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"\n", "### AI-Powered Pet Care Assistant\n", "\n", "---\n", "\n", "🐶 **Dogs & Cats** | 📊 **Data Analysis** | 🔍 **Similarity Search** | 🧠 **Embeddings** | 🤖 **AI Generation** | 🌐 **Gradio App**\n", "\n", "---\n", "\n", "**HopePet AI** is an intelligent pet-care assistant designed to help pet owners understand symptoms, estimate urgency, retrieve similar cases, and receive safe first-step guidance.\n", "\n", "The project combines data analysis, recommendation logic, embeddings, and AI-based text generation to support responsible pet-care decisions.\n", "\n", "---\n", "\n", "### Created by: Shaked and Maya 💗\n" ], "metadata": { "id": "xknKl1EiiD--" } }, { "cell_type": "markdown", "source": [ "

\n", "\n", "---\n", "\n", "

" ], "metadata": { "id": "bKRTdGzdcUes" } }, { "cell_type": "markdown", "source": [ "# **Part 1: Synthetic Data Generation**\n", "\n", "## Project Problem\n", "\n", "Many dog and cat owners do not always know how serious a pet-related problem is.\n", "For example, a pet owner may notice vomiting, appetite loss, hiding, limping, anxiety, low energy, or emergency signs, but may not know whether the situation is low urgency, requires monitoring, requires a veterinarian soon, or needs emergency care.\n", "\n", "Searching online can be confusing because the answers are often too general, stressful, or not adapted to the pet’s specific situation.\n", "\n", "Therefore, the problem this project addresses is the need for a clear, structured, and responsible first-step pet-care assistant.\n", "\n", "The app does not replace a veterinarian. Its purpose is to help users understand the possible urgency level, safe first steps, and when they should contact a veterinarian.\n", "\n", "---\n", "\n", "## App Goal\n", "\n", "The goal of PawPal AI is to help dog and cat owners receive responsible first-step guidance when they notice a problem with their pet.\n", "\n", "The user provides structured information about the pet and the situation, such as:\n", "\n", "* Pet type\n", "* Age group\n", "* Medical background\n", "* Main symptom\n", "* Secondary symptoms\n", "* Symptom duration\n", "* Appetite status\n", "* Water intake\n", "* Energy level\n", "* Pain signs\n", "* Emergency signs\n", "* Free text description\n", "\n", "Based on this information, the app retrieves similar synthetic pet-care cases and generates a personalized response that includes:\n", "\n", "* Possible problem category\n", "* Urgency level\n", "* Triage reasoning\n", "* Safe first steps\n", "* Recommended next step\n", "* Veterinary warning\n", "* Similar cases used by the system\n", "\n", "---\n", "\n", "## Synthetic Dataset Description\n", "\n", "For this project, I created a synthetic text dataset called:\n", "\n", "`pawpal_ai_final_petcare_dataset_10000.csv`\n", "\n", "The dataset contains 10,000 synthetic pet-care cases and 33 columns.\n", "\n", "Each row represents one realistic but fictional case of a dog or cat owner describing a problem with their pet.\n", "\n", "The dataset includes structured information such as:\n", "\n", "* Pet type\n", "* Pet age\n", "* Age group\n", "* Medical background\n", "* Main symptom\n", "* Secondary symptoms\n", "* Symptom duration\n", "* Appetite status\n", "* Water intake\n", "* Energy level\n", "* Pain signs\n", "* Emergency signs\n", "* Problem category\n", "* Urgency level\n", "* Recommended next step\n", "* Triage reason\n", "* Safe first steps\n", "* User question\n", "* Detailed advice\n", "* Vet warning\n", "* Retrieval text\n", "\n", "The dataset is synthetic, meaning it does not contain real users, real pets, or private personal information.\n", "\n", "---\n", "\n", "## How the Dataset Was Generated\n", "\n", "The dataset was generated using an AI language model with structured prompting techniques.\n", "\n", "Instead of asking the model to simply “create pet-care data”, I used detailed prompts that defined:\n", "\n", "* The role of the model\n", "* The required output structure\n", "* The allowed categories\n", "* The pet-care context\n", "* Safety rules\n", "* The required columns\n", "* The expected writing style\n", "* The need to avoid medical diagnosis\n", "* The need to recommend contacting a veterinarian when appropriate\n", "\n", "The prompts were designed to generate realistic but fictional pet-care cases.\n", "\n", "The generation process focused on responsible and safe guidance. The model was instructed not to provide a final medical diagnosis and not to replace a veterinarian.\n", "\n", "---\n", "\n", "## Example Prompting Strategy\n", "\n", "The prompt asked the AI model to generate structured pet-care cases with fields such as pet type, age group, symptoms, urgency level, triage reason, safe first steps, and vet warning.\n", "\n", "The prompt also included safety instructions:\n", "\n", "* Do not diagnose the pet.\n", "* Do not replace a veterinarian.\n", "* Use responsible and cautious language.\n", "* Recommend veterinary care when emergency signs appear.\n", "* Create realistic but synthetic examples only.\n", "* Do not include real personal information.\n", "\n", "This prompting method helped create a dataset that is useful for a responsible AI pet-care assistant.\n", "\n", "---\n", "\n", "## Why This Dataset Fits the Project\n", "\n", "This dataset fits the PawPal AI app because each row contains both structured information and natural language text.\n", "\n", "The most important column for the recommendation component is `retrieval_text`.\n", "\n", "This column combines the important details of each case into one searchable text, including the pet type, symptoms, urgency level, and recommended next step.\n", "\n", "The app will later use this column to create embeddings and retrieve the most similar cases to the user’s input.\n", "\n", "---\n", "\n", "## Summary\n", "\n", "In Part 1, I created a synthetic text dataset for PawPal AI, an AI-powered pet-care assistant for dog and cat owners.\n", "\n", "The dataset contains 10,000 synthetic examples. Each example represents a fictional pet-care case with structured fields, urgency level, safe first steps, and veterinary warning.\n", "\n", "The data was generated using an AI language model with structured prompting techniques. The prompts were designed to create realistic, safe, and responsible examples without using real personal information.\n", "\n", "This dataset will be used in the next parts of the project for EDA, embeddings-based recommendation, generation, and the final Gradio application.\n" ], "metadata": { "id": "toHqJkdMrj8O" } }, { "cell_type": "markdown", "source": [ "

\n", "\n", "---\n", "\n", "

" ], "metadata": { "id": "OgOX9Kd66Pnj" } }, { "cell_type": "markdown", "source": [ "# **Part 2: Dataset and Exploratory Data Analysis**\n", "\n", "**Step 1 - Project and Dataset Overview**\n", "\n", "In this part, I performed Exploratory Data Analysis on the PawPal AI synthetic dataset.\n", "\n", "The goal of the EDA was to understand the structure of the dataset, check the quality of the generated data, identify possible issues created by the AI generation process, and prepare the dataset for the next stages of the project.\n", "\n", "The dataset contains synthetic pet-care cases for dogs and cats. Each row represents one fictional case and includes structured information such as pet type, age group, symptoms, appetite, water intake, energy level, urgency level, safe first steps, recommended next step, and veterinary warning.\n", "\n", "During the EDA, I checked the dataset size, missing values, duplicate rows, category distributions, urgency level distribution, text lengths, and possible inconsistencies.\n", "\n", "Since AI-generated data can contain mistakes, hallucinations, or inconsistent outputs, I also looked for suspicious cases, such as emergency signs with low urgency, missing optional symptom fields, or unusual category values.\n", "\n", "The EDA helped confirm that the dataset is suitable for the PawPal AI application, while also showing which fields need cleaning before using the data for embeddings and recommendation.\n", "\n", "\n", "\n", "\n" ], "metadata": { "id": "IAdo5YUxsxJM" } }, { "cell_type": "markdown", "source": [ "### **Step 2 - Data Loading and Initial Inspection**\n", "\n", "In this step, I load the PawPal AI synthetic dataset.\n", "The dataset contains pet-care cases for dogs and cats, and each row represents one synthetic user case that will later be used for EDA, recommendation, and generation." ], "metadata": { "id": "wp5aYBcKODTT" } }, { "cell_type": "code", "source": [ "# Import libraries\n", "from google.colab import files\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "uploaded = files.upload()\n", "file_name = list(uploaded.keys())[0]\n", "# Read the dataset\n", "df = pd.read_csv(file_name)\n", "# Display the first rows\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 693 }, "id": "y7Jq0RakNHdx", "outputId": "e5086aa3-c823-49ad-c8dc-523c8d17cba8" }, "execution_count": 1, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving HopePet_dataset.csv to HopePet_dataset.csv\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " case_id pet_type pet_age_years pet_age_group pet_sex neutered_status \\\n", "0 1 Dog 12.0 Senior Male Not neutered/spayed \n", "1 2 Dog 16.0 Senior Female Neutered/Spayed \n", "2 3 Cat 0.3 Kitten Male Unknown \n", "3 4 Cat 19.0 Senior Male Neutered/Spayed \n", "4 5 Cat 0.5 Kitten Male Not neutered/spayed \n", "\n", " breed_size vaccination_status environment medical_background \\\n", "0 Medium Up to date Indoor mostly No known medical condition \n", "1 Unknown Up to date Indoor mostly No known medical condition \n", "2 Small Up to date Indoor only Unknown \n", "3 Medium Not up to date Indoor only Diabetes \n", "4 Small Unknown Mostly outdoor Unknown \n", "\n", " ... recommended_next_step \\\n", "0 ... Monitor at home and use general care guidance \n", "1 ... Contact a veterinarian as soon as possible \n", "2 ... Contact an emergency veterinary clinic immedia... \n", "3 ... Monitor closely and contact a veterinarian if ... \n", "4 ... Monitor at home and use general care guidance \n", "\n", " user_question \\\n", "0 My senior dog has eating too fast and also sho... \n", "1 My senior dog has drinking more water and also... \n", "2 My kitten with an unknown medical background h... \n", "3 My senior cat with diabetes has diarrhea and a... \n", "4 My kitten with an unknown medical background h... \n", "\n", " short_recommendation \\\n", "0 Make gradual food changes and monitor appetite... \n", "1 Contact a veterinarian as soon as possible and... \n", "2 Contact an emergency veterinarian immediately. \n", "3 Monitor your pet closely and contact a veterin... \n", "4 Make gradual food changes and monitor appetite... \n", "\n", " detailed_advice \\\n", "0 This case involves a nutrition-related concern... \n", "1 This case may require prompt veterinary attent... \n", "2 This case includes emergency warning signs in ... \n", "3 This case involves a health-related concern in... \n", "4 This case involves a nutrition-related concern... \n", "\n", " vet_warning \\\n", "0 Contact a veterinarian if symptoms continue, w... \n", "1 Contact a veterinarian as soon as possible, es... \n", "2 Seek emergency veterinary care immediately, es... \n", "3 Contact a veterinarian if symptoms continue, w... \n", "4 Contact a veterinarian if symptoms continue, w... \n", "\n", " keywords \\\n", "0 dog, senior, nutrition, low, eating too fast, ... \n", "1 dog, senior, nutrition, high, drinking more wa... \n", "2 cat, kitten, emergency, cannot stand, 2-3 days... \n", "3 cat, senior, health, medium, diarrhea, diabete... \n", "4 cat, kitten, nutrition, low, eating too fast, ... \n", "\n", " retrieval_text \\\n", "0 Pet type: Dog. Age group: Senior. Age years: 1... \n", "1 Pet type: Dog. Age group: Senior. Age years: 1... \n", "2 Pet type: Cat. Age group: Kitten. Age years: 0... \n", "3 Pet type: Cat. Age group: Senior. Age years: 1... \n", "4 Pet type: Cat. Age group: Kitten. Age years: 0... \n", "\n", " triage_reason \\\n", "0 Low urgency because no emergency warning signs... \n", "1 High urgency because the case involves drinkin... \n", "2 Emergency-level triage because the case includ... \n", "3 Medium urgency because symptoms should be moni... \n", "4 Low urgency because no emergency warning signs... \n", "\n", " safe_first_steps \\\n", "0 Make food changes gradually, keep fresh water ... \n", "1 Make food changes gradually, keep fresh water ... \n", "2 Keep the pet calm, do not give human medicatio... \n", "3 Keep the pet calm, monitor appetite, water int... \n", "4 Make food changes gradually, keep fresh water ... \n", "\n", " data_quality_notes \n", "0 appetite_status corrected from Not eating to I... \n", "1 water_intake corrected from Normal to Drinking... \n", "2 secondary_symptoms cleaned to remove energy-le... \n", "3 No automated correction needed; final cleanup ... \n", "4 secondary_symptoms cleaned to remove energy-le... \n", "\n", "[5 rows x 33 columns]" ], "text/html": [ "\n", "
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case_idpet_typepet_age_yearspet_age_grouppet_sexneutered_statusbreed_sizevaccination_statusenvironmentmedical_background...recommended_next_stepuser_questionshort_recommendationdetailed_advicevet_warningkeywordsretrieval_texttriage_reasonsafe_first_stepsdata_quality_notes
01Dog12.0SeniorMaleNot neutered/spayedMediumUp to dateIndoor mostlyNo known medical condition...Monitor at home and use general care guidanceMy senior dog has eating too fast and also sho...Make gradual food changes and monitor appetite...This case involves a nutrition-related concern...Contact a veterinarian if symptoms continue, w...dog, senior, nutrition, low, eating too fast, ...Pet type: Dog. Age group: Senior. Age years: 1...Low urgency because no emergency warning signs...Make food changes gradually, keep fresh water ...appetite_status corrected from Not eating to I...
12Dog16.0SeniorFemaleNeutered/SpayedUnknownUp to dateIndoor mostlyNo known medical condition...Contact a veterinarian as soon as possibleMy senior dog has drinking more water and also...Contact a veterinarian as soon as possible and...This case may require prompt veterinary attent...Contact a veterinarian as soon as possible, es...dog, senior, nutrition, high, drinking more wa...Pet type: Dog. Age group: Senior. Age years: 1...High urgency because the case involves drinkin...Make food changes gradually, keep fresh water ...water_intake corrected from Normal to Drinking...
23Cat0.3KittenMaleUnknownSmallUp to dateIndoor onlyUnknown...Contact an emergency veterinary clinic immedia...My kitten with an unknown medical background h...Contact an emergency veterinarian immediately.This case includes emergency warning signs in ...Seek emergency veterinary care immediately, es...cat, kitten, emergency, cannot stand, 2-3 days...Pet type: Cat. Age group: Kitten. Age years: 0...Emergency-level triage because the case includ...Keep the pet calm, do not give human medicatio...secondary_symptoms cleaned to remove energy-le...
34Cat19.0SeniorMaleNeutered/SpayedMediumNot up to dateIndoor onlyDiabetes...Monitor closely and contact a veterinarian if ...My senior cat with diabetes has diarrhea and a...Monitor your pet closely and contact a veterin...This case involves a health-related concern in...Contact a veterinarian if symptoms continue, w...cat, senior, health, medium, diarrhea, diabete...Pet type: Cat. Age group: Senior. Age years: 1...Medium urgency because symptoms should be moni...Keep the pet calm, monitor appetite, water int...No automated correction needed; final cleanup ...
45Cat0.5KittenMaleNot neutered/spayedSmallUnknownMostly outdoorUnknown...Monitor at home and use general care guidanceMy kitten with an unknown medical background h...Make gradual food changes and monitor appetite...This case involves a nutrition-related concern...Contact a veterinarian if symptoms continue, w...cat, kitten, nutrition, low, eating too fast, ...Pet type: Cat. Age group: Kitten. Age years: 0...Low urgency because no emergency warning signs...Make food changes gradually, keep fresh water ...secondary_symptoms cleaned to remove energy-le...
