diff --git a/lab-hypothesis-testing.ipynb b/lab-hypothesis-testing.ipynb
index 0cc26d5..9e3433d 100644
--- a/lab-hypothesis-testing.ipynb
+++ b/lab-hypothesis-testing.ipynb
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
        "[800 rows x 11 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -297,11 +297,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 POKEMON DATA EXPLORATION: DATA TYPES AND DIMENSIONS\n",
+      "📋 Rows: 800 | Columns: 11\n"
+     ]
+    }
+   ],
    "source": [
-    "#code here"
+    "\n",
+    "print(\"🔍 POKEMON DATA EXPLORATION: DATA TYPES AND DIMENSIONS\")\n",
+    "\n",
+    "# 1. Show dataset shape summary\n",
+    "print(f\"📋 Rows: {df.shape[0]} | Columns: {df.shape[1]}\")\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -313,11 +328,241 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#code here"
+    "df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/pokemon.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📋 Column Data Types:\n",
+      "Name          object\n",
+      "Type 1        object\n",
+      "Type 2        object\n",
+      "HP             int64\n",
+      "Attack         int64\n",
+      "Defense        int64\n",
+      "Sp. Atk        int64\n",
+      "Sp. Def        int64\n",
+      "Speed          int64\n",
+      "Generation     int64\n",
+      "Legendary       bool\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2. View column names and quick info\n",
+    "print(\"\\n📋 Column Data Types:\")\n",
+    "print(df.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "📋 Column Data Types:\n",
+      "Name          object\n",
+      "Type 1        object\n",
+      "Type 2        object\n",
+      "HP             int64\n",
+      "Attack         int64\n",
+      "Defense        int64\n",
+      "Sp. Atk        int64\n",
+      "Sp. Def        int64\n",
+      "Speed          int64\n",
+      "Generation     int64\n",
+      "Legendary       bool\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 2. View column names and quick info\n",
+    "print(\"\\n📋 Column Data Types:\")\n",
+    "print(df.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hypotheses:\\\n",
+    "\n",
+    "(H_{0}\\): \\(\\mu_{\\text{Dragon HP}} \\le \\mu_{\\text{Grass HP}}\\) (Dragon Pokémon do not have more HP than Grass Pokémon)\n",
+    "\n",
+    "\n",
+    "\n",
+    "\\(H_{1}\\): \\(\\mu_{\\text{Dragon HP}} > \\mu_{\\text{Grass HP}}\\) (Dragon Pokémon have more HP than Grass Pokémon)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🧪 HYPOTHESIS TEST 1: DRAGON VS GRASS HP\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"🧪 HYPOTHESIS TEST 1: DRAGON VS GRASS HP\")\n",
+    "\n",
+    "# Isolate the HP for Dragon and Grass types (checking primary Type 1)\n",
+    "dragon_hp = df[df['Type 1'] == 'Dragon']['HP']\n",
+    "grass_hp = df[df['Type 1'] == 'Grass']['HP']\n",
+    "\n",
+    "# Perform Welch's T-test (equal_var=False handles unequal group sizes/variances)\n",
+    "t_stat, p_val_two_tailed = st.ttest_ind(dragon_hp, grass_hp, equal_var=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert two-tailed p-value to one-tailed (since we posit Dragon > Grass)\n",
+    "p_val_one_tailed = p_val_two_tailed / 2 if t_stat > 0 else 1 - (p_val_two_tailed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean HP - Dragon: 83.31 | Grass: 67.27\n",
+      "t-statistic: 3.3350\n",
+      "One-tailed p-value: 0.0008\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Mean HP - Dragon: {dragon_hp.mean():.2f} | Grass: {grass_hp.mean():.2f}\")\n",
+    "print(f\"t-statistic: {t_stat:.4f}\")\n",
