T51_capstone project VII.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "766ebcf6",
   "metadata": {},
   "source": [
    "## T51 Capstone project VII"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02175c18",
   "metadata": {},
   "source": [
    "### Loading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60bcc307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>13.2</td>\n",
       "      <td>236</td>\n",
       "      <td>58</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>10.0</td>\n",
       "      <td>263</td>\n",
       "      <td>48</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>8.1</td>\n",
       "      <td>294</td>\n",
       "      <td>80</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>8.8</td>\n",
       "      <td>190</td>\n",
       "      <td>50</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>9.0</td>\n",
       "      <td>276</td>\n",
       "      <td>91</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         City  Murder  Assault  UrbanPop  Rape\n",
       "0     Alabama    13.2      236        58  21.2\n",
       "1      Alaska    10.0      263        48  44.5\n",
       "2     Arizona     8.1      294        80  31.0\n",
       "3    Arkansas     8.8      190        50  19.5\n",
       "4  California     9.0      276        91  40.6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"UsArrests.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eec2e00",
   "metadata": {},
   "source": [
    "### Exploroatory Data Analysis (EDA) and preprocessing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fa306ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50 entries, 0 to 49\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   City      50 non-null     object \n",
      " 1   Murder    50 non-null     float64\n",
      " 2   Assault   50 non-null     int64  \n",
      " 3   UrbanPop  50 non-null     int64  \n",
      " 4   Rape      50 non-null     float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 2.1+ KB\n",
      "(50, 5)\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e09f8229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City        0.0\n",
       "Murder      0.0\n",
       "Assault     0.0\n",
       "UrbanPop    0.0\n",
       "Rape        0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the percetage of null values in each column\n",
    "df.isnull().sum() * 100 / df.shape [0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b370c467",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City        50\n",
      "Murder      43\n",
      "Assault     45\n",
      "UrbanPop    36\n",
      "Rape        48\n",
      "dtype: int64\n",
      "\n",
      "Feature that has 1 unique entry: []\n"
     ]
    }
   ],
   "source": [
    "# to upderstand the diversity of values in that feature\n",
    "#call the numbers of features/types out\n",
    "#to understand the distrition of the values in each feature\n",
    "#..size file\n",
    "unic_en = df.nunique()\n",
    "print(unic_en)\n",
    "\n",
    "# find entries = 1 unique entry\n",
    "feat = unic_en[unic_en == 1].index\n",
    "feat = feat.tolist()\n",
    "\n",
    "print(\"\\nFeature that has 1 unique entry:\", feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e15cd52e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Murder</th>\n",
       "      <th>Assault</th>\n",
       "      <th>UrbanPop</th>\n",
       "      <th>Rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.78800</td>\n",
       "      <td>170.760000</td>\n",
       "      <td>65.540000</td>\n",
       "      <td>21.232000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.35551</td>\n",
       "      <td>83.337661</td>\n",
       "      <td>14.474763</td>\n",
       "      <td>9.366385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.80000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.07500</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>15.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.25000</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>20.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.25000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>77.750000</td>\n",
       "      <td>26.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.40000</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Murder     Assault   UrbanPop       Rape\n",
       "count  50.00000   50.000000  50.000000  50.000000\n",
       "mean    7.78800  170.760000  65.540000  21.232000\n",
       "std     4.35551   83.337661  14.474763   9.366385\n",
       "min     0.80000   45.000000  32.000000   7.300000\n",
       "25%     4.07500  109.000000  54.500000  15.075000\n",
       "50%     7.25000  159.000000  66.000000  20.100000\n",
       "75%    11.25000  249.000000  77.750000  26.175000\n",
       "max    17.40000  337.000000  91.000000  46.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting a basic statistic out\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11af0db8",
   "metadata": {},
   "source": [
    "#### The UsArrests dataset has 50 rows and 5 columns, including City is a object datatype, Murder and Rape are float, Assault and UrbanPop are numerical data. \n",
    "#### The dataset has no missing values and none has unique entry equal 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dca32f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to visualise representation of the distribution \n",
    "#and spread of each variable in the dataset, we need to import libaries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7ad7cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the correclation between variables in the dataset before narrow down to specific graph\n",
    "# Compute the correlation matrix\n",
    "corr = df.corr()\n",
    "\n",
    "# Plot the correlation heatmap\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4e7092b",
   "metadata": {},
   "source": [
    "### The correlation between the Assault and Murder variales in the UsArrests dataset is 0.8. This indicates that  there is a strong positive correlation between these two variables. The values of the Assault variable increase, means the values of the Murder variable are also likely to go up.\n",
    "\n",
    "### While the correlation between the Assault and Rape variables in the same dataset is 0.67. This indicates that there is a moderate positive correlation between these two variables. It means that as the Assault values increase, the values of the Rap values are also increase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8cd718ef",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms of each variable\n",
    "df.hist(bins=30, figsize=(10,8))\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35038d6b",
   "metadata": {},
   "source": [
    "The distribution on each variable is not normally distributed because it has multiple spikes. Therefore, the data has a complex distrition and or the data is spread out. We may need to transform the data to make it more normally distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b21cc4c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot scatter plot of the relationship between Assault and Murder\n",
    "plt.scatter(x=df['Assault'], y=df['Murder'])\n",
    "plt.xlabel('Assault')\n",
    "plt.ylabel('Murder')\n",
    "plt.title('Scatter Plot of Assault and Murder')\n",
    "plt.show()\n",
    "\n",
    "# Plot scatter plot of the relationship between Assault and Rape\n",
    "plt.scatter(x=df['Assault'], y=df['Rape'])\n",
    "plt.xlabel('Assault')\n",
    "plt.ylabel('Rape')\n",
    "plt.title('Scatter Plot of Assault and Rape')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ebc064",
   "metadata": {},
   "source": [
    "#### The scatterplot shows the postive relations between the two variables and clearly the Assault and Murder indicated the strong link than the Assault and Rape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9f0d4d49",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot box plot to check to visualise the relationship between variables and to check for outliers\n",
    "sns.boxplot(data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f1ddb2",
   "metadata": {},
   "source": [
    "### Principal Composent Analysis (PCA), preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "397c3ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "City        0\n",
      "Murder      0\n",
      "Assault     0\n",
      "UrbanPop    0\n",
      "Rape        0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#double check before performing PCA\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "c770a456",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           PC1        PC2            City\n",
      "0    64.802164  11.448007         Alabama\n",
      "1    92.827450  17.982943          Alaska\n",
      "2   124.068216  -8.830403         Arizona\n",
      "3    18.340035  16.703911        Arkansas\n",
      "4   107.422953 -22.520070      California\n",
      "5    34.975986 -13.719584        Colorado\n",
      "6   -60.887282 -12.932530     Connecticut\n",
      "7    66.731025  -1.353798        Delaware\n",
      "8   165.244370  -6.274690         Florida\n",
      "9    40.535177   7.290240         Georgia\n",
      "10 -123.536106 -24.291208          Hawaii\n",
      "11  -51.797002   9.469191           Idaho\n",
      "12   78.992097 -12.897060        Illinois\n",
      "13  -57.550961  -2.846265         Indiana\n",
      "14 -115.586790   3.342131            Iowa\n",
      "15  -55.789694  -3.157234          Kansas\n",
      "16  -62.383181  10.673271        Kentucky\n",
      "17   78.277631   4.294917       Louisiana\n",
      "18  -89.261044  11.487827           Maine\n",