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df" } }, "metadata": {}, "execution_count": 1 } ] }, { "cell_type": "markdown", "source": [ "### **Dataset Shape**\n", "\n", "Before analyzing the data, I check the number of rows and columns. \n", "This helps confirm that the dataset meets the project requirement of at least 10,000 text examples." ], "metadata": { "id": "GqnCQeoxOWxA" } }, { "cell_type": "code", "source": [ "# Check number of rows and columns\n", "df.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dOl_HNBtOgk9", "outputId": "d16dab05-b124-4874-fa23-3d6bb6a01b8c" }, "execution_count": 2, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(10000, 33)" ] }, "metadata": {}, "execution_count": 2 } ] }, { "cell_type": "markdown", "source": [ "### **Dataset Columns**\n", "\n", "In this section, I inspect the column names to understand what information exists in the dataset." ], "metadata": { "id": "1bohyJzWOrUN" } }, { "cell_type": "code", "source": [ "# Display all column names\n", "df.columns.tolist()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Im62pQsQOvwe", "outputId": "ec1a2ee5-0d26-42ae-ff4e-31878cd952e2" }, "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['case_id',\n", " 'pet_type',\n", " 'pet_age_years',\n", " 'pet_age_group',\n", " 'pet_sex',\n", " 'neutered_status',\n", " 'breed_size',\n", " 'vaccination_status',\n", " 'environment',\n", " 'medical_background',\n", " 'recent_change',\n", " 'main_symptom',\n", " 'secondary_symptoms',\n", " 'symptom_duration',\n", " 'appetite_status',\n", " 'water_intake',\n", " 'energy_level',\n", " 'pain_signs',\n", " 'emergency_signs',\n", " 'previous_occurrence',\n", " 'problem_category',\n", " 'urgency_level',\n", " 'safety_disclaimer_level',\n", " 'recommended_next_step',\n", " 'user_question',\n", " 'short_recommendation',\n", " 'detailed_advice',\n", " 'vet_warning',\n", " 'keywords',\n", " 'retrieval_text',\n", " 'triage_reason',\n", " 'safe_first_steps',\n", " 'data_quality_notes']" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "markdown", "source": [ "## **Step 3 - Data Quality Checks**\n", "\n" ], "metadata": { "id": "Oc64frL9ZtL5" } }, { "cell_type": "markdown", "source": [ "**1. Missing Values Check**\n", "\n", "In this step, I check whether the dataset contains missing values.\n", "This is important because AI-generated datasets can sometimes include incomplete fields.\n", "For this project, missing values may appear in optional symptom-related columns, such as emergency signs, pain signs, or secondary symptoms. These fields are not always reported in every pet-care case, so I first identify them before deciding how to handle them." ], "metadata": { "id": "amGfSk9Ga3al" } }, { "cell_type": "code", "source": [ "# Count missing values in each column\n", "missing_values = df.isna().sum().sort_values(ascending=False)\n", "\n", "# Keep only columns that contain missing values\n", "missing_values_table = missing_values[missing_values > 0].reset_index()\n", "\n", "# Rename columns for readability\n", "missing_values_table.columns = [\"Column\", \"Missing Values\"]\n", "\n", "# Calculate missing percentage\n", "missing_values_table[\"Missing Percentage\"] = (\n", " missing_values_table[\"Missing Values\"] / len(df) * 100\n", ").round(2)\n", "\n" ], "metadata": { "id": "oG5hlHqAhAee" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "# Style the missing values table for clearer presentation\n", "missing_values_table.style \\\n", " .background_gradient(subset=[\"Missing Values\"], cmap=\"Pastel1\") \\\n", " .format({\"Missing Percentage\": \"{:.2f}%\"}) \\\n", " .set_caption(\"Missing Values Summary\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"18px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#F8D7DA\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 166 }, "id": "9BnpnWbUcVcw", "outputId": "39ea66c7-c284-4ac7-e5f9-3a581169a823" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Missing Values Summary
 ColumnMissing ValuesMissing Percentage
0emergency_signs731873.18%
1pain_signs426642.66%
2secondary_symptoms2072.07%
\n" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "# Create a clean table only for columns with missing values\n", "missing_values_to_plot = missing_values[missing_values > 0]\n", "\n", "# Define pastel pink and blue colors\n", "colors = [\"#A8DADC\", \"#F7A8B8\", \"#BDE0FE\", \"#FFCAD4\"]\n", "\n", "# Create figure\n", "plt.figure(figsize=(11, 6))\n", "plt.gca().set_facecolor(\"#FFF9FB\")\n", "\n", "# Create horizontal bar chart\n", "bars = plt.barh(\n", " missing_values_to_plot.index[::-1],\n", " missing_values_to_plot.values[::-1],\n", " color=colors[:len(missing_values_to_plot)],\n", " edgecolor=\"white\",\n", " linewidth=3\n", ")\n", "\n", "# Add title and labels\n", "plt.title(\"Missing Values by Column\", fontsize=20, fontweight=\"bold\", pad=18)\n", "plt.xlabel(\"Number of Missing Values\", fontsize=12)\n", "plt.ylabel(\"Column\", fontsize=12)\n", "\n", "# Add value labels next to bars\n", "for bar in bars:\n", " width = bar.get_width()\n", " plt.text(\n", " width + 50,\n", " bar.get_y() + bar.get_height() / 2,\n", " f\"{int(width)}\",\n", " va=\"center\",\n", " fontsize=12,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", " )\n", "\n", "# Clean chart style\n", "plt.grid(axis=\"x\", linestyle=\"--\", alpha=0.25)\n", "plt.gca().spines[\"top\"].set_visible(False)\n", "plt.gca().spines[\"right\"].set_visible(False)\n", "plt.gca().spines[\"left\"].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "zBxbtc1DcsJ4", "outputId": "9983312f-3671-42af-a44f-f8b9c6a51659" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**2. Handling Missing Values**\n", "\n", "In this step, I handle the missing values in the dataset. \n", "The missing values appear in optional symptom-related columns, such as `emergency_signs`, `pain_signs`, and `secondary_symptoms`.\n", "\n", "These fields are optional because not every pet-care case includes emergency signs, pain signs, or secondary symptoms. \n", "Therefore, I replace the missing values with `\"None\"` instead of deleting rows.\n", "\n", "In the same step, I also create a final table that shows how many missing values remain after the cleaning process." ], "metadata": { "id": "BDpSrLtkgpvP" } }, { "cell_type": "code", "source": [ "# Clean column names by removing extra spaces\n", "df.columns = df.columns.str.strip()\n", "\n", "# Define optional symptom-related columns\n", "optional_columns = [\"emergency_signs\", \"pain_signs\", \"secondary_symptoms\"]\n", "\n", "# Keep only columns that actually exist in the dataset\n", "existing_optional_columns = [col for col in optional_columns if col in df.columns]\n", "\n", "# Replace missing values in optional columns with \"None\"\n", "for col in existing_optional_columns:\n", " df[col] = df[col].fillna(\"None\")\n", "\n", "# Count missing values after cleaning for the handled columns\n", "remaining_missing = df[existing_optional_columns].isna().sum().reset_index()\n", "\n", "# Rename columns\n", "remaining_missing.columns = [\"Column\", \"Missing Values Remaining\"]\n", "\n", "# Add missing percentage after cleaning\n", "remaining_missing[\"Missing Percentage Remaining\"] = (\n", " remaining_missing[\"Missing Values Remaining\"] / len(df) * 100\n", ").round(2)\n", "\n", "# Add cleaning action\n", "remaining_missing[\"Action Taken\"] = 'Missing values replaced with \"None\"'\n", "\n", "# Display styled table\n", "remaining_missing.style \\\n", " .set_caption(\"Missing Values Remaining After Cleaning\") \\\n", " .background_gradient(subset=[\"Missing Values Remaining\"], cmap=\"Pastel1\") \\\n", " .format({\"Missing Percentage Remaining\": \"{:.2f}%\"}) \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#BDE0FE\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 168 }, "id": "WTilIOriiZSf", "outputId": "ff64a11d-a94a-43af-856b-34a904a7a9d3" }, "execution_count": 6, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Missing Values Remaining After Cleaning
 ColumnMissing Values RemainingMissing Percentage RemainingAction Taken
0emergency_signs00.00%Missing values replaced with \"None\"
1pain_signs00.00%Missing values replaced with \"None\"
2secondary_symptoms00.00%Missing values replaced with \"None\"
\n" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "**3. Duplicate Entries Check**\n", "\n", "In this step, I check whether the dataset contains duplicate entries. \n", "Duplicate rows can be a problem because AI-generated datasets may sometimes repeat the same example more than once.\n", "\n", "For this project, duplicate rows could reduce the diversity of the dataset and may affect the recommendation system. \n", "Therefore, I check for full duplicate rows and remove them only if they exist." ], "metadata": { "id": "GVVnjMAtiyPf" } }, { "cell_type": "code", "source": [ "# Count duplicate rows before cleaning\n", "duplicate_rows_before = df.duplicated().sum()\n", "\n", "# Save the number of rows before removing duplicates\n", "rows_before = len(df)\n", "\n", "# Remove full duplicate rows if they exist\n", "df = df.drop_duplicates()\n", "\n", "# Count duplicate rows after cleaning\n", "duplicate_rows_after = df.duplicated().sum()\n", "\n", "# Save the number of rows after removing duplicates\n", "rows_after = len(df)\n", "\n", "# Create duplicate cleaning summary table\n", "duplicate_summary = pd.DataFrame({\n", " \"Check\": [\n", " \"Rows before duplicate cleaning\",\n", " \"Duplicate rows found\",\n", " \"Rows after duplicate cleaning\",\n", " \"Duplicate rows remaining\"\n", " ],\n", " \"Result\": [\n", " rows_before,\n", " duplicate_rows_before,\n", " rows_after,\n", " duplicate_rows_after\n", " ]\n", "})\n", "\n", "# Display styled table\n", "duplicate_summary.style \\\n", " .set_caption(\"Duplicate Entries Cleaning Summary\") \\\n", " .background_gradient(subset=[\"Result\"], cmap=\"Pastel1\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#F7A8B8\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 224 }, "id": "Of6Ekh8oi8a0", "outputId": "2a13bbd0-32bb-4221-b571-a625d8ee128c" }, "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Duplicate Entries Cleaning Summary
 CheckResult
0Rows before duplicate cleaning10000
1Duplicate rows found0
2Rows after duplicate cleaning10000
3Duplicate rows remaining0
\n" ] }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "markdown", "source": [ "**4. scaling/normalize issues**\n", "\n", "In this step, I check whether the dataset contains numeric columns that may require scaling or normalization.\n", "\n", "Most of the PawPal AI dataset is categorical or text-based, such as pet type, symptoms, urgency level, recommendations, and advice. These columns do not need scaling.\n", "\n", "The main meaningful numeric column is `pet_age_years`, which represents the pet's age. \n", "For EDA, I keep the original age values because they are easy to understand. \n", "However, I also create a normalized version of the age column, which may be useful later for machine learning models.\n", "\n", "The `case_id` column is not normalized because it is only an identifier, not a real numeric feature." ], "metadata": { "id": "IByvScf_jfQF" } }, { "cell_type": "code", "source": [ "# Import display for clean table output\n", "from IPython.display import display\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "# Make sure column names are clean\n", "df.columns = df.columns.str.strip()\n", "\n", "# Convert pet_age_years to numeric in case it was loaded as text\n", "df[\"pet_age_years\"] = pd.to_numeric(df[\"pet_age_years\"], errors=\"coerce\")\n", "\n", "# Identify numeric columns in the dataset\n", "numeric_columns = df.select_dtypes(include=[\"int64\", \"float64\"]).columns.tolist()\n", "\n", "# Create a table that summarizes numeric columns\n", "numeric_summary = []\n", "\n", "for col in numeric_columns:\n", " numeric_summary.append({\n", " \"Column\": col,\n", " \"Minimum Value\": df[col].min(),\n", " \"Maximum Value\": df[col].max(),\n", " \"Mean\": round(df[col].mean(), 2),\n", " \"Standard Deviation\": round(df[col].std(), 2),\n", " \"Needs Scaling?\": \"No - ID column\" if col == \"case_id\" else \"Yes - useful for ML\" if col == \"pet_age_years\" else \"Optional\"\n", " })\n", "\n", "numeric_summary_table = pd.DataFrame(numeric_summary)\n", "\n", "# Display styled numeric summary table\n", "numeric_summary_table.style \\\n", " .set_caption(\"Scaling / Normalization Check\") \\\n", " .background_gradient(subset=[\"Maximum Value\"], cmap=\"Pastel1\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#BDE0FE\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 137 }, "id": "XlsvGF9GjsYF", "outputId": "837f74e6-0c61-4d3b-9321-d409f0dd9f8a" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Scaling / Normalization Check
 ColumnMinimum ValueMaximum ValueMeanStandard DeviationNeeds Scaling?
0case_id1.00000010000.0000005000.5000002886.900000No - ID column
1pet_age_years0.20000020.0000006.1000005.860000Yes - useful for ML
\n" ] }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "markdown", "source": [ "**Normalize Pet Age**\n", "\n", "In this step, I create a normalized version of `pet_age_years` using Min-Max normalization. \n", "The normalized values range between 0 and 1.\n", "\n", "This does not replace the original age column. \n", "The original column is kept for interpretation, while the normalized column can be useful later for modeling." ], "metadata": { "id": "Bha-ybxPjzGQ" } }, { "cell_type": "code", "source": [ "# Calculate minimum and maximum pet age\n", "age_min = df[\"pet_age_years\"].min()\n", "age_max = df[\"pet_age_years\"].max()\n", "\n", "# Create normalized age column using Min-Max normalization\n", "df[\"pet_age_years_normalized\"] = (\n", " df[\"pet_age_years\"] - age_min\n", ") / (\n", " age_max - age_min\n", ")\n", "\n", "# Create a small table to compare original and normalized age values\n", "age_normalization_preview = df[\n", " [\"pet_age_years\", \"pet_age_years_normalized\"]\n", "].head(10)\n", "\n", "# Display styled table\n", "age_normalization_preview.style \\\n", " .set_caption(\"Original vs Normalized Pet Age\") \\\n", " .background_gradient(subset=[\"pet_age_years_normalized\"], cmap=\"Pastel1\") \\\n", " .format({\"pet_age_years_normalized\": \"{:.3f}\"}) \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#F7A8B8\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 388 }, "id": "filxoav_j33h", "outputId": "3d15997f-ea55-4b7c-dee5-88b5ceff0b37" }, "execution_count": 9, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Original vs Normalized Pet Age
 pet_age_yearspet_age_years_normalized
012.0000000.596
116.0000000.798
20.3000000.005
319.0000000.949
40.5000000.015
54.0000000.192
619.0000000.949
711.0000000.545
80.6000000.020
913.0000000.646
\n" ] }, "metadata": {}, "execution_count": 9 } ] }, { "cell_type": "markdown", "source": [ "**Normalized Age Visualization**\n", "\n", "This graph shows how the original pet age values were transformed into normalized values.\n", "\n", "The original age is measured in years, while the normalized age is scaled between 0 and 1. \n", "This makes the numeric feature easier to use in future machine learning models, while still keeping the original age column for interpretation." ], "metadata": { "id": "OuBrM66IkBvt" } }, { "cell_type": "code", "source": [ "# Make sure pet_age_years is numeric\n", "df[\"pet_age_years\"] = pd.to_numeric(df[\"pet_age_years\"], errors=\"coerce\")\n", "\n", "# Create normalized age column if it does not already exist\n", "age_min = df[\"pet_age_years\"].min()\n", "age_max = df[\"pet_age_years\"].max()\n", "\n", "df[\"pet_age_years_normalized\"] = (\n", " df[\"pet_age_years\"] - age_min\n", ") / (\n", " age_max - age_min\n", ")\n", "\n", "# Create a clean table for plotting\n", "age_plot = df[[\"pet_age_years\", \"pet_age_years_normalized\"]].drop_duplicates()\n", "age_plot = age_plot.sort_values(\"pet_age_years\")\n", "\n", "# Create figure\n", "plt.figure(figsize=(10, 6))\n", "plt.gca().set_facecolor(\"#FFF9FB\")\n", "\n", "# Plot normalization line\n", "plt.plot(\n", " age_plot[\"pet_age_years\"],\n", " age_plot[\"pet_age_years_normalized\"],\n", " color=\"#F7A8B8\",\n", " linewidth=4,\n", " marker=\"o\",\n", " markersize=6,\n", " markerfacecolor=\"#BDE0FE\",\n", " markeredgecolor=\"white\",\n", " markeredgewidth=1.5\n", ")\n", "\n", "# Add title and labels\n", "plt.title(\"Pet Age Normalization: Original Age to 0–1 Scale\", fontsize=18, fontweight=\"bold\", pad=15)\n", "plt.xlabel(\"Original Pet Age in Years\", fontsize=12)\n", "plt.ylabel(\"Normalized Age Value\", fontsize=12)\n", "\n", "# Add explanation labels on graph\n", "plt.text(\n", " age_min,\n", " 0.05,\n", " \"Youngest pet\\nnormalized close to 0\",\n", " fontsize=10,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", ")\n", "\n", "plt.text(\n", " age_max - 4,\n", " 0.9,\n", " \"Oldest pet\\nnormalized close to 1\",\n", " fontsize=10,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", ")\n", "\n", "# Clean chart style\n", "plt.grid(axis=\"both\", linestyle=\"--\", alpha=0.25)\n", "plt.gca().spines[\"top\"].set_visible(False)\n", "plt.gca().spines[\"right\"].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "8eA96smWlODX", "outputId": "a8661e37-3d48-4e4a-a266-14d623ed1974" }, "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**5. Fixing Typos and Category Consistency**\n", "\n", "In this step, I check whether the main categorical columns contain typos, inconsistent spelling, or unexpected values.\n", "\n", "AI-generated datasets can sometimes include small mistakes, such as different capitalization, extra spaces, or labels that do not match the expected categories.\n", "\n", "To handle this, I first clean extra spaces from text columns and then check whether the main categorical columns contain only valid values." ], "metadata": { "id": "UMjFhILoleUm" } }, { "cell_type": "code", "source": [ "# Remove extra spaces from all text columns\n", "text_columns = df.select_dtypes(include=\"object\").columns\n", "\n", "for col in text_columns:\n", " df[col] = df[col].astype(str).str.strip()\n", "\n", "# Define expected values for important categorical columns\n", "expected_values = {\n", " \"pet_type\": [\"Dog\", \"Cat\"],\n", " \"pet_age_group\": [\"Puppy\", \"Kitten\", \"Adult\", \"Senior\"],\n", " \"urgency_level\": [\"Low\", \"Medium\", \"High\", \"Emergency\"],\n", " \"problem_category\": [\"Health\", \"Behavior\", \"Emergency\", \"Training\", \"Anxiety\", \"Nutrition\", \"Grooming\"],\n", " \"appetite_status\": [\"Normal\", \"Reduced\", \"Not eating\", \"Increased\", \"Unknown\"],\n", " \"water_intake\": [\"Normal\", \"Drinking less\", \"Drinking more\", \"Not drinking\", \"Unknown\"],\n", " \"energy_level\": [\"Normal\", \"Slightly tired\", \"Very tired\", \"Restless\", \"Cannot stand\", \"Unknown\"],\n", " \"vaccination_status\": [\"Up to date\", \"Not up to date\", \"Unknown\"],\n", " \"neutered_status\": [\"Neutered/Spayed\", \"Not neutered/spayed\", \"Unknown\"],\n", " \"breed_size\": [\"Small\", \"Medium\", \"Large\", \"Unknown\"],\n", " \"environment\": [\"Indoor only\", \"Indoor mostly\", \"Outdoor access\", \"Mostly outdoor\", \"Unknown\"]\n", "}\n", "\n", "# Check for unexpected values\n", "typo_check_results = []\n", "\n", "for col, valid_values in expected_values.items():\n", " if col in df.columns:\n", " actual_values = set(df[col].dropna().unique())\n", " unexpected_values = actual_values - set(valid_values)\n", "\n", " typo_check_results.append({\n", " \"Column\": col,\n", " \"Number of Unexpected Values\": len(unexpected_values),\n", " \"Unexpected Values\": list(unexpected_values) if len(unexpected_values) > 0 else \"None\"\n", " })\n", "\n", "# Create results table\n", "typo_check_table = pd.DataFrame(typo_check_results)\n", "\n", "# Display styled table\n", "typo_check_table.style \\\n", " .set_caption(\"Typo and Category Consistency Check\") \\\n", " .background_gradient(subset=[\"Number of Unexpected Values\"], cmap=\"Pastel1\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#BDE0FE\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 419 }, "id": "VwPqEpptmCdL", "outputId": "bd914034-b894-48cd-ac55-9e8b47b024ac" }, "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Typo and Category Consistency Check
 ColumnNumber of Unexpected ValuesUnexpected Values
0pet_type0None
1pet_age_group0None
2urgency_level0None
3problem_category0None
4appetite_status0None
5water_intake0None
6energy_level0None
7vaccination_status0None
8neutered_status0None
9breed_size0None
10environment0None