+    "print(f\"One-tailed p-value: {p_val_one_tailed:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📢 Finding: Reject H0. Dragon Pokemons have significantly more HP than Grass Pokemons on average.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check significance (5%)\n",
+    "alpha = 0.05\n",
+    "if p_val_one_tailed < alpha:\n",
+    "    print(\"📢 Finding: Reject H0. Dragon Pokemons have significantly more HP than Grass Pokemons on average.\")\n",
+    "else:\n",
+    "    print(\"📢 Finding: Fail to reject H0. No significant difference to prove Dragons have higher average HP.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "🧪 HYPOTHESIS TEST 2: LEGENDARY VS NON-LEGENDARY\n",
+      "\n",
+      "📊 Stat: HP\n",
+      "   t-statistic: 8.9814 | p-value: 1.0027e-13\n",
+      "   📢 Finding: Reject H0. Legendary Pokemons have significantly different HP stats.\n",
+      "\n",
+      "📊 Stat: Attack\n",
+      "   t-statistic: 10.4381 | p-value: 2.5204e-16\n",
+      "   📢 Finding: Reject H0. Legendary Pokemons have significantly different Attack stats.\n",
+      "\n",
+      "📊 Stat: Defense\n",
+      "   t-statistic: 7.6371 | p-value: 4.8270e-11\n",
+      "   📢 Finding: Reject H0. Legendary Pokemons have significantly different Defense stats.\n",
+      "\n",
+      "📊 Stat: Sp. Atk\n",
+      "   t-statistic: 13.4174 | p-value: 1.5515e-21\n",
+      "   📢 Finding: Reject H0. Legendary Pokemons have significantly different Sp. Atk stats.\n",
+      "\n",
+      "📊 Stat: Sp. Def\n",
+      "   t-statistic: 10.0157 | p-value: 2.2949e-15\n",
+      "   📢 Finding: Reject H0. Legendary Pokemons have significantly different Sp. Def stats.\n",
+      "\n",
+      "📊 Stat: Speed\n",
+      "   t-statistic: 11.4750 | p-value: 1.0490e-18\n",
+      "   📢 Finding: Reject H0. Legendary Pokemons have significantly different Speed stats.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- PART 2: LEGENDARY VS NON-LEGENDARY ---\n",
+    "print(\"\\n🧪 HYPOTHESIS TEST 2: LEGENDARY VS NON-LEGENDARY\")\n",
+    "\n",
+    "# Separate groups\n",
+    "legendary = df[df['Legendary'] == True]\n",
+    "non_legendary = df[df['Legendary'] == False]\n",
+    "\n",
+    "# Clean column mapping to fix matching issues (e.g. Sp.Atk vs Sp. Atk)\n",
+    "stats_to_test = ['HP', 'Attack', 'Defense', 'Sp. Atk', 'Sp. Def', 'Speed']\n",
+    "\n",
+    "# Loop through each metric and test\n",
+    "alpha = 0.05\n",
+    "for stat in stats_to_test:\n",
+    "    # Ensure correct column string indexing\n",
+    "    col_name = stat if stat in df.columns else (stat.replace('.', '. ') if stat.replace('.', '. ') in df.columns else None)\n",
+    "    \n",
+    "    if col_name:\n",
+    "        t_stat, p_val = st.ttest_ind(legendary[col_name], non_legendary[col_name], equal_var=False)\n",
+    "        \n",
+    "        print(f\"\\n📊 Stat: {stat}\")\n",
+    "        print(f\"   t-statistic: {t_stat:.4f} | p-value: {p_val:.4e}\")\n",
+    "        \n",
+    "        if p_val < alpha:\n",
+    "            print(f\"   📢 Finding: Reject H0. Legendary Pokemons have significantly different {stat} stats.\")\n",
+    "        else:\n",
+    "            print(f\"   📢 Finding: Fail to reject H0. No significant difference found for {stat}.\")\n",
+    "    else:\n",
+    "        print(f\"⚠️ Column {stat} not found in the dataset.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Key Insights for your Lab CommentsDragon vs. Grass: The true dataset shows that Dragon types exhibit a noticeably higher baseline base health pool (\\(p < 0.05\\)). This successfully rejects \\(H_{0}\\).Legendary vs. Non-Legendary: Legendary Pokémon yield extremely small p-values (typically \\(p < 0.001\\)) across all 6 base stats. You can confidently comment that Legendary Pokémon are structurally scaled differently in game balance across every single attribute."
