Enrichment\n",
        "No API needed — runs locally and instantly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "do-M5kLNFGjS",
        "outputId": "463075b2-d481-4bc7-b087-a22f88033dfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🔍 Computing TextBlob sentiment...\n",
            "✅ Done — 425 reviews labelled\n",
            "\n",
            "Sentiment distribution:\n",
            "sentiment_label\n",
            "Positive    207\n",
            "Neutral     168\n",
            "Negative     50\n"
          ]
        }
      ],
      "source": [
        "def get_sentiment(text):\n",
        "    score = round(TextBlob(str(text)).sentiment.polarity, 3)\n",
        "    if score > 0.1:   label = 'Positive'\n",
        "    elif score < -0.1: label = 'Negative'\n",
        "    else:              label = 'Neutral'\n",
        "    return label, score\n",
        "\n",
        "print('🔍 Computing TextBlob sentiment...')\n",
        "results = df_sample['review_text'].apply(get_sentiment)\n",
        "df_sample['sentiment_label'] = results.apply(lambda x: x[0])\n",
        "df_sample['sentiment_score'] = results.apply(lambda x: x[1])\n",
        "\n",
        "print(f'✅ Done — {len(df_sample)} reviews labelled')\n",
        "print(f'\\nSentiment distribution:')\n",
        "print(df_sample['sentiment_label'].value_counts().to_string())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcud7PwIFGjS"
      },
      "source": [
        "---\n",
        "## 🧬 Step 7 — Synthetic Data Generation\n",
        "Pre-written food review pools (same approach as class notebook).\n",
        "Generated with LLM assistance and hardcoded — **no API calls**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m9KLWGrgFGjS",
        "outputId": "ea5f9654-ff90-47aa-d73f-17401d289969"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Food synthetic review pools ready\n",
            "   Positive: 20 reviews\n",
            "   Neutral: 20 reviews\n",
            "   Negative: 20 reviews\n"
          ]
        }
      ],
      "source": [
        "# ── FOOD-SPECIFIC SYNTHETIC REVIEW POOLS ─────────────────────────\n",
        "# Created by prompting an LLM:\n",
        "# \"Write 20 realistic Amazon food product reviews that are [positive/neutral/negative].\n",
        "#  Vary the products: snacks, beverages, condiments, health foods.\"\n",
        "\n",
        "synthetic_reviews_by_sentiment = {\n",
        "\n",
        "    'Positive': [\n",
        "        \"Absolutely the best version of this product I have ever tried. The flavour is authentic and the quality is clearly a step above the supermarket equivalent.\",\n",
        "        \"I ordered this on a whim and it has become a household staple. We go through a pack every week. The taste is consistent and the price is fair for the quality.\",\n",
        "        \"Exceptional product. Fresh, well packaged and exactly as described. The flavour profile is complex and satisfying. Will definitely be reordering regularly.\",\n",
        "        \"This exceeded my expectations completely. I was sceptical but the reviews were right. Outstanding flavour and the ingredients list is clean and simple.\",\n",
        "        \"Best purchase I have made on this platform. The product arrived quickly, was well protected and tasted incredible straight out of the packaging.\",\n",
        "        \"I have tried many similar products and this is head and shoulders above the competition. Rich, full flavoured and zero artificial aftertaste.\",\n",
        "        \"Five stars without hesitation. This is the kind of quality you expect from a specialist supplier. The freshness is obvious and the taste is superb.\",\n",
        "        \"My whole family loves this. We have been buying it for six months and the quality has been absolutely consistent every single time. Highly recommend.\",\n",
        "        \"Genuinely delicious. I bought this as a gift and the recipient immediately asked where to get more. That says everything about the quality.\",\n",
        "        \"Perfect product. The packaging keeps everything fresh, the flavour is exactly right and the portion size is generous. Great value for money.\",\n",
        "        \"I am so glad I found this. It has transformed the way I cook and the flavour it adds is remarkable. Worth every penny and I will never go back.\",\n",
        "        \"Outstanding quality from start to finish. The aroma when you open the packaging is wonderful and the taste lives up to it completely.\",\n",
        "        \"This is now my go-to product in this category. Consistent quality, brilliant taste and it keeps well. Cannot recommend it highly enough.\",\n",
        "        \"Honestly surprised by how good this is. The description does not do it justice. Rich, satisfying and made with ingredients you can actually read.\",\n",
        "        \"Subscribed after the first order. The value is excellent for the quality and my morning routine has genuinely improved since I started using it.\",\n",
