{ "cells": [ { "cell_type": "markdown", "id": "879ccf76", "metadata": {}, "source": [ "# Capstone 4 — Session 4: Data Visualization Techniques\n", "\n", "**Run timestamp:** `2026-02-19 01:49:22`\n", "\n", "## Goal\n", "- I converted Week 3 and Week 4 analysis into required visual outputs and documented interpretation-ready findings.\n", "- I generated and saved all required plots for categorical comparisons and correlation/relationship analysis.\n", "\n", "## Inputs\n", "- `NSMES1988updated.csv` from `Capstone 2/outputs/`\n", "\n", "## Outputs\n", "- All exports go to `./outputs/` (and plots to `./outputs/plots/` when applicable)\n", "\n", "## Libraries (documented)\n", "- `pandas`: I used it to prepare grouped summaries and correlation inputs for plotting.\n", "- `matplotlib`: I used it to build and save all required charts to `outputs/plots/`.\n", "\n", "## Key dataset note\n", "- `age` is encoded as **Age in years (divided by 10)** (e.g., `6.9` = 69 years)." ] }, { "cell_type": "markdown", "id": "588d060d", "metadata": {}, "source": [ "## C?-T0 — Runtime setup (paths + output folders)\n", "I used this setup cell first to initialize reproducible paths and plotting output folders." ] }, { "cell_type": "code", "execution_count": 9, "id": "9e6dc683", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Capstone: 4 | Session: Session 4: Data Visualization Techniques\n", "Run timestamp: 2026-02-19 00:32:05\n", "Output directory: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\n", "Plots directory: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\n" ] } ], "source": [ "from pathlib import Path\n", "from datetime import datetime\n", "\n", "try:\n", " from IPython.display import display as _ipy_display\n", "except Exception:\n", " _ipy_display = None\n", "\n", "def show(value):\n", " if _ipy_display is not None:\n", " _ipy_display(value)\n", " else:\n", " print(value)\n", "\n", "# --- Project metadata ---\n", "CAPSTONE = 4\n", "SESSION_TITLE = 'Session 4: Data Visualization Techniques'\n", "\n", "print(f\"Capstone: {CAPSTONE} | Session: {SESSION_TITLE}\")\n", "print(\"Run timestamp:\", datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n", "\n", "CWD = Path.cwd()\n", "if (CWD / f\"Capstone {CAPSTONE}\").exists():\n", " BASE_DIR = CWD / f\"Capstone {CAPSTONE}\"\n", "elif CWD.name == f\"Capstone {CAPSTONE}\":\n", " BASE_DIR = CWD\n", "elif (CWD / \"Incremental_Capstone\" / f\"Capstone {CAPSTONE}\").exists():\n", " BASE_DIR = CWD / \"Incremental_Capstone\" / f\"Capstone {CAPSTONE}\"\n", "else:\n", " BASE_DIR = CWD\n", "\n", "# --- Paths ---\n", "def first_existing_path(candidates):\n", " for candidate in candidates:\n", " candidate = Path(candidate).expanduser()\n", " if candidate.exists():\n", " return candidate\n", " return None\n", "\n", "def resolve_dataset_path(default_filename: str) -> Path:\n", " \"\"\"Resolve dataset path from known local runtime locations (non-interactive).\"\"\"\n", " path = first_existing_path([\n", " BASE_DIR / default_filename,\n", " CWD / default_filename,\n", " CWD / \"Incremental_Capstone\" / f\"Capstone {CAPSTONE}\" / default_filename,\n", " ])\n", " if path is None:\n", " path = BASE_DIR / default_filename\n", "\n", " if not path.exists():\n", " raise FileNotFoundError(f\"Dataset not found: {path}\")\n", " return path\n", "\n", "OUTPUT_DIR = BASE_DIR / \"outputs\"\n", "PLOTS_DIR = OUTPUT_DIR / \"plots\"\n", "OUTPUT_DIR.mkdir(exist_ok=True)\n", "PLOTS_DIR.mkdir(parents=True, exist_ok=True)\n", "\n", "print(\"Output directory:\", OUTPUT_DIR)\n", "print(\"Plots directory:\", PLOTS_DIR)\n" ] }, { "cell_type": "markdown", "id": "6fceed7f", "metadata": {}, "source": [ "## C?-T1 — Imports (ONLY what you use)\n", "I documented each import with why I used it and what it enabled in this capstone." ] }, { "cell_type": "code", "execution_count": 10, "id": "52664ee2", "metadata": {}, "outputs": [], "source": [ "import pandas as pd # DataFrames + aggregation/pivot/correlation preparation\n", "import matplotlib.pyplot as plt # Plot creation and export to outputs/plots" ] }, { "cell_type": "markdown", "id": "9baf5c57", "metadata": {}, "source": [ "## C?-T2 — Load dataset\n", "I loaded the instructed handoff dataset from Capstone 2 using default + fallback paths." ] }, { "cell_type": "code", "execution_count": 11, "id": "1834cff1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local default not found; using fallback: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 2\\outputs\\NSMES1988updated.csv\n", "Loaded: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 2\\outputs\\NSMES1988updated.csv\n", "Shape: (4406, 20)\n" ] }, { "data": { "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
visitsnvisitsovisitsnovisitsemergencyhospitalhealthchronicadlregionagegendermarriedschoolincomeemployedinsurancemedicaidage_yearsincome_dollars
0500001average2normalother6.9maleyes62.8810yesyesno6928810
1102020average2normalother7.4femaleyes102.7478noyesno7427478
21300033poor4limitedother6.6femaleno100.6532nonoyes666532
31605011poor2limitedother7.6maleyes30.6588noyesno766588
4300000average2limitedother7.9femaleyes60.6588noyesno796588
\n", "
