Mod3_lab2.ipynb

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      "source": [
        "# **FOUNDATIONS OF MODERN MACHINE LEARNING, IIIT Hyderabad**\n",
        "### MODULE 3: CLASSIFICATION-1\n",
        "### LAB-2 : Implementing KNN from scratch and visualize Algorithm performance\n",
        "#### Module Coordinator: Jashn Arora\n",
        "\n",
        "---"
      ],
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      "source": [
        "# **Section 1: Implementing KNN**"
      ],
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      "source": [
        "In the last lab we had started discussing about KNN or K Nearest Neighbour method for clasification. We used the pre-built scikit-learn library for KNN. Now let's see how to implement this algorithm from scratch  "
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import preprocessing\n",
        "from collections import Counter"
      ],
      "execution_count": 2,
      "outputs": []
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        "def predict(X_train, y_train, X_test, k):\n",
        "    distances = []\n",
        "    targets = []\n",
        "\n",
        "    for i in range(len(X_train)):\n",
        "        # compute and store L2 distance\n",
        "        distances.append([np.sqrt(np.sum(np.square(X_test - X_train[i, :]))), i])\n",
        "\n",
        "    distances = sorted(distances)\n",
        "\n",
        "    for i in range(k):\n",
        "        index = distances[i][1]\n",
        "        targets.append(y_train[index])\n",
        "\n",
        "    # return most common target\n",
        "    return Counter(targets).most_common(1)[0][0]"
      ],
      "execution_count": 3,
      "outputs": []
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        "def k_nearest_neighbor(X_train, y_train, X_test, k):\n",
        "\n",
        "    assert k <= len(X_train), \"[!] K cannot be larger than number of samples.\"\n",
        "\n",
        "    # loop over all observations\n",
        "    predictions = []\n",
        "    for i in range(len(X_test)):\n",
        "        predictions.append(predict(X_train, y_train, X_test[i, :], k))\n",
        "\n",
        "    return np.asarray(predictions)"
      ],
      "execution_count": 4,
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      "source": [
        "**Note** : If k = 1 then the algorithm will simply return the label of the nearest neighbour. When we give k > 1 the most common label out of the given labels in the k neighbours will be selected.The code for 1 NN is given as follows and does not have to be so complicated."
      ],
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      "source": [
        "def NN1(traindata, trainlabel, query):\n",
        "  diff  = traindata - query  # find the difference between features. Numpy automatically takes care of the size here\n",
        "  sq = diff*diff # square the differences\n",
        "  dist = sq.sum(1) # add up the squares\n",
        "  label = trainlabel[np.argmin(dist)] # our predicted label is the label of the training data which has the least distance from the query\n",
        "  return label"
      ],
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      "execution_count": 5,
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      "source": [
        "Let us define a metric 'Accuracy' to see how good our learning algorithm is. Accuracy is the ratio of the number of correctly classified samples to the total number of samples. The higher the accuracy, the better the algorithm."
      ]
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      "cell_type": "code",
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      "source": [
        "def Accuracy(gtlabel, predlabel):\n",
        "  assert len(gtlabel)==len(predlabel), \"Length of the groundtruth labels and predicted labels should be the same\"\n",
        "  correct = (gtlabel==predlabel).sum() # count the number of times the groundtruth label is equal to the predicted label.\n",
        "  return correct/len(gtlabel)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 1.1 - Iris Dataset\n",
        "Let's try it out on Iris Dataset present in the scikit learn library"
      ],
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      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "data = load_iris()\n",
        "\n",
        "df = pd.DataFrame(data=data.data, columns=data.feature_names)\n",
        "\n",
        "df[\"target\"] = data.target\n",
        "\n",
        "df.head()"
      ],
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      "execution_count": 7,
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            "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   \n",
              "\n",
              "   target  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "X = np.array(df[['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']])\n",
        "y = np.array(df['target'])\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=17)"
      ],
      "metadata": {
        "id": "r8SHprUbKtuI"