\n" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "markdown", "source": [ "## **Outlier Detection & Handling**\n", "\n", "\n" ], "metadata": { "id": "T2bT0sgCmRKL" } }, { "cell_type": "markdown", "source": [ "**1. Pet Age**\n", "\n", "In this step, I check for outliers in the `pet_age_years` column.\n", "\n", "Pet age is the main meaningful numeric feature in this dataset. \n", "Outliers in this column may indicate unrealistic AI-generated values, such as negative ages or extremely high pet ages.\n", "\n", "I use the IQR method to detect unusual age values. \n", "However, I do not automatically remove all outliers, because very young pets and senior pets can be realistic and important for a pet-care assistant.\n", "\n", "Instead, I flag the outliers and decide whether they should be kept or removed based on whether they are realistic." ], "metadata": { "id": "t_UkUZA4myJ1" } }, { "cell_type": "code", "source": [ "# Convert pet_age_years to numeric\n", "df[\"pet_age_years\"] = pd.to_numeric(df[\"pet_age_years\"], errors=\"coerce\")\n", "\n", "# Calculate Q1, Q3, and IQR\n", "Q1 = df[\"pet_age_years\"].quantile(0.25)\n", "Q3 = df[\"pet_age_years\"].quantile(0.75)\n", "IQR = Q3 - Q1\n", "\n", "# Define outlier bounds using the IQR method\n", "lower_bound = Q1 - 1.5 * IQR\n", "upper_bound = Q3 + 1.5 * IQR\n", "\n", "# Flag IQR outliers\n", "df[\"age_iqr_outlier\"] = (\n", " (df[\"pet_age_years\"] < lower_bound) |\n", " (df[\"pet_age_years\"] > upper_bound)\n", ")\n", "\n", "# Flag unrealistic age values\n", "# Negative ages or extremely high pet ages would be considered unrealistic\n", "df[\"age_unrealistic_outlier\"] = (\n", " (df[\"pet_age_years\"] < 0) |\n", " (df[\"pet_age_years\"] > 30)\n", ")\n", "\n", "# Create summary table\n", "age_outlier_summary = pd.DataFrame({\n", " \"Metric\": [\n", " \"Q1\",\n", " \"Q3\",\n", " \"IQR\",\n", " \"Lower IQR Bound\",\n", " \"Upper IQR Bound\",\n", " \"IQR Outliers Found\",\n", " \"Unrealistic Age Values Found\"\n", " ],\n", " \"Value\": [\n", " round(Q1, 2),\n", " round(Q3, 2),\n", " round(IQR, 2),\n", " round(lower_bound, 2),\n", " round(upper_bound, 2),\n", " int(df[\"age_iqr_outlier\"].sum()),\n", " int(df[\"age_unrealistic_outlier\"].sum())\n", " ]\n", "})\n", "\n", "# Display styled summary table\n", "age_outlier_summary.style \\\n", " .set_caption(\"Pet Age Outlier Summary\") \\\n", " .background_gradient(subset=[\"Value\"], cmap=\"Pastel1\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#BDE0FE\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 294 }, "id": "rbgdoHLam1PV", "outputId": "d5457b25-17ce-4427-83c6-9bbbcc7a00c6" }, "execution_count": 12, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Pet Age Outlier Summary
 MetricValue
0Q10.800000
1Q311.000000
2IQR10.200000
3Lower IQR Bound-14.500000
4Upper IQR Bound26.300000
5IQR Outliers Found0.000000
6Unrealistic Age Values Found0.000000
\n" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "# Calculate the median for visualization\n", "median_age = df[\"pet_age_years\"].median()\n", "\n", "# Create figure\n", "plt.figure(figsize=(12, 6))\n", "plt.gca().set_facecolor(\"#FFF9FB\")\n", "\n", "# Plot histogram\n", "plt.hist(\n", " df[\"pet_age_years\"],\n", " bins=20,\n", " color=\"#BDE0FE\",\n", " edgecolor=\"white\",\n", " linewidth=1.5,\n", " alpha=0.9\n", ")\n", "\n", "# Highlight the IQR range\n", "plt.axvspan(Q1, Q3, color=\"#F7A8B8\", alpha=0.25, label=\"IQR Range (Q1 to Q3)\")\n", "\n", "# Add key lines\n", "plt.axvline(Q1, color=\"#CDB4DB\", linestyle=\"--\", linewidth=2, label=f\"Q1 = {Q1:.1f}\")\n", "plt.axvline(median_age, color=\"#A8DADC\", linestyle=\"-\", linewidth=3, label=f\"Median = {median_age:.1f}\")\n", "plt.axvline(Q3, color=\"#F7A8B8\", linestyle=\"--\", linewidth=2, label=f\"Q3 = {Q3:.1f}\")\n", "plt.axvline(upper_bound, color=\"#FF8FAB\", linestyle=\":\", linewidth=3, label=f\"Upper Outlier Bound = {upper_bound:.1f}\")\n", "\n", "# Add title and labels\n", "plt.title(\"Pet Age Distribution and Outlier Check\", fontsize=20, fontweight=\"bold\", pad=18)\n", "plt.xlabel(\"Pet Age in Years\", fontsize=12)\n", "plt.ylabel(\"Number of Cases\", fontsize=12)\n", "\n", "# Add annotation\n", "plt.text(\n", " 13,\n", " plt.ylim()[1] * 0.85,\n", " \"No outliers were detected\\nin pet age values\",\n", " fontsize=12,\n", " fontweight=\"bold\",\n", " color=\"#444\",\n", " bbox=dict(facecolor=\"white\", edgecolor=\"#F7A8B8\", boxstyle=\"round,pad=0.5\")\n", ")\n", "\n", "# Clean chart style\n", "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.25)\n", "plt.gca().spines[\"top\"].set_visible(False)\n", "plt.gca().spines[\"right\"].set_visible(False)\n", "\n", "plt.legend(frameon=False)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "-OAlDgj0nZqT", "outputId": "29b90206-5e7b-4c27-96c4-794c374a0b02" }, "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Decision and Justification**\n", "\n", "The pet age outlier analysis was performed using the IQR method.\n", "\n", "The results show that the lower IQR bound is -14.5 and the upper IQR bound is 26.3. \n", "Since pet age cannot be negative, the lower bound is only a mathematical result of the IQR formula and does not indicate a real issue.\n", "\n", "The dataset contains 0 IQR outliers and 0 unrealistic age values. \n", "This means there are no negative ages, no extremely unrealistic ages, and no age values that need to be removed.\n", "\n", "Therefore, I decided to keep all rows in the dataset. \n", "No rows were removed because the pet age values are realistic and suitable for the PawPal AI application." ], "metadata": { "id": "akjqPCeZnjy4" } }, { "cell_type": "markdown", "source": [ " **2. Text Length**\n", "\n", "In this step, I check for outliers in the text length of two important columns: `user_question` and `retrieval_text`.\n", "\n", "Since this is a text-based dataset, unusually short or unusually long texts may indicate AI-generated problems. \n", "For example, a very short user question may not contain enough information, while an extremely long text may be noisy or inconsistent.\n", "\n", "I use word count and the IQR method to identify text-length outliers. \n", "The goal is not to automatically delete rows, but to flag unusual text lengths and decide whether they are still useful for the project." ], "metadata": { "id": "k2wlaew4oDzp" } }, { "cell_type": "code", "source": [ "# Define the important text columns for outlier detection\n", "text_columns_for_outliers = [\"user_question\", \"retrieval_text\"]\n", "\n", "# Create word count columns\n", "for col in text_columns_for_outliers:\n", " df[f\"{col}_word_count\"] = df[col].astype(str).str.split().str.len()\n", "\n", "# Function to calculate IQR outlier information\n", "def calculate_text_outliers(data, column):\n", " Q1 = data[column].quantile(0.25)\n", " Q3 = data[column].quantile(0.75)\n", " IQR = Q3 - Q1\n", "\n", " lower_bound = Q1 - 1.5 * IQR\n", " upper_bound = Q3 + 1.5 * IQR\n", "\n", " outlier_flag = (data[column] < lower_bound) | (data[column] > upper_bound)\n", "\n", " return Q1, Q3, IQR, lower_bound, upper_bound, outlier_flag\n", "\n", "# Store results\n", "text_outlier_results = []\n", "\n", "for col in text_columns_for_outliers:\n", " word_count_col = f\"{col}_word_count\"\n", "\n", " Q1, Q3, IQR, lower_bound, upper_bound, outlier_flag = calculate_text_outliers(\n", " df,\n", " word_count_col\n", " )\n", "\n", " # Save outlier flag in the dataset\n", " df[f\"{col}_length_outlier\"] = outlier_flag\n", "\n", " # Add results to summary table\n", " text_outlier_results.append({\n", " \"Text Column\": col,\n", " \"Minimum Words\": int(df[word_count_col].min()),\n", " \"Q1\": round(Q1, 2),\n", " \"Median\": round(df[word_count_col].median(), 2),\n", " \"Q3\": round(Q3, 2),\n", " \"Maximum Words\": int(df[word_count_col].max()),\n", " \"Lower Bound\": round(lower_bound, 2),\n", " \"Upper Bound\": round(upper_bound, 2),\n", " \"Outliers Found\": int(outlier_flag.sum())\n", " })\n", "\n", "# Create summary table\n", "text_outlier_summary = pd.DataFrame(text_outlier_results)\n", "\n", "# Display styled table\n", "text_outlier_summary.style \\\n", " .set_caption(\"Text Length Outlier Summary\") \\\n", " .background_gradient(subset=[\"Outliers Found\"], cmap=\"Pastel1\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#BDE0FE\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 137 }, "id": "noul4xvToHYn", "outputId": "a840439d-58ba-45b0-b68f-c6647d731c45" }, "execution_count": 14, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Text Length Outlier Summary
 Text ColumnMinimum WordsQ1MedianQ3Maximum WordsLower BoundUpper BoundOutliers Found
0user_question1130.00000034.00000037.0000004719.50000047.500000178
1retrieval_text114129.000000132.000000135.000000147120.000000144.00000093
\n" ] }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "code", "source": [ "# Prepare data for visualization\n", "plot_data = pd.DataFrame({\n", " \"Column\": [\"user_question\", \"retrieval_text\"],\n", " \"Normal Texts\": [\n", " len(df) - df[\"user_question_length_outlier\"].sum(),\n", " len(df) - df[\"retrieval_text_length_outlier\"].sum()\n", " ],\n", " \"Text-Length Outliers\": [\n", " df[\"user_question_length_outlier\"].sum(),\n", " df[\"retrieval_text_length_outlier\"].sum()\n", " ]\n", "})\n", "\n", "# Create figure\n", "plt.figure(figsize=(11, 6))\n", "plt.gca().set_facecolor(\"#FFF9FB\")\n", "\n", "# X positions\n", "x = range(len(plot_data))\n", "bar_width = 0.35\n", "\n", "# Create grouped bars\n", "normal_bars = plt.bar(\n", " [i - bar_width / 2 for i in x],\n", " plot_data[\"Normal Texts\"],\n", " width=bar_width,\n", " color=\"#BDE0FE\",\n", " edgecolor=\"white\",\n", " linewidth=2,\n", " label=\"Normal Texts\"\n", ")\n", "\n", "outlier_bars = plt.bar(\n", " [i + bar_width / 2 for i in x],\n", " plot_data[\"Text-Length Outliers\"],\n", " width=bar_width,\n", " color=\"#F7A8B8\",\n", " edgecolor=\"white\",\n", " linewidth=2,\n", " label=\"Text-Length Outliers\"\n", ")\n", "\n", "# Add value labels\n", "for bars in [normal_bars, outlier_bars]:\n", " for bar in bars:\n", " height = bar.get_height()\n", " plt.text(\n", " bar.get_x() + bar.get_width() / 2,\n", " height + 50,\n", " f\"{int(height)}\",\n", " ha=\"center\",\n", " va=\"bottom\",\n", " fontsize=11,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", " )\n", "\n", "# Add title and labels\n", "plt.title(\"Text Length Outliers by Column\", fontsize=20, fontweight=\"bold\", pad=18)\n", "plt.xlabel(\"Text Column\", fontsize=12)\n", "plt.ylabel(\"Number of Cases\", fontsize=12)\n", "plt.xticks(x, plot_data[\"Column\"], fontsize=12)\n", "\n", "# Clean chart style\n", "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.25)\n", "plt.gca().spines[\"top\"].set_visible(False)\n", "plt.gca().spines[\"right\"].set_visible(False)\n", "\n", "plt.legend(frameon=False)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "APSTBURIoPDS", "outputId": "837d522d-f36d-4b61-95d9-6dd72b17daba" }, "execution_count": 15, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Decision and Justification**\n", "\n", "The text-length outlier analysis found 178 outliers in the `user_question` column and 93 outliers in the `retrieval_text` column.\n", "\n", "Even though these rows are marked as outliers by the IQR method, I decided to keep them in the dataset. \n", "The reason is that these values are not necessarily data errors. In a text-based pet-care dataset, some users may write shorter questions, while others may provide more detailed descriptions.\n", "\n", "The graph also shows that the number of text-length outliers is very small compared to the full dataset of 10,000 examples. \n", "Most rows are normal-length texts: 9,822 normal `user_question` rows and 9,907 normal `retrieval_text` rows.\n", "\n", "Therefore, I kept the text-length outliers and only flagged them for awareness. \n", "Rows would only be removed if the text was empty, unreadable, or clearly unrelated to the project." ], "metadata": { "id": "UEYp5i3DokJ3" } }, { "cell_type": "markdown", "source": [ "## **Step 4 - Descriptive Statistics**\n", "\n", "\n" ], "metadata": { "id": "kgO9eSEzpEiz" } }, { "cell_type": "markdown", "source": [ "### **1. Numeric Summary**\n", "\n", "In this step, I summarize the main numeric features in the dataset.\n", "\n", "The goal is to understand the central patterns in the data using statistics such as mean, median, minimum, maximum, and standard deviation.\n", "\n", "For this dataset, the most important numeric features are:\n", "\n", "- `pet_age_years` — the pet's age\n", "- `user_question_word_count` — length of the generated user question\n", "- `retrieval_text_word_count` — length of the retrieval text used later for embeddings\n", "\n", "These statistics help reveal whether the values are reasonable and whether the text fields are consistent enough for the next stages of the project." ], "metadata": { "id": "gSBgeG6VpV_c" } }, { "cell_type": "markdown", "source": [ "**Smart Numeric Summary**\n", "\n", "This table summarizes the numeric features and adds extra descriptive measures such as range, IQR, skewness, and mean-median difference.\n" ], "metadata": { "id": "lG4zDOVreF4d" } }, { "cell_type": "code", "source": [ "numeric_summary_df.style \\\n", " .set_caption(\"Smart Numeric Summary\") \\\n", " .hide(axis=\"index\") \\\n", " .format({\n", " \"Mean\": \"{:.2f}\",\n", " \"Median\": \"{:.2f}\",\n", " \"Min\": \"{:.2f}\",\n", " \"Max\": \"{:.2f}\",\n", " \"Range\": \"{:.2f}\",\n", " \"IQR\": \"{:.2f}\",\n", " \"Skewness\": \"{:.2f}\"\n", " }) \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"22px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#2F3A56\"),\n", " (\"text-align\", \"center\"),\n", " (\"padding\", \"14px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#DDEBFF\"),\n", " (\"color\", \"#2F3A56\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"10px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"color\", \"#333333\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"10px\"),\n", " (\"font-size\", \"13px\")\n", " ]\n", " }\n", " ]) \\\n", " .apply(\n", " lambda row: [\n", " \"background-color: #FFF7FB\" if row.name % 2 == 0 else \"background-color: #F7FBFF\"\n", " for _ in row\n", " ],\n", " axis=1\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 223 }, "id": "1_rlArtOd7Ua", "outputId": "9f701b90-c032-414d-fa03-c4bd74868902" }, "execution_count": 24, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Smart Numeric Summary
FeatureMeanMedianMinMaxRangeIQRSkewnessInsight
pet_age_years6.104.000.2020.0019.8010.200.72Right-skewed tendency
user_question_word_count33.0134.0011.0047.0036.007.00-0.65Mean and median are close
retrieval_text_word_count131.84132.00114.00147.0033.006.00-0.28Mean and median are close
\n" ] }, "metadata": {}, "execution_count": 24 } ] }, { "cell_type": "markdown", "source": [ "**Insight:**\n", "\n", "The smart numeric summary gives more information than a regular descriptive table.\n", "\n", "It shows not only the average and median values, but also the spread, range, and skewness of each numeric feature.\n", "This helps understand whether the numeric columns are balanced, concentrated, or affected by extreme values.\n", "." ], "metadata": { "id": "wepp-OX5p1QQ" } }, { "cell_type": "markdown", "source": [ "**Numeric Differences Between Risk Groups**\n", "\n", "This table compares key numeric features between lower-risk cases and high-risk cases." ], "metadata": { "id": "fR8cG1HmfVS_" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Numeric Differences Between Risk Groups\n", "# ==========================================\n", "\n", "# Create risk groups\n", "df[\"risk_group\"] = df[\"urgency_level\"].apply(\n", " lambda x: \"High/Emergency Risk\" if x in [\"High\", \"Emergency\"] else \"Low/Medium Risk\"\n", ")\n", "\n", "# Create word-count columns\n", "df[\"user_question_word_count\"] = df[\"user_question\"].astype(str).apply(lambda x: len(x.split()))\n", "df[\"retrieval_text_word_count\"] = df[\"retrieval_text\"].astype(str).apply(lambda x: len(x.split()))\n", "\n", "# Features to compare\n", "numeric_features = {\n", " \"Pet Age\": \"pet_age_years\",\n", " \"User Question Length\": \"user_question_word_count\",\n", " \"Retrieval Text Length\": \"retrieval_text_word_count\"\n", "}\n", "\n", "comparison_rows = []\n", "\n", "for feature_name, col in numeric_features.items():\n", " low_medium_avg = df[df[\"risk_group\"] == \"Low/Medium Risk\"][col].mean()\n", " high_risk_avg = df[df[\"risk_group\"] == \"High/Emergency Risk\"][col].mean()\n", " difference = high_risk_avg - low_medium_avg\n", "\n", " comparison_rows.append({\n", " \"Feature\": feature_name,\n", " \"Low/Medium Average\": round(low_medium_avg, 2),\n", " \"High/Emergency Average\": round(high_risk_avg, 2),\n", " \"Difference\": round(difference, 2)\n", " })\n", "\n", "risk_numeric_comparison = pd.DataFrame(comparison_rows)\n", "\n", "risk_numeric_comparison.style \\\n", " .set_caption(\"Numeric Differences Between Risk Groups\") \\\n", " .hide(axis=\"index\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"20px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#2F3A56\"),\n", " (\"padding\", \"12px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#DDEBFF\"),\n", " (\"color\", \"#2F3A56\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"10px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"background-color\", \"#FFF7FB\"),\n", " (\"color\", \"#333333\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"10px\"),\n", " (\"font-size\", \"13px\")\n", " ]\n", " }\n", " ])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 216 }, "id": "zwWbL1pXfYiM", "outputId": "5269d034-0cfd-47e9-941c-0d2c119ee1cc" }, "execution_count": 27, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Numeric Differences Between Risk Groups
FeatureLow/Medium AverageHigh/Emergency AverageDifference
Pet Age5.6700006.4700000.800000
User Question Length30.73000034.9800004.250000
Retrieval Text Length131.280000132.3200001.040000
\n" ] }, "metadata": {}, "execution_count": 27 } ] }, { "cell_type": "markdown", "source": [ "**Insight**:\n", "\n", "The table shows small numeric differences between lower-risk and high-risk cases.\n", "\n", "High-risk cases have a slightly higher average pet age (6.47 years) compared to lower-risk cases (5.67 years).\n", "They also have longer user questions on average, with about 4.25 more words.\n", "\n", "However, the retrieval text length is almost the same between the two groups.\n", "This suggests that urgency is not explained mainly by numeric features, but more by symptoms, emergency signs, appetite, energy level, and medical context." ], "metadata": { "id": "gi9hp895fjEI" } }, { "cell_type": "markdown", "source": [ "### **2. Categorical Statistics Summary**\n", "\n", "After examining the numeric variables, I also examined the main categorical variables in the dataset.\n", "\n", "This step is important because most of the dataset is based on categorical pet-care information, such as pet type, age group, urgency level, problem category, appetite status, energy level, and recommended next step.\n", "\n", "The goal was to understand how many unique values each categorical column contains and which value is the most common in each column.\n" ], "metadata": { "id": "v6t5Z-Nhs6pv" } }, { "cell_type": "markdown", "source": [ "**Categorical Descriptive Statistics**\n", "\n", "\n", "This table summarizes the main categorical columns in the dataset, including the number of records, unique values, most common value, and its frequency.," ], "metadata": { "id": "unys2rNTck5Z" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Beautiful Styled Categorical Summary Table\n", "# ==========================================\n", "\n", "categorical_columns = [\n", " \"pet_type\",\n", " \"pet_age_group\",\n", " \"problem_category\",\n", " \"urgency_level\",\n", " \"medical_background\",\n", " \"recommended_next_step\"\n", "]\n", "\n", "categorical_summary = df[categorical_columns].describe()\n", "\n", "categorical_summary.style \\\n", " .set_caption(\"Categorical Statistics Summary\") \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"22px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#444\"),\n", " (\"padding\", \"12px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th.col_heading\",\n", " \"props\": [\n", " (\"background-color\", \"#A8DADC\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"font-size\", \"14px\"),\n", " (\"text-align\", \"center\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"8px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th.row_heading\",\n", " \"props\": [\n", " (\"background-color\", \"#F9C6D3\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"font-size\", \"14px\"),\n", " (\"text-align\", \"center\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"8px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"font-size\", \"14px\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"8px\")\n", " ]\n", " }\n", " ]) \\\n", " .apply(\n", " lambda x: [\n", " \"background-color: #FDF1F5\" if x.name == \"count\" else\n", " \"background-color: #EEF7FF\" if x.name == \"unique\" else\n", " \"background-color: #FFF4E5\" if x.name == \"top\" else\n", " \"background-color: #F3F0FF\" if x.name == \"freq\" else\n", " \"\"\n", " for _ in x\n", " ],\n", " axis=1\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "id": "c7-x662Ds_N2", "outputId": "10695342-7742-4ce3-e231-53e4cfe24bbe" }, "execution_count": 20, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Categorical Statistics Summary
 pet_typepet_age_groupproblem_categoryurgency_levelmedical_backgroundrecommended_next_step
count100001000010000100001000010000
unique2474124
topDogAdultHealthMediumNo known medical conditionMonitor closely and contact a veterinarian if symptoms continue or worsen
freq502533871681283046992830