" ], "text/plain": [ " visits nvisits ovisits novisits emergency hospital health chronic \\\n", "0 5 0 0 0 0 1 average 2 \n", "1 1 0 2 0 2 0 average 2 \n", "2 13 0 0 0 3 3 poor 4 \n", "3 16 0 5 0 1 1 poor 2 \n", "4 3 0 0 0 0 0 average 2 \n", "\n", " adl region age gender married school income employed insurance \\\n", "0 normal other 6.9 male yes 6 2.8810 yes yes \n", "1 normal other 7.4 female yes 10 2.7478 no yes \n", "2 limited other 6.6 female no 10 0.6532 no no \n", "3 limited other 7.6 male yes 3 0.6588 no yes \n", "4 limited other 7.9 female yes 6 0.6588 no yes \n", "\n", " medicaid age_years income_dollars \n", "0 no 69 28810 \n", "1 no 74 27478 \n", "2 yes 66 6532 \n", "3 no 76 6588 \n", "4 no 79 6588 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "DEFAULT_DATASET = \"NSMES1988updated.csv\"\n", "\n", "try:\n", " dataset_path = resolve_dataset_path(DEFAULT_DATASET)\n", "except FileNotFoundError:\n", " fallback = first_existing_path([\n", " BASE_DIR.parent / \"Capstone 2\" / \"outputs\" / \"NSMES1988updated.csv\",\n", " CWD / \"Capstone 2\" / \"outputs\" / \"NSMES1988updated.csv\",\n", " CWD / \"Incremental_Capstone\" / \"Capstone 2\" / \"outputs\" / \"NSMES1988updated.csv\",\n", " ])\n", " if fallback is None:\n", " raise\n", " dataset_path = fallback\n", " print(\"Local default not found; using fallback:\", dataset_path)\n", "\n", "df = pd.read_csv(dataset_path)\n", "if \"age_years\" not in df.columns and \"age\" in df.columns:\n", " df[\"age_years\"] = (df[\"age\"] * 10).round(0)\n", "if \"income_dollars\" not in df.columns and \"income\" in df.columns:\n", " df[\"income_dollars\"] = (df[\"income\"] * 10000).round(0)\n", "\n", "print(\"Loaded:\", dataset_path)\n", "print(\"Shape:\", df.shape)\n", "show(df.head())\n" ] }, { "cell_type": "markdown", "id": "95078c91", "metadata": {}, "source": [ "## C?-T3 — Validation checks\n", "- Confirm expected columns exist\n", "- Confirm key dtypes\n", "- Check missing values\n", "\n", "**Results Capture:**\n", "- What I did: I validated expected schema/dtypes and checked missing values prior to plotting.\n", "- What I found: shape=`(4406, 20)` and no missing values; plotting fields required for tasks are available.\n", "- Caveats: derived pairs (`age` vs `age_years`, `income` vs `income_dollars`) naturally dominate top correlation rankings." ] }, { "cell_type": "code", "execution_count": 12, "id": "669a4bee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing expected columns: []\n", "\n", "Dtypes:\n" ] }, { "data": { "text/plain": [ "visits int64\n", "nvisits int64\n", "ovisits int64\n", "novisits int64\n", "emergency int64\n", "hospital int64\n", "health str\n", "chronic int64\n", "adl str\n", "region str\n", "age float64\n", "gender str\n", "married str\n", "school int64\n", "income float64\n", "employed str\n", "insurance str\n", "medicaid str\n", "age_years int64\n", "income_dollars int64\n", "dtype: object" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Missing values (count):\n" ] }, { "data": { "text/plain": [ "visits 0\n", "nvisits 0\n", "ovisits 0\n", "novisits 0\n", "emergency 0\n", "dtype: int64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "expected_cols = [\n", " \"visits\", \"nvisits\", \"ovisits\", \"novisits\", \"emergency\", \"hospital\",\n", " \"health\", \"chronic\", \"adl\", \"region\", \"age\", \"gender\",\n", " \"married\", \"school\", \"income\", \"employed\", \"insurance\", \"medicaid\",\n", " \"age_years\", \"income_dollars\"\n", "]\n", "\n", "missing_cols = [c for c in expected_cols if c not in df.columns]\n", "print(\"Missing expected columns:\", missing_cols)\n", "\n", "print(\"\\nDtypes:\")\n", "show(df.dtypes)\n", "\n", "print(\"\\nMissing values (count):\")\n", "na_counts = df.isna().sum().sort_values(ascending=False)\n", "show(na_counts[na_counts > 0] if (na_counts > 0).any() else na_counts.head())\n" ] }, { "cell_type": "markdown", "id": "9636d399", "metadata": {}, "source": [ "## C4-T3 — State plotting library choice + reasons\n", "**PDF requirement:** State which library you used for plotting and why.\n", "\n", "### What I completed\n", "- I used Matplotlib for all required visualizations and file exports.\n", "\n", "### Results Capture\n", "- Library choice: **Matplotlib**.\n", "- Reasons: strong pandas compatibility, clear customization, and straightforward PNG export to `outputs/plots/`.\n", "\n", "### Code evidence\n", "- The next cell prints the plotting-library choice summary used in this submission." ] }, { "cell_type": "code", "execution_count": 13, "id": "76b039e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plotting library: matplotlib\n", "Reason: direct pandas compatibility, flexible formatting, and reliable PNG export for grading artifacts.\n" ] } ], "source": [ "print(\"Plotting library: matplotlib\")\n", "print(\"Reason: direct pandas compatibility, flexible formatting, and reliable PNG export for grading artifacts.\")\n" ] }, { "cell_type": "markdown", "id": "257398e2", "metadata": {}, "source": [ "## C4-T4 — Plot Week 3 categorical analysis for Health and Region\n", "**PDF requirement:** Plot the Week 3 categorical analysis for Health and Region.\n", "\n", "### What I completed\n", "- I transformed Week 3 category analysis into saved bar-chart visualizations for region and health, plus a grouped region-health visits chart.