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      "execution_count": 8,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Making our predictions\n",
        "predictions = k_nearest_neighbor(X_train, y_train, X_test, 7)\n",
        "\n",
        "# evaluating accuracy\n",
        "accuracy = Accuracy(y_test, predictions)\n",
        "print(\"The accuracy of our classifier is {} %\".format(100*accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUYb80o6LHUt",
        "outputId": "3d6b1e5d-40c4-4e1e-be4f-11a7d35f49be"
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      "execution_count": 9,
      "outputs": [
        {
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          "text": [
            "The accuracy of our classifier is 94.0 %\n"
          ]
        }
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "iwQ3qP-UXY5x"
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      "source": [
        "Voila! You have implemented your own version of the K-Nearest Neighbours algorithm, which works very well on the Iris Dataset. Congratulations!  \n",
        "\n",
        "Now try out the sklearn implementation and compare your results.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0jJj5_7dg-o",
        "outputId": "c1815fb0-7d41-47f9-9e7f-3f9b7d60751c"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "sklearn_knn = KNeighborsClassifier(n_neighbors=7)\n",
        "sklearn_knn.fit(X_train,y_train)\n",
        "sklearn_predictions = sklearn_knn.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, sklearn_predictions)\n",
        "print(\"The accuracy of Sklearn classifier is {} %\".format(100*accuracy))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of Sklearn classifier is 94.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Section 1.2: Weighted NN\n",
        "For KNN, If k is too small, the algorithm would be more sensitive to outliers. If k is too large, then the neighborhood may include too many points from other classes. We also take the label with max votes in the neighbourhood. Another choice can be that instead of giving equal weights to each of the neighbours we can give weighted points in the neighbourhood. So we weigh points by the inverse of their distance. Therefore, closer points will be given a higher priority as compared to the far off points.\n",
        "\n",
        "An easy way to implement this is by specifying the 'weights' parameter as distance when defining the sklearn KNN function. For more information go through this [site](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)"
      ],
      "metadata": {
        "id": "8Yi2tX6h0_Ka"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hXZP4wXqtmK"
      },
      "source": [
        "## TASK\n",
        "## Modify the KNN function you wrote to return all the K-nearest neighbours along with their distances,\n",
        "## instead of just the output that was most common. You don't need to find out accuracy, just modify the function\n",
        "## and return the k-nearest neighbours and distances."
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##Task Solution\n",
        "# KNN function to return all k-nearest neighbours\n",
        "def predict1(X_train, y_train, X_test, k):\n",
        "    distances = []\n",
        "    targets = []\n",
        "\n",
        "    for i in range(len(X_train)):\n",
        "        # compute and store L2 distance\n",
        "        distances.append([np.sqrt(np.sum(np.square(X_test - X_train[i, :]))), i])\n",
        "\n",
        "    distances = sorted(distances)\n",
        "    for i in range(k):\n",
        "        index = distances[i][1]\n",
        "        targets.append(y_train[index])\n",
        "    return Counter(targets)"
      ],
      "metadata": {
        "id": "NEjgGv-ao4Re"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##Task Solution\n",
        "# KNN function to return all k-nearest neighbours\n",
        "def predict1(X_train, y_train, X_test, k):\n",
        "    distances = []\n",
        "    targets = []\n",
        "\n",
        "    for i in range(len(X_train)):\n",
        "        # compute and store L2 distance\n",
        "        distances.append([np.sqrt(np.sum(np.square(X_test - X_train[i, :]))), i])\n",
        "\n",
        "    distances = sorted(distances)\n",
        "    for i in range(k):\n",
        "        index = distances[i][1]\n",
        "        targets.append(y_train[index])\n",
        "    return Counter(targets)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "uwQf61Rmo81s"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Making predictions\n",
        "predictions = k_nearest_neighbor(X_train, y_train, X_test, 7)\n",
        "print(predictions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_n9skML6o_hE",
        "outputId": "282128e6-27fe-413c-fcc3-e0d0abb71195"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 1 1 1 1 2 1 2 2 0 1 0 2 0 0 2 2 2 1 0 2 1 1 1 1 1 0 1 0 1 0 0 1 1 1\n",
            " 2 1 0 1 1 0 1 2 1 1 2 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SxcBnLl8GHWz"
      },
      "source": [
        "# **Section 2: Visualizing Data**  \n",
        "\n",
        "We will look into something called **Voronoi** diagrams.  \n",