        "        \"Fantastic product that delivers on every single claim on the label. Fresh, flavourful and made the right way. A cut above everything else I have tried.\",\n",
        "        \"The whole household is obsessed. We have already reordered three times. Great flavour, clean label and arrives perfectly packaged every time.\",\n",
        "        \"Exactly what I was looking for. High quality ingredients, honest flavour and no unnecessary additives. This is how food should be made.\",\n",
        "        \"Brilliant find. I bought it based on reviews and the reviews were absolutely right. Delicious, fresh and great for the price. Will reorder always.\",\n",
        "        \"A product that genuinely lives up to its reputation. The quality is premium, the taste is exceptional and the packaging is thoughtful and secure.\",\n",
        "    ],\n",
        "\n",
        "    'Neutral': [\n",
        "        \"Decent product that does what it says. Nothing groundbreaking but a reliable choice. I would buy it again if I could not find anything better locally.\",\n",
        "        \"It is fine. The flavour is acceptable and the quality is what you would expect at this price point. Not remarkable but not disappointing either.\",\n",
        "        \"Average experience overall. The product is edible and the packaging is adequate. I expected more based on the description but it is not bad.\",\n",
        "        \"Mixed feelings. Some of the product was great and some was underwhelming. Inconsistent quality across the batch which was a bit frustrating.\",\n",
        "        \"It does the job. Not the most exciting product in the category but reliable and practical. A reasonable everyday option at a fair price.\",\n",
        "        \"Three stars because it is neither impressive nor terrible. Solid middle ground product. Functional and consistent if not particularly memorable.\",\n",
        "        \"I have bought better and I have bought worse. This sits comfortably in the middle. Worth trying but manage your expectations accordingly.\",\n",
        "        \"The flavour is pleasant enough without being outstanding. Would work well as part of a recipe but perhaps not as a standalone snack.\",\n",
        "        \"Not sure if I would reorder. The quality is acceptable but there is nothing that distinguishes this from a cheaper supermarket alternative.\",\n",
        "        \"Reasonable product for the price. Arrived in good condition and tasted fine. Just nothing special that would make me prioritise it over other options.\",\n",
        "        \"Okay but not memorable. The ingredients are fine, the texture is acceptable, the flavour is inoffensive. A three-star product in every sense.\",\n",
        "        \"Does what it says on the label, no more and no less. I appreciate the clean ingredients but the overall experience was a bit flat.\",\n",
        "        \"Perfectly serviceable. I used it up without complaint but also without enthusiasm. There are probably better options in this category if you look around.\",\n",
        "        \"Average quality, average taste, average experience. Not a bad product but certainly not a great one. Probably will not repurchase at this price.\",\n",
        "        \"It is fine for what it is. The convenience factor is decent and the flavour is acceptable. Just do not expect anything extraordinary.\",\n",
        "        \"A middle-of-the-road purchase. Not the worst thing I have bought and not the best. Gets the job done without exciting or disappointing.\",\n",
        "        \"The product is what it is. Good enough for regular use but I would not recommend it to anyone looking for something exceptional.\",\n",
        "        \"Three stars feels right. Competent product, no major issues, but also no reason to choose it over competitors unless the price is right.\",\n",
        "        \"Perfectly adequate. There is nothing wrong with it but there is also nothing that makes it stand out. A background product rather than a star.\",\n",
        "        \"It gets the job done. Taste is neutral, texture is acceptable, packaging is functional. Nothing to rave about but nothing to complain about either.\",\n",
        "    ],\n",
        "\n",
        "    'Negative': [\n",
        "        \"Extremely disappointed. The product looked nothing like the photos and the taste was completely off. This does not match the description at all.\",\n",
        "        \"Arrived in terrible condition. The packaging was crushed and the product inside was damaged beyond use. No protective padding whatsoever.\",\n",
        "        \"Worst food purchase I have made online. The smell when I opened it was immediately wrong. Clearly old stock being sold as fresh. Do not buy.\",\n",
        "        \"The product is fine but the portion size is a complete joke for the price. I cannot believe the amount you actually get. Total rip off.\",\n",
        "        \"Flavour is artificial and overwhelming. Nothing like the natural product it claims to be. The ingredient list does not match the front of the pack.\",\n",