\n", "\n", "### Results Capture\n", "- Saved plot artifacts:\n", " - `outputs/plots/region_counts.png`\n", " - `outputs/plots/health_counts.png`\n", " - `outputs/plots/mean_visits_region_health.png`\n", "- Interpretation highlights:\n", " - `other` region has the largest count, while `west` has the smallest.\n", " - `average` health status dominates the sample compared with `excellent` and `poor`.\n", " - Mean visits vary across region-health segments.\n", "\n", "### Artifacts\n", "- `outputs/plots/region_counts.png (example)`\n", "- `outputs/plots/health_*.png`\n", "\n", "### Code evidence\n", "- The next cell contains all categorical plotting commands I ran and saved." ] }, { "cell_type": "code", "execution_count": 14, "id": "12f8b070", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAG4CAYAAAC5CgR7AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAQQdJREFUeJzt3Q98jfX///HXZrP5N7NhIzOShISQj3+VLBMVpT9KWhLl408osUJIrST/SqSPv8Wn/xRK/saniJlE/qaURbaKWdQ27Pxur/f3dp3fOWzazs7sXNvjfrtdnZ3runbOda4Onud9Xtfr7edwOBwCAAAA2Ix/UR8AAAAA4AmCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLADYxE8//SR+fn4yadIkKc5uvPFGswDAPyHIAii2fvjhB3n00Ufl8ssvl+DgYAkJCZE2bdrItGnT5O+//xZf8Prrr8v8+fPFl+jxaGC2loCAALnsssvkoYcekiNHjhT14QGAU8D//xEAio8VK1bI3XffLUFBQfLggw/K1VdfLVlZWfLll1/K8OHDZffu3TJ79myfCLKVK1c2IdHXjB8/XmrXri0ZGRny9ddfm4Cr5++7774zHwwKy6pVqwrtsQEULwRZAMXOoUOHpEePHhIdHS3r1q2TatWqObcNGDBADh48aIIuLu6WW26R5s2bm58feeQRE7hfeukl+eSTT+See+4ptNNXunRp/tcAyBNKCwAUOxMnTpRTp07JnDlz3EKs5YorrpDHH3/cef/s2bPy3HPPSZ06dcwIbq1ateTpp5+WzMxMt9/Tr9nHjh17wePp/q4jqtZX81999ZUMGzZMqlSpIuXKlZM77rhDfvvtN7ff05HhDRs2OL/Gz2tt6JQpU0xQL1OmjNxwww1mlNQyb94881jffPPNBb/3wgsvSKlSpTwqEWjXrp2zZMPVvn375K677pKwsDAzUqvhV8Pu+Xbu3GmOVY+5Ro0aMmHCBOexav3vxWpkU1NTpU+fPhIREWGeo3HjxrJgwYJca4h1tN36/9miRQtJTEzM9+sF4PsYkQVQ7CxbtszUxbZu3TpP++too4YiDWNPPPGEbNmyRRISEmTv3r2yZMkSj49j0KBBUqlSJXn22WdNyJo6daoMHDhQ3n33XbNd7+s+5cuXl2eeecas06D2TxYuXCh//vmnGV3Wr/215vemm26SXbt2md/X16HbFi1aJE2bNnX7XV2nIVFrXvPLCpv6miwaxLXuWB9v5MiRJrC/99570q1bN/nwww9NeFcanNu3b2+CZnx8vNnvP//5jwma/0TrmfWYdSRdz5+WO7z//vvmw0NaWprbhxK1ePFic360PlqfTz/Y3HnnnfLjjz9KYGBgvl83AB/mAIBi5OTJkw79q61r16552n/Hjh1m/0ceecRt/ZNPPmnWr1u3zrlO7z/77LMXPEZ0dLQjLi7OeX/evHlm35iYGEd2drZz/dChQx2lSpVypKWlOdc1bNjQccMNN+TpWA8dOmQet0yZMo5ffvnFuX7Lli1mvT6+5b777nNUr17dce7cOee67du3m/30+C7GOv41a9Y4fvvtN0dycrLjgw8+cFSpUsURFBRk7ls6dOjgaNSokSMjI8O5Tl9z69atHXXr1nWuGzRokMPPz8/xzTffONf98ccfjrCwMPNc+tosej5cz8nUqVPNPm+//bZzXVZWlqNVq1aO8uXLO9LT093OT3h4uOP48ePOfT/++GOzftmyZXk6zwDsg9ICAMVKenq6ua1QoUKe9v/000/NrZYAuNKRWVWQWtp+/fqZEUHXr+bPnTsnP//8sxSEjna6jqhed9110rJlS+drUXqB29GjR2X9+vVuo7H6tX737t3z9DwxMTGmLCIqKsqM8uooqpYMaFmAOn78uKlB1npZHQH9/fffzfLHH39IbGysfP/9984ShpUrV0qrVq2kSZMmzsfXUoSePXv+43Ho64qMjJT77rvPuU5HVgcPHmxKSLQ0w9W9997rNmpslUToiCyA4oUgC6BY0RZbSoNVXmio9Pf3N3WzrjQ4hYaGFih01qxZ0+2+Fa5OnDghBVG3bt0L1l155ZVudaY333yzqQ/W8Kqys7Plv//9r3Tt2jXPIX/GjBmyevVq+eCDD6Rz584mpLqWAuhX/TpQPXr0aBN4XRctp7BqW5Wex/PPscpp3fn0d/U16/8nV/Xr13duvxTnHYDvoUYWQLELstWrV3e7+CkvXEdO80tHWXOiF1Xl5P+qFAqXPvf9998vb775pmnxpRee6QjtAw88kOfH0JFeq2uBjgK3bdvWPOb+/ftNXa+GY/Xkk0+aEdic5CWoeltRnncAlxYjsgCKnVtvvdVcWb958+Z/3Fev/NdApl+Du0pJSTEXEul215E9XedKe9P++uuvHh+rJwH6/GNVBw4cMF0QXGl5gZZa6MVvOjKrI6W5Bc68hEO9AE7D8GuvvWbW6QV11tf8WoaQ02KN/up51BHc8+W07nz6u/qareDs2i3B2g6gZCLIAih2nnrqKVPPqd0INJCeT0OuXumv9Ctzq4OAq8mTJ5vbLl26ONdpO6eNGze67adtnnIbkc0LPc7zw/E/Wbp0qVv7rK1bt5pOC9r31dU111xjFu0OoB0EtLeuztLlKe0coKO0eq60W0LVqlXNujfeeCPHMO/aakwDtH6w2LFjh3Od1thapQ8Xo/+Pjh075uz2YLVMe/XVV83IsLb0AlAyUVoAoNjRwKktmPSiH62jdJ3Za9OmTc7WTUr7kcbFxZlAqoFSQ5EGQ23HpV+na8soiwbjxx57zFwspTWo3377rXz++edmogBPNWvWTGbOnGl6qurX8BoOtZXWxeh++jV///79Ta9bDZbh4eEmwJ9PX7t+9a/yU1aQG50VTWdM0165ei60jlaPpVGjRtK3b18zSqsfHjS0/vLLL+YcKT22t99+25w3bTlmtd/SelYNtBcbmdaL5jQs6/+zpKQkM/KsdbtaLqGvPa81vwCKoaJumwAAheXAgQOOvn37OmrVquUoXbq0o0KFCo42bdo4Xn31Vbd2UWfOnHGMGzfOUbt2bUdgYKAjKirKER8f77aP0lZWI0aMcFSuXNlRtmxZR2xsrOPgwYO5tt9KTEx0+/3169eb9XprOXbsmKNLly7m2HTbxVpxWe2lXn75Zccrr7xijlPbYbVr187x7bff5vg7v/76q2n5deWVV+b5vOV2/NY5qFOnjlnOnj1r1v3www+OBx980BEZGWnO32WXXea49dZbTcsuV9p6S49Vj7lGjRqOhIQEx/Tp081z6XnIrf2WSklJcfTu3duce/1/qS2/zm8j5np+zpdb6zQA9uan/ynqMA0AKBzaaUC7F4wZM8Z0F/A1Q4YMMaOt2kYrt4u0ACA31MgCQDGmJQBaw9urV6+iPhQzQ5cr7Tf71ltvmdIEQiwAT1AjCwDFkE5UsGfPHnn++edNre/5HQ2Kgk6IoBeHad2y1tHOmTPHdFXwxZFiAPZAaQEAFEMaGPXCtjZt2piLrFxnAisqTz/9tLlISy8C04u7rr32WjNxgrbpAgBPEGQBAABgS9TIAgAAwJYIsgAAALAlLvbKI50aUadm1MbbBZmTHQAAALnTzrB//vmnVK9eXfz9Lz7mSpDNIw2xUVFRed0dAAAABZCcnCw1atS46D4E2TyypkDUkxoSElKQ/y8AAADIhbbl08HDvEw/TZDNI6ucQEMsQRYAAKBw5aWUk4u9AAAAYEsEWQAAANgSQRYAAAC2RJAFAACALRFkAQAAYEsEWQAAANgSQRYAAAC2RJAFAACALRFkAQAAYEsEWQAAANhSkQbZjRs3ym233SbVq1c305AtXbr0gn327t0rt99+u1SsWFHKlSsnLVq0kMOHDzu3Z2RkyIABAyQ8PFzKly8v3bt3l5SUFLfH0P27dOkiZcuWlapVq8rw4cPl7Nmzl+Q1AgAAoBgG2dOnT0vjxo1lxowZOW7/4YcfpG3btnLVVVfJF198ITt37pTRo0dLcHCwc5+hQ4fKsmXL5P3335cNGzbI0aNH5c4773RuP3funAmxWVlZsmnTJlmwYIHMnz9fxowZc0leIwAAAAqHn8PhcIgP0BHZJUuWSLdu3ZzrevToIYGBgfLWW2/l+DsnT56UKlWqyOLFi+Wuu+4y6/bt2yf169eXzZs3y7/+9S/57LPP5NZbbzUBNyIiwuwza9YsGTFihPz2229SunTpPB1fenq6GRXW5wwJCfHKawaAkqrWyBVFfQi28tOLXYr6EIBLJj+Zy2drZLOzs2XFihVy5ZVXSmxsrCkJaNmypVv5QVJSkpw5c0ZiYmKc63T0tmbNmibIKr1t1KiRM8QqfTw9Sbt37871+TMzM80+rgsAAAB8h88G2dTUVDl16pS8+OKL0qlTJ1m1apXccccdpmxASwjUsWPHzIhqaGio2+9qaNVt1j6uIdbabm3LTUJCgvk0YC1RUVGF8CoBAABQ7IKsjsiqrl27mjrYJk2ayMiRI02ZgJYGFLb4+HgzpG0tycnJhf6cAAAAKAZBtnLlyhIQECANGjRwW6/1r1bXgsjISHMRV1pamts+2rVAt1n7nN/FwLpv7ZOToKAgU5fhugAAAMB3+GyQ1ZIBbbW1f/9+t/UHDhyQ6Oho83OzZs3MxWBr1651btf9Nei2atXK3NfbXbt2mVIFy+rVq00wPT8kAwAAwD4CivLJtQb24MGDzvuHDh2SHTt2SFhYmLlgS/u93nvvvXL99ddL+/btZeXKlabVlrbiUlq72qdPHxk2bJj5HQ2ngwYNMuFVOxaojh07msDaq1cvmThxoqmLHTVqlOk9q6OuAAAAsKciDbLbtm0zAdWigVTFxcWZXq96cZfWw+qFV4MHD5Z69erJhx9+aHrLWqZMmSL+/v5mIgTtNKAdCV5//XXn9lKlSsny5culf//+JuDqpAr6+OPHj7/ErxYAAADFso+sr6OPLAB4D31k84c+sihJ0otDH1kAAADgYgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbKtIgu3HjRrntttukevXq4ufnJ0uXLs1138cee8zsM3XqVLf1x48fl549e0pISIiEhoZKnz595NSpU2777Ny5U9q1ayfBwcESFRUlEydOLLTXBAAAgBIQZE+fPi2NGzeWGTNmXHS/JUuWyNdff20C7/k0xO7evVtWr14ty5cvN+G4X79+zu3p6enSsWNHiY6OlqSkJHn55Zdl7NixMnv27EJ5TQAAALg0AqQI3XLLLWa5mCNHjsigQYPk888/ly5durht27t3r6xcuVISExOlefPmZt2rr74qnTt3lkmTJpngu2jRIsnKypK5c+dK6dKlpWHDhrJjxw6ZPHmyW+AFAACAvfh0jWx2drb06tVLhg8fbgLo+TZv3mzKCawQq2JiYsTf31+2bNni3Of66683IdYSGxsr+/fvlxMnTlyiVwIAAIBiNSL7T1566SUJCAiQwYMH57j92LFjUrVqVbd1un9YWJjZZu1Tu3Ztt30iIiKc2ypVqpTjY2dmZprFtUQBAAAAvsNnR2S1nnXatGkyf/58c5HXpZaQkCAVK1Z0LnqRGAAAAHyHzwbZ//3vf5Kamio1a9Y0o6y6/Pzzz/LEE09IrVq1zD6RkZFmH1dnz541nQx0m7VPSkqK2z7WfWufnMTHx8vJkyedS3JyciG8SgAAABS70gKtjdV6V1da26rre/fube63atVK0tLSzOhts2bNzLp169aZ2tqWLVs693nmmWfkzJkzEhgYaNZph4N69erlWlaggoKCzAIAAADfVKRBVvu9Hjx40Hn/0KFDpqOA1rjqSGx4eLjb/hpEdRRVQ6iqX7++dOrUSfr27SuzZs0yYXXgwIHSo0cPZ6uu+++/X8aNG2f6y44YMUK+++47U7IwZcqUS/xqAQAAUGyC7LZt26R9+/bO+8OGDTO3cXFxpjY2L7S9lobXDh06mG4F3bt3l+nTpzu3a33rqlWrZMCAAWbUtnLlyjJmzBhabwEAANicn8PhcBT1QdiBdi3QUKz1sjqLGADAc7VGruD05cNPL7r3UQeKs/R8ZC6fvdgLAAAAuBiCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsCWCLAAAAGyJIAsAAABbIsgCAADAlgiyAAAAsKUiDbIbN26U2267TapXry5+fn6ydOlS57YzZ87IiBEjpFGjRlKuXDmzz4MPPihHjx51e4zjx49Lz549JSQkREJDQ6VPnz5y6tQpt3127twp7dq1k+DgYImKipKJEydestcIAACAYhhkT58+LY0bN5YZM2ZcsO2vv/6S7du3y+jRo83tRx99JPv375fbb7/dbT8Nsbt375bVq1fL8uXLTTju16+fc3t6erp07NhRoqOjJSkpSV5++WUZO3aszJ49+5K8RgAAABQOP4fD4RAfoCOyS5YskW7duuW6T2Jiolx33XXy888/S82aNWXv3r3SoEEDs7558+Zmn5UrV0rnzp3ll19+MaO4M2fOlGeeeUaOHTsmpUuXNvuMHDnSjP7u27cvz8engbhixYpy8uRJM/oLAPBcrZErOH358NOLXThfKDHS85G5bFUjqy9IA6+WEKjNmzebn60Qq2JiYsTf31+2bNni3Of66693hlgVGxtrRndPnDiR63NlZmaaE+m6AAAAwHfYJshmZGSYmtn77rvPmc51lLVq1apu+wUEBEhYWJjZZu0TERHhto9139onJwkJCebTgLVobS0AAAB8hy2CrF74dc8994hWQWipwKUQHx9vRoCtJTk5+ZI8LwAAAPImQGwSYrUudt26dW61EpGRkZKamuq2/9mzZ00nA91m7ZOSkuK2j3Xf2icnQUFBZgEAAIBv8rdDiP3+++9lzZo1Eh4e7ra9VatWkpaWZroRWDTsZmdnS8uWLZ37aCcDfSyLdjioV6+eVKpU6RK+GgAAABSbIKv9Xnfs2GEWdejQIfPz4cOHTfC86667ZNu2bbJo0SI5d+6cqWnVJSsry+xfv3596dSpk/Tt21e2bt0qX331lQwcOFB69OhhOhao+++/31zopf1ltU3Xu+++K9OmTZNhw4YV5UsHAACAndtvffHFF9K+ffsL1sfFxZler7Vr187x99avXy833nij+VnLCDS8Llu2zHQr6N69u0yfPl3Kly/vNiHCgAEDTJuuypUry6BBg8yFY/lB+y0A8B7ab+UP7bdQkqTno/2Wz/SR9XUEWQDwHoJs/hBkUZKkF9c+sgAAAIBtuhbg4hjVyB9GNQAAKD4YkQUAAIAtEWQBAABgSwRZAAAA2BJBFgAAALZEkAUAAIAtEWQBAABgSwRZAAAA2BJBFgAAALZEkAUAAIAtEWQBAABgSwRZAAAA2BJBFgAAALZEkAUAAIAtEWQBAABgSwRZAAAA2BJBFgAAALZEkAUAAIAtEWQBAABgSwRZAAAA2BJBFgAAALZEkAUAAIAtEWQBAABgS0UaZDdu3Ci33XabVK9eXfz8/GTp0qVu2x0Oh4wZM0aqVasmZcqUkZiYGPn+++/d9jl+/Lj07NlTQkJCJDQ0VPr06SOnTp1y22fnzp3Srl07CQ4OlqioKJk4ceIleX0AAAAopkH29OnT0rhxY5kxY0aO2zVwTp8+XWbNmiVbtmyRcuXKSWxsrGRkZDj30RC7e/duWb16tSxfvtyE4379+jm3p6enS8eOHSU6OlqSkpLk5ZdflrFjx8rs2bMvyWsEAABA4QiQInTLLbeYJSc6Gjt16lQZNWqUdO3a1axbuHChREREmJHbHj16yN69e2XlypWSmJgozZs3N/u8+uqr0rlzZ5k0aZIZ6V20aJFkZWXJ3LlzpXTp0tKwYUPZsWOHTJ482S3wAgAAwF58tkb20KFDcuzYMVNOYKlYsaK0bNlSNm/ebO7rrZYTWCFW6f7+/v5mBNfa5/rrrzch1qKjuvv375cTJ05c0tcEAACAYjIiezEaYpWOwLrS+9Y2va1atarb9oCAAAkLC3Pbp3bt2hc8hrWtUqVKOT5/ZmamWVxLFAAAAOA7fHZEtqglJCSYEWBr0YvEAAAA4Dt8NshGRkaa25SUFLf1et/aprepqalu28+ePWs6Gbjuk9NjuD5HTuLj4+XkyZPOJTk52UuvDAAAAMU6yGo5gAbNtWvXun29r7WvrVq1Mvf1Ni0tzXQjsKxbt06ys7NNLa21j3YyOHPmjHMf7XBQr169XMsKVFBQkGnp5boAAADAdxRpkNV+r9pBQBfrAi/9+fDhw6av7JAhQ2TChAnyySefyK5du+TBBx80nQi6detm9q9fv7506tRJ+vbtK1u3bpWvvvpKBg4caDoa6H7q/vvvNxd6aX9ZbdP17rvvyrRp02TYsGFF+dIBAABg54u9tm3bJu3bt3fet8JlXFyczJ8/X5566inTa1bbZOnIa9u2bU27LZ3YwKLttTS8dujQwXQr6N69u+k9a9H61lWrVsmAAQOkWbNmUrlyZTPJAq23AAAA7M3PoQ1b8Y+0rEFDsdbL+lKZQa2RK4r6EGzlpxe7FPUhAODvrnzj7y6UJOn5yFw+WyMLAAAAXAxBFgAAALZEkAUAAEDJCbKXX365/PHHHxes1wuydBsAAADgk0H2p59+knPnzl2wXqd0PXLkiDeOCwAAAPBe+y3t52r5/PPPzRVlFg22OnlBrVq18vOQAAAAQOEHWWsiAp2sQHu9ugoMDDQh9pVXXvHsSAAAAIDCCrI69as1fWxiYqKZXAAAAACwzcxeOpUsAAAAYMsparUeVpfU1FTnSK1l7ty53jg2AAAAwLtBdty4cTJ+/Hhp3ry5VKtWzdTMAgAAAD4fZGfNmiXz58+XXr16ef+IAAAAgMLqI5uVlSWtW7f25FcBAACAoguyjzzyiCxevNg7RwAAAABcqtKCjIwMmT17tqxZs0auueYa00PW1eTJkz15WAAAAKBwg+zOnTulSZMm5ufvvvvObRsXfgEAAMBng+z69eu9fyQAAABAYdfIAgAAALYckW3fvv1FSwjWrVtXkGMCAAAACifIWvWxljNnzsiOHTtMvWxcXJwnDwkAAAAUfpCdMmVKjuvHjh0rp06d8uQhAQAAgKKrkX3ggQdk7ty53nxIAAAAoPCD7ObNmyU4ONibDwkAAAB4r7TgzjvvdLvvcDjk119/lW3btsno0aM9eUgAPqjWyBVFfQi28dOLXYr6EACgxPEoyFasWNHtvr+/v9SrV0/Gjx8vHTt29NaxAQAAAN4NsvPmzZNL4dy5c+YCsrfffluOHTsm1atXl4ceekhGjRrlbP+lo8HPPvusvPnmm5KWliZt2rSRmTNnSt26dZ2Pc/z4cRk0aJAsW7bMhO7u3bvLtGnTpHz58pfkdQAAAMBHgqwlKSlJ9u7da35u2LChNG3aVLzppZdeMqF0wYIF5vG1dKF3795mRHjw4MFmn4kTJ8r06dPNPrVr1zalDbGxsbJnzx5nvW7Pnj1N6cPq1atNqzB9jH79+snixYu9erwAAADw8SCbmpoqPXr0kC+++EJCQ0PNOh0N1YkS3nnnHalSpYpXDm7Tpk3StWtX6dLl/2rPatWqJf/9739l69atztHYqVOnmhFa3U8tXLhQIiIiZOnSpeYYNWivXLlSEhMTpXnz5mafV199VTp37iyTJk0yo7wAAAAoIV0L9Gv6P//8U3bv3m2+ttdFJ0NIT093jpR6Q+vWrWXt2rVy4MABc//bb7+VL7/8Um655RZz/9ChQ6bkICYmxvk7OlrbsmVL00FB6a2GbSvEKt1fSwy2bNmS63NnZmaa1+O6AAAAwOYjsjrCuWbNGqlfv75zXYMGDWTGjBlevdhr5MiRJkBeddVVUqpUKVMz+/zzz5tSAaUhVukIrCu9b23T26pVq7ptDwgIkLCwMOc+OUlISJBx48Z57bUAAADAB4Jsdna2BAYGXrBe1+k2b3nvvfdk0aJFppZVa2R1GtwhQ4aYcoDCngo3Pj5ehg0b5ryvgToqKqpQnxMAABQMbQNLVutAj0oLbrrpJnn88cfl6NGjznVHjhyRoUOHSocOHbx2cMOHDzejslrr2qhRI+nVq5d5Dh0tVZGRkeY2JSXF7ff0vrVNb7Wm19XZs2dNOYS1T06CgoIkJCTEbQEAAIDNg+xrr71mRij14qs6deqYRTsG6Dq9kMpb/vrrL1PL6kpLDKxRX31ODaNaR2vRY9Da11atWpn7eqsXommHBcu6devMY2gtLQAAAEpQaYF+xb59+3ZTJ7tv3z6zTutlXS+68obbbrvN1MTWrFnTlBZ88803MnnyZHn44YfNdu0lq6UGEyZMMH1jrfZbWnrQrVs353F16tRJ+vbtK7NmzTLttwYOHGhGeelYAAAAUEKCrI5kagj8+uuvzVftN998s1nUyZMnTdjUsNiuXTuvHJyO7mow/fe//23KAzR4PvroozJmzBjnPk899ZScPn3a9IXVkde2bduai9GsHrJK62z1uLXswZoQQXvPAgAAoIQEWe3ZqiObOdWLatsrDZk6YuqtIFuhQgXznLrkRkdldWpcXXKjHQqY/AAAAKAE18hqH1f9mj432nrLtRYVAAAA8Ikgq90Acmq75dqf9bfffvPGcQEAAADeC7KXXXaZmcErNzt37pRq1arl5yEBAACAwg+ynTt3NhdfZWRkXLDt77//lmeffVZuvfVWz44EAAAAKKyLvUaNGiUfffSRXHnllaYLQL169cx6bcGl09PqFLLPPPNMfh4SAAAAKPwgGxERIZs2bZL+/fubKVwdDoezc0BsbKwJs7oPAAAA4HMTIkRHR8unn34qJ06ckIMHD5owq5MRVKpUqXCOEAAAAPDWzF5Kg2uLFi08/XUAAADg0l3sBQAAAPgKgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlnw+yR44ckQceeEDCw8OlTJky0qhRI9m2bZtzu8PhkDFjxki1atXM9piYGPn+++/dHuP48ePSs2dPCQkJkdDQUOnTp4+cOnWqCF4NAAAASkSQPXHihLRp00YCAwPls88+kz179sgrr7wilSpVcu4zceJEmT59usyaNUu2bNki5cqVk9jYWMnIyHDuoyF29+7dsnr1alm+fLls3LhR+vXrV0SvCgAAAN4QID7spZdekqioKJk3b55zXe3atd1GY6dOnSqjRo2Srl27mnULFy6UiIgIWbp0qfTo0UP27t0rK1eulMTERGnevLnZ59VXX5XOnTvLpEmTpHr16kXwygAAAFCsR2Q/+eQTEz7vvvtuqVq1qjRt2lTefPNN5/ZDhw7JsWPHTDmBpWLFitKyZUvZvHmzua+3Wk5ghVil+/v7+5sR3NxkZmZKenq62wIAAADf4dNB9scff5SZM2dK3bp15fPPP5f+/fvL4MGDZcGCBWa7hlilI7Cu9L61TW81BLsKCAiQsLAw5z45SUhIMKHYWnRkGAAAAL7Dp4Nsdna2XHvttfLCCy+Y0Vita+3bt6+phy1s8fHxcvLkSeeSnJxc6M8JAACAYhJktRNBgwYN3NbVr19fDh8+bH6OjIw0tykpKW776H1rm96mpqa6bT979qzpZGDtk5OgoCDT5cB1AQAAgO/w6SCrHQv279/vtu7AgQMSHR3tvPBLw+jatWud27WWVWtfW7VqZe7rbVpamiQlJTn3WbdunRnt1VpaAAAA2JNPdy0YOnSotG7d2pQW3HPPPbJ161aZPXu2WZSfn58MGTJEJkyYYOpoNdiOHj3adCLo1q2bcwS3U6dOzpKEM2fOyMCBA01HAzoWAAAA2JdPB9kWLVrIkiVLTL3q+PHjTVDVdlvaF9by1FNPyenTp039rI68tm3b1rTbCg4Odu6zaNEiE147dOhguhV0797d9J4FAACAffl0kFW33nqrWXKjo7IacnXJjXYoWLx4cSEdIQAAAIqCT9fIAgAAALkhyAIAAMCWCLIAAACwJYIsAAAAbIkgCwAAAFsiyAIAAMCWCLIAAACwJYIsAAAAbIkgCwAAAFsiyAIAAMCWCLIAAACwJYIsAAAAbIkgCwAAAFsiyAIAAMCWCLIAAACwJYIsAAAAbIkgCwAAAFsiyAIAAMCWCLIAAACwJYIsAAAAbIkgCwAAAFsiyAIAAMCWCLIAAACwJYIsAAAAbMlWQfbFF18UPz8/GTJkiHNdRkaGDBgwQMLDw6V8+fLSvXt3SUlJcfu9w4cPS5cuXaRs2bJStWpVGT58uJw9e7YIXgEAAABKXJBNTEyUN954Q6655hq39UOHDpVly5bJ+++/Lxs2bJCjR4/KnXfe6dx+7tw5E2KzsrJk06ZNsmDBApk/f76MGTOmCF4FAAAASlSQPXXqlPTs2VPefPNNqVSpknP9yZMnZc6cOTJ58mS56aabpFmzZjJv3jwTWL/++muzz6pVq2TPnj3y9ttvS5MmTeSWW26R5557TmbMmGHCLQAAAOzJFkFWSwd0VDUmJsZtfVJSkpw5c8Zt/VVXXSU1a9aUzZs3m/t626hRI4mIiHDuExsbK+np6bJ79+5L+CoAAADgTQHi49555x3Zvn27KS0437Fjx6R06dISGhrqtl5Dq26z9nENsdZ2a1tuMjMzzWLR4AsAAADf4dMjssnJyfL444/LokWLJDg4+JI+d0JCglSsWNG5REVFXdLnBwAAgI2DrJYOpKamyrXXXisBAQFm0Qu6pk+fbn7WkVWtc01LS3P7Pe1aEBkZaX7W2/O7GFj3rX1yEh8fb2pwrUVDNQAAAHyHTwfZDh06yK5du2THjh3OpXnz5ubCL+vnwMBAWbt2rfN39u/fb9pttWrVytzXW30MDcSW1atXS0hIiDRo0CDX5w4KCjL7uC4AAADwHT5dI1uhQgW5+uqr3daVK1fO9Iy11vfp00eGDRsmYWFhJmwOGjTIhNd//etfZnvHjh1NYO3Vq5dMnDjR1MWOGjXKXECmYRUAAAD25NNBNi+mTJki/v7+ZiIEvThLOxK8/vrrzu2lSpWS5cuXS//+/U3A1SAcFxcn48ePL9LjBgAAQAkLsl988YXbfb0ITHvC6pKb6Oho+fTTTy/B0QEAAOBS8ekaWQAAACA3BFkAAADYEkEWAAAAtkSQBQAAgC0RZAEAAGBLBFkAAADYEkEWAAAAtkSQBQAAgC0RZAEAAGBLBFkAAADYEkEWAAAAtkSQBQAAgC0RZAEAAGBLBFkAAADYEkEWAAAAtkSQBQAAgC0RZAEAAGBLBFkAAADYEkEWAAAAtkSQBQAAgC0RZAEAAGBLBFkAAADYEkEWAAAAtkSQBQAAgC0RZAEAAGBLPh9kExISpEWLFlKhQgWpWrWqdOvWTfbv3++2T0ZGhgwYMEDCw8OlfPny0r17d0lJSXHb5/Dhw9KlSxcpW7aseZzhw4fL2bNnL/GrAQAAQIkJshs2bDAh9euvv5bVq1fLmTNnpGPHjnL69GnnPkOHDpVly5bJ+++/b/Y/evSo3Hnnnc7t586dMyE2KytLNm3aJAsWLJD58+fLmDFjiuhVAQAAoKACxMetXLnS7b4GUB1RTUpKkuuvv15Onjwpc+bMkcWLF8tNN91k9pk3b57Ur1/fhN9//etfsmrVKtmzZ4+sWbNGIiIipEmTJvLcc8/JiBEjZOzYsVK6dOkienUAAAAotiOy59PgqsLCwsytBlodpY2JiXHuc9VVV0nNmjVl8+bN5r7eNmrUyIRYS2xsrKSnp8vu3btzfJ7MzEyz3XUBAACA77BVkM3OzpYhQ4ZImzZt5Oqrrzbrjh07ZkZUQ0ND3fbV0KrbrH1cQ6y13dqWW21uxYoVnUtUVFQhvSoAAAAU+yCrtbLfffedvPPOO4X+XPHx8Wb011qSk5ML/TkBAABQjGpkLQMHDpTly5fLxo0bpUaNGs71kZGR5iKutLQ0t1FZ7Vqg26x9tm7d6vZ4VlcDa5/zBQUFmQUAAAC+yedHZB0OhwmxS5YskXXr1knt2rXdtjdr1kwCAwNl7dq1znXankvbbbVq1crc19tdu3ZJamqqcx/tgBASEiINGjS4hK8GAAAAJWZEVssJtCPBxx9/bHrJWjWtWrdapkwZc9unTx8ZNmyYuQBMw+mgQYNMeNWOBUrbdWlg7dWrl0ycONE8xqhRo8xjM+oKAABgTz4fZGfOnGlub7zxRrf12mLroYceMj9PmTJF/P39zUQI2m1AOxK8/vrrzn1LlSplyhL69+9vAm65cuUkLi5Oxo8ff4lfDQAAAEpMkNXSgn8SHBwsM2bMMEtuoqOj5dNPP/Xy0QEAAKCo+HyNLAAAAJATgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALAAAAWyLIAgAAwJZKVJCdMWOG1KpVS4KDg6Vly5aydevWoj4kAAAAeKjEBNl3331Xhg0bJs8++6xs375dGjduLLGxsZKamlrUhwYAAAAPlJggO3nyZOnbt6/07t1bGjRoILNmzZKyZcvK3Llzi/rQAAAA4IESEWSzsrIkKSlJYmJinOv8/f3N/c2bNxfpsQEAAMAzAVIC/P7773Lu3DmJiIhwW6/39+3bl+PvZGZmmsVy8uRJc5ueni6+JDvzr6I+BFvxtf9/vo73V97x3uK9VZh4f+Udf2/Z/71lHZPD4fjHfUtEkPVEQkKCjBs37oL1UVFRRXI88I6KUzmTKBy8t1CYeH+hJL63/vzzT6lYseJF9ykRQbZy5cpSqlQpSUlJcVuv9yMjI3P8nfj4eHNxmCU7O1uOHz8u4eHh4ufnV+jHbGf6SUoDf3JysoSEhBT14aCY4f0F3luwG/7eyh8didUQW7169X/ct0QE2dKlS0uzZs1k7dq10q1bN2cw1fsDBw7M8XeCgoLM4io0NPSSHG9xoSGWIAveX7Ab/u4C762i908jsSUqyCodXY2Li5PmzZvLddddJ1OnTpXTp0+bLgYAAACwnxITZO+991757bffZMyYMXLs2DFp0qSJrFy58oILwAAAAGAPJSbIKi0jyK2UAN6jJRk68cT5pRkA7y/4Mv7uAu8t+/Fz5KW3AQAAAOBjSsSECAAAACh+CLIAAACwJYIsAAAAbIkgCwAAAFsiyAIASqQzZ85IQECAfPfdd0V9KCiGDh8+bGaoOp+u023wDoIsvGLhwoWSmZl5wfqsrCyzDSiIhx9+2ExXeD6d1ES3AZ4IDAyUmjVryrlz5ziB8LratWub/vXn0+nudRu8g/Zb