Day 19 of 75 DataSci.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb7b376f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7434cafd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Iris dataset\n",
    "from sklearn.datasets import load_iris\n",
    "data = load_iris()\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df['species'] = data.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e3f752b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 5 rows of the dataset:\n",
      "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
      "0                5.1               3.5                1.4               0.2   \n",
      "1                4.9               3.0                1.4               0.2   \n",
      "2                4.7               3.2                1.3               0.2   \n",
      "3                4.6               3.1                1.5               0.2   \n",
      "4                5.0               3.6                1.4               0.2   \n",
      "\n",
      "   species  \n",
      "0        0  \n",
      "1        0  \n",
      "2        0  \n",
      "3        0  \n",
      "4        0  \n"
     ]
    }
   ],
   "source": [
    "print(\"First 5 rows of the dataset:\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8db800e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Summary statistics:\n",
      "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
      "count         150.000000        150.000000         150.000000   \n",
      "mean            5.843333          3.057333           3.758000   \n",
      "std             0.828066          0.435866           1.765298   \n",
      "min             4.300000          2.000000           1.000000   \n",
      "25%             5.100000          2.800000           1.600000   \n",
      "50%             5.800000          3.000000           4.350000   \n",
      "75%             6.400000          3.300000           5.100000   \n",
      "max             7.900000          4.400000           6.900000   \n",
      "\n",
      "       petal width (cm)     species  \n",
      "count        150.000000  150.000000  \n",
      "mean           1.199333    1.000000  \n",
      "std            0.762238    0.819232  \n",
      "min            0.100000    0.000000  \n",
      "25%            0.300000    0.000000  \n",
      "50%            1.300000    1.000000  \n",
      "75%            1.800000    2.000000  \n",
      "max            2.500000    2.000000  \n",
      "\n",
      "Unique species:\n",
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nSummary statistics:\")\n",
    "print(df.describe())\n",
    "\n",
    "print(\"\\nUnique species:\")\n",
    "print(df['species'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77878b96",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "incomplete input (3513167384.py, line 7)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[7], line 7\u001b[1;36m\u001b[0m\n\u001b[1;33m    X_scaled = scaler.fit_transform(df.drop('species', axis=1)\u001b[0m\n\u001b[1;37m                                                              ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m incomplete input\n"
     ]
    }
   ],
   "source": [
    "sns.pairplot(df, hue='species', palette=\"viridis\", markers=[\"o\", \"s\", \"D\"])\n",
    "plt.suptitle(\"Pair Plot of Iris Dataset Features\", y=1.02)\n",
    "plt.show()\n",
    "\n",
    "# Step 3: Scaling the Data\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(df.drop('species', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "844401c4",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_scaled' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[8], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m kmeans \u001b[38;5;241m=\u001b[39m KMeans(n_clusters\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, init\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mk-means++\u001b[39m\u001b[38;5;124m'\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m----> 2\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcluster\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m kmeans\u001b[38;5;241m.\u001b[39mfit_predict(\u001b[43mX_scaled\u001b[49m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'X_scaled' is not defined"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, init='k-means++', random_state=42)\n",
    "df['cluster'] = kmeans.fit_predict(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b5b958e2",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sns' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Plot the clusters with Sepal Length and Sepal Width\u001b[39;00m\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[1;32m----> 3\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241m.\u001b[39mscatterplot(x\u001b[38;5;241m=\u001b[39mdf\u001b[38;5;241m.\u001b[39miloc[:, \u001b[38;5;241m0\u001b[39m], y\u001b[38;5;241m=\u001b[39mdf\u001b[38;5;241m.\u001b[39miloc[:, \u001b[38;5;241m1\u001b[39m], hue\u001b[38;5;241m=\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcluster\u001b[39m\u001b[38;5;124m'\u001b[39m], palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdeep\u001b[39m\u001b[38;5;124m\"\u001b[39m, style\u001b[38;5;241m=\u001b[39mdf[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mspecies\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(data\u001b[38;5;241m.\u001b[39mfeature_names[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(data\u001b[38;5;241m.\u001b[39mfeature_names[\u001b[38;5;241m1\u001b[39m])\n",
      "\u001b[1;31mNameError\u001b[0m: name 'sns' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the clusters with Sepal Length and Sepal Width\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x=df.iloc[:, 0], y=df.iloc[:, 1], hue=df['cluster'], palette=\"deep\", style=df['species'])\n",
    "plt.xlabel(data.feature_names[0])\n",
    "plt.ylabel(data.feature_names[1])\n",
    "plt.title(\"K-Means Clustering of Iris Data\")\n",
    "plt.legend(title=\"Cluster\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3552278",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}