    ]
   },
   {
@@ -463,6 +708,102 @@
     "df.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 DATASET COMPONENT SUMMARY:\n",
+      "   Houses Close to Facility (n): 6829\n",
+      "   Houses Far from Facility (n):   10171\n",
+      "   Mean Price (Close): $246,951.98\n",
+      "   Mean Price (Far):   $180,678.44\n",
+      "\n",
+      "🧪 HYPOTHESIS TEST METRICS:\n",
+      "   Calculated t-statistic: 37.9923\n",
+      "   One-tailed p-value: 1.5032e-301\n",
+      "\n",
+      "📢 Finding: Reject H0. Houses close to a school or hospital are significantly more expensive.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scipy.stats as st\n",
+    "\n",
+    "# 1. Load dataset\n",
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/california_housing.csv\"\n",
+    "df = pd.read_csv(url)\n",
+    "\n",
+    "# Defined Reference Targets\n",
+    "school_coords = (-118, 34)\n",
+    "hospital_coords = (-122, 37)\n",
+    "\n",
+    "\n",
+    "# 2. Distance function (Optimized vector application)\n",
+    "def calc_euclidean(row, target):\n",
+    "    target_lon, target_lat = target\n",
+    "    return np.sqrt(\n",
+    "        (row[\"longitude\"] - target_lon) ** 2\n",
+    "        + (row[\"latitude\"] - target_lat) ** 2\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# Apply calculations to append distance metrics\n",
+    "df[\"dist_to_school\"] = df.apply(\n",
+    "    calc_euclidean, axis=1, target=school_coords\n",
+    ")\n",
+    "df[\"dist_to_hospital\"] = df.apply(\n",
+    "    calc_euclidean, axis=1, target=hospital_coords\n",
+    ")\n",
+    "\n",
+    "# 3. Categorize locations based on the 0.50 threshold criteria\n",
+    "df[\"is_close\"] = (df[\"dist_to_school\"] < 0.50) | (\n",
+    "    df[\"dist_to_hospital\"] < 0.50\n",
+    ")\n",
+    "\n",
+    "# Isolate target price vectors\n",
+    "close_prices = df[df[\"is_close\"] == True][\"median_house_value\"].dropna()\n",
+    "far_prices = df[df[\"is_close\"] == False][\"median_house_value\"].dropna()\n",
+    "\n",
+    "# 4. Statistical Testing: One-Tailed Welch's T-Test\n",
+    "# equal_var=False addresses unequal variance and uneven group sizes (Welch's correction)\n",
+    "t_stat, p_val_two_tailed = st.ttest_ind(\n",
+    "    close_prices, far_prices, equal_var=False\n",
+    ")\n",
+    "\n",
+    "# Convert two-tailed test to a one-tailed directional output (Close > Far)\n",
+    "p_val_one_tailed = (\n",
+    "    p_val_two_tailed / 2 if t_stat > 0 else 1 - (p_val_two_tailed / 2)\n",
+    ")\n",
+    "\n",
+    "# 5. Output Results Display\n",
+    "print(\"🔍 DATASET COMPONENT SUMMARY:\")\n",
+    "print(f\"   Houses Close to Facility (n): {len(close_prices)}\")\n",
+    "print(f\"   Houses Far from Facility (n):   {len(far_prices)}\")\n",
+    "print(f\"   Mean Price (Close): ${close_prices.mean():,.2f}\")\n",
+    "print(f\"   Mean Price (Far):   ${far_prices.mean():,.2f}\")\n",
+    "\n",
+    "print(\"\\n🧪 HYPOTHESIS TEST METRICS:\")\n",
+    "print(f\"   Calculated t-statistic: {t_stat:.4f}\")\n",
+    "print(f\"   One-tailed p-value: {p_val_one_tailed:.4e}\")\n",
+    "\n",
+    "alpha = 0.05\n",
+    "if p_val_one_tailed < alpha and t_stat > 0:\n",
+    "    print(\n",
+    "        \"\\n📢 Finding: Reject H0. Houses close to a school or hospital are significantly more expensive.\"\n",
+    "    )\n",
+    "else:\n",
+    "    print(\n",
+    "        \"\\n📢 Finding: Fail to reject H0. Proximity to these coordinates does not yield higher average values.\"\n",
+    "    )\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -498,7 +839,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -512,7 +853,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,