Task #2 - Prediction using Unsupervised ML.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UID9RK1qDlVB"
   },
   "source": [
    "# **Viroang Rawat**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qnC6-B-KUBWO"
   },
   "source": [
    "# **Task #2 - Prediction using Unsupervised ML**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 195
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 835,
     "status": "ok",
     "timestamp": 1544240454142,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "kO_1kOEGDTws",
    "outputId": "24bb4b2a-2342-4c91-c702-b127a5391152"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "# Load the iris dataset\n",
    "iris = datasets.load_iris()\n",
    "iris_df = pd.DataFrame(iris.data, columns = iris.feature_names)\n",
    "iris_df.head() # See the first 5 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q_pPmK9GIKMz"
   },
   "source": [
    "#### How do you find the optimum number of clusters for K Means? How does one determine the value of K?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1649,
     "status": "ok",
     "timestamp": 1544110062756,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "WevSKogFEalU",
    "outputId": "d0161248-67f2-48a4-cf4b-224f8faae045"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Finding the optimum number of clusters for k-means classification\n",
    "\n",
    "x = iris_df.iloc[:, [0, 1, 2, 3]].values\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "wcss = []\n",
    "\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', \n",
    "                    max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "# Plotting the results onto a line graph, \n",
    "# `allowing us to observe 'The elbow'\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The elbow method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS') # Within cluster sum of squares\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IUXmLTh4Ih6r"
   },
   "source": [
    "You can clearly see why it is called 'The elbow method' from the above graph, the optimum clusters is where the elbow occurs. This is when the within cluster sum of squares (WCSS) doesn't decrease significantly with every iteration.\n",
    "\n",
    "From this we choose the number of clusters as ** '3**'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aJbyXuNGIXI9"
   },
   "outputs": [],
   "source": [
    "# Applying kmeans to the dataset / Creating the kmeans classifier\n",
    "kmeans = KMeans(n_clusters = 3, init = 'k-means++',\n",
    "                max_iter = 300, n_init = 10, random_state = 0)\n",
    "y_kmeans = kmeans.fit_predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 364
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 670,
     "status": "ok",
     "timestamp": 1544110172140,
     "user": {
      "displayName": "A M Aditya",
      "photoUrl": "https://lh3.googleusercontent.com/-WI8p7JNWLic/AAAAAAAAAAI/AAAAAAAAAfs/vS8ElgH0p0c/s64/photo.jpg",
      "userId": "15341571102300750919"
     },
     "user_tz": -480
    },
    "id": "Q42-XPJjIyXv",
    "outputId": "12284613-40c5-41c6-93ba-6b66fae0aa5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f9c1571d68>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualising the clusters - On the first two columns\n",
    "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \n",
    "            s = 100, c = 'red', label = 'Iris-setosa')\n",
    "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \n",
    "            s = 100, c = 'blue', label = 'Iris-versicolour')\n",
    "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\n",
    "            s = 100, c = 'green', label = 'Iris-virginica')\n",
    "\n",
    "# Plotting the centroids of the clusters\n",
    "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \n",
    "            s = 100, c = 'yellow', label = 'Centroids')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Lue5nDJrJIJm"
   },
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "KMeans_Clustering.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python3.6",
   "language": "python",
   "name": "python3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}