        "\n",
        "**Note**: Ideally, we should perform data visualization to see what the data looks like before we apply any Machine Learning algorithm.  Only for the purpose of this lab session, we're explaining it after you've applied KNN."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aDsDfmXG79k"
      },
      "source": [
        "### Voronoi Diagrams  \n",
        "\n",
        "In simple terms, Voronoi diagrams help you to visualize the dataset by partioning the plane into regions that are close to a given set of points. These regions are also called Voronoi cells.  \n",
        "\n",
        "Note that the cells/regions depend on the Distance metric being used. One way of interpreting this is by understanding that the distance metric decides the degree to which a 'point' or 'seed' in the Voronoi diagram has influence.  For each seed there is a corresponding region, called a Voronoi cell, consisting of all points of the plane closer to that seed than to any other.\n",
        "\n",
        "This [link](https://en.wikipedia.org/wiki/Voronoi_diagram#Illustration) provides a wonderful illustration of Voronoi plots for 20 points in two cases: (1) Using Euclidean distance, and (2) Using Manhattan distance.  \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdcdjthOKwra"
      },
      "source": [
        "Since our Iris data is 4-dimensional (as it has 4 attributes), we need to convert into a form that can be represented in 2-D.   \n",
        "\n",
        "While there are methods to visualize data higher than 2-dimensions, that is beyond scope for now.  \n",
        "\n",
        "For simplicity, we just take the first two columns of the iris dataset attributes and observe the Voronoi diagram generated for that.  \n",
        "Alternatively, one can also perform PCA (Principal Component Analysis), to reduce the 4D data to just two dimensions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNOPcS3f7sZg"
      },
      "source": [
        "#@title Plotting Voronoi regions\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
        "\n",
        "def voronoi_finite_polygons_2d(vor, radius=None):\n",
        "    \"\"\"\n",
        "    Reconstruct infinite voronoi regions in a 2D diagram to finite\n",
        "    regions.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    vor : Voronoi\n",
        "        Input diagram\n",
        "    radius : float, optional\n",
        "        Distance to 'points at infinity'.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    regions : list of tuples\n",
        "        Indices of vertices in each revised Voronoi regions.\n",
        "    vertices : list of tuples\n",
        "        Coordinates for revised Voronoi vertices. Same as coordinates\n",
        "        of input vertices, with 'points at infinity' appended to the\n",
        "        end.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    if vor.points.shape[1] != 2:\n",
        "        raise ValueError(\"Requires 2D input\")\n",
        "\n",
        "    new_regions = []\n",
        "    new_vertices = vor.vertices.tolist()\n",
        "\n",
        "    center = vor.points.mean(axis=0)\n",
        "    if radius is None:\n",
        "        radius = vor.points.ptp().max()\n",
        "\n",
        "    # Construct a map containing all ridges for a given point\n",
        "    all_ridges = {}\n",
        "    for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):\n",
        "        all_ridges.setdefault(p1, []).append((p2, v1, v2))\n",
        "        all_ridges.setdefault(p2, []).append((p1, v1, v2))\n",
        "\n",
        "    # Reconstruct infinite regions\n",
        "    for p1, region in enumerate(vor.point_region):\n",
        "        vertices = vor.regions[region]\n",
        "\n",
        "        if all(v >= 0 for v in vertices):\n",
        "            # finite region\n",
        "            new_regions.append(vertices)\n",
        "            continue\n",
        "\n",
        "        # reconstruct a non-finite region\n",
        "        ridges = all_ridges[p1]\n",
        "        new_region = [v for v in vertices if v >= 0]\n",
        "\n",
        "        for p2, v1, v2 in ridges:\n",
        "            if v2 < 0:\n",
        "                v1, v2 = v2, v1\n",
        "            if v1 >= 0:\n",
        "                # finite ridge: already in the region\n",
        "                continue\n",
        "\n",
        "            # Compute the missing endpoint of an infinite ridge\n",
        "\n",
        "            t = vor.points[p2] - vor.points[p1] # tangent\n",
        "            t /= np.linalg.norm(t)\n",
        "            n = np.array([-t[1], t[0]])  # normal\n",
        "\n",
        "            midpoint = vor.points[[p1, p2]].mean(axis=0)\n",
        "            direction = np.sign(np.dot(midpoint - center, n)) * n\n",
        "            far_point = vor.vertices[v2] + direction * radius\n",
        "\n",
        "            new_region.append(len(new_vertices))\n",
        "            new_vertices.append(far_point.tolist())\n",
        "\n",