      "19  129.330136   5.007031        Maryland\n",
      "20  -21.266283 -19.450179   Massachusetts\n",
      "21   85.451527  -5.904557        Michigan\n",
      "22  -98.954816  -5.209601       Minnesota\n",
      "23   86.856358  27.428420     Mississippi\n",
      "24    7.986289  -5.275641        Missouri\n",
      "25  -62.483635   9.510502         Montana\n",
      "26  -69.096544   0.211196        Nebraska\n",
      "27   83.613578 -15.102184          Nevada\n",
      "28 -114.777355   4.734558   New Hampshire\n",
      "29  -10.815725 -23.137339      New Jersey\n",
      "30  114.868163   0.336453      New Mexico\n",
      "31   84.294231 -15.923966        New York\n",
      "32  164.325514  31.096615  North Carolina\n",
      "33 -127.495597  16.135039    North Dakota\n",
      "34  -50.086822 -12.279324            Ohio\n",
      "35  -19.693723  -3.370131        Oklahoma\n",
      "36  -11.150240  -3.866068          Oregon\n",
      "37  -64.689142  -8.911547    Pennsylvania\n",
      "38    3.063973 -18.373970    Rhode Island\n",
      "39  107.281069  23.536116  South Carolina\n",
      "40  -86.106720  16.597859    South Dakota\n",
      "41   17.506264   6.506576       Tennessee\n",
      "42   31.291122 -12.984957           Texas\n",
      "43  -49.913397 -17.648458            Utah\n",
      "44 -124.714469  27.313559         Vermont\n",
      "45  -14.817448   1.752615        Virginia\n",
      "46  -25.075839  -9.967967      Washington\n",
      "47  -91.544647  22.952878   West Virginia\n",
      "48 -118.176328  -5.507579       Wisconsin\n",
      "49  -10.434539   5.924453         Wyoming\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "df = pd.read_csv(\"UsArrests.csv\")\n",
    "#the model is initialised with n_components=2\n",
    "#it will keep 2 new variables that capture the most information from the original variables\n",
    "pca = PCA(n_components=2)\n",
    "df_pca = pca.fit_transform(df.drop([\"City\"], axis=1))\n",
    "#the df_pca will stored with the transformed data\n",
    "df_pca = pd.DataFrame(df_pca, columns=[\"PC1\",\"PC2\"])\n",
    "df_pca[\"City\"] = df[\"City\"]\n",
    "\n",
    "print(df_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d5d68314",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PC1     0\n",
      "PC2     0\n",
      "City    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#double check for missing values\n",
    "print(df_pca.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd2da44",
   "metadata": {},
   "source": [
    "### Report from PCA finding:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ac7948fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              PC1           PC2\n",
      "PC1  1.000000e+00  1.641348e-16\n",
      "PC2  1.641348e-16  1.000000e+00\n"
     ]
    }
   ],
   "source": [
    "#to calculates the Pearson correlation matrix for the transformed data\n",
    "print(df_pca.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2611b51c",
   "metadata": {},
   "source": [
    "#### The correlation matrix shows the linear relationship between the two new variables, which is PC1 and PC2. PC1+PC1 has correlation between itself of 1.000000e+00 score and it means a positive linear relationship. While PC1 and PC2 are with correlation of  1.641348e-16, which shows weak linear relationship between the two variables. \n",
    "\n",
    "#### The finding shows the PC1 captures a significant number of information fromt he original variables, while PC2 captures very less information from the original variables.\n",
    "\n",
    "#### Moreover, it can explain the PC1 represents a single linear combination of the variables and the PC2 as a residual component , captures information that not captured by PC1. To retain most of the information from the original data, only PC1 needs to be retained.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e400802f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard deviation: [83.73240025 14.21240185]\n"
     ]
    }
   ],
   "source": [
    "#calculates the standard deviation of each principal component in df_pca\n",
    "#to see how much their values are spread out from their mean values\n",
    "std = df_pca.describe().transpose()[\"std\"]\n",
    "print(f\"Standard deviation: {std.values}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4888f100",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of Variance Explained: [0.96553422 0.02781734]\n"
     ]
    }
   ],
   "source": [
    "#the explained variance ratio of each component\n",
    "#it gives the contribution of each component to the total variability of the data\n",
    "#where the sum of the explained variances is equal to 1\n",
    "print(f\"Proportion of Variance Explained: {pca.explained_variance_ratio_}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a0e51ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cumulative Proportion: [7011.11485102 7213.10721735]\n"
     ]
    }
   ],
   "source": [
    "#the cumulative proportion of the variance explained by each component\n",
    "print(f\"Cumulative Proportion: {np.cumsum(pca.explained_variance_)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a5a9929",
   "metadata": {},
   "source": [
    "It shows the first two components together explain over 98% of the variablility in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "2fac0bf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Standardize the features\n",
    "X = (df - df.mean()) / df.std()\n",
    "\n",
    "# Apply PCA to the standardized features\n",
    "pca = PCA(n_components=2)\n",
    "df_pca = pca.fit_transform(X)\n",
    "\n",
    "# Plot the biplot\n",
    "def biplot(score, coeff, labels=None):\n",
    "    xs = score[:,0]\n",
    "    ys = score[:,1]\n",
    "    n = coeff.shape[0]\n",
    "    scalex = 1.0/(xs.max() - xs.min())\n",
    "    scaley = 1.0/(ys.max() - ys.min())\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.scatter(xs * scalex, ys * scaley, s=5)\n",
    "    for i in range(n):\n",
    "        ax.arrow(0, 0, coeff[i,0], coeff[i,1], color='r', alpha=0.5)\n",
    "        if labels is None:\n",
    "            ax.text(coeff[i,0]*1.15, coeff[i,1]*1.15, \"Var\"+str(i+1), color='g', ha='center', va='center')\n",
    "        else:\n",
    "            ax.text(coeff[i,0]*1.15, coeff[i,1]*1.15, labels[i], color='g', ha='center', va='center')\n",
    "    plt.xlabel(\"PC1\")\n",
    "    plt.ylabel(\"PC2\")\n",
    "    plt.grid()\n",
    "\n",
    "biplot(df_pca, np.transpose(pca.components_), list(df.columns))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75dcb730",
   "metadata": {},
   "source": [
    "#### Each point represents one state/city in the UsArrests dataset, and the arrows represent the weight of each feature in the formation of the two principal components. The finding shows that there is a moderate amount of correlation betweenth e features but not a strong relationship. However, it is still contributing to the variation in the dat along both dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "b0eb49ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#to calculate the linkage matrix that stores the information about the distances between the data points\n",
    "import scipy.cluster.hierarchy as shc\n",
    "\n",
    "# Preprocess the data\n",
    "#select only numerical columns for clustering\n",
    "X = df.iloc[:,1:].values\n",
    "\n",
    "# Perform Hierarchical Clustering\n",
    "plt.figure(figsize=(10, 7))  \n",
    "#to plot the dendrogram that represents the hierarchical relationship between the dta points\n",
    "dend = shc.dendrogram(shc.linkage(X, method='ward'))\n",
    "#ward method to minimizes the variance of the distance betweenthe clusters being merged\n",
    "plt.title(\"Dendrogram\")\n",
    "plt.xlabel(\"States/Cities\")\n",
    "plt.ylabel(\"Euclidean distances\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223a1bd3",
   "metadata": {},
   "source": [
    "#### The first set of long vertical lines, near the y-axis represent a high level of dissimilarity betweent he initial clusters. The second set of long vertical lines, further from the y-axis, represent a lower level of dissimilarity as the distance betweent he lines is shorter.\n",
    "\n",
    "#### The finding indicates that the data points within these latter clusters are more similar to each other. the branching of the dendrogram and the shorter vertical lines within these branches further refine the structure of the clusters, dividing the data points into smaller ones and more  homogeneous groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "6d0241d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import the KMeans class \n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# initialise an instance of the KMeans class\n",
    "#set to 3, Number of clusters\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "# Fitting the k-means algorithm on scaled data\n",
    "kmeans.fit(df_pca)\n",
    "# Predicting the clusters\n",
    "pred_clusters = kmeans.predict(df_pca)\n",
    "\n",
    "# Plotting the cluster assignments and cluster centers\n",
    "plt.scatter(df_pca[:,0], df_pca[:,1], c=pred_clusters)\n",
    "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], marker='*', s=200, linewidths=3, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edc74930",
   "metadata": {},
   "source": [
    "#### The finding shows that the data has been separated ito 3 clusters based on their patterns of similarity. The K-Means algorithm grouped the dat points into 3 groups that represent with 3 stars. Each star represent the centerr of a cluster. The clusters are differentiated based on their location in the PCA transformed space."
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}