\n" ] }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "markdown", "source": [ "**Categorical Feature Summary**\n", "\n", "This table summarizes the main categorical features and shows how dominant the most common value is in each column." ], "metadata": { "id": "wB-6XFVYc9cT" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Categorical Feature Summary\n", "# ==========================================\n", "\n", "categorical_summary = []\n", "\n", "for col in categorical_columns:\n", " value_counts = df[col].value_counts(dropna=False)\n", " top_value = value_counts.index[0]\n", " top_count = value_counts.iloc[0]\n", " top_pct = round((top_count / len(df)) * 100, 2)\n", " unique_count = df[col].nunique(dropna=False)\n", "\n", " # Add a simple interpretation of dominance\n", " if top_pct >= 70:\n", " dominance_level = \"Very dominant\"\n", " elif top_pct >= 50:\n", " dominance_level = \"Dominant\"\n", " elif top_pct >= 35:\n", " dominance_level = \"Moderate\"\n", " else:\n", " dominance_level = \"Balanced\"\n", "\n", " categorical_summary.append({\n", " \"Column\": col,\n", " \"Unique Values\": unique_count,\n", " \"Most Frequent Value\": top_value,\n", " \"Top Value Count\": top_count,\n", " \"Top Value (%)\": top_pct,\n", " \"Dominance Level\": dominance_level\n", " })\n", "\n", "categorical_summary_df = pd.DataFrame(categorical_summary)\n", "\n", "# Sort by how dominant the most common value is\n", "categorical_summary_df = categorical_summary_df.sort_values(\n", " \"Top Value (%)\",\n", " ascending=False\n", ")\n", "\n", "# Display styled table\n", "categorical_summary_df.style \\\n", " .set_caption(\"Categorical Feature Summary\") \\\n", " .format({\"Top Value (%)\": \"{:.2f}%\"}) \\\n", " .set_table_styles([\n", " {\n", " \"selector\": \"caption\",\n", " \"props\": [\n", " (\"caption-side\", \"top\"),\n", " (\"font-size\", \"22px\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"color\", \"#333\"),\n", " (\"padding\", \"12px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"th\",\n", " \"props\": [\n", " (\"background-color\", \"#BDE0FE\"),\n", " (\"color\", \"#333\"),\n", " (\"font-weight\", \"bold\"),\n", " (\"text-align\", \"center\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"8px\")\n", " ]\n", " },\n", " {\n", " \"selector\": \"td\",\n", " \"props\": [\n", " (\"text-align\", \"center\"),\n", " (\"border\", \"1px solid white\"),\n", " (\"padding\", \"8px\")\n", " ]\n", " }\n", " ]) \\\n", " .background_gradient(\n", " subset=[\"Top Value (%)\"],\n", " cmap=\"RdPu\"\n", " )" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 310 }, "id": "ueBcaejEtGz9", "outputId": "4db2e300-3812-4aa0-c0ee-3dabc30929c0" }, "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Categorical Feature Summary
 ColumnUnique ValuesMost Frequent ValueTop Value CountTop Value (%)Dominance Level
0pet_type2Dog502550.25%Dominant
4medical_background12No known medical condition469946.99%Moderate
1pet_age_group4Adult338733.87%Balanced
3urgency_level4Medium283028.30%Balanced
5recommended_next_step4Monitor closely and contact a veterinarian if symptoms continue or worsen283028.30%Balanced
2problem_category7Health168116.81%Balanced
\n" ] }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "markdown", "source": [ "**Insight:** \n", "The categorical summary shows that the dataset includes several meaningful structured features. Some columns, such as `pet_type` and `urgency_level`, have a small and controlled number of categories, while others, such as `medical_background`, include more diverse values. This is useful because the dataset remains structured while still providing enough variety for recommendation and generation." ], "metadata": { "id": "ng8bZMYltKu-" } }, { "cell_type": "markdown", "source": [ "**Number of Unique Values in Categorical Features**\n", "\n", "This chart shows how many unique values exist in each selected categorical feature.\n" ], "metadata": { "id": "g515p5Q7dWnV" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Number of Unique Values in Categorical Features\n", "# ==========================================\n", "\n", "from matplotlib.ticker import MaxNLocator\n", "\n", "# Count unique values in each categorical column\n", "unique_counts = pd.Series({\n", " col: df[col].nunique()\n", " for col in categorical_columns\n", "}).sort_values()\n", "\n", "# Clean labels for display\n", "display_labels = [\n", " label.replace(\"_\", \" \").title()\n", " for label in unique_counts.index\n", "]\n", "\n", "# Pastel colors\n", "pastel_colors = [\n", " \"#CDB4DB\",\n", " \"#FFC8DD\",\n", " \"#FFAFCC\",\n", " \"#BDE0FE\",\n", " \"#A2D2FF\",\n", " \"#B8E0D2\"\n", "]\n", "\n", "colors = pastel_colors[:len(unique_counts)]\n", "\n", "# Create figure\n", "fig, ax = plt.subplots(figsize=(10, 5.5))\n", "fig.patch.set_facecolor(\"#FFF9FB\")\n", "ax.set_facecolor(\"#FFF9FB\")\n", "\n", "# Create horizontal bar chart\n", "bars = ax.barh(\n", " display_labels,\n", " unique_counts.values,\n", " color=colors,\n", " edgecolor=\"white\",\n", " linewidth=2\n", ")\n", "\n", "# Add value labels\n", "for bar in bars:\n", " width = bar.get_width()\n", " ax.text(\n", " width + 0.15,\n", " bar.get_y() + bar.get_height() / 2,\n", " str(int(width)),\n", " va=\"center\",\n", " fontsize=11,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", " )\n", "\n", "# Title and labels\n", "ax.set_title(\n", " \"Number of Unique Values in Categorical Features\",\n", " fontsize=18,\n", " fontweight=\"bold\",\n", " pad=16\n", ")\n", "\n", "ax.set_xlabel(\"Number of Unique Values\", fontsize=12)\n", "ax.set_ylabel(\"Categorical Feature\", fontsize=12)\n", "\n", "# Make x-axis show whole numbers only\n", "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n", "\n", "# Clean design\n", "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.25)\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "ax.spines[\"left\"].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 557 }, "id": "KijtAvv7dZer", "outputId": "d7a2b885-a2a0-44d7-cd58-d661f2df2c0a" }, "execution_count": 22, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Insight:**\n", "\n", "The chart shows which main symptoms have the highest percentage of high-risk cases.\n", "\n", "This visualization is important because it highlights symptoms that are more frequently connected to **High** or **Emergency** urgency levels.\n", "Symptoms with a higher high-risk percentage may require closer attention in the HopePet AI application.\n", "\n", "The result helps validate that the `main_symptom` column is meaningful for urgency estimation.\n", "If certain symptoms consistently appear with higher urgency, this supports the idea that symptom type should influence the recommendation process.\n", "\n", "Overall, this graph provides useful insight for the next stages of the project because it shows which symptoms are most important for identifying potentially serious pet-care cases.\n" ], "metadata": { "id": "rajFlCXadd3R" } }, { "cell_type": "markdown", "source": [ "## **Step 5 - Exploratory Data Visualization**" ], "metadata": { "id": "sscqQK7O2KUh" } }, { "cell_type": "markdown", "source": [ "In this section, I visualize the main distributions in the dataset. These visualizations help understand the balance of pet types, age groups, problem categories, urgency levels, and medical backgrounds in the HOPEPET dataset." ], "metadata": { "id": "tdFsMf3t2UPH" } }, { "cell_type": "markdown", "source": [ "### **1. Distribution of Pet Types**\n", "\n", "In this visualization, I examined the distribution of pet types in the dataset.\n", "\n", "The goal was to check whether the synthetic dataset is balanced between dogs and cats.\n", "This is important because HopePet AI is designed to support both dog owners and cat owners.\n", "\n" ], "metadata": { "id": "I3O62uSZvN1f" } }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# Define logical order for important categorical fields\n", "urgency_order = [\"Low\", \"Medium\", \"High\", \"Emergency\"]\n", "age_order = [\"Puppy\", \"Kitten\", \"Adult\", \"Senior\"]" ], "metadata": { "id": "KNMC7vyB2RQj" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# Soft pastel color palette\n", "PINK = \"#f48fb1\"\n", "LIGHT_BLUE = \"#81d4fa\"\n", "LAVENDER = \"#ce93d8\"\n", "MINT = \"#a5d6a7\"\n", "PEACH = \"#ffcc80\"\n", "SOFT_RED = \"#ef9a9a\"\n", "SOFT_YELLOW = \"#fff59d\"\n", "SOFT_GREY = \"#cfd8dc\"\n", "\n", "palette = [PINK, LIGHT_BLUE, LAVENDER, MINT, PEACH, SOFT_RED, SOFT_YELLOW, SOFT_GREY]\n", "\n", "plt.rcParams[\"figure.figsize\"] = (8, 5)\n", "plt.rcParams[\"axes.titlesize\"] = 16\n", "plt.rcParams[\"axes.labelsize\"] = 12\n", "plt.rcParams[\"xtick.labelsize\"] = 11\n", "plt.rcParams[\"ytick.labelsize\"] = 11\n", "\n", "def add_value_labels(ax, spacing=5):\n", " for container in ax.containers:\n", " ax.bar_label(container, padding=spacing, fontsize=10)" ], "metadata": { "id": "suRmSyp03sAO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "pet_counts = df[\"pet_type\"].value_counts()\n", "\n", "plt.figure(figsize=(7, 7))\n", "colors = [LIGHT_BLUE, PINK]\n", "\n", "wedges, texts, autotexts = plt.pie(\n", " pet_counts,\n", " labels=pet_counts.index,\n", " autopct=\"%1.1f%%\",\n", " startangle=90,\n", " colors=colors,\n", " textprops={\"fontsize\": 12}\n", ")\n", "\n", "centre_circle = plt.Circle((0, 0), 0.70, fc=\"white\")\n", "fig = plt.gcf()\n", "fig.gca().add_artist(centre_circle)\n", "\n", "plt.title(\"Distribution of Pet Types\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 600 }, "id": "Z_lez8W-2bUB", "outputId": "48b90417-f130-475e-a09b-c9f94c445918" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "\n", "**Result**\n", "\n", "The dataset is almost perfectly balanced between dogs and cats.\n", "\n", "Dogs represent **50.2%** of the dataset, while cats represent **49.8%**.\n", "This is a positive result because the dataset does not strongly favor one pet type over the other.\n", "\n", "This balance helps make the application more suitable for both dog-related and cat-related pet-care cases." ], "metadata": { "id": "a5TVc8z9vpCc" } }, { "cell_type": "markdown", "source": [ "### **2. Distribution of Pet Age Groups**\n", "\n", "In this visualization, I examined how the cases are distributed across different pet age groups.\n", "\n", "The goal was to understand whether the dataset includes pets from different life stages, such as puppies, kittens, adults, and seniors.\n", "This is important because pet age can affect symptoms, urgency level, and the type of recommendation the user may need.\n", "\n", "\n" ], "metadata": { "id": "w1rPcNRWu61t" } }, { "cell_type": "code", "source": [ "age_order = [\"Puppy\", \"Kitten\", \"Adult\", \"Senior\"]\n", "age_counts = df[\"pet_age_group\"].value_counts().reindex(age_order)\n", "\n", "fig, ax = plt.subplots(figsize=(8, 5))\n", "bars = ax.bar(age_counts.index, age_counts.values, color=[PINK, LIGHT_BLUE, LAVENDER, MINT])\n", "\n", "ax.set_title(\"Distribution of Pet Age Groups\")\n", "ax.set_xlabel(\"Pet Age Group\")\n", "ax.set_ylabel(\"Number of Cases\")\n", "\n", "add_value_labels(ax)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 494 }, "id": "uEgjbi_02l6z", "outputId": "96ca205d-db38-4178-e2fe-43ede15c376e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The dataset includes all main pet age groups.\n", "\n", "There are **1,701 puppy cases**, **1,690 kitten cases**, **3,387 adult cases**, and **3,222 senior cases**.\n", "\n", "Adult and senior pets appear more frequently than puppies and kittens.\n", "This makes sense for a pet-care assistant because many health-related and urgency-related questions may involve adult or older pets.\n", "\n", "Overall, the dataset provides examples from different life stages, which supports the goal of building a useful pet-care recommendation system." ], "metadata": { "id": "adtF-C7vvsmy" } }, { "cell_type": "markdown", "source": [ "### **3. Distribution of Problem Categories**\n", "\n", "In this visualization, I examined the distribution of pet-care problem categories in the dataset.\n", "\n", "The goal was to check whether the synthetic dataset covers a wide range of pet-care situations, such as health, behavior, emergency, training, anxiety, nutrition, and grooming.\n" ], "metadata": { "id": "eU_xsxZQvwWV" } }, { "cell_type": "code", "source": [ "category_counts = df[\"problem_category\"].value_counts().sort_values()\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "bars = ax.barh(category_counts.index, category_counts.values, color=palette[:len(category_counts)])\n", "\n", "ax.set_title(\"Distribution of Problem Categories\")\n", "ax.set_xlabel(\"Number of Cases\")\n", "ax.set_ylabel(\"Problem Category\")\n", "\n", "add_value_labels(ax)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 571 }, "id": "YIS8mUUY2rUB", "outputId": "895a6122-d7dd-40b2-a225-59119491f8f3" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The dataset includes several important pet-care categories.\n", "\n", "The largest category is Health with 1,681 cases.\n", "Other major categories include Behavior with 1,583 cases, Emergency with 1,569 cases, Training with 1,438 cases, Anxiety with 1,375 cases, Nutrition with 1,271 cases, and Grooming with 1,083 cases.\n", "\n", "Although the categories are not perfectly equal, each category has a meaningful number of examples.\n", "This is a positive result because HopePet AI needs a diverse dataset in order to retrieve similar cases for different types of pet-care problems." ], "metadata": { "id": "6dmU0PcOv8iT" } }, { "cell_type": "markdown", "source": [ "### **4. Distribution of Urgency Levels**\n", "\n", "In this visualization, I examined the distribution of urgency levels in the dataset.\n", "\n", "The goal was to check how many cases are labeled as Low, Medium, High, or Emergency.\n", "This is important because urgency level is one of the main outputs of the HopePet AI application." ], "metadata": { "id": "VMJmZGMCwbky" } }, { "cell_type": "code", "source": [ "urgency_order = [\"Low\", \"Medium\", \"High\", \"Emergency\"]\n", "urgency_counts = df[\"urgency_level\"].value_counts().reindex(urgency_order)\n", "\n", "fig, ax = plt.subplots(figsize=(8, 5))\n", "bars = ax.bar(\n", " urgency_counts.index,\n", " urgency_counts.values,\n", " color=[MINT, LIGHT_BLUE, PEACH, PINK]\n", ")\n", "\n", "ax.set_title(\"Distribution of Urgency Levels\")\n", "ax.set_xlabel(\"Urgency Level\")\n", "ax.set_ylabel(\"Number of Cases\")\n", "\n", "add_value_labels(ax)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 494 }, "id": "xI3gFUpi2xXM", "outputId": "af3344da-b059-43b1-fce9-eae63f4707db" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The dataset includes all four urgency levels: Low, Medium, High, and Emergency.\n", "\n", "There are 1,808 Low cases, 2,830 Medium cases, 2,680 High cases, and 2,682 Emergency cases.\n", "\n", "This shows that the dataset does include low-risk cases, so not all examples are severe or emergency-related.\n", "At the same time, Medium, High, and Emergency cases are strongly represented, which is useful because the application needs to identify situations that may require faster veterinary attention.\n", "\n", "Overall, the urgency distribution supports the goal of the application because it allows the system to distinguish between low-risk cases and more urgent pet-care situations." ], "metadata": { "id": "MdU4LWOewjrB" } }, { "cell_type": "markdown", "source": [ "### **5. Top 10 Medical Background Categories**\n", "\n", "In this visualization, I examined the top 10 most common medical background categories in the dataset.\n", "\n", "The goal was to understand the most frequent pre-existing medical conditions or background health statuses represented in the synthetic dataset. \n", "This is important because a pet’s medical background can influence symptom severity, urgency level, and the type of recommendation generated by the HopePet AI application.\n" ], "metadata": { "id": "8nAl7YPox8bp" } }, { "cell_type": "code", "source": [ "medical_counts = df[\"medical_background\"].value_counts().head(10).sort_values()\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "bars = ax.barh(medical_counts.index, medical_counts.values, color=palette[:len(medical_counts)])\n", "\n", "ax.set_title(\"Top 10 Medical Background Categories\")\n", "ax.set_xlabel(\"Number of Cases\")\n", "ax.set_ylabel(\"Medical Background\")\n", "\n", "add_value_labels(ax)\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 571 }, "id": "m3cnSuE421qd", "outputId": "259d4f9d-341d-45ff-bc8b-18666e934baf" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "\n", "**Result**\n", "\n", "The most common medical background category in the dataset is **No known medical condition**, with **4,699 cases**. \n", "This is followed by **Unknown** with **909 cases**, and then several specific medical conditions such as **Allergies** (**719 cases**), **Digestive issues** (**575 cases**), **Kidney disease** (**562 cases**), **Diabetes** (**493 cases**), **Arthritis** (**476 cases**), **Skin sensitivity** (**388 cases**), **Heart disease** (**348 cases**), and **Obesity** (**328 cases**).\n", "\n", "This result shows that the dataset includes both healthy pets and pets with a variety of existing medical conditions. \n", "This is useful for the application because it allows HopePet AI to handle a wider range of realistic pet-care situations, including cases where the pet’s background health may affect the recommended action." ], "metadata": { "id": "aKTBFJg4yzBQ" } }, { "cell_type": "markdown", "source": [ "**Step 5 Summary**\n", "\n", "The visualizations show that the HOPEPET dataset is diverse and well-structured. The pet type distribution is almost perfectly balanced, with 50.2% dog cases and 49.8% cat cases, which helps the app support both types of pet owners. The dataset also includes all major age groups, with adult and senior pets being the most common, while puppies and kittens are still strongly represented.\n", "\n", "The problem category distribution shows that the dataset covers a wide range of pet-care situations, including health, behavior, emergency, training, anxiety, nutrition, and grooming. The urgency levels are also well represented, with Medium, High, and Emergency cases appearing more frequently than Low cases. This is useful because HOPEPET needs to provide responsible guidance for cases that may require monitoring, veterinary attention, or emergency care.\n", "\n", "The medical background distribution shows that many pets have no known medical condition, but the dataset also includes important health backgrounds such as allergies, digestive issues, kidney disease, diabetes, arthritis, skin sensitivity, heart disease, and obesity. This adds important context for the recommendation system because the same symptom can require different guidance depending on the pet’s medical history." ], "metadata": { "id": "9L-BTlBG4mMh" } }, { "cell_type": "markdown", "source": [ "## **Step 6 - Relationship and Consistency Analysis**" ], "metadata": { "id": "rAr0eDZX5B-u" } }, { "cell_type": "markdown", "source": [ "In this section, I analyze relationships between important variables in the HOPEPET dataset. The goal is to check whether the synthetic dataset follows logical and responsible patterns between problem categories, urgency levels, emergency signs, and recommended next steps." ], "metadata": { "id": "M9doValL5QBT" } }, { "cell_type": "markdown", "source": [ "### **1. Problem Category vs Urgency Level**\n", "\n", "In this visualization, I examined the relationship between the problem category and the urgency level.\n", "\n", "The goal was to understand how urgency levels are distributed inside each problem category. \n", "Instead of only counting how many cases exist in each category, this graph shows the percentage of Low, Medium, High, and Emergency cases within every category.\n", "\n", "This is important for HopePet AI because urgency level is one of the main outputs of the application. \n", "By checking this relationship, I can evaluate whether the synthetic labels make sense and whether certain problem categories are naturally linked to higher or lower urgency." ], "metadata": { "id": "87vqkkGk1NgO" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Problem Category vs Urgency Level (%)\n", "# 100% Stacked Bar Chart\n", "# ==========================================\n", "\n", "# Define the order of categories and urgency levels\n", "category_order = [\"Health\", \"Behavior\", \"Emergency\", \"Training\", \"Anxiety\", \"Nutrition\", \"Grooming\"]\n", "urgency_order = [\"Low\", \"Medium\", \"High\", \"Emergency\"]\n", "\n", "# Create percentage table\n", "category_urgency_pct = pd.crosstab(\n", " df[\"problem_category\"],\n", " df[\"urgency_level\"],\n", " normalize=\"index\"\n", ").reindex(index=category_order, columns=urgency_order, fill_value=0) * 100\n", "\n", "# Define pastel colors\n", "colors = {\n", " \"Low\": \"#A8D5BA\",\n", " \"Medium\": \"#8ECAE6\",\n", " \"High\": \"#FFD6A5\",\n", " \"Emergency\": \"#F4A6C1\"\n", "}\n", "\n", "# Create figure\n", "fig, ax = plt.subplots(figsize=(13, 8))\n", "fig.patch.set_facecolor(\"#FFF9FB\")\n", "ax.set_facecolor(\"#FFF9FB\")\n", "\n", "# Start plotting stacked bars\n", "left = np.zeros(len(category_urgency_pct))\n", "\n", "for urgency in urgency_order:\n", " values = category_urgency_pct[urgency].values\n", "\n", " bars = ax.barh(\n", " category_urgency_pct.index,\n", " values,\n", " left=left,\n", " color=colors[urgency],\n", " edgecolor=\"white\",\n", " linewidth=2,\n", " label=urgency,\n", " height=0.72\n", " )\n", "\n", " # Add percentage labels inside bars\n", " for i, (bar, value) in enumerate(zip(bars, values)):\n", " if value >= 7: # show labels only if segment is large enough\n", " ax.text(\n", " left[i] + value / 2,\n", " bar.get_y() + bar.get_height() / 2,\n", " f\"{value:.1f}%\",\n", " ha=\"center\",\n", " va=\"center\",\n", " fontsize=10,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", " )\n", "\n", " left += values\n", "\n", "# Add title and labels\n", "ax.set_title(\n", " \"Problem Category vs Urgency Level (%)\",\n", " fontsize=20,\n", " fontweight=\"bold\",\n", " pad=20\n", ")\n", "ax.set_xlabel(\"Percentage of Cases\", fontsize=13)\n", "ax.set_ylabel(\"Problem Category\", fontsize=13)\n", "\n", "# Format x-axis\n", "ax.set_xlim(0, 100)\n", "ax.set_xticks(np.arange(0, 101, 10))\n", "ax.set_xticklabels([f\"{i}%\" for i in range(0, 101, 10)], fontsize=11)\n", "ax.tick_params(axis=\"y\", labelsize=12)\n", "\n", "# Put Health at the top\n", "ax.invert_yaxis()\n", "\n", "# Add grid\n", "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.25)\n", "\n", "# Remove extra borders\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "ax.spines[\"left\"].set_visible(False)\n", "\n", "# Add legend\n", "ax.legend(\n", " title=\"Urgency Level\",\n", " bbox_to_anchor=(0.5, 1.08),\n", " loc=\"upper center\",\n", " ncol=4,\n", " frameon=False,\n", " fontsize=11,\n", " title_fontsize=12\n", ")\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 807 }, "id": "QGUAOmSW0oXj", "outputId": "b318a761-4d7b-4160-d112-fdc2f67cb728" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The graph shows clear differences between problem categories and urgency levels.