        "        # sort region counterclockwise\n",
        "        vs = np.asarray([new_vertices[v] for v in new_region])\n",
        "        c = vs.mean(axis=0)\n",
        "        angles = np.arctan2(vs[:,1] - c[1], vs[:,0] - c[0])\n",
        "        new_region = np.array(new_region)[np.argsort(angles)]\n",
        "\n",
        "        # finish\n",
        "        new_regions.append(new_region.tolist())\n",
        "\n",
        "    return new_regions, np.asarray(new_vertices)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "9NIVhxz8KvG3",
        "outputId": "30112ec2-92ea-4698-ca17-b31fb54b80a7"
      },
      "source": [
        "## Plotting Voronoi regions for the Iris dataset\n",
        "\n",
        "points = []\n",
        "xpts = np.array(df['sepal length (cm)'])\n",
        "ypts = np.array(df['sepal width (cm)'])\n",
        "for i in range(len(xpts)):\n",
        "  points.append([xpts[i],ypts[i]])\n",
        "# print(points)\n",
        "points = np.array(points)\n",
        "# compute Voronoi tesselation\n",
        "vor = Voronoi(points)\n",
        "\n",
        "regions, vertices = voronoi_finite_polygons_2d(vor)\n",
        "\n",
        "for region in regions:\n",
        "    polygon = vertices[region]\n",
        "    plt.fill(*zip(*polygon), alpha=0.4)\n",
        "\n",
        "plt.plot(points[:,0], points[:,1], 'ko')\n",
        "plt.xlim(vor.min_bound[0] - 0.1, vor.max_bound[0] + 0.1)\n",
        "plt.ylim(vor.min_bound[1] - 0.1, vor.max_bound[1] + 0.1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSK-GI0Knurk"
      },
      "source": [
        "## Section 2.2: Understanding Decision Boundaries  \n",
        "So you have seen the Voronoi diagram of the dataset, implemented KNN, and also seen your algorithm's performance in terms of accuracy? Impressive!  \n",
        "Wouldn't it also be great to know how exactly these 'votes' or neighbours are decided through some kind of visualization?  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytP_AImALiZX"
      },
      "source": [
        "### Decision Boundary\n",
        "\n",
        "While the Voronoi diagram gave us a good idea of the points present in our dataset, to understand how KNN performed on our dataset we can plot decision boundaries. Decision boundaries, as the name suggests, divide the plane into different regions of classification.  \n",
        "\n",
        "Note that here again, for simplicity, we have only considered first two attributes of the DataFrame (ie, Sepal Length and Sepal Width).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8P8Pqav4DI4N"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "def decision_boundary_plot(x_dec,y_dec,k):\n",
        "  h = .02  # step size in the mesh\n",
        "\n",
        "  # Create color maps\n",
        "  n = len(set(y_dec))\n",
        "  cmap_light = ListedColormap(['pink', 'green', 'cyan','yellow'][:n])\n",
        "  cmap_bold = ['pink', 'darkgreen', 'blue','yellow'][:n]\n",
        "\n",
        "  for weights in ['uniform', 'distance']:\n",
        "      # we create an instance of Neighbours Classifier and fit the data.\n",
        "      clf = KNeighborsClassifier(n_neighbors=k, weights=weights)\n",
        "      clf.fit(x_dec, y_dec)\n",
        "\n",
        "      # Plot the decision boundary. For that, we will assign a color to each\n",
        "      # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
        "      x_min, x_max = x_dec[:, 0].min() - 1, x_dec[:, 0].max() + 1\n",
        "      y_min, y_max = x_dec[:, 1].min() - 1, x_dec[:, 1].max() + 1\n",
        "      xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                          np.arange(y_min, y_max, h))\n",
        "      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "\n",
        "      # Put the result into a color plot\n",
        "      Z = Z.reshape(xx.shape)\n",
        "      plt.figure(figsize=(8, 6))\n",
        "      plt.contourf(xx, yy, Z, cmap=cmap_light)\n",
        "\n",
        "      # Plot also the training points\n",
        "      sns.scatterplot(x=x_dec[:, 0], y=x_dec[:, 1], hue=y_dec,\n",
        "                      palette=cmap_bold, alpha=1.0, edgecolor=\"black\")\n",
        "      plt.xlim(xx.min(), xx.max())\n",
        "      plt.ylim(yy.min(), yy.max())\n",
        "      plt.title(\"Multi-Classification (k = %i, weights = '%s')\"% (k, weights))\n",
        "\n",
        "  plt.show()\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bgAO62vWKPHt",
        "outputId": "b37a9496-ddba-492e-a7ae-523077deb57d"
      },
      "source": [
        "x_pts = X[:,:2]\n",
        "y_pts = y\n",
        "decision_boundary_plot(x_pts,y_pts,7)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFRJidIgr6gt"
      },
      "source": [
        "## TASK-2\n",
        "## In the above cells, we saw the Voronoi diagram of the data and plotted the KNN decision boundaries\n",
        "## by only considering two attributes of the dataset. You must be already familiar with PCA.\n",
        "## Apply PCA on the dataset above to reduce it to two dimensions.\n",
        "## Plot the Voronoi diagram and Decision boundaries after that."