        "        \"I received the wrong item and when I contacted support the response was dismissive and unhelpful. Still waiting for a resolution two weeks later.\",\n",
        "        \"Mouldy on arrival. Clearly stored badly before dispatch. The best before date had not passed but the product was inedible. Shocking quality control.\",\n",
        "        \"The sodium level in this product is dangerous. Over 2000mg per serving is not something that should be on shelves without a clear health warning.\",\n",
        "        \"Absolutely terrible. I followed the preparation instructions exactly and the result was inedible. I cannot understand how this product has positive reviews.\",\n",
        "        \"Leaking packaging meant the product was ruined and contaminated other items in the delivery. Completely unacceptable and a waste of money.\",\n",
        "        \"Do not believe the health claims on this packaging. They are wildly misleading and the actual nutritional content tells a very different story.\",\n",
        "        \"The quality has clearly dropped since previous orders. Same product but completely different taste. Either the recipe has changed or the ingredients.\",\n",
        "        \"I have been sick after eating this product. I cannot prove it was the cause but the timing and symptoms were very suspicious. Removing from my diet.\",\n",
        "        \"Ordered twice hoping the first bad experience was a fluke. Both times the same problem. Clearly a systemic issue with this product. Giving up.\",\n",
        "        \"The product contains allergens not listed on the website description. This is a serious safety issue that needs to be addressed immediately.\",\n",
        "        \"Stale, tasteless and overpriced. I have bought the supermarket version for a third of the price and it is genuinely better. Complete waste of money.\",\n",
        "        \"Customer service was appalling when I reported the issue. Dismissive, slow and ultimately unhelpful. Will not be using this seller again.\",\n",
        "        \"The texture is completely wrong for this type of product. It was gummy and unpleasant where it should have been light and crisp. Inedible.\",\n",
        "        \"One star because zero is not an option. Every aspect of this purchase was a disappointment from the misleading photos to the awful flavour.\",\n",
        "        \"Returned immediately. The product was clearly not as advertised and the quality was so poor that I questioned whether it was genuine at all.\",\n",
        "    ]\n",
        "}\n",
        "\n",
        "print('✅ Food synthetic review pools ready')\n",
        "for s, r in synthetic_reviews_by_sentiment.items():\n",
        "    print(f'   {s}: {len(r)} reviews')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PpvNBJpZFGjT",
        "outputId": "3f8f6b0e-876c-4483-9a10-2271c28b91af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Generated 60 synthetic food reviews across 6 products\n",
            "\n",
            "Synthetic sentiment distribution:\n",
            "sentiment_label\n",
            "Positive    30\n",
            "Negative    20\n",
            "Neutral     10\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            product_name   category  rating sentiment_label  \\\n",
              "0  Organic Matcha Powder  Beverages       5        Positive   \n",
              "1  Organic Matcha Powder  Beverages       5        Positive   \n",
              "2  Organic Matcha Powder  Beverages       5        Positive   \n",
              "3  Organic Matcha Powder  Beverages       5        Positive   \n",
              "4  Organic Matcha Powder  Beverages       5        Positive   \n",
              "\n",
              "                                                                                                               review_text  \n",
              "0  This exceeded my expectations completely. I was sceptical but the reviews were right. Outstanding flavour and the in...  \n",
              "1  Absolutely the best version of this product I have ever tried. The flavour is authentic and the quality is clearly a...  \n",
              "2  Genuinely delicious. I bought this as a gift and the recipient immediately asked where to get more. That says everyt...  \n",
              "3  My whole family loves this. We have been buying it for six months and the quality has been absolutely consistent eve...  \n",
              "4  The whole household is obsessed. We have already reordered three times. Great flavour, clean label and arrives perfe...  "
            ],
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_name</th>\n",
              "      <th>category</th>\n",
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              "      <td>Organic Matcha Powder</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Organic Matcha Powder</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>5</td>\n",
              "      <td>Positive</td>\n",
              "      <td>Genuinely delicious. I bought this as a gift and the recipient immediately asked where to get more. That says everyt...</td>\n",
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              "    <tr>\n",
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              "      <td>Organic Matcha Powder</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>5</td>\n",
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              "      <td>The whole household is obsessed. We have already reordered three times. Great flavour, clean label and arrives perfe...</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_synthetic[['product_name','category','rating','sentiment_label','review_text']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Organic Matcha Powder\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Beverages\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Absolutely the best version of this product I have ever tried. The flavour is authentic and the quality is clearly a step above the supermarket equivalent.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# ── SYNTHETIC FOOD PRODUCTS ───────────────────────────────────────\n",
        "# Underrepresented product/scenario combinations not in the Kaggle data\n",
        "synthetic_products = [\n",
        "    {'product_id':'SYN001','product_name':'Organic Matcha Powder',     'category':'Beverages',        'rating':5,'sentiment_label':'Positive', 'n_reviews':10},\n",
        "    {'product_id':'SYN002','product_name':'Gluten-Free Pasta',          'category':'Pasta & Grains',   'rating':2,'sentiment_label':'Negative', 'n_reviews':10},\n",
        "    {'product_id':'SYN003','product_name':'Truffle Sea Salt Flakes',    'category':'Oils & Condiments','rating':4,'sentiment_label':'Positive', 'n_reviews':10},\n",
        "    {'product_id':'SYN004','product_name':'Vegan Protein Bar',          'category':'Health Foods',     'rating':3,'sentiment_label':'Neutral',  'n_reviews':10},\n",
        "    {'product_id':'SYN005','product_name':'Raw Cashew Butter',          'category':'Spreads',          'rating':1,'sentiment_label':'Negative', 'n_reviews':10},\n",
        "    {'product_id':'SYN006','product_name':'Smoked Paprika Almonds',     'category':'Snacks',           'rating':5,'sentiment_label':'Positive', 'n_reviews':10},\n",
        "]\n",
        "\n",
        "# Generate synthetic reviews — same logic as class notebook (random.sample)\n",
        "synthetic_rows = []\n",
        "for product in synthetic_products:\n",
        "    pool    = synthetic_reviews_by_sentiment[product['sentiment_label']]\n",
        "    sampled = random.sample(pool, product['n_reviews'])\n",
        "    for review_text in sampled:\n",
        "        _, score = get_sentiment(review_text)\n",
        "        synthetic_rows.append({\n",
        "            'product_id'      : product['product_id'],\n",
        "            'product_name'    : product['product_name'],\n",
        "            'category'        : product['category'],\n",
        "            'rating'          : product['rating'],\n",
        "            'review_title'    : product['sentiment_label'] + ' experience',\n",
        "            'review_text'     : review_text,\n",
        "            'sentiment_label' : product['sentiment_label'],\n",
        "            'sentiment_score' : score,\n",
        "            'helpful_votes'   : random.randint(0, 60),\n",
        "            'total_votes'     : random.randint(0, 70),\n",
        "            'is_synthetic'    : True\n",
        "        })\n",
        "\n",
        "df_synthetic = pd.DataFrame(synthetic_rows)\n",
        "print(f'✅ Generated {len(df_synthetic)} synthetic food reviews across {len(synthetic_products)} products')\n",
        "print(f'\\nSynthetic sentiment distribution:')\n",
        "print(df_synthetic['sentiment_label'].value_counts().to_string())\n",
        "df_synthetic[['product_name','category','rating','sentiment_label','review_text']].head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyfLzpDOFGjT"
      },
      "source": [
        "---\n",
        "## 💾 Step 8 — Combine Real + Synthetic & Save"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDyyg1LXFGjT",
        "outputId": "cac9b6f5-cee5-423a-f21f-52043c85d702"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📦 Final dataset:\n",
            "   Real reviews (Kaggle) : 425\n",
            "   Synthetic reviews     : 60\n",
            "   Total rows            : 485\n",
            "   Unique real products  : 10\n",
            "   Synthetic products    : 6\n",
            "\n",
            "💾 Saved to: food_reviews_enriched.csv\n",
            "   ➡️  Load this in Notebook 2!\n"
          ]
        }
      ],
      "source": [
        "df_sample['is_synthetic'] = False\n",
        "df_final = pd.concat([df_sample, df_synthetic], ignore_index=True, sort=False)\n",
        "\n",
        "# Final type cleanup\n",
        "df_final['rating']          = pd.to_numeric(df_final['rating'], errors='coerce').fillna(3).astype(int)\n",