8IpSpUrJr7/+KlWrVnVb/8cff5h1/EOBwnh//f777xIZGSlnz57lBMMjc+bMkY8++kjeeustCQsL4yzCa/z9/SUlJUWqVKnitv7nn3+WBg0amA/iKLgSNSECCo9+VeLn53fB+l9++SXP8yUD50tPTzfvLV10RDY4ONi5TT8cffrppxeEWyA/XnvtNTl48KBUr15doqOjpVy5cm7bt2/fzglFvgwbNszc6r+Jo0ePlrJly7r9vbVlyxYzuyi8gyCLAmnatKn5w6pLhw4dTL2Z6x/YQ4cOSadOnTjL8EhoaKjz/XXllVdesF3Xjxs3jrMLj3Xr1o2zB6/65ptvzK1+AN+1a5eULl3auU1/bty4sTz55JOcdS+htAAFYoUIvX3iiSekfPnybn9ga9WqJd27d3f7gwzk1YYNG8w/BjfddJN8+OGHbl/96ntKR9B0JA0AfE3v3r1l2rRpEhISUtSHUqwRZOEVCxYskB49epi5ygFv05oyvSgnp/IVoKDS0tLkgw8+kB9++EGGDx9uPjBpSUFERIRcdtllnGB4rVRq3bp1ctVVV5kF3kHXAniFjpi5Xp25detWGTJkiMyePZszjALbu3evfPXVV877M2bMMDVm999/v5w4cYIzDI/t3LnTlK289NJLMmnSJBNqlV4AFh8fz5mFx+655x5Tg63+/vtvad68uVnXqFEj8w0TvIMgC6/QQLF+/Xrz87FjxyQmJsaE2WeeeUbGjx/PWUaB6CiZjmYorTnTiyk6d+5sarCtCysAT+j756GHHpLvv//e7WJCfX9t3LiRkwqP6funXbt25uclS5aYMin9oDR9+nSZMGECZ9ZLCLLwCm0oft1115mf33vvPfOJc9OmTbJo0SKZP38+ZxkFooFV29UoHcm47bbb5IUXXjAjs5999hlnFx5LTEyURx999IL1WlKgH8oBT508edJZ179y5UpzvYh2MOjSpYv54ATvIMjCazPkWPWxa9askdtvv938rHVA2v8TKAi9sOuvv/5yvr86duxoftZ/JKyRWsAT+vdWTu+hAwcOXND/E8iPqKgo2bx5s+kXq0HW+ntLy6FcR/9RMARZeEXDhg1l1qxZ8r///U9Wr17tbLl19OhRCQ8P5yyjQNq2bWu+An7uuedMyYqOaFhho0aNGpxdeEw/dGv5k34YV3pBoU4fOmLECDOCBnhKrxPp2bOn+TuqWrVqcuONNzpLDvRbS3gHXQvgFV988YXccccdZmQjLi5O5s6da9Y//fTTsm/fPnPhBOApDRb//ve/JTk5WQYPHix9+vQx64cOHWr6FWvNGeDp17933XWXbNu2zUy6oe3ctKSgVatWZsKN8ydIAPJD31f699bNN9/sbE+5YsUK0yO7TZs2nEwvIMjCazRQaJCtVKmSc91PP/1kaoKYfQmAL/vyyy9NB4NTp07Jtddeay5YBbwhKyvL1PnXqVPHbdIgeAdBFl6j893ryKz2YtQuBhUqVDClBdoM2nWiBKAgMjIyzD8Mrmg4DsDXaF3/oEGDTJ91qxTq8ssvN+v0YsKRI0cW9SEWC3w0gNca1mtdrH4FnJmZab5G0SCrvRn1vtbPAp7SiyW0ZlE7Yvzxxx85fhsAeGrt2rVmSU1NlezsbLdtVpkUkF/ah/jbb781AzyuU7XraP/YsWMJsl7CxV7wiscff9w0e9arMcuUKeNcr3Wz+g8EUBBPPfWUmRFn5syZ5irz//znP2ZaZK1nXLhwIScXHtP3kV5Nrn9P/f777+bvMNcF8NTSpUvNhAh6sarrrIR6cbR+cwnvYEQWXqHdCrRvrLZJclWrVi05cuQIZxkFsmzZMhNY9apfnb9cm4xfccUVEh0dbXoV65XBgCf02yLtdd2rVy9OILxKZ7vM6foQ/YaJ6ba9hxFZeIV+HZfT17u//PKLKTEACuL48eOmtsyqh9X7Skc6mH0JBaH11q1bt+Ykwuv0W0rtUGCxwqt+o6RdMeAdBFl4hX41N3XqVLc/sHr177PPPmumegQKQkOsXvVrTbKhtbLWSK22sQE89cgjj8jixYs5gfA6nX1QW1D279/fXAw9bdo082/lvHnz5Pnnn+eMewldC+AVOvIaGxtr5pLWqff0k6jeVq5c2YyY0X4LBTFlyhQpVaqU6SGrM3vpFLX6XtMm9pMnTzY12kBe6eQart8m6VXl11xzjVkCAwPd9tX3F+CpH3/8URISEsxFX1ZrN71wlQkRvIcgC6/RT5zvvPOOWy9GrV10vfgL8FaXjKSkJFMnq+EDyI/27dvned/169dzcuGRBx980LzXrr/+etNDFoWDIAvAdn1kmaccgK/TshX9RlI7FGiHlRtuuMFcsKq3devWLerDKzaokYVX1KxZ03z6nDNnjvkqBfAmvZDwueeeM03EdXIN6z02evRo854DPPXwww+bqWlzurJctwGe0ou6dBIE7a8+ceJE83fXK6+8Yur8a9SowYn1EoIsvFbUrqNkOgGCft0bFRUlDzzwgLz55pumVhYoCL0wQlsk6T8Gri3err76avOPBeAprY/9+++/L1iv6+hRDG/QadvDw8PNrV6cqtPUVqlShZPrJZQWwOt+/fVX2bBhgyxfvlzefffdXFtzAXmlH47eeOMN6dChg2nnphdOaCeDffv2mTY2NK5HfqWnp5sLBjVc6Idt12Chf19pRwydQlSn2QY8oR0LdFavb775RurXr+8sLdCaWX3fwTuYEAFenVf6yy+/NH9w9QIJ/cOrI2b6BxcoCJ1UQ8Ps+fRDknYuAPJLR8a0TaAuV1555QXbdb3O+gV46sUXXzQfkLQN5Z133pnj+wwFR5CFV2hDcetTpwZXHcngUye8pUGDBmb2OJ3Jy9UHH3wgTZs25UQj3/TDto7I3nTTTfLhhx9KWFiYc5uWr+h7TS/QATyl/ybqt5M6uKO1sfq+skZldSHYegdBFl6hX/GWK1fOFLHrooGWr07gLWPGjJG4uDgzMqujsB999JHs37/f1DBqCQuQXxoolE60obPFzZ07V/bu3WvWNWzY0Hx4AgqicePGZtH+10pLorQn9oABAyi58yJqZOEVOrKxa9cu88lTP4FqyxHr06f20evbty9nGgWiI7Ljx493ayyuAVdnygE8tW3bNunUqZO5WPW6664z6xITE83FXqtWrTLvM8DTfxd1VFb/XdRFS++0Nlt7X+u/jRpqUXAEWRTKH15tVv/aa6/JokWL+OQJwGe1a9fO1F9rhxW9mtya3EV7gGqbN/1QDnhCv5XUD906KmuVFOj7jWm1vYsgC6/Yvn2726dO7cuoU/BZzZ+7du3KmUaBZWVlSWpqqvlwdH4fY8ATOvOgjpppSZSrPXv2mKm29SJWwBMrVqwwwVVLV1B4qJGFV+hXcnrRjYZWLSPQC70qVqzI2YVXaHskbU6/adOmC0b/9epy2rvBUxoytGH9+UE2OTnZtHoDPNWlSxdO3iVAkIVXHD9+nE+dKDQPPfSQ+dpXL+yqVq2aCa+AN9x7773Sp08fmTRpkum+or766isZPny43HfffZxkwMdRWgDA52lHDK27Pn/UDPBGuYqG1lmzZpnaWBUYGCj9+/c3fUCDgoI4yYAPI8iiQIXseR0Z0xFbwFMtWrQwV/i2bduWk4hCobWwP/zwg/m5Tp06UrZsWc40YAMEWRRojnLLH3/8IRMmTJDY2FgzZajavHmzfP755zJ69GgZOnQoZxr5om1qXFskjRo1Sl544QVzEaGOmLniYgoAKJkIsvCK7t27m36xAwcOdFuvLbjWrFkjS5cu5UwjX/z9/d1G/K0Lu1xxsRcAlGwEWXhF+fLlZceOHaYfo6uDBw9KkyZNTC89ID90Yo38ztIEAChZ6FoArwgPD5ePP/5YnnjiCbf1uk63AfnlGk61PVJUVFSOI7LaJgkAUDIRZOEV48aNMzPh6IQILVu2NOu2bNkiK1euNDPmAAVRu3Zt+fXXX6Vq1aoXXESo2+gjCwAlE0EWXuvzWb9+fZk+fbp89NFHZp3e11m+rGALeCqn+lilJSvBwcGcWAAooaiRBeCzhg0bZm6nTZtmZoxzbYmko7A66l+qVCnTwB4AUPIwIosCtUey2h65tkrKCe2R4IlvvvnGOSK7a9cuKV26tHOb/ty4cWN58sknObkAUEIxIguP6UiYVbd4fqskC+2R4A29e/c2ZSsVKlTghAIAnAiyKFB7pDZt2khAQMA/tkqiPRI8debMGSlTpoxp73b11VdzIgEATpQWwGOu4VR/zsjIkJ07d0pqaqpkZ2dzZuEVOotXzZo16UwAALgAI7LwCm2z9eCDD8rvv/9+4ZvMz48QggKZM2eO6Ybx1ltvSVhYGGcTAGAQZOEVdevWlY4dO8qYMWMkIiKCswqvatq0qZklTssMoqOjpVy5cm7bt2/fzhkHgBKI0gJ4RUpKimmVRIhFYejWrRsnFgBwAUZk4RUPP/ywufCrT58+nFEAAHBJEGThFX/99ZfcfffdUqVKFWnUqJG5QMfV4MGDOdMosKSkJNm7d6/5uWHDhqbkAABQchFk4bWLcR577DEzXWh4eLhbT1n9+ccff+RMw2PaCaNHjx7yxRdfSGhoqFmXlpYm7du3l3feecd8gAIAlDwEWXhFZGSkGXUdOXKkmRwB8KZ7773XfBhauHCh1K9f36zbs2ePxMXFyRVXXCH//e9/OeEAUAIRZOEV2hIpMTFR6tSpwxmF11WsWFHWrFkjLVq0cFu/detW0y1DR2cBACUPQ2fwCh0Ze/fddzmbKBQ6wcb5dddK1zH5BgCUXIzIwiu0rEC/9m3cuLFcc801F4SOyZMnc6bhsa5du5pRVy0hqF69ull35MgR6dmzp1SqVEmWLFnC2QWAEoggC6/Qi25yfZP5+cm6des40/BYcnKy3H777bJ7926Jiooy6w4fPmw6ZHzyySdSo0YNzi4AlEAEWQC24HA4ZO3atc72W3rRV0xMTFEfFgCgCBFkAdiChlhdtBXX+XWxc+fOLbLjAgAUHaaoBeDzxo0bJ+PHj5fmzZtLtWrV3PoUAwBKLkZkAfg8Da8TJ06UXr16FfWhAAB8CO23APi8rKwsad26dVEfBgDAxxBkAfi8Rx55RBYvXlzUhwEA8DHUyALweRkZGTJ79mwzuxd9igEAFmpkAfg8+hQDAHJCkAUAAIAtUSMLAAAAWyLIAgAAwJYIsgAAALAlgiwAAABsiSALACXE/PnzJTQ0tKgPAwC8hiALAD7goYceEj8/P7MEBgZK7dq15amnnjI9dL3l3nvvlQMHDnjt8QCgqDEhAgD4iE6dOsm8efPkzJkzkpSUJHFxcSbYvvTSS155/DJlypgFAIoLRmQBwEcEBQVJZGSkREVFSbdu3SQmJkZWr15ttmVnZ0tCQoIZqdUw2rhxY/nggw/cfv+TTz6RunXrSnBwsJlEYsGCBSYIp6Wl5VpaMHPmTKlTp46ULl1a6tWrJ2+99Zbbdv39//znP3LHHXdI2bJlzePr8wCALyDIAoAP+u6772TTpk0mYCoNsQsXLpRZs2bJ7t27ZejQofLAAw/Ihg0bzPZDhw7JXXfdZQLwt99+K48++qg888wzF32OJUuWyOOPPy5PPPGEeT79nd69e8v69evd9hs3bpzcc889snPnTuncubP07NlTjh8/XoivHgDyhpm9AMBHamTffvttM5p69uxZyczMFH9/f3nvvffk1ltvlbCwMFmzZo20atXK+TuPPPKI/PXXX7J48WIZOXKkrFixQnbt2uXcPmrUKHn++eflxIkTZiRWR2SHDBniHKFt06aNNGzYUGbPnu38HQ2sp0+fNo9ljcjq4zz33HPmvm4rX768fPbZZ6YUAgCKEjWyAOAjtBxAv+rXsDhlyhQJCAiQ7t27mxFYDaw333yz2/5ZWVnStGlT8/P+/fulRYsWbtuvu+66iz7f3r17pV+/fm7rNNxOmzbNbd0111zj/LlcuXISEhIiqampHr9OAPAWgiwA+AgNiVdccYX5ee7cuaYOds6cOXL11VebdTpKetlll11QV1vYtIuCKx2l1ZpdAChqBFkA8EFaVvD000/LsGHDTMssDayHDx+WG264Icf99UKtTz/91G1dYmLiRZ+jfv368tVXX5nuCBa936BBAy+9CgAoXARZAPBRd999twwfPlzeeOMNefLJJ80FXjoS2rZtWzl58qQJnfo1vwZRvVBr8uTJMmLECOnTp4/s2LHD1MRaI6g50cfWmlgtT9AOCcuWLZOPPvrI1OICgB0QZAHAR2mN7MCBA2XixImmK0GVKlVM94Iff/zRXLx17bXXmlFbpW25tB2XdiDQGle9KEy7FvTv3z/X8gPtcKD7Tpo0yXQv0MfQPrY33njjJX6lAOAZuhYAQDGlHQu0XVdycnJRHwoAFApGZAGgmHj99ddN54Lw8HBTdvDyyy+bEV0AKK4IsgBQTHz//fcyYcIEM1lBzZo1TZlBfHx8UR8WABQaSgsAAABgS0xRCwAAAFsiyAIAAMCWCLIAAACwJYIsAAAAbIkgCwAAAFsiyAIAAMCWCLIAAACwJYIsAAAAbIkgCwAAALGj/wfWN5ogiqCVngAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\\region_counts.png\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\\health_counts.png\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\\mean_visits_region_health.png\n" ] } ], "source": [ "# Plot 1: counts by region\n", "if \"region\" in df.columns:\n", " region_counts = df[\"region\"].value_counts()\n", " fig, ax = plt.subplots(figsize=(7, 4.5))\n", " region_counts.sort_index().plot(kind=\"bar\", title=\"Count by Region\", ax=ax)\n", " ax.set_xlabel(\"Region\")\n", " ax.set_ylabel(\"Count\")\n", " out = PLOTS_DIR / \"region_counts.png\"\n", " fig.tight_layout()\n", " fig.savefig(out, dpi=150)\n", " plt.show()\n", " print(\"Saved:\", out)\n", "\n", "# Plot 2: counts by health\n", "if \"health\" in df.columns:\n", " fig, ax = plt.subplots(figsize=(7, 4.5))\n", " df[\"health\"].value_counts().sort_index().plot(kind=\"bar\", title=\"Count by Health Category\", ax=ax)\n", " ax.set_xlabel(\"Health\")\n", " ax.set_ylabel(\"Count\")\n", " out = PLOTS_DIR / \"health_counts.png\"\n", " fig.tight_layout()\n", " fig.savefig(out, dpi=150)\n", " plt.show()\n", " print(\"Saved:\", out)\n", "\n", "# Plot 3: mean visits by region and health\n", "if set([\"region\", \"health\", \"visits\"]).issubset(df.columns):\n", " pivot_visits = pd.pivot_table(df, index=\"region\", columns=\"health\", values=\"visits\", aggfunc=\"mean\")\n", " fig, ax = plt.subplots(figsize=(8, 5))\n", " pivot_visits.plot(kind=\"bar\", ax=ax)\n", " ax.set_title(\"Mean Visits by Region and Health\")\n", " ax.set_xlabel(\"Region\")\n", " ax.set_ylabel(\"Mean Visits\")\n", " out = PLOTS_DIR / \"mean_visits_region_health.png\"\n", " fig.tight_layout()\n", " fig.savefig(out, dpi=150)\n", " plt.show()\n", " print(\"Saved:\", out)\n" ] }, { "cell_type": "markdown", "id": "3e2a6f93", "metadata": {}, "source": [ "## C4-T5 — Plot Week 4 analysis + correlation\n", "**PDF requirement:** Plot Week 4 analysis and correlation. Provide detailed report and observations.\n", "\n", "### What I completed\n", "- I computed numeric correlations, exported a correlation-matrix figure, and generated required scatter plots for key relationships.\n", "\n", "### Results Capture\n", "- Correlation highlights: `emergency~hospital=0.476`, `ovisits~novisits=0.467`, `visits~chronic=0.262`.\n", "- Derived pairs `age~age_years` and `income~income_dollars` reached `1.0` as expected.\n", "- Saved artifacts: `correlation_matrix.png`, `scatter_income_vs_visits.png`, `scatter_age_vs_visits.png`, `scatter_income_vs_emergency.png`.\n", "\n", "### Artifacts\n", "- `outputs/plots/correlation_matrix.png`\n", "- `outputs/plots/scatter_*.png`\n", "\n", "### Code evidence\n", "- The next cell contains all correlation and scatter plotting commands I executed." ] }, { "cell_type": "code", "execution_count": 15, "id": "2bc193cf", "metadata": {}, "outputs": [ { "data": { "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
visitsnvisitsovisitsnovisitsemergencyhospitalchronicageschoolincomeage_yearsincome_dollars
visits1.0000000.2263650.0681440.0784680.1587480.2407890.2618860.0034040.064433-0.0049510.003404-0.004951
nvisits0.2263651.0000000.0011290.0417680.0490560.0504010.037113-0.0416400.0859740.010731-0.0416400.010731
ovisits0.0681440.0011291.0000000.4669230.0653880.1105730.100805-0.040028-0.012156-0.004267-0.040028-0.004267
novisits0.0784680.0417680.4669231.0000000.0240830.0653010.040748-0.032813-0.011733-0.007786-0.032813-0.007786
emergency0.1587480.0490560.0653880.0240831.0000000.4760610.2038450.061023-0.071192-0.0264620.061023-0.026462
hospital0.2407890.0504010.1105730.0653010.4760611.0000000.2335240.074122-0.036082-0.0141830.074122-0.014183
chronic0.2618860.0371130.1008050.0407480.2038450.2335241.0000000.099758-0.065829-0.0473970.099758-0.047397
age0.003404-0.041640-0.040028-0.0328130.0610230.0741220.0997581.000000-0.142996-0.0731301.000000-0.073130
school0.0644330.085974-0.012156-0.011733-0.071192-0.036082-0.065829-0.1429961.0000000.259199-0.1429960.259199
income-0.0049510.010731-0.004267-0.007786-0.026462-0.014183-0.047397-0.0731300.2591991.000000-0.0731301.000000
age_years0.003404-0.041640-0.040028-0.0328130.0610230.0741220.0997581.000000-0.142996-0.0731301.000000-0.073130
income_dollars-0.0049510.010731-0.004267-0.007786-0.026462-0.014183-0.047397-0.0731300.2591991.000000-0.0731301.000000
\n", "
" ], "text/plain": [ " visits nvisits ovisits novisits emergency hospital \\\n", "visits 1.000000 0.226365 0.068144 0.078468 0.158748 0.240789 \n", "nvisits 0.226365 1.000000 0.001129 0.041768 0.049056 0.050401 \n", "ovisits 0.068144 0.001129 1.000000 0.466923 0.065388 0.110573 \n", "novisits 0.078468 0.041768 0.466923 1.000000 0.024083 0.065301 \n", "emergency 0.158748 0.049056 0.065388 0.024083 1.000000 0.476061 \n", "hospital 0.240789 0.050401 0.110573 0.065301 0.476061 1.000000 \n", "chronic 0.261886 0.037113 0.100805 0.040748 0.203845 0.233524 \n", "age 0.003404 -0.041640 -0.040028 -0.032813 0.061023 0.074122 \n", "school 0.064433 0.085974 -0.012156 -0.011733 -0.071192 -0.036082 \n", "income -0.004951 0.010731 -0.004267 -0.007786 -0.026462 -0.014183 \n", "age_years 0.003404 -0.041640 -0.040028 -0.032813 0.061023 0.074122 \n", "income_dollars -0.004951 0.010731 -0.004267 -0.007786 -0.026462 -0.014183 \n", "\n", " chronic age school income age_years \\\n", "visits 0.261886 0.003404 0.064433 -0.004951 0.003404 \n", "nvisits 0.037113 -0.041640 0.085974 0.010731 -0.041640 \n", "ovisits 0.100805 -0.040028 -0.012156 -0.004267 -0.040028 \n", "novisits 0.040748 -0.032813 -0.011733 -0.007786 -0.032813 \n", "emergency 0.203845 0.061023 -0.071192 -0.026462 0.061023 \n", "hospital 0.233524 0.074122 -0.036082 -0.014183 0.074122 \n", "chronic 1.000000 0.099758 -0.065829 -0.047397 0.099758 \n", "age 0.099758 1.000000 -0.142996 -0.073130 1.000000 \n", "school -0.065829 -0.142996 1.000000 0.259199 -0.142996 \n", "income -0.047397 -0.073130 0.259199 1.000000 -0.073130 \n", "age_years 0.099758 1.000000 -0.142996 -0.073130 1.000000 \n", "income_dollars -0.047397 -0.073130 0.259199 1.000000 -0.073130 \n", "\n", " income_dollars \n", "visits -0.004951 \n", "nvisits 0.010731 \n", "ovisits -0.004267 \n", "novisits -0.007786 \n", "emergency -0.026462 \n", "hospital -0.014183 \n", "chronic -0.047397 \n", "age -0.073130 \n", "school 0.259199 \n", "income 1.000000 \n", "age_years -0.073130 \n", "income_dollars 1.000000 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\\correlation_matrix.png\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\\scatter_income_vs_visits.png\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\\scatter_age_vs_visits.png\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Saved: c:\\DEV_Projects\\SIMPLILEARN\\CAPSTONE_Applied_Data_Science_with _Python\\Incremental_Capstone\\Capstone 4\\outputs\\plots\\scatter_income_vs_emergency.png\n" ] } ], "source": [ "num_df = df.select_dtypes(include=[\"number\"])\n", "corr = num_df.corr(numeric_only=True)\n", "show(corr)\n", "\n", "fig, ax = plt.subplots(figsize=(10, 8))\n", "cax = ax.imshow(corr, cmap=\"coolwarm\", vmin=-1, vmax=1)\n", "fig.colorbar(cax, ax=ax, fraction=0.046, pad=0.04)\n", "ax.set_xticks(range(len(corr.columns)))\n", "ax.set_yticks(range(len(corr.columns)))\n", "ax.set_xticklabels(corr.columns, rotation=90)\n", "ax.set_yticklabels(corr.columns)\n", "ax.set_title(\"Correlation Matrix (Numeric Features)\")\n", "\n", "out = PLOTS_DIR / \"correlation_matrix.png\"\n", "fig.tight_layout()\n", "fig.savefig(out, bbox_inches=\"tight\", dpi=150)\n", "plt.show()\n", "print(\"Saved:\", out)\n", "\n", "scatter_specs = [\n", " (\"income_dollars\", \"visits\", \"scatter_income_vs_visits.png\", \"Income vs Visits\"),\n", " (\"age_years\", \"visits\", \"scatter_age_vs_visits.png\", \"Age vs Visits\"),\n", " (\"income_dollars\", \"emergency\", \"scatter_income_vs_emergency.png\", \"Income vs Emergency Visits\")\n", "]\n", "\n", "for x, y, fname, title in scatter_specs:\n", " if x in df.columns and y in df.columns:\n", " fig, ax = plt.subplots(figsize=(6.5, 4.5))\n", " ax.scatter(df[x], df[y], alpha=0.3, s=12)\n", " ax.set_title(title)\n", " ax.set_xlabel(x)\n", " ax.set_ylabel(y)\n", " out = PLOTS_DIR / fname\n", " fig.tight_layout()\n", " fig.savefig(out, dpi=150)\n", " plt.show()\n", " print(\"Saved:\", out)\n" ] }, { "cell_type": "markdown", "id": "68c58d07", "metadata": {}, "source": [ "## Final section — Conclusions (required)\n", "- I completed the required visualization deliverables using the instructed Capstone 2 handoff dataset.\n", "- I produced and interpreted categorical analysis charts for health and region.\n", "- I delivered correlation and scatter-relationship visuals with caveats documented for derived-feature pairs.\n", "- I saved all grading artifacts under `outputs/plots/` and updated `WORK_SUMMARY.md` accordingly.\n" ] }, { "cell_type": "code", "execution_count": 16, "id": "94e35f26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Capstone 4 completed: C4-T3 to C4-T5\n", "Artifacts saved under outputs/plots\n" ] } ], "source": [ "print(\"Capstone 4 completed: C4-T3 to C4-T5\")\n", "print(\"Artifacts saved under outputs/plots\")\n" ] } ], "metadata": { "kernelspec": { "display_name": ".venv_clean", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.0" } }, "nbformat": 4, "nbformat_minor": 5 }