\n", "\n", "The **Emergency** category is labeled as **100% Emergency**, which is expected and shows strong consistency in the dataset. \n", "The **Health** category has a large percentage of **High urgency** cases, with **54.0%** labeled as High and **19.5%** labeled as Emergency. This makes sense because health-related symptoms may require faster attention.\n", "\n", "Categories such as **Training**, **Nutrition**, and **Grooming** contain more Low and Medium urgency cases compared to Health and Emergency. \n", "For example, **Training** includes **35.0% Low** and **36.0% Medium** cases, while **Grooming** includes **31.6% Low** and **36.7% Medium** cases.\n", "\n", "This result shows that the dataset does not treat all pet-care problems as equally urgent. \n", "Instead, urgency levels change according to the type of problem, which supports the logic of the HopePet AI application.\n", "\n", "Overall, this visualization confirms that the dataset contains meaningful relationships between problem categories and urgency levels." ], "metadata": { "id": "X2n1u7f01x4Y" } }, { "cell_type": "markdown", "source": [ "### **2. Pet Age Group and Urgency Level Bubble Chart**\n", "\n", "In this visualization, I examined the relationship between pet age group and urgency level.\n", "\n", "The goal was to understand how urgency levels are distributed across different life stages, including puppies, kittens, adult pets, and senior pets. \n", "The bubble size represents the number of cases in each combination of age group and urgency level.\n", "\n", "This is important for HopePet AI because age can influence the level of concern. \n", "For example, symptoms in puppies, kittens, or senior pets may sometimes require closer attention than the same symptoms in healthy adult pets." ], "metadata": { "id": "wn6Wtf_S2Uqa" } }, { "cell_type": "code", "source": [ "age_order_existing = [age for age in age_order if age in df[\"pet_age_group\"].unique()]\n", "\n", "age_urgency_counts = pd.crosstab(\n", " df[\"pet_age_group\"],\n", " df[\"urgency_level\"]\n", ").reindex(index=age_order_existing, columns=urgency_order, fill_value=0)\n", "\n", "fig, ax = plt.subplots(figsize=(10, 6))\n", "\n", "for y_idx, age in enumerate(age_urgency_counts.index):\n", " for x_idx, urgency in enumerate(age_urgency_counts.columns):\n", " count = age_urgency_counts.loc[age, urgency]\n", " bubble_size = np.sqrt(count) * 22\n", "\n", " ax.scatter(\n", " x_idx,\n", " y_idx,\n", " s=bubble_size,\n", " color=PINK if urgency == \"Emergency\" else LIGHT_BLUE if urgency == \"High\" else LAVENDER if urgency == \"Medium\" else MINT,\n", " alpha=0.75,\n", " edgecolors=\"white\",\n", " linewidth=1.5\n", " )\n", "\n", " ax.text(\n", " x_idx,\n", " y_idx,\n", " str(count),\n", " ha=\"center\",\n", " va=\"center\",\n", " fontsize=9,\n", " color=\"black\"\n", " )\n", "\n", "ax.set_title(\"Pet Age Group and Urgency Level Bubble Chart\", fontsize=16)\n", "ax.set_xlabel(\"Urgency Level\")\n", "ax.set_ylabel(\"Pet Age Group\")\n", "\n", "ax.set_xticks(np.arange(len(urgency_order)))\n", "ax.set_xticklabels(urgency_order)\n", "\n", "ax.set_yticks(np.arange(len(age_urgency_counts.index)))\n", "ax.set_yticklabels(age_urgency_counts.index)\n", "\n", "ax.grid(True, linestyle=\"--\", alpha=0.3)\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 607 }, "id": "ASqcirNu5cpM", "outputId": "d91fd3eb-0a36-47b0-dc8e-809d0f30e158" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The bubble chart shows that all age groups appear across all urgency levels, which means the dataset includes a wide range of pet-care situations for puppies, kittens, adults, and seniors.\n", "\n", "Adult and senior pets have the largest number of cases overall. \n", "For example, adult pets have **1,037 Medium** cases, **839 High** cases, and **898 Emergency** cases. \n", "Senior pets also show high representation, with **1,022 High** cases and **879 Emergency** cases.\n", "\n", "Puppies and kittens appear less frequently than adults and seniors, but they are still represented across all urgency levels. \n", "For example, puppies include **316 Low**, **537 Medium**, **377 High**, and **471 Emergency** cases, while kittens include **305 Low**, **509 Medium**, **442 High**, and **434 Emergency** cases.\n", "\n", "Overall, this visualization shows that the dataset contains urgency variation across all pet age groups. \n", "This supports the HopePet AI application because the system can learn from examples involving different life stages and different levels of urgency." ], "metadata": { "id": "EUIATxmy2f2m" } }, { "cell_type": "markdown", "source": [ "### **3. Emergency Signs and Urgency Distribution**\n", "\n", "In this visualization, I examined the relationship between emergency signs and urgency level.\n", "\n", "The goal was to check whether cases with reported emergency signs are labeled with higher urgency levels. \n", "This validation step is important because emergency signs should strongly influence the urgency level in a pet-care assistant application.\n", "\n", "The graph compares two groups: cases with no emergency signs and cases with emergency signs. \n", "For each group, the chart shows the percentage distribution of Low, Medium, High, and Emergency urgency levels." ], "metadata": { "id": "F-hofUca2lwy" } }, { "cell_type": "code", "source": [ "### Graph 9 - Emergency Signs and Urgency Distribution\n", "\n", "def classify_emergency_signs(value):\n", " value = str(value).strip().lower()\n", " if value in [\"none\", \"nan\", \"\"]:\n", " return \"No emergency signs\"\n", " return \"Has emergency signs\"\n", "\n", "df[\"has_emergency_signs\"] = df[\"emergency_signs\"].apply(classify_emergency_signs)\n", "\n", "emergency_profile_pct = pd.crosstab(\n", " df[\"has_emergency_signs\"],\n", " df[\"urgency_level\"],\n", " normalize=\"index\"\n", ").reindex(columns=urgency_order, fill_value=0) * 100\n", "\n", "fig, ax = plt.subplots(figsize=(10, 5))\n", "\n", "left = np.zeros(len(emergency_profile_pct))\n", "\n", "for urgency, color in zip(urgency_order, [MINT, LIGHT_BLUE, PEACH, PINK]):\n", " values = emergency_profile_pct[urgency].values\n", " ax.barh(\n", " emergency_profile_pct.index,\n", " values,\n", " left=left,\n", " label=urgency,\n", " color=color\n", " )\n", "\n", " for i, value in enumerate(values):\n", " if value > 3:\n", " ax.text(\n", " left[i] + value / 2,\n", " i,\n", " f\"{value:.1f}%\",\n", " ha=\"center\",\n", " va=\"center\",\n", " fontsize=10\n", " )\n", "\n", " left += values\n", "\n", "ax.set_title(\"Emergency Signs and Urgency Distribution (%)\")\n", "ax.set_xlabel(\"Percentage of Cases\")\n", "ax.set_ylabel(\"Emergency Signs\")\n", "ax.legend(title=\"Urgency Level\", bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 507 }, "id": "1ZBIpnCv6GPd", "outputId": "ba9d3fee-6cc3-430f-c4a5-18bcdb51a8bb" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The graph shows a very clear relationship between emergency signs and urgency level.\n", "\n", "Cases with **emergency signs** are labeled as **100.0% Emergency**. \n", "This is an important consistency result because emergency signs should always lead to the highest urgency level.\n", "\n", "Cases with **no emergency signs** are distributed across Low, Medium, and High urgency levels. \n", "Specifically, these cases include **24.7% Low**, **38.7% Medium**, and **36.6% High**.\n", "\n", "This result shows that the dataset treats reported emergency signs as a strong indicator of emergency urgency. \n", "At the same time, cases without emergency signs can still be Medium or High urgency, depending on other symptoms such as appetite, energy level, duration, or pain signs.\n", "\n", "Overall, this validation supports the logic of the HopePet AI application because emergency signs are consistently linked to emergency-level recommendations." ], "metadata": { "id": "_A4CUnCj2oFy" } }, { "cell_type": "markdown", "source": [ "### **4. Recommended Next Step by Urgency Level**\n", "\n", "In this visualization, I examined the relationship between urgency level and the recommended next step.\n", "\n", "The goal was to validate whether the recommendation labels match the urgency labels in a logical way. \n", "This is important because the application should give a safer and more appropriate next step when the urgency level increases.\n", "\n", "The heatmap shows how many cases from each urgency level are assigned to each recommended next step." ], "metadata": { "id": "5_sbhC3T21ri" } }, { "cell_type": "code", "source": [ "### Graph 10 - Recommended Next Step by Urgency Level\n", "\n", "step_label_map = {\n", " \"Monitor at home and use general care guidance\": \"Home\\nmonitoring\",\n", " \"Monitor closely and contact a veterinarian if symptoms continue or worsen\": \"Close\\nmonitoring\",\n", " \"Contact a veterinarian as soon as possible\": \"Vet\\nsoon\",\n", " \"Contact an emergency veterinary clinic immediately\": \"Emergency\\nclinic\"\n", "}\n", "\n", "df[\"recommended_next_step_short\"] = df[\"recommended_next_step\"].map(step_label_map)\n", "\n", "short_step_order = [\"Home\\nmonitoring\", \"Close\\nmonitoring\", \"Vet\\nsoon\", \"Emergency\\nclinic\"]\n", "\n", "recommendation_table = pd.crosstab(\n", " df[\"urgency_level\"],\n", " df[\"recommended_next_step_short\"]\n", ").reindex(index=urgency_order, columns=short_step_order, fill_value=0)\n", "\n", "fig, ax = plt.subplots(figsize=(9, 5))\n", "\n", "im = ax.imshow(recommendation_table.values, cmap=\"RdPu\", aspect=\"auto\")\n", "\n", "ax.set_title(\"Recommended Next Step by Urgency Level\")\n", "ax.set_xlabel(\"Recommended Next Step\")\n", "ax.set_ylabel(\"Urgency Level\")\n", "\n", "ax.set_xticks(np.arange(len(recommendation_table.columns)))\n", "ax.set_xticklabels(recommendation_table.columns)\n", "\n", "ax.set_yticks(np.arange(len(recommendation_table.index)))\n", "ax.set_yticklabels(recommendation_table.index)\n", "\n", "for i in range(recommendation_table.shape[0]):\n", " for j in range(recommendation_table.shape[1]):\n", " value = recommendation_table.iloc[i, j]\n", " ax.text(j, i, str(value), ha=\"center\", va=\"center\", color=\"black\", fontsize=11)\n", "\n", "plt.colorbar(im, label=\"Number of Cases\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 506 }, "id": "fNzcfP7x6Li9", "outputId": "273b2ef0-941d-4e37-dc4e-6f1bce30d8f6" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The heatmap shows a perfect one-to-one relationship between urgency level and recommended next step.\n", "\n", "All **Low** urgency cases are assigned to **Home monitoring**, with **1,808 cases**. \n", "All **Medium** urgency cases are assigned to **Close monitoring**, with **2,830 cases**. \n", "All **High** urgency cases are assigned to **Vet soon**, with **2,680 cases**. \n", "All **Emergency** urgency cases are assigned to **Emergency clinic**, with **2,682 cases**.\n", "\n", "This result shows strong consistency between the urgency labels and the recommended next-step labels.\n", "\n", "From a data-quality perspective, this is positive because the recommendation column follows a clear and logical structure. \n", "However, it also means that `recommended_next_step` is highly dependent on `urgency_level`, so this column should be interpreted carefully in later modeling or recommendation stages.\n", "\n", "Overall, this validation confirms that the dataset provides safe and consistent next-step recommendations according to urgency level." ], "metadata": { "id": "2Fsn1sme-TLG" } }, { "cell_type": "markdown", "source": [ "### **5. High-Risk Cases by Medical Background**\n", "\n", "In this visualization, I examined which medical background categories are most associated with high-risk cases.\n", "\n", "For this analysis, high-risk cases are defined as cases labeled either **High** or **Emergency** urgency. \n", "The goal was to understand whether certain medical backgrounds are linked to a higher percentage of urgent cases.\n", "\n", "This chart ranks medical background categories by the percentage of high-risk cases." ], "metadata": { "id": "7eNLW_a64n-_" } }, { "cell_type": "code", "source": [ "# Create high-risk indicator: High or Emergency urgency\n", "df[\"is_high_risk\"] = df[\"urgency_level\"].isin([\"High\", \"Emergency\"])\n", "\n", "# Calculate high-risk statistics by medical background\n", "medical_risk = (\n", " df.groupby(\"medical_background\")\n", " .agg(\n", " total_cases=(\"urgency_level\", \"count\"),\n", " high_risk_cases=(\"is_high_risk\", \"sum\")\n", " )\n", " .reset_index()\n", ")\n", "\n", "# Calculate high-risk percentage\n", "medical_risk[\"high_risk_pct\"] = (\n", " medical_risk[\"high_risk_cases\"] / medical_risk[\"total_cases\"] * 100\n", ")\n", "\n", "# Keep only categories with enough cases\n", "medical_risk = medical_risk[medical_risk[\"total_cases\"] >= 100]\n", "\n", "# Sort from lowest to highest for clean horizontal ranking\n", "medical_risk = medical_risk.sort_values(\"high_risk_pct\", ascending=True)\n", "\n", "# Create figure\n", "fig, ax = plt.subplots(figsize=(13, 8))\n", "fig.patch.set_facecolor(\"#FFF9FB\")\n", "ax.set_facecolor(\"#FFF9FB\")\n", "\n", "# Y positions\n", "y_positions = range(len(medical_risk))\n", "\n", "# Draw soft horizontal lines\n", "ax.hlines(\n", " y=y_positions,\n", " xmin=0,\n", " xmax=medical_risk[\"high_risk_pct\"],\n", " color=\"#F3B6C8\",\n", " linewidth=5,\n", " alpha=0.65\n", ")\n", "\n", "# Draw main dots\n", "ax.scatter(\n", " medical_risk[\"high_risk_pct\"],\n", " y_positions,\n", " s=280,\n", " color=\"#D63384\",\n", " edgecolor=\"white\",\n", " linewidth=2.5,\n", " zorder=3\n", ")\n", "\n", "# Add percentage and count labels\n", "for y, (_, row) in zip(y_positions, medical_risk.iterrows()):\n", " ax.text(\n", " row[\"high_risk_pct\"] + 1,\n", " y,\n", " f\"{row['high_risk_pct']:.1f}% ({int(row['high_risk_cases'])}/{int(row['total_cases'])})\",\n", " va=\"center\",\n", " fontsize=10.5,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", " )\n", "\n", "# Add average reference line\n", "average_risk = medical_risk[\"high_risk_pct\"].mean()\n", "\n", "ax.axvline(\n", " average_risk,\n", " color=\"#6C4A78\",\n", " linestyle=\"--\",\n", " linewidth=2,\n", " alpha=0.85\n", ")\n", "\n", "ax.text(\n", " average_risk + 0.5,\n", " len(medical_risk) - 0.4,\n", " f\"Average: {average_risk:.1f}%\",\n", " fontsize=11,\n", " fontweight=\"bold\",\n", " color=\"#6C4A78\"\n", ")\n", "\n", "# Set labels and title\n", "ax.set_title(\n", " \"High-Risk Cases by Medical Background\",\n", " fontsize=21,\n", " fontweight=\"bold\",\n", " pad=18\n", ")\n", "\n", "ax.set_xlabel(\"High or Emergency Cases (%)\", fontsize=12)\n", "ax.set_ylabel(\"Medical Background\", fontsize=12)\n", "\n", "# Set y-axis labels\n", "ax.set_yticks(y_positions)\n", "ax.set_yticklabels(medical_risk[\"medical_background\"], fontsize=11)\n", "\n", "# Format x-axis\n", "ax.set_xlim(0, medical_risk[\"high_risk_pct\"].max() + 12)\n", "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.22)\n", "\n", "# Clean borders\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "ax.spines[\"left\"].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 807 }, "id": "WTzq_y465Njy", "outputId": "326e37c8-a7d6-468f-d38b-a32cd1b6d6de" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The chart shows that some medical background categories have a higher percentage of high-risk cases than others.\n", "\n", "The highest high-risk percentages appear in categories such as **Dental problems**, **Obesity**, **Recent surgery**, **Heart disease**, **Digestive issues**, and **Kidney disease**. \n", "For example, **Dental problems** has **65.6%** high-risk cases, while **Obesity** has **64.3%** high-risk cases.\n", "\n", "Categories such as **No known medical condition** and **Unknown** have lower high-risk percentages compared to many specific medical-background categories.\n", "\n", "This suggests that medical background is relevant when evaluating urgency. \n", "Pets with existing medical conditions may be more likely to appear in High or Emergency urgency cases.\n", "\n", "Overall, this visualization supports the idea that medical background can help HopePet AI understand risk and provide safer recommendations." ], "metadata": { "id": "8Xm3-MGG5VAz" } }, { "cell_type": "markdown", "source": [ "### **6. Recommended Next Step by Problem Category**\n", "\n", "In this visualization, I examined the relationship between problem category and the recommended next step.\n", "\n", "The goal was to understand what type of recommendation is most common within each problem category.\n", "This is important because HopePet AI should provide recommendations that match the type of pet-care problem, such as health, emergency, anxiety, grooming, nutrition, training, or behavior.\n", "\n", "The heatmap shows the percentage distribution of recommended next steps inside each problem category.\n" ], "metadata": { "id": "9KHCFzIu5fgR" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import textwrap\n", "\n", "# Function for wrapping long labels\n", "def wrap_labels(labels, width=22):\n", " return [\n", " \"\\n\".join(textwrap.wrap(str(label), width=width))\n", " for label in labels\n", " ]\n", "\n", "# Problem category order - only categories that exist in the dataset\n", "category_order = [\n", " category for category in df[\"problem_category\"].dropna().unique()\n", "]\n", "\n", "# Recommended next step order - only steps that exist in the dataset\n", "existing_steps = [\n", " step for step in df[\"recommended_next_step\"].dropna().unique()\n", "]\n", "\n", "# Create percentage cross-tab\n", "category_next_step_pct = pd.crosstab(\n", " df[\"problem_category\"],\n", " df[\"recommended_next_step\"],\n", " normalize=\"index\"\n", ").reindex(index=category_order, columns=existing_steps, fill_value=0) * 100\n", "\n", "# Create heatmap\n", "fig, ax = plt.subplots(figsize=(13, 7))\n", "\n", "im = ax.imshow(category_next_step_pct.values, cmap=\"PuBu\", aspect=\"auto\")\n", "\n", "ax.set_title(\"Recommended Next Step by Problem Category (%)\", fontsize=16)\n", "ax.set_xlabel(\"Recommended Next Step\")\n", "ax.set_ylabel(\"Problem Category\")\n", "\n", "ax.set_xticks(np.arange(len(category_next_step_pct.columns)))\n", "ax.set_xticklabels(\n", " wrap_labels(category_next_step_pct.columns, width=22),\n", " rotation=35,\n", " ha=\"right\"\n", ")\n", "\n", "ax.set_yticks(np.arange(len(category_next_step_pct.index)))\n", "ax.set_yticklabels(category_next_step_pct.index)\n", "\n", "# Add percentage labels inside the heatmap\n", "for i in range(category_next_step_pct.shape[0]):\n", " for j in range(category_next_step_pct.shape[1]):\n", " value = category_next_step_pct.iloc[i, j]\n", " ax.text(\n", " j,\n", " i,\n", " f\"{value:.1f}%\",\n", " ha=\"center\",\n", " va=\"center\",\n", " color=\"black\",\n", " fontsize=9\n", " )\n", "\n", "plt.colorbar(im, label=\"Percentage of Cases\")\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 707 }, "id": "-bi1hBpSWCpC", "outputId": "7810286a-5e0e-4a98-f3f4-a7a606488fac" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The heatmap shows that the recommended next step changes according to the problem category.\n", "\n", "The **Emergency** category is strongly consistent, with **100.0%** of cases recommending to **contact an emergency veterinary clinic immediately**.\n", "This is expected because emergency-related problems should always lead to urgent veterinary attention.\n", "\n", "For the **Health** category, the most common recommendation is to **contact a veterinarian as soon as possible**, with **54.0%** of cases.\n", "This makes sense because health-related symptoms often require professional veterinary evaluation.\n", "\n", "Categories such as **Training**, **Grooming**, **Nutrition**, **Anxiety**, and **Behavior** show more variation.\n", "Many of these cases recommend either home monitoring, general guidance, or close monitoring with veterinary contact if symptoms continue or worsen.\n", "\n", "Overall, this visualization shows that the dataset links different problem categories to different types of recommended actions.\n", "This supports the logic of the HopePet AI application because recommendations are not random; they depend on the type and seriousness of the pet-care problem.\n" ], "metadata": { "id": "fKFX0b1N-gTz" } }, { "cell_type": "markdown", "source": [ "### **6. High-Risk Rate by Appetite Status and Energy Level**\n", "\n", "In this visualization, I examined the relationship between appetite status, energy level, and high-risk urgency.\n", "\n", "For this analysis, high-risk cases are defined as cases labeled either **High** or **Emergency** urgency. \n", "The goal was to identify which combinations of appetite and energy level are most strongly associated with higher-risk pet-care situations.\n", "\n", "This is important for HopePet AI because appetite and energy level are two key user inputs in the application. \n", "Understanding this relationship can help validate the risk logic used later in the recommendation system." ], "metadata": { "id": "jV9RZuXW6Ifd" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# High-Risk Rate by Appetite and Energy Level\n", "# ==========================================\n", "\n", "# Create high-risk indicator\n", "df[\"is_high_risk\"] = df[\"urgency_level\"].isin([\"High\", \"Emergency\"])\n", "\n", "# Define clear order for rows and columns\n", "appetite_order = [\"Normal\", \"Reduced\", \"Not eating\", \"Increased\", \"Unknown\"]\n", "energy_order = [\"Normal\", \"Slightly tired\", \"Very tired\", \"Restless\", \"Cannot stand\", \"Unknown\"]\n", "\n", "# Keep only values that actually exist in the dataset\n", "existing_appetite = [x for x in appetite_order if x in df[\"appetite_status\"].unique()]\n", "existing_energy = [x for x in energy_order if x in df[\"energy_level\"].unique()]\n", "\n", "# Calculate high-risk percentage for each combination\n", "risk_matrix = (\n", " df.groupby([\"appetite_status\", \"energy_level\"])[\"is_high_risk\"]\n", " .mean()\n", " .unstack()\n", " .reindex(index=existing_appetite, columns=existing_energy)\n", " * 100\n", ")\n", "\n", "# Calculate counts for each combination\n", "count_matrix = (\n", " df.groupby([\"appetite_status\", \"energy_level\"])\n", " .size()\n", " .unstack()\n", " .reindex(index=existing_appetite, columns=existing_energy)\n", ")\n", "\n", "# Create figure\n", "fig, ax = plt.subplots(figsize=(13, 7))\n", "fig.patch.set_facecolor(\"#FFF9FB\")\n", "ax.set_facecolor(\"#FFF9FB\")\n", "\n", "# Create heatmap\n", "im = ax.imshow(\n", " risk_matrix,\n", " cmap=\"RdPu\",\n", " aspect=\"auto\",\n", " vmin=0,\n", " vmax=100\n", ")\n", "\n", "# Add text labels inside cells\n", "for i in range(risk_matrix.shape[0]):\n", " for j in range(risk_matrix.shape[1]):\n", " value = risk_matrix.iloc[i, j]\n", " count = count_matrix.iloc[i, j]\n", "\n", " if not pd.isna(value):\n", " ax.text(\n", " j,\n", " i,\n", " f\"{value:.1f}%\\n(n={int(count)})\",\n", " ha=\"center\",\n", " va=\"center\",\n", " fontsize=10,\n", " fontweight=\"bold\",\n", " color=\"#222\"\n", " )\n", "\n", "# Set axis labels\n", "ax.set_xticks(np.arange(len(risk_matrix.columns)))\n", "ax.set_yticks(np.arange(len(risk_matrix.index)))\n", "\n", "ax.set_xticklabels(risk_matrix.columns, rotation=30, ha=\"right\", fontsize=11)\n", "ax.set_yticklabels(risk_matrix.index, fontsize=11)\n", "\n", "# Add title and labels\n", "ax.set_title(\n", " \"High-Risk Rate by Appetite Status and Energy Level\",\n", " fontsize=20,\n", " fontweight=\"bold\",\n", " pad=18\n", ")\n", "\n", "ax.set_xlabel(\"Energy Level\", fontsize=12)\n", "ax.set_ylabel(\"Appetite Status\", fontsize=12)\n", "\n", "# Add colorbar\n", "cbar = plt.colorbar(im, ax=ax)\n", "cbar.set_label(\"High or Emergency Cases (%)\", fontsize=11)\n", "\n", "# Clean style\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Find the highest-risk combination\n", "risk_long = risk_matrix.stack().reset_index()\n", "risk_long.columns = [\"Appetite Status\", \"Energy Level\", \"High-Risk Percentage\"]\n", "\n", "count_long = count_matrix.stack().reset_index()\n", "count_long.columns = [\"Appetite Status\", \"Energy Level\", \"Number of Cases\"]\n", "\n", "risk_summary = risk_long.merge(\n", " count_long,\n", " on=[\"Appetite Status\", \"Energy Level\"]\n", ")\n", "\n", "risk_summary = risk_summary.sort_values(\n", " \"High-Risk Percentage\",\n", " ascending=False\n", ")\n", "\n", "risk_summary.head(10)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "LS_8rnba6C5s", "outputId": "cb3c75ac-3235-495a-d1e8-2342008bb585" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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Appetite StatusEnergy LevelHigh-Risk PercentageNumber of Cases
4NormalCannot stand100.000000100
10ReducedCannot stand100.000000209
22IncreasedCannot stand100.0000002
28UnknownCannot stand100.00000071
16Not eatingCannot stand100.000000251
14Not eatingVery tired94.899329745
8ReducedVery tired83.333333648
17Not eatingUnknown80.212014283
13Not eatingSlightly tired75.000000748
12Not eatingNormal74.488189635
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "risk_summary", "summary": "{\n \"name\": \"risk_summary\",\n \"rows\": 30,\n \"fields\": [\n {\n \"column\": \"Appetite Status\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Reduced\",\n \"Not eating\",\n \"Increased\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Energy Level\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"Cannot stand\",\n \"Very tired\",\n \"Restless\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High-Risk Percentage\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 31.821045882673193,\n \"min\": 10.44776119402985,\n \"max\": 100.0,\n \"num_unique_values\": 26,\n \"samples\": [\n 58.56,\n 30.601092896174865,\n 100.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Number of Cases\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 265,\n \"min\": 2,\n \"max\": 806,\n \"num_unique_values\": 30,\n \"samples\": [\n 95,\n 456,\n 524\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 34 } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The heatmap shows how the percentage of high-risk cases changes according to appetite status and energy level.\n", "\n", "Combinations that include lower appetite and lower energy are expected to show higher risk percentages. \n", "This is important because pets that are not eating and also appear very tired or weak may require closer attention.\n", "\n", "The chart helps identify which user inputs are most strongly connected to High or Emergency urgency levels. \n", "This is useful for HopePet AI because the application uses appetite status and energy level as part of its risk assessment.\n", "\n", "Overall, this visualization supports the idea that appetite and energy are important signals for estimating urgency and generating safer recommendations." ], "metadata": { "id": "ki6xL6-46U4i" } }, { "cell_type": "markdown", "source": [ "### **7. Risk Lift by Recent Change**\n", "\n", "In this visualization, I examined whether different recent changes are associated with higher or lower risk compared to the overall dataset average.\n", "\n", "For this analysis, high-risk cases are defined as cases labeled either **High** or **Emergency** urgency.\n", "Instead of only showing the high-risk percentage for each recent change, this graph shows the **risk lift**.\n", "\n", "Risk lift means how much higher or lower each recent-change category is compared to the overall high-risk rate in the dataset.\n", "\n", "This is important for HopePet AI because recent changes may provide useful context for understanding the pet’s condition and improving the recommendation process.\n" ], "metadata": { "id": "Y5AyXzOt7YGB" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Risk Lift by Recent Change\n", "# ==========================================\n", "\n", "# Create high-risk indicator\n", "df[\"is_high_risk\"] = df[\"urgency_level\"].isin([\"High\", \"Emergency\"])\n", "\n", "# Calculate overall high-risk rate\n", "overall_high_risk_rate = df[\"is_high_risk\"].mean() * 100\n", "\n", "# Calculate high-risk rate by recent change\n", "recent_change_risk = (\n", " df.groupby(\"recent_change\")\n", " .agg(\n", " total_cases=(\"urgency_level\", \"count\"),\n", " high_risk_cases=(\"is_high_risk\", \"sum\")\n", " )\n", " .reset_index()\n", ")\n", "\n", "# Calculate high-risk percentage\n", "recent_change_risk[\"high_risk_pct\"] = (\n", " recent_change_risk[\"high_risk_cases\"] / recent_change_risk[\"total_cases\"] * 100\n", ")\n", "\n", "# Calculate risk lift compared to overall average\n", "recent_change_risk[\"risk_lift\"] = (\n", " recent_change_risk[\"high_risk_pct\"] - overall_high_risk_rate\n", ")\n", "\n", "# Keep only categories with enough cases\n", "recent_change_risk = recent_change_risk[recent_change_risk[\"total_cases\"] >= 100]\n", "\n", "# Sort by risk lift\n", "recent_change_risk = recent_change_risk.sort_values(\"risk_lift\")\n", "\n", "# Define colors: blue = lower than average, pink = higher than average\n", "bar_colors = [\n", " \"#BDE0FE\" if value < 0 else \"#F7A8B8\"\n", " for value in recent_change_risk[\"risk_lift\"]\n", "]\n", "\n", "# Create figure\n", "fig, ax = plt.subplots(figsize=(13, 7))\n", "fig.patch.set_facecolor(\"#FFF9FB\")\n", "ax.set_facecolor(\"#FFF9FB\")\n", "\n", "# Create horizontal diverging bar chart\n", "bars = ax.barh(\n", " recent_change_risk[\"recent_change\"],\n", " recent_change_risk[\"risk_lift\"],\n", " color=bar_colors,\n", " edgecolor=\"white\",\n", " linewidth=2\n", ")\n", "\n", "# Add vertical zero line\n", "ax.axvline(\n", " 0,\n", " color=\"#444\",\n", " linewidth=2,\n", " alpha=0.8\n", ")\n", "\n", "# Add value labels\n", "for bar, (_, row) in zip(bars, recent_change_risk.iterrows()):\n", " value = row[\"risk_lift\"]\n", " label_position = value + 0.4 if value >= 0 else value - 0.4\n", " ha = \"left\" if value >= 0 else \"right\"\n", "\n", " ax.text(\n", " label_position,\n", " bar.get_y() + bar.get_height() / 2,\n", " f\"{value:+.1f} pts\",\n", " va=\"center\",\n", " ha=ha,\n", " fontsize=11,\n", " fontweight=\"bold\",\n", " color=\"#333\"\n", " )\n", "\n", "# Add title and labels\n", "ax.set_title(\n", " \"Risk Lift by Recent Change\",\n", " fontsize=22,\n", " fontweight=\"bold\",\n", " pad=18\n", ")\n", "\n", "ax.set_xlabel(\"Difference from Overall High-Risk Rate (percentage points)\", fontsize=12)\n", "ax.set_ylabel(\"Recent Change\", fontsize=12)\n", "\n", "# Add explanation labels\n", "ax.text(\n", " recent_change_risk[\"risk_lift\"].min(),\n", " len(recent_change_risk) - 0.4,\n", " \"Lower than average risk\",\n", " fontsize=10,\n", " fontweight=\"bold\",\n", " color=\"#3A5F7D\"\n", ")\n", "\n", "ax.text(\n", " recent_change_risk[\"risk_lift\"].max(),\n", " len(recent_change_risk) - 0.4,\n", " \"Higher than average risk\",\n", " fontsize=10,\n", " fontweight=\"bold\",\n", " color=\"#9B3D63\",\n", " ha=\"right\"\n", ")\n", "\n", "# Clean style\n", "ax.grid(axis=\"x\", linestyle=\"--\", alpha=0.25)\n", "ax.spines[\"top\"].set_visible(False)\n", "ax.spines[\"right\"].set_visible(False)\n", "ax.spines[\"left\"].set_visible(False)\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 707 }, "id": "eDD1elQM8VPs", "outputId": "a1d0d373-7814-4762-b916-c31a6f626bf5" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "This graph shows which recent changes are associated with higher or lower risk compared to the overall high-risk rate in the dataset.\n", "\n", "Categories on the right side of the zero line have a higher-than-average percentage of High or Emergency cases.\n", "Categories on the left side have a lower-than-average percentage of High or Emergency cases.\n", "\n", "This visualization gives more insight than a regular bar chart because it does not only show raw percentages.\n", "It shows whether each recent change increases or decreases risk compared to the dataset baseline.\n", "\n", "Overall, this graph supports the decision to include `recent_change` as an input feature in the HopePet AI application, because recent changes can provide useful context for understanding the pet’s symptoms and estimating urgency.\n" ], "metadata": { "id": "9Dxq4If-8gSs" } }, { "cell_type": "markdown", "source": [ "### **8. Bonus Visualization: HopePet AI Decision Flow**\n", "\n", "In this bonus visualization, I created an interactive Sankey diagram to show the decision flow inside the dataset.\n", "\n", "The graph connects three important fields:\n", "\n", "- `problem_category`\n", "- `urgency_level`\n", "- `recommended_next_step`\n", "\n", "The goal is to understand how pet-care problem categories flow into urgency levels, and how urgency levels then lead to recommended next steps.\n", "\n", "This visualization is important because it gives a high-level view of the logic behind the HopePet AI application. \n", "It helps show whether the dataset supports a clear and responsible decision path from the user’s problem to the final recommendation." ], "metadata": { "id": "eGW-nNyC64k3" } }, { "cell_type": "code", "source": [ "# ==========================================\n", "# Bonus Visualization: HopePet AI Decision Flow\n", "# Problem Category → Urgency Level → Recommended Next Step\n", "# ==========================================\n", "\n", "import plotly.graph_objects as go\n", "import pandas as pd\n", "\n", "# Create a flow table from problem category to urgency level\n", "category_to_urgency = (\n", " df.groupby([\"problem_category\", \"urgency_level\"])\n", " .size()\n", " .reset_index(name=\"count\")\n", ")\n", "\n", "# Create a flow table from urgency level to recommended next step\n", "urgency_to_step = (\n", " df.groupby([\"urgency_level\", \"recommended_next_step\"])\n", " .size()\n", " .reset_index(name=\"count\")\n", ")\n", "\n", "# Create unique labels for all nodes\n", "problem_categories = sorted(df[\"problem_category\"].unique().tolist())\n", "\n", "urgency_order = [\"Low\", \"Medium\", \"High\", \"Emergency\"]\n", "urgency_levels = [\n", " urgency for urgency in urgency_order\n", " if urgency in df[\"urgency_level\"].unique()\n", "]\n", "\n", "recommended_steps = sorted(df[\"recommended_next_step\"].unique().tolist())\n", "\n", "all_labels = problem_categories + urgency_levels + recommended_steps\n", "\n", "# Create label-to-index dictionary\n", "label_to_index = {\n", " label: index\n", " for index, label in enumerate(all_labels)\n", "}\n", "\n", "# Build source, target, and value lists\n", "sources = []\n", "targets = []\n", "values = []\n", "\n", "# Add flows: Problem Category → Urgency Level\n", "for _, row in category_to_urgency.iterrows():\n", " sources.append(label_to_index[row[\"problem_category\"]])\n", " targets.append(label_to_index[row[\"urgency_level\"]])\n", " values.append(row[\"count\"])\n", "\n", "# Add flows: Urgency Level → Recommended Next Step\n", "for _, row in urgency_to_step.iterrows():\n", " sources.append(label_to_index[row[\"urgency_level\"]])\n", " targets.append(label_to_index[row[\"recommended_next_step\"]])\n", " values.append(row[\"count\"])\n", "\n", "# Define node colors\n", "node_colors = []\n", "\n", "for label in all_labels:\n", " if label in problem_categories:\n", " node_colors.append(\"#BDE0FE\") # pastel blue\n", " elif label in urgency_levels:\n", " if label == \"Low\":\n", " node_colors.append(\"#B8E0D2\")\n", " elif label == \"Medium\":\n", " node_colors.append(\"#FFD6A5\")\n", " elif label == \"High\":\n", " node_colors.append(\"#F7A8B8\")\n", " else:\n", " node_colors.append(\"#CDB4DB\")\n", " else:\n", " node_colors.append(\"#FFF1F5\") # soft pink\n", "\n", "# Create Sankey figure\n", "fig = go.Figure(\n", " data=[\n", " go.Sankey(\n", " arrangement=\"snap\",\n", " node=dict(\n", " pad=22,\n", " thickness=22,\n", " line=dict(color=\"white\", width=1.5),\n", " label=all_labels,\n", " color=node_colors\n", " ),\n", " link=dict(\n", " source=sources,\n", " target=targets,\n", " value=values,\n", " color=\"rgba(180, 190, 220, 0.35)\"\n", " )\n", " )\n", " ]\n", ")\n", "\n", "# Update layout\n", "fig.update_layout(\n", " title=dict(\n", " text=\"HopePet AI Decision Flow: Problem Category → Urgency Level → Recommended Next Step\",\n", " x=0.5,\n", " font=dict(size=22)\n", " ),\n", " font=dict(size=12),\n", " paper_bgcolor=\"#FFF9FB\",\n", " plot_bgcolor=\"#FFF9FB\",\n", " height=750,\n", " margin=dict(l=30, r=30, t=90, b=30)\n", ")\n", "\n", "fig.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 767 }, "id": "ghYuUqEA6t7O", "outputId": "71be8a1f-348e-48cd-f3f4-8bc6d8c70956" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "**Result**\n", "\n", "The Sankey diagram shows the full decision flow of the dataset from problem category to urgency level and then to the recommended next step.\n", "\n", "The visualization shows that different problem categories lead to different urgency patterns. \n", "For example, emergency-related cases flow strongly into the Emergency urgency level, while categories such as training, grooming, nutrition, anxiety, and behavior are more spread across Low, Medium, and High urgency levels.\n", "\n", "The second part of the flow shows how urgency levels connect to recommended next steps. \n", "Low urgency cases flow into home monitoring, Medium cases flow into closer monitoring, High cases flow into contacting a veterinarian soon, and Emergency cases flow into contacting an emergency veterinary clinic immediately.\n", "\n", "This is an important validation result because it shows that the dataset contains a clear decision structure. \n", "The recommendation path is not random; it follows a logical flow from the type of problem, to urgency level, and then to the final recommended action.\n", "\n", "Overall, this bonus visualization supports the design of HopePet AI as a recommendation system that combines problem classification, urgency estimation, and responsible next-step guidance." ], "metadata": { "id": "BoTEDkBS6-lz" } }, { "cell_type": "markdown", "source": [ "

\n", "\n", "---\n", "\n", "

" ], "metadata": { "id": "mmucBzLt6Qk9" } }, { "cell_type": "markdown", "source": [ "# **Part 3 : Recommendation with Embeddings**\n", "\n", "In this part, I build the recommendation component of HOPEPET.\n", "\n", "Since the dataset is text-based, I use text embedding models to convert each pet-care case into a numerical vector. Then, I compare the user input vector to the dataset vectors and retrieve the top 3 most similar cases.\n", "\n", "After retrieving the similar cases, the system creates one generated response for the user. This response is based on the retrieved cases and includes the predicted problem category, urgency level, recommended next step, safe guidance, and a veterinary warning.\n", "\n", "This creates the full pipeline:\n", "\n", "User input → Text embedding → Similar case recommendation → Generated response" ], "metadata": { "id": "M_HCdRVptJPI" } }, { "cell_type": "markdown", "source": [ "## 1. Task Definition\n", "\n", "The recommendation task in HOPEPET is a text-to-vector similarity task.\n", "\n", "Each row in the dataset contains a `retrieval_text` field. This field combines the most important case details, such as pet type, age group, symptoms, medical background, urgency level, and recommended next step.\n", "\n", "The embedding model converts each `retrieval_text` into a vector. When the user describes a new pet-care problem, the same model converts the user input into a vector. Then, the system compares the user vector to all dataset vectors and retrieves the most similar cases.\n", "\n", "The retrieved cases are later used to create one final generated answer for the user." ], "metadata": { "id": "RdP8-8oFHhEQ" } }, { "cell_type": "code", "source": [ "!pip install -q sentence-transformers faiss-cpu" ], "metadata": { "id": "ecnHQRQZ2UgX", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c6774f26-91f9-44e5-81c7-9f7bad8f67f1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.5/18.5 MB\u001b[0m \u001b[31m48.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import numpy as np\n", "import time\n", "import pickle\n", "\n", "from sentence_transformers import SentenceTransformer\n", "from sklearn.metrics.pairwise import cosine_similarity" ], "metadata": { "id": "PrpoeseZ6UhL" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 2. Preparing the Text for Embeddings\n", "\n", "For the embedding-based recommendation system, I use the `retrieval_text` column. This column is better than using only the user question because it contains more context about the pet-care case." ], "metadata": { "id": "6Et6aBPDHrN6" } }, { "cell_type": "code", "source": [ "df[\"retrieval_text\"] = df[\"retrieval_text\"].astype(str)\n", "\n", "texts = df[\"retrieval_text\"].tolist()\n", "\n", "print(\"Number of texts:\", len(texts))\n", "print(\"\\nExample retrieval text:\")\n", "print(texts[0])" ], "metadata": { "id": "dRQ5aWKx6Ujn" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "The `retrieval_text` column will be converted into embeddings. Each embedding represents one pet-care case as a numerical vector that can be compared to a new user input." ], "metadata": { "id": "iSe1712MHyqO" } }, { "cell_type": "markdown", "source": [ "## 3. Selecting Three Hugging Face Embedding Models\n", "\n", "To find the best embedding model for HOPEPET, I compare three different Hugging Face text embedding models.\n", "\n", "The models are evaluated based on:\n", "\n", "- Retrieval quality\n", "- Runtime\n", "- Embedding size\n", "- Practical usability for the final Gradio app" ], "metadata": { "id": "ZqkicDxHH2O1" } }, { "cell_type": "code", "source": [ "embedding_models = {\n", " \"all-MiniLM-L6-v2\": \"sentence-transformers/all-MiniLM-L6-v2\",\n", " \"paraphrase-MiniLM-L3-v2\": \"sentence-transformers/paraphrase-MiniLM-L3-v2\",\n", " \"all-mpnet-base-v2\": \"sentence-transformers/all-mpnet-base-v2\"\n", "}\n", "\n", "embedding_models" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yqDub74hH6K6", "outputId": "b5a3f445-5057-4f39-dd0d-40d3c62cce6d" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'all-MiniLM-L6-v2': 'sentence-transformers/all-MiniLM-L6-v2',\n", " 'paraphrase-MiniLM-L3-v2': 'sentence-transformers/paraphrase-MiniLM-L3-v2',\n", " 'all-mpnet-base-v2': 'sentence-transformers/all-mpnet-base-v2'}" ] }, "metadata": {}, "execution_count": 39 } ] }, { "cell_type": "markdown", "source": [ "### 4. Evaluation Setup\n", "\n", "To compare the models efficiently, I use a sample of the dataset for evaluation. This allows me to test the models without spending too much time embedding the full dataset three separate times." ], "metadata": { "id": "mORQID35H8Yt" } }, { "cell_type": "code", "source": [ "sample_df = df.sample(n=1000, random_state=42).reset_index(drop=True)\n", "sample_texts = sample_df[\"retrieval_text\"].astype(str).tolist()\n", "\n", "sample_df.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "O4t80Tj9H-CM", "outputId": "09b406fa-27f0-4aa2-bb3a-7a3da5d73a47" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(1000, 43)" ] }, "metadata": {}, "execution_count": 40 } ] }, { "cell_type": "markdown", "source": [ "### 5. Test Queries for Model Evaluation\n", "\n", "I created several test queries that represent different types of pet-care problems. Each query has an expected problem category. A good embedding model should retrieve similar cases from the correct category." ], "metadata": { "id": "b-eZExJDICJu" } }, { "cell_type": "code", "source": [ "test_queries = [\n", " {\n", " \"query\": \"My dog is vomiting and has no appetite since yesterday.\",\n", " \"expected_category\": \"Health\"\n", " },\n", " {\n", " \"query\": \"My cat is bleeding and breathing very fast.\",\n", " \"expected_category\": \"Emergency\"\n", " },\n", " {\n", " \"query\": \"My puppy keeps biting my hands during playtime.\",\n", " \"expected_category\": \"Training\"\n", " },\n", " {\n", " \"query\": \"My dog is shaking and hiding during fireworks.\",\n", " \"expected_category\": \"Anxiety\"\n", " },\n", " {\n", " \"query\": \"My cat has dry skin and needs grooming help.\",\n", " \"expected_category\": \"Grooming\"\n", " },\n", " {\n", " \"query\": \"My dog refuses to eat the new food I bought.\",\n", " \"expected_category\": \"Nutrition\"\n", " }\n", "]" ], "metadata": { "id": "Gy9RIqHCIFej" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 6. Evaluation Function\n", "\n", "In this section, I create a function that evaluates each embedding model. \n", "The function embeds a sample of the dataset, embeds each test query, retrieves the most similar cases, and checks whether the expected category appears in the top results.\n", "\n", "For each model, I measure:\n", "\n", "- Embedding time\n", "- Embedding dimension\n", "- Top 3 retrieval accuracy\n", "- Top 5 retrieval accuracy" ], "metadata": { "id": "Be2HtZwyIT8k" } }, { "cell_type": "code", "source": [ "def evaluate_embedding_model(model_name, model_path, sample_texts, sample_df, test_queries):\n", " start_time = time.time()\n", "\n", " # Load embedding model\n", " model = SentenceTransformer(model_path)\n", "\n", " # Create embeddings for the sample dataset\n", " sample_embeddings = model.encode(\n", " sample_texts,\n", " convert_to_numpy=True,\n", " show_progress_bar=True,\n", " normalize_embeddings=True\n", " )\n", "\n", " embedding_time = time.time() - start_time\n", " embedding_dimension = sample_embeddings.shape[1]\n", "\n", " top3_hits = 0\n", " top5_hits = 0\n", " detailed_results = []\n", "\n", " for item in test_queries:\n", " query = item[\"query\"]\n", " expected_category = item[\"expected_category\"]\n", "\n", " # Embed the user query\n", " query_embedding = model.encode(\n", " [query],\n", " convert_to_numpy=True,\n", " normalize_embeddings=True\n", " )\n", "\n", " # Calculate similarity between query and dataset sample\n", " similarities = cosine_similarity(query_embedding, sample_embeddings)[0]\n", "\n", " # Sort by highest similarity\n", " ranked_indices = np.argsort(similarities)[::-1]\n", "\n", " top3_indices = ranked_indices[:3]\n", " top5_indices = ranked_indices[:5]\n", "\n", " top3_categories = sample_df.iloc[top3_indices][\"problem_category\"].tolist()\n", " top5_categories = sample_df.iloc[top5_indices][\"problem_category\"].tolist()\n", "\n", " top3_hit = expected_category in top3_categories\n", " top5_hit = expected_category in top5_categories\n", "\n", " if top3_hit:\n", " top3_hits += 1\n", "\n", " if top5_hit:\n", " top5_hits += 1\n", "\n", " detailed_results.append({\n", " \"model\": model_name,\n", " \"query\": query,\n", " \"expected_category\": expected_category,\n", " \"top3_categories\": top3_categories,\n", " \"top5_categories\": top5_categories,\n", " \"top3_hit\": top3_hit,\n", " \"top5_hit\": top5_hit\n", " })\n", "\n", " summary = {\n", " \"model\": model_name,\n", " \"embedding_dimension\": embedding_dimension,\n", " \"embedding_time_seconds\": round(embedding_time, 2),\n", " \"top3_accuracy\": round(top3_hits / len(test_queries), 2),\n", " \"top5_accuracy\": round(top5_hits / len(test_queries), 2)\n", " }\n", "\n", " return summary, pd.DataFrame(detailed_results)" ], "metadata": { "id": "jUCXdt_AIcs3" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 7. Evaluating the Three Embedding Models\n", "\n", "Now I run the evaluation function on the three selected Hugging Face embedding models." ], "metadata": { "id": "dNMbZ7pkIf2b" } }, { "cell_type": "code", "source": [ "evaluation_summaries = []\n", "detailed_results = {}\n", "\n", "for model_name, model_path in embedding_models.items():\n", " print(f\"Evaluating model: {model_name}\")\n", "\n", " summary, results_df = evaluate_embedding_model(\n", " model_name=model_name,\n", " model_path=model_path,\n", " sample_texts=sample_texts,\n", " sample_df=sample_df,\n", " test_queries=test_queries\n", " )\n", "\n", " evaluation_summaries.append(summary)\n", " detailed_results[model_name] = results_df\n", "\n", "evaluation_df = pd.DataFrame(evaluation_summaries)\n", "evaluation_df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "ea8b2164d581401581468fc23086bebd", "7df2cd3521a94f5ab56839bbbf3b2542", "a7770d8703f94bca88ceef297e37cfa4", 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"name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:112: UserWarning:\n", "\n", "\n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", "\n", "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "modules.json: 0%| | 0.00/349 [00:00\n", "
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modelembedding_dimensionembedding_time_secondstop3_accuracytop5_accuracy
0all-MiniLM-L6-v238484.591.001.0
1paraphrase-MiniLM-L3-v238423.560.831.0
2all-mpnet-base-v2768624.281.001.0
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\n", " \n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "evaluation_df", "summary": "{\n \"name\": \"evaluation_df\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"all-MiniLM-L6-v2\",\n \"paraphrase-MiniLM-L3-v2\",\n \"all-mpnet-base-v2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"embedding_dimension\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 221,\n \"min\": 384,\n \"max\": 768,\n \"num_unique_values\": 2,\n \"samples\": [\n 768,\n 384\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"embedding_time_seconds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 330.6192360304121,\n \"min\": 23.56,\n \"max\": 624.28,\n \"num_unique_values\": 3,\n \"samples\": [\n 84.59,\n 23.56\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top3_accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0981495457622364,\n \"min\": 0.83,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.83,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top5_accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 1.0,\n \"max\": 1.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 43 } ] }, { "cell_type": "markdown", "source": [ "**Insight:** \n", "The model comparison shows that both `all-MiniLM-L6-v2` and `all-mpnet-base-v2` achieved perfect Top 3 and Top 5 accuracy on the test queries. However, `all-mpnet-base-v2` was much slower and produced larger embeddings with 768 dimensions. The `paraphrase-MiniLM-L3-v2` model was the fastest, but it had lower Top 3 accuracy, which means it was less reliable for retrieving the most relevant cases.\n", "\n", "Based on this comparison, `all-MiniLM-L6-v2` provides the best balance between retrieval quality, runtime, embedding size, and practical usability for the final HOPEPET app." ], "metadata": { "id": "y7bgc5MeMEzL" } }, { "cell_type": "markdown", "source": [ "## 8. Model Comparison\n", "\n", "The table below compares the three embedding models based on embedding dimension, runtime, Top 3 accuracy, and Top 5 accuracy." ], "metadata": { "id": "I2B7mloOIlva" } }, { "cell_type": "code", "source": [ "evaluation_df.sort_values(\n", " by=[\"top3_accuracy\", \"top5_accuracy\", \"embedding_time_seconds\"],\n", " ascending=[False, False, True]\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "UeuGuMltIoR1", "outputId": "f463578d-0a23-4736-9cdd-7ff58c5a52a8" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " model embedding_dimension embedding_time_seconds \\\n", "0 all-MiniLM-L6-v2 384 84.59 \n", "2 all-mpnet-base-v2 768 624.28 \n", "1 paraphrase-MiniLM-L3-v2 384 23.56 \n", "\n", " top3_accuracy top5_accuracy \n", "0 1.00 1.0 \n", "2 1.00 1.0 \n", "1 0.83 1.0 " ], "text/html": [ "\n", "
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modelembedding_dimensionembedding_time_secondstop3_accuracytop5_accuracy
0all-MiniLM-L6-v238484.591.001.0
2all-mpnet-base-v2768624.281.001.0
1paraphrase-MiniLM-L3-v238423.560.831.0
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \")\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"model\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"all-MiniLM-L6-v2\",\n \"all-mpnet-base-v2\",\n \"paraphrase-MiniLM-L3-v2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"embedding_dimension\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 221,\n \"min\": 384,\n \"max\": 768,\n \"num_unique_values\": 2,\n \"samples\": [\n 768,\n 384\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"embedding_time_seconds\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 330.6192360304121,\n \"min\": 23.56,\n \"max\": 624.28,\n \"num_unique_values\": 3,\n \"samples\": [\n 84.59,\n 624.28\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top3_accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0981495457622364,\n \"min\": 0.83,\n \"max\": 1.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.83,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top5_accuracy\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 1.0,\n \"max\": 1.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 44 } ] }, { "cell_type": "markdown", "source": [ "The best model should provide a good balance between retrieval quality and runtime. Since HOPEPET will later run as a Gradio app, the selected model should also be lightweight and practical for real-time use." ], "metadata": { "id": "0JlKaFrsJBmG" } }, { "cell_type": "markdown", "source": [ "**Important note:** \n", "The accuracy score in this evaluation is not a traditional classification accuracy. It represents a retrieval hit rate. A Top 3 hit means that the expected problem category appeared somewhere within the three most similar retrieved cases. Therefore, a score of 1.00 means that the model retrieved at least one relevant category in the Top 3 results for all test queries." ], "metadata": { "id": "8-QzJ3CUM9dm" } }, { "cell_type": "markdown", "source": [ "### 9. Detailed Retrieval Results\n", "\n", "In this section, I inspect the retrieved categories for each test query and each model." ], "metadata": { "id": "mgTlBTiTJECK" } }, { "cell_type": "code", "source": [ "detailed_results[\"all-MiniLM-L6-v2\"]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 341 }, "id": "geCcYvKEJHiA", "outputId": "2d330cc9-d685-41fb-b0ce-dea7cb4b280c" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " model query \\\n", "0 all-MiniLM-L6-v2 My dog is vomiting and has no appetite since y... \n", "1 all-MiniLM-L6-v2 My cat is bleeding and breathing very fast. \n", "2 all-MiniLM-L6-v2 My puppy keeps biting my hands during playtime. \n", "3 all-MiniLM-L6-v2 My dog is shaking and hiding during fireworks. \n", "4 all-MiniLM-L6-v2 My cat has dry skin and needs grooming help. \n", "5 all-MiniLM-L6-v2 My dog refuses to eat the new food I bought. \n", "\n", " expected_category top3_categories \\\n", "0 Health [Health, Health, Health] \n", "1 Emergency [Emergency, Emergency, Emergency] \n", "2 Training [Training, Anxiety, Training] \n", "3 Anxiety [Anxiety, Anxiety, Anxiety] \n", "4 Grooming [Grooming, Grooming, Grooming] \n", "5 Nutrition [Nutrition, Nutrition, Nutrition] \n", "\n", " top5_categories top3_hit top5_hit \n", "0 [Health, Health, Health, Health, Health] True True \n", "1 [Emergency, Emergency, Emergency, Emergency, E... True True \n", "2 [Training, Anxiety, Training, Anxiety, Training] True True \n", "3 [Anxiety, Anxiety, Anxiety, Behavior, Anxiety] True True \n", "4 [Grooming, Grooming, Grooming, Grooming, Groom... True True \n", "5 [Nutrition, Nutrition, Nutrition, Nutrition, N... True True " ], "text/html": [ "\n", "
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modelqueryexpected_categorytop3_categoriestop5_categoriestop3_hittop5_hit
0all-MiniLM-L6-v2My dog is vomiting and has no appetite since y...Health[Health, Health, Health][Health, Health, Health, Health, Health]TrueTrue
1all-MiniLM-L6-v2My cat is bleeding and breathing very fast.Emergency[Emergency, Emergency, Emergency][Emergency, Emergency, Emergency, Emergency, E...TrueTrue
2all-MiniLM-L6-v2My puppy keeps biting my hands during playtime.Training[Training, Anxiety, Training][Training, Anxiety, Training, Anxiety, Training]TrueTrue
3all-MiniLM-L6-v2My dog is shaking and hiding during fireworks.Anxiety[Anxiety, Anxiety, Anxiety][Anxiety, Anxiety, Anxiety, Behavior, Anxiety]TrueTrue
4all-MiniLM-L6-v2My cat has dry skin and needs grooming help.Grooming[Grooming, Grooming, Grooming][Grooming, Grooming, Grooming, Grooming, Groom...TrueTrue
5all-MiniLM-L6-v2My dog refuses to eat the new food I bought.Nutrition[Nutrition, Nutrition, Nutrition][Nutrition, Nutrition, Nutrition, Nutrition, N...TrueTrue
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modelqueryexpected_categorytop3_categoriestop5_categoriestop3_hittop5_hit
0paraphrase-MiniLM-L3-v2My dog is vomiting and has no appetite since y...Health[Nutrition, Health, Emergency][Nutrition, Health, Emergency, Health, Health]TrueTrue
1paraphrase-MiniLM-L3-v2My cat is bleeding and breathing very fast.Emergency[Emergency, Emergency, Emergency][Emergency, Emergency, Emergency, Emergency, E...TrueTrue
2paraphrase-MiniLM-L3-v2My puppy keeps biting my hands during playtime.Training[Emergency, Emergency, Emergency][Emergency, Emergency, Emergency, Training, Be...FalseTrue
3paraphrase-MiniLM-L3-v2My dog is shaking and hiding during fireworks.Anxiety[Anxiety, Training, Anxiety][Anxiety, Training, Anxiety, Emergency, Emerge...TrueTrue
4paraphrase-MiniLM-L3-v2My cat has dry skin and needs grooming help.Grooming[Grooming, Grooming, Grooming][Grooming, Grooming, Grooming, Grooming, Groom...TrueTrue
5paraphrase-MiniLM-L3-v2My dog refuses to eat the new food I bought.Nutrition[Nutrition, Health, Nutrition][Nutrition, Health, Nutrition, Health, Nutrition]TrueTrue
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modelqueryexpected_categorytop3_categoriestop5_categoriestop3_hittop5_hit
0all-mpnet-base-v2My dog is vomiting and has no appetite since y...Health[Health, Health, Emergency][Health, Health, Emergency, Emergency, Nutrition]TrueTrue
1all-mpnet-base-v2My cat is bleeding and breathing very fast.Emergency[Emergency, Emergency, Emergency][Emergency, Emergency, Emergency, Emergency, E...TrueTrue
2all-mpnet-base-v2My puppy keeps biting my hands during playtime.Training[Training, Behavior, Behavior][Training, Behavior, Behavior, Training, Behav...TrueTrue
3all-mpnet-base-v2My dog is shaking and hiding during fireworks.Anxiety[Anxiety, Anxiety, Anxiety][Anxiety, Anxiety, Anxiety, Anxiety, Anxiety]TrueTrue
4all-mpnet-base-v2My cat has dry skin and needs grooming help.Grooming[Grooming, Grooming, Grooming][Grooming, Grooming, Grooming, Grooming, Groom...TrueTrue
5all-mpnet-base-v2My dog refuses to eat the new food I bought.Nutrition[Nutrition, Nutrition, Nutrition][Nutrition, Nutrition, Nutrition, Nutrition, N...TrueTrue
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "results_df", "summary": "{\n \"name\": \"results_df\",\n \"rows\": 6,\n \"fields\": [\n {\n \"column\": \"model\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"all-mpnet-base-v2\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"query\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"My dog is vomiting and has no appetite since yesterday.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"expected_category\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 6,\n \"samples\": [\n \"Health\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top3_categories\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top5_categories\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top3_hit\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 1,\n \"samples\": [\n true\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"top5_hit\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 1,\n \"samples\": [\n true\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 47 } ] }, { "cell_type": "markdown", "source": [ "**Insight:** \n", "The detailed retrieval results show how each model performed on specific pet-care queries. `all-MiniLM-L6-v2` retrieved relevant categories for all test queries in the Top 3 results. `all-mpnet-base-v2` also performed well, but required much more runtime. `paraphrase-MiniLM-L3-v2` was faster, but made one less accurate Top 3 retrieval, especially in the training-related query.\n", "\n", "This confirms that the selected model should not be chosen only by speed. For HOPEPET, retrieval quality is important because the app uses similar cases to support safe and relevant recommendations." ], "metadata": { "id": "li49_jM7MKBA" } }, { "cell_type": "markdown", "source": [ "### 10. Choosing the Best Embedding Model" ], "metadata": { "id": "7YaxYd4NMiEs" } }, { "cell_type": "markdown", "source": [ "After evaluating the three Hugging Face embedding models, I selected `sentence-transformers/all-MiniLM-L6-v2` as the final embedding model for HOPEPET.\n", "\n", "This model achieved perfect Top 3 and Top 5 accuracy on the test queries, while keeping the embedding size relatively small with 384 dimensions. It was also much faster than `all-mpnet-base-v2`, which achieved similar accuracy but required significantly more runtime.\n", "\n", "The selected model provides the best balance between accuracy, speed, model size, and practical usability for a Gradio-based pet-care recommendation app." ], "metadata": { "id": "48WisPrkMk4O" } }, { "cell_type": "markdown", "source": [ "### 11. Creating Final Embeddings for the Full Dataset\n", "\n", "After selecting the best model, I create embeddings for all 10,000 cases in the dataset using the `retrieval_text` column." ], "metadata": { "id": "OMXNrmKqNReP" } }, { "cell_type": "code", "source": [ "final_model_name = \"sentence-transformers/all-MiniLM-L6-v2\"\n", "\n", "final_model = SentenceTransformer(final_model_name)\n", "\n", "final_embeddings = final_model.encode(\n", " df[\"retrieval_text\"].astype(str).tolist(),\n", " convert_to_numpy=True,\n", " show_progress_bar=True,\n", " normalize_embeddings=True\n", ")\n", "\n", "final_embeddings.shape" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 98, "referenced_widgets": [ "90af670f2f83470f9866474565db1e6c", "a7099d9f5943476f804ae5f07d616517", "aae2e88f98f74c6682bd36a1d4de24d0", "57038a478c1e4c5090046db26e227688", "9f7090bd4e6a4d4d9b0ba55f536d938d", "7a6c621b7fb84d25baf74dfd1748b665", "45bb4625a5cd45d284413fa47cf19a9d", "86ed79d39a2e494194dc3616418af09d", "caddd03ffd4343a5a4ff13ab2e3539f2", "d6af647ab6134ceaa9955e4647908e1b", "1d4c5104a4914ac3accd53b4951c5a93", "d801924fbc5743908cfbb7d2c7cb6528", "289e8c8782b74158a38e803b3374972f", "8049d8cca34842db86fea25cd4f1e787", "69533283a86245d196de435b3956fa20", "ffe46b7090aa457c9941f62ed76858a3", "71e65493430e4696a423658033f8a3b0", "7df9dc0f3b064aaba0590adb8a0044ce", "726627dc7f354410944ce5f393d181e3", "248e70bfa3ac448e8348c5d3eebbd6ab", "372203b199d8499589c2af8831b4484a", "a11a6aa0a758419488abf09063f0f800" ] }, "id": "rNN_Ig8ENUJN", "outputId": "b93cb8c6-8da0-4140-9fde-3e4e15662139" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/103 [00:00\n", "
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case_idpet_typepet_age_groupproblem_categoryurgency_levelrecommended_next_stepshort_recommendationvet_warningsimilarity_score
76377638CatKittenHealthHighContact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.654079
56455646CatSeniorHealthHighContact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.649469
27252726CatSeniorNutritionHighContact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.645897
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case_idpet_typepet_age_groupproblem_categoryurgency_levelrecommended_next_stepshort_recommendationvet_warningsimilarity_score
245246DogSeniorAnxietyHighContact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.553960
38583859DogPuppyAnxietyHighContact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.536797
66776678DogSeniorAnxietyHighContact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.529838
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"recommend_similar_cases(test_input, top_k=3)\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"case_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3224,\n \"min\": 246,\n \"max\": 6678,\n \"num_unique_values\": 3,\n \"samples\": [\n 246,\n 3859,\n 6678\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pet_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Dog\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pet_age_group\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Puppy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"problem_category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Anxiety\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"urgency_level\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"High\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"recommended_next_step\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Contact a veterinarian as soon as possible\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"short_recommendation\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Contact a veterinarian as soon as possible and monitor symptoms closely.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vet_warning\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Contact a veterinarian as soon as possible, especially if the pet is not eating, seems weak, has repeated symptoms, shows pain, or the condition is worsening.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"similarity_score\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 3,\n \"samples\": [\n 0.5539603233337402\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 53 } ] }, { "cell_type": "markdown", "source": [ "The sanity checks show that the recommendation function retrieves cases that are semantically related to the user input. This confirms that the embedding-based retrieval system can be used as the recommendation layer of HOPEPET." ], "metadata": { "id": "DHgE5TSXN5fD" } }, { "cell_type": "markdown", "source": [ "### 15. Full Recommendation Pipeline with Generated Response\n", "\n", "In this step, I combine the recommendation system with a generated user-facing response. The system retrieves the top 3 similar cases and then creates one final response based on the most relevant retrieved case." ], "metadata": { "id": "k4ZhtFzkN8u6" } }, { "cell_type": "code", "source": [ "def generate_response_from_recommendations(user_input, recommendations):\n", " top_case = recommendations.iloc[0]\n", "\n", " generated_response = f\"\"\"\n", "User question:\n", "{user_input}\n", "\n", "Based on the most similar retrieved case, this situation is most related to the category: {top_case[\"problem_category\"]}.\n", "\n", "Estimated urgency level:\n", "{top_case[\"urgency_level\"]}\n", "\n", "Recommended next step:\n", "{top_case[\"recommended_next_step\"]}\n", "\n", "Guidance:\n", "{top_case[\"short_recommendation\"]}\n", "\n", "Veterinary warning:\n", "{top_case[\"vet_warning\"]}\n", "\n", "Important note:\n", "This response is based on similar cases retrieved from the HOPEPET dataset. It provides general guidance only and does not replace professional veterinary advice.\n", "\"\"\"\n", "\n", " return generated_response" ], "metadata": { "id": "hnuTlFjcN_Re" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "user_input = \"My senior cat stopped eating today and seems very tired.\"\n", "\n", "recommendations = recommend_similar_cases(user_input, top_k=3)\n", "\n", "print(\"Top 3 Similar Cases:\")\n", "display(recommendations)\n", "\n", "print(\"\\nGenerated Response:\")\n", "print(generate_response_from_recommendations(user_input, recommendations))" ], "metadata": { "id": "h7MFSefqOBTN" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**Insight:** \n", "This example demonstrates the full HOPEPET recommendation pipeline. The user input is first converted into an embedding and compared to the saved dataset embeddings. The system retrieves the top 3 most similar cases and then creates one final response based on the most relevant retrieved case. The generated response includes the problem category, urgency level, recommended next step, guidance, and a veterinary warning." ], "metadata": { "id": "3Ygu--yeOEjG" } }, { "cell_type": "markdown", "source": [ "### **Summary of part 3**\n", "\n", "In this part, I built the recommendation system for HOPEPET using text embeddings. I tested three Hugging Face embedding models and selected `sentence-transformers/all-MiniLM-L6-v2` because it provided the best balance between retrieval quality, runtime, embedding size, and practical usability.\n", "\n", "After selecting the final model, I created embeddings for all 10,000 cases in the dataset and saved them to a `.npy` file. Then, I built a recommendation function that receives a user input, converts it into an embedding, compares it to the dataset embeddings, and returns the top 3 most similar cases.\n", "\n", "The sanity checks show that the system retrieves relevant cases for different user inputs, such as a cat not eating or a dog showing anxiety during fireworks. Finally, I created a full pipeline that uses the retrieved cases to generate one user-facing response with category, urgency level, recommended next step, guidance, and a veterinary warning." ], "metadata": { "id": "uyKMFIq8OHmN" } }, { "cell_type": "markdown", "source": [ "

\n", "\n", "---\n", "\n", "

" ], "metadata": { "id": "tF91mUTe8viw" } }, { "cell_type": "markdown", "source": [ "# **Part 4 : Generation**\n", "\n", "In this part, I add a Generative AI component to HOPEPET.\n", "\n", "The goal is to generate a clear and helpful user-facing response based on the user's pet-care problem and the top 3 similar cases retrieved by the embedding-based recommendation system.\n", "\n", "This creates a retrieval-augmented generation pipeline:\n", "\n", "User input → Similar case recommendation → Hugging Face generative model → Final AI response" ], "metadata": { "id": "R4ph-eftqYo-" } }, { "cell_type": "markdown", "source": [ "### 1. Generation Task Definition\n", "\n", "The generation task is text generation.\n", "\n", "The system receives:\n", "- The user's pet-care question\n", "- The top 3 similar cases retrieved from the dataset\n", "- The urgency level, recommended next step, safe guidance, and veterinary warning from the retrieved cases\n", "\n", "Then, a Hugging Face generative language model creates one final response for the user.\n", "\n", "The response should be clear, safe, and responsible. It should not replace professional veterinary advice." ], "metadata": { "id": "pYM-GNU6UAws" } }, { "cell_type": "code", "source": [ "!pip install -q transformers sentencepiece accelerate" ], "metadata": { "id": "UUWrk_LetTBB" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import torch\n", "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM" ], "metadata": { "id": "yVA8R0U1qvf-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "generation_model_name = \"google/flan-t5-base\"\n", "\n", "generation_tokenizer = AutoTokenizer.from_pretrained(generation_model_name)\n", "generation_model = AutoModelForSeq2SeqLM.from_pretrained(generation_model_name)\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "generation_model = generation_model.to(device)\n", "\n", "print(\"Generation model loaded:\", generation_model_name)\n", "print(\"Device:\", device)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 345, "referenced_widgets": [ "e50dfa292ca543f4b1a1de87d06746ec", "00070f76a18a4d94908ff0b3f0acd9fc", "d7403bb90f0a4965a5e29b568d18eee6", "eaffc0b0d9c84b02ba3e36ec8a7420d4", "ebdcc8ee18be4654bdc752a474178bbe", "58f7d70e2f3a44adaca712e44be07dfd", "feb057606a934b859c971929a10ce266", "63d82bdbadd24c36b1f9a50edc392b51", "f397e057e6944fb6af38897aa78135cf", "f5f84ef8e29146cc99d49ea0d78943fd", "05e8063b2ef541a38c9f60ed11b5a531", "f911cd0b34b0464eb26fc45e0665d390", "7471570d5c44474999d40ed48efde8f6", "880138325a2a4f339efba39b924a52c7", "8758088c03e94d0598f81532ec51c5ee", "07012097baa3419cb93d7ae7e054cefd", "984db6420fd04b72a2312916a8a40c68", "8eef16b0c4d245d382c11d5701f657e1", "453ce25a0a0e40c2b420216e87062079", "af5b271361f5412facb607c3f6ab43d3", "48fd15e9b2a84989ad4d23ffe089dcb2", "2424f3a7a19b4e428a0be20f83b96115", "0351847756c04fb6af09bc269a206dbf", "b001d177c1df4e6984c9679dddd00d66", "4da52504bb74479eb68d1e34bfdfc701", "4ca6f20306b140e0bb813650e3dcbc92", "8f37978f0c91461fb3d9f5e78074704c", "1769cb77823d498f897bcf3249200499", "da9ed6912b9c491386a63ddc8e9b0b1d", "21bc789808584afd936675b54dc9f767", "5db49e337f6c423780d43209dbd9e724", "6cd243c7b3e3432794570073303b2d63", "346586790540481cba11c53bdb706c39", "e66cd7f5fb7d47838c889198b0c36f6c", "c5171f81203d4645985b3a03738699e6", "8b9aa29d0945470cb062ce87b70b1dc1", "a917c5560f3a489eacd721f6bb09a3bb", "70891fe8f54a4decaf9a7f17fd6e5919", "9169e44d396e464eaaf9f6923a039490", "a1323a3ee2324d88bbcb7a084004c488", "5254cd9a0ef54ebeaff94bb7c1034c67", "3dcfbc44c58e4877abd34bf57bf024f1", "a2316d18700542f78662b8e5f9318f7e", "a649ec9cbc2b4b588d7b1f91272b26d9", "17c2880b48424799a00ba1cf58e99bbc", "845b61578bd3411485b2c27e31213a9a", "4b2f94f785564d4b9c46f2cb8e51754c", "c80ec954f1a84c3d9f764ce91b9f5af7", "32054dc64818462b8132724aed0adef1", "a46b15546be54d75b865da58b44a2554", "1e58e3011b65450a935b32fab11acc96", "7673aa40acd54bdea7a0f66b1a3a236a", "a512162f37fc4d35889fac6f4e6c5413", "c110fa22dbaf4289a2293faecd7aae0a", "00445f3ea16a48edba72d7016a97a733", "18e7e39b570549bf9fec6bc91d36db8c", "7e0adf37bc854526b185870fc1a8aab6", "496565e672a145bb9e8b9d638dec629e", "f31ae91bbbbe4ec6bec6e5c84bfa5d9d", "a23fdf1dd0184182949ad803b5c44aba", "a453dc3c0adb4df981352bb38c1e722d", "03636c6adb7c45e1906b21c621519f8a", "9074680c98374a37b4e42b4d108f9f66", "a41b208f97ba46bfa821ed30a73e6e34", "ffeff46620b4430b953b43366b85a367", "dc637c10f5b14e579dbf75817500a8d9", "0d54cc6ba7f64951a3f4e763666f0232", "9db46db45b404ed09463c76938b210b2", "46e672928c214fbd9bd2e81b38607d81", "908da5a5660645cfb2a8751882b1d1d2", "cb7623f3f2f74b5aacb55bfe7e0f5eca", "f48a2cd79af844eaabc2013f82553346", "9cf30e2e094b4462b6754db04d6c53cf", "2b2581b0b2ed48cfbc8f18ac64e4c361", "10a04a7b0e0c45c09cf429d759d1a376", "63a8da27e172455ba61dfb9cf4fb2377", "eccf27adda1f43759e92443b48546845", "a6dec92452d9427e882e9cf53056432e", "8d0340e1a4d6466f82c19fc2634d4075", "0b72b8fc34ab40019056b2d3dd64acf1", "e764ad9370714bec8ff2e473db41b05e", "d71d015f107d4702b35d7ee93f22ab92", "db70fd7071104163b246f0c0e253e5b4", "55a6b32112944093b4bc659041d3d7ce", "d3a05b482d434131a298e0ba715823e3", "d33174bfc57b4399be856c86ccbc5961", "f14d7a24a370445b97c89a1f7772d9ba", "fec047f9402c41469ae7a532174dbc65" ] }, "id": "LiuYehL0UHGK", "outputId": "9779f671-8438-41e0-df5d-f6674a537882" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.40k [00:00\n", "
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My senior cat stopped eating today and seems very tired. 2. High urgency 3. Contact a veterinarian as soon as possible 4. Make food changes gradually 5. Contact a veterinarian as soon as possible\n", "\n", "Important note:\n", "This app provides general guidance only and does not replace professional veterinary advice.\n", "If your pet has difficulty breathing, seizures, bleeding, possible poisoning, repeated vomiting, or cannot stand, contact a veterinarian or emergency clinic immediately.\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Insight:** \n", "This example shows that the generation component uses the retrieved similar cases to create one final user-facing response. The response includes the main concern, urgency level, recommended next step, safe first steps, and a veterinary warning." ], "metadata": { "id": "_L8JCzlUVwQ-" } }, { "cell_type": "code", "source": [ "user_input = \"My dog is shaking and hiding during fireworks.\"\n", "\n", "recommendations = recommend_cases_for_generation(user_input, top_k=3)\n", "\n", "print(\"Top 3 Similar Cases:\")\n", "display(recommendations)\n", "\n", "print(\"\\nGenerated AI Response:\")\n", "print(generate_ai_response(user_input, recommendations))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 528 }, "id": "TWc71Q8cVzCR", "outputId": "c1b7acd7-44cf-48b0-c6a7-0ba882dc023b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Top 3 Similar Cases:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " case_id pet_type pet_age_group problem_category urgency_level \\\n", "245 246 Dog Senior Anxiety High \n", "3858 3859 Dog Puppy Anxiety High \n", "6677 6678 Dog Senior Anxiety High \n", "\n", " triage_reason \\\n", "245 High urgency because the case involves shaking... \n", "3858 High urgency because the case involves shaking... \n", "6677 High urgency because the case involves shaking... \n", "\n", " safe_first_steps \\\n", "245 Create a quiet safe space, reduce triggers whe... \n", "3858 Create a quiet safe space, reduce triggers whe... \n", "6677 Create a quiet safe space, reduce triggers whe... \n", "\n", " recommended_next_step \\\n", "245 Contact a veterinarian as soon as possible \n", "3858 Contact a veterinarian as soon as possible \n", "6677 Contact a veterinarian as soon as possible \n", "\n", " short_recommendation \\\n", "245 Contact a veterinarian as soon as possible and... \n", "3858 Contact a veterinarian as soon as possible and... \n", "6677 Contact a veterinarian as soon as possible and... \n", "\n", " vet_warning similarity_score \n", "245 Contact a veterinarian as soon as possible, es... 0.553960 \n", "3858 Contact a veterinarian as soon as possible, es... 0.536797 \n", "6677 Contact a veterinarian as soon as possible, es... 0.529838 " ], "text/html": [ "\n", "
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case_idpet_typepet_age_groupproblem_categoryurgency_leveltriage_reasonsafe_first_stepsrecommended_next_stepshort_recommendationvet_warningsimilarity_score
245246DogSeniorAnxietyHighHigh urgency because the case involves shaking...Create a quiet safe space, reduce triggers whe...Contact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.553960
38583859DogPuppyAnxietyHighHigh urgency because the case involves shaking...Create a quiet safe space, reduce triggers whe...Contact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.536797
66776678DogSeniorAnxietyHighHigh urgency because the case involves shaking...Create a quiet safe space, reduce triggers whe...Contact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.529838
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My dog is shaking and hiding during fireworks. 2. High urgency 3. Contact a veterinarian as soon as possible 4. Create a quiet safe space, reduce triggers when possible, keep a calm routine, and avoid forcing the pet into stressful situations 5. Contact a veterinarian as soon as possible, especially if the pet is not eating, seems weak, has repeated symptoms, shows pain, or the condition is worsening.\n", "\n", "Important note:\n", "This app provides general guidance only and does not replace professional veterinary advice.\n", "If your pet has difficulty breathing, seizures, bleeding, possible poisoning, repeated vomiting, or cannot stand, contact a veterinarian or emergency clinic immediately.\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "**Insight:** \n", "This example tests the generation component on an anxiety-related case. The model uses the retrieved cases to generate a response that connects the user's input to stress or anxiety and provides safe guidance." ], "metadata": { "id": "4lpPoR8aWEec" } }, { "cell_type": "markdown", "source": [ "### 7. Full Generation Pipeline\n", "\n", "This function combines the recommendation system and the generation model into one complete pipeline." ], "metadata": { "id": "H7_U7NokWGbk" } }, { "cell_type": "code", "source": [ "def hopepet_generation_pipeline(user_input, top_k=3):\n", " recommendations = recommend_cases_for_generation(user_input, top_k=top_k)\n", " ai_response = generate_ai_response(user_input, recommendations)\n", "\n", " return recommendations, ai_response" ], "metadata": { "id": "sAronIz0WH-0" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "user_input = \"My puppy keeps biting my hands during playtime.\"\n", "\n", "recommendations, ai_response = hopepet_generation_pipeline(user_input)\n", "\n", "print(\"Top 3 Similar Cases:\")\n", "display(recommendations)\n", "\n", "print(\"\\nGenerated AI Response:\")\n", "print(ai_response)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 528 }, "id": "qgKpehMiWKg3", "outputId": "a9d16437-c649-45d2-c266-42a9ca4caade" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Top 3 Similar Cases:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " case_id pet_type pet_age_group problem_category urgency_level \\\n", "582 583 Dog Puppy Training High \n", "4369 4370 Dog Puppy Training Low \n", "6563 6564 Dog Puppy Training Medium \n", "\n", " triage_reason \\\n", "582 High urgency because the case involves chewing... \n", "4369 Low urgency because no emergency warning signs... \n", "6563 Medium urgency because symptoms should be moni... \n", "\n", " safe_first_steps \\\n", "582 Use calm positive reinforcement, reward desire... \n", "4369 Use calm positive reinforcement, reward desire... \n", "6563 Use calm positive reinforcement, reward desire... \n", "\n", " recommended_next_step \\\n", "582 Contact a veterinarian as soon as possible \n", "4369 Monitor at home and use general care guidance \n", "6563 Monitor closely and contact a veterinarian if ... \n", "\n", " short_recommendation \\\n", "582 Contact a veterinarian as soon as possible and... \n", "4369 Use consistent positive reinforcement and redi... \n", "6563 Use consistent positive reinforcement and redi... \n", "\n", " vet_warning similarity_score \n", "582 Contact a veterinarian as soon as possible, es... 0.515320 \n", "4369 Contact a veterinarian if symptoms continue, w... 0.512847 \n", "6563 Contact a veterinarian if symptoms continue, w... 0.511279 " ], "text/html": [ "\n", "
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case_idpet_typepet_age_groupproblem_categoryurgency_leveltriage_reasonsafe_first_stepsrecommended_next_stepshort_recommendationvet_warningsimilarity_score
582583DogPuppyTrainingHighHigh urgency because the case involves chewing...Use calm positive reinforcement, reward desire...Contact a veterinarian as soon as possibleContact a veterinarian as soon as possible and...Contact a veterinarian as soon as possible, es...0.515320
43694370DogPuppyTrainingLowLow urgency because no emergency warning signs...Use calm positive reinforcement, reward desire...Monitor at home and use general care guidanceUse consistent positive reinforcement and redi...Contact a veterinarian if symptoms continue, w...0.512847
65636564DogPuppyTrainingMediumMedium urgency because symptoms should be moni...Use calm positive reinforcement, reward desire...Monitor closely and contact a veterinarian if ...Use consistent positive reinforcement and redi...Contact a veterinarian if symptoms continue, w...0.511279
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "recommendations", "summary": "{\n \"name\": \"recommendations\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"case_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3025,\n \"min\": 583,\n \"max\": 6564,\n \"num_unique_values\": 3,\n \"samples\": [\n 583,\n 4370,\n 6564\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pet_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Dog\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"pet_age_group\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Puppy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"problem_category\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Training\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"urgency_level\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"High\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"triage_reason\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"High urgency because the case involves chewing objects with risk factors such as duration, age, appetite, water intake, energy, or pain signs.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"safe_first_steps\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"Use calm positive reinforcement, reward desired behavior, keep sessions short, and avoid punishment-based training.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"recommended_next_step\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Contact a veterinarian as soon as possible\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"short_recommendation\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Use consistent positive reinforcement and redirect the behavior calmly.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vet_warning\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"Contact a veterinarian as soon as possible, especially if the pet is not eating, seems weak, has repeated symptoms, shows pain, or the condition is worsening.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"similarity_score\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 3,\n \"samples\": [\n 0.5153201818466187\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Generated AI Response:\n", "1. My puppy keeps biting my hands during playtime. 2. Estimated urgency level 3. Recommended next step: Contact a veterinarian as soon as possible 4. Use calm positive reinforcement, reward desired behavior, keep sessions short, and avoid punishment-based training 5. Contact a veterinarian if symptoms continue, worsen, last more than 24-48 hours, or include appetite loss, vomiting, weakness, pain, or behavior changes.\n", "\n", "Important note:\n", "This app provides general guidance only and does not replace professional veterinary advice.\n", "If your pet has difficulty breathing, seizures, bleeding, possible poisoning, repeated vomiting, or cannot stand, contact a veterinarian or emergency clinic immediately.\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "### **Summary of part 4**\n", "\n", "In this step, I added a Generative AI component to HOPEPET using a Hugging Face text generation model.\n", "\n", "The generation component uses the user's input together with the top 3 similar cases retrieved by the embedding-based recommendation system. The retrieved cases provide context such as problem category, urgency level, safe first steps, recommended next step, and veterinary warning.\n", "\n", "The final pipeline creates one user-facing AI response that is clear, responsible, and based on similar cases from the dataset. This makes the app more useful because the user does not only receive similar cases, but also receives a generated explanation and guidance." ], "metadata": { "id": "R9jy9OHfWPjx" } }, { "cell_type": "markdown", "source": [ "

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