        "df_final['sentiment_score'] = pd.to_numeric(df_final['sentiment_score'], errors='coerce').fillna(0.0).round(3)\n",
        "df_final['helpful_votes']   = pd.to_numeric(df_final['helpful_votes'], errors='coerce').fillna(0).astype(int)\n",
        "\n",
        "OUTPUT = 'food_reviews_enriched.csv'\n",
        "df_final.to_csv(OUTPUT, index=False)\n",
        "\n",
        "print('📦 Final dataset:')\n",
        "print(f'   Real reviews (Kaggle) : {len(df_sample)}')\n",
        "print(f'   Synthetic reviews     : {len(df_synthetic)}')\n",
        "print(f'   Total rows            : {len(df_final)}')\n",
        "print(f'   Unique real products  : {df_sample[\"product_id\"].nunique()}')\n",
        "print(f'   Synthetic products    : {df_synthetic[\"product_id\"].nunique()}')\n",
        "print(f'\\n💾 Saved to: {OUTPUT}')\n",
        "print(f'   ➡️  Load this in Notebook 2!')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "9rJ5UnRwFGjT",
        "outputId": "cda58d33-f15a-41f0-bd20-62935587e6ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipykernel_2503/3320424086.py:39: UserWarning: Glyph 11088 (\\N{WHITE MEDIUM STAR}) missing from font(s) Liberation Sans.\n",
            "  plt.tight_layout()\n",
            "/tmp/ipykernel_2503/3320424086.py:40: UserWarning: Glyph 11088 (\\N{WHITE MEDIUM STAR}) missing from font(s) Liberation Sans.\n",
            "  plt.savefig('02_enriched_summary.png', dpi=150, bbox_inches='tight')\n",
            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 11088 (\\N{WHITE MEDIUM STAR}) missing from font(s) Liberation Sans.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "📊 Saved: 02_enriched_summary.png\n"
          ]
        }
      ],
      "source": [
        "# Final summary charts\n",
        "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
        "fig.suptitle('Final Enriched Dataset — Amazon Food Reviews', fontsize=15, fontweight='bold')\n",
        "\n",
        "# 1. Sentiment distribution\n",
        "sc = df_final['sentiment_label'].value_counts()\n",
        "clr = [{'Positive':'#27ae60','Neutral':'#f39c12','Negative':'#e74c3c'}.get(s,'grey') for s in sc.index]\n",
        "sc.plot(kind='bar', ax=axes[0,0], color=clr, edgecolor='white', width=0.6)\n",
        "axes[0,0].set_title('Sentiment Distribution', fontweight='bold')\n",
        "axes[0,0].set_xticklabels(sc.index, rotation=0)\n",
        "for p in axes[0,0].patches:\n",
        "    axes[0,0].annotate(str(int(p.get_height())),\n",
        "                       (p.get_x()+p.get_width()/2, p.get_height()),\n",
        "                       ha='center', va='bottom', fontsize=10)\n",
        "\n",
        "# 2. Real vs Synthetic\n",
        "tc = df_final['is_synthetic'].map({False:'Real',True:'Synthetic'}).value_counts()\n",
        "axes[0,1].pie(tc, labels=tc.index, autopct='%1.1f%%', startangle=90,\n",
        "              colors=['#3498db','#e67e22'])\n",
        "axes[0,1].set_title('Real vs Synthetic Reviews', fontweight='bold')\n",
        "\n",
        "# 3. Rating distribution\n",
        "df_final['rating'].value_counts().sort_index().plot(\n",
        "    kind='bar', ax=axes[1,0],\n",
        "    color=['#e74c3c','#e67e22','#f1c40f','#2ecc71','#27ae60'], edgecolor='white')\n",
        "axes[1,0].set_title('Rating Distribution (Combined)', fontweight='bold')\n",
        "axes[1,0].set_xticklabels(['1⭐','2⭐','3⭐','4⭐','5⭐'], rotation=0)\n",
        "\n",
        "# 4. Sentiment score histogram\n",
        "df_final['sentiment_score'].plot(\n",
        "    kind='hist', bins=25, ax=axes[1,1], color='#9b59b6', edgecolor='white', alpha=0.85)\n",
        "axes[1,1].axvline(x=0, color='black', linestyle='--', alpha=0.6, label='Neutral (0)')\n",
        "axes[1,1].axvline(df_final['sentiment_score'].mean(), color='red', linestyle='--', alpha=0.7,\n",
        "                  label=f'Mean ({df_final[\"sentiment_score\"].mean():.2f})')\n",
        "axes[1,1].set_title('Sentiment Score Distribution (TextBlob)', fontweight='bold')\n",
        "axes[1,1].set_xlabel('-1.0 = Very Negative  →  +1.0 = Very Positive')\n",
        "axes[1,1].legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('02_enriched_summary.png', dpi=150, bbox_inches='tight')\n",
        "plt.show()\n",
        "print('📊 Saved: 02_enriched_summary.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fl3ecHwoFGjT"
      },
      "source": [
        "---\n",
        "## ✅ Notebook 1 Complete!\n",
        "\n",
        "| Step | Output |\n",
        "|---|---|\n",
        "| Real data | Amazon Fine Food Reviews (Kaggle) — properly filtered |\n",
        "| Synthetic data | 60 food reviews across 6 new products |\n",
        "| Sentiment | TextBlob labels + scores on every review |\n",
        "| **Output file** | `food_reviews_enriched.csv` |\n",
        "| **Cost** | **$0.00** |\n",
        "\n",
        "### ➡️ Next: Open `notebook_2_analysis.ipynb` and load `food_reviews_enriched.csv`"
      ]
    }
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