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##Applying PCA\n",
        "from sklearn.preprocessing import StandardScaler as ss\n",
        "from sklearn.decomposition import PCA\n",
        "scaler=ss().fit(df).transform(df)\n",
        "pca=PCA(n_components=2)\n",
        "pca.fit(scaler)\n",
        "reduced=pca.transform(scaler)\n"
      ],
      "metadata": {
        "id": "zqDxHqdZpXer"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reduced.shape\n",
        "#thus,the dataset is reduced into two dimensions.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfw11sbVpZks",
        "outputId": "1e199da6-162f-4bcc-bcbb-b6cc968d19a1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=pd.DataFrame(reduced,columns=[\"PC1\",\"PC2\"])"
      ],
      "metadata": {
        "id": "P9F4WCwXpcOn"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting Voronai diagram for the reduced dataset\n",
        "points_ = []\n",
        "xpts = np.array(df1[\"PC1\"])\n",
        "ypts = np.array(df1[\"PC2\"])\n",
        "for i in range(len(xpts)):\n",
        "  points_.append([xpts[i],ypts[i]])\n",
        "# print(points_)\n",
        "points_ = np.array(points_)\n",
        "# compute Voronoi tesselation\n",
        "reduced = Voronoi(points_)\n",
        "regions1, vertices1 = voronoi_finite_polygons_2d(reduced)\n",
        "\n",
        "for region in regions1:\n",
        "    polygon = vertices1[region]\n",
        "    plt.fill(*zip(*polygon), alpha=0.4)\n",
        "\n",
        "plt.plot(points_[:,0], points_[:,1], 'ko')\n",
        "plt.xlim(reduced.min_bound[0] - 0.1, reduced.max_bound[0] + 0.1)\n",
        "plt.ylim(reduced.min_bound[1] - 0.1, reduced.max_bound[1] + 0.1)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "GEnI6yeHpmOD",
        "outputId": "3214e58e-3f6e-419e-8cfa-c9ffe748d1bd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gti-Jgu_VBDl"
      },
      "source": [
        "## Section 2.3: Confusion Matrix  \n",
        "In classification problems, a confusion matrix, also known as an error matrix, is a table that allows visualization of the performance of an algorithm, typically a supervised learning one. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPfc8YFBA8Oh"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XMXIM9apA99s",
        "outputId": "595bce64-a0a3-4094-b916-2f11abb66d8b"
      },
      "source": [
        "# print(confusion_matrix(y_test,predictions))\n",
        "pd.crosstab(y_test, predictions, rownames=['True'], colnames=['Predicted'], margins=True)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Predicted   0   1   2  All\n",
              "True                      \n",
              "0          13   0   0   13\n",
              "1           0  22   0   22\n",
              "2           0   3  12   15\n",
              "All        13  25  12   50"
            ],
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b0ae595c-2f7b-4bf0-a23e-4207577df2b7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b0ae595c-2f7b-4bf0-a23e-4207577df2b7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8c239804-b8f1-4b3e-9a28-899b7d96c8d9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8c239804-b8f1-4b3e-9a28-899b7d96c8d9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8c239804-b8f1-4b3e-9a28-899b7d96c8d9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "5cgYG0E5UHdy",
        "outputId": "fb90cb03-551d-4afc-c551-e8378d567d39"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, predictions)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"viridis\" ,fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 23.52222222222222, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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rMoZhGIqMjNSTTz6pUaNGSZJyc3MVHh6ulJQU9enTp1SxUPkAAMBiBbK5Zbscubm5kqRq1apJkrZu3aozZ86offv2zj4NGzbU9ddfr40bNxY7xr59+5SVlWU6Jzg4WDExMSWeUxwWnAIAYLECwz3/1nc4HHI4HKY2u90uu91+wfMKCws1YsQI3XHHHWrSpIkkKSsrSxUqVFBISIipb3h4uLKysood53x7eHh4qc8pDpUPAACuEMnJyQoODjZtycnJFz1vyJAh2rlzp+bOneuBKC+OygcAABYrdNO/9ZOSkpSYmGhqu1jVY+jQoVqyZInWrVun6667ztkeERGh06dPKycnx1T9yM7OVkRERLFjnW/Pzs5WjRo1TOe0aNGi1J+DygcAABZz15oPu92uKlWqmLaSkg/DMDR06FAtXLhQX3/9terUMS96jY6OVvny5bVq1SpnW1pamjIyMtSqVatix6xTp44iIiJM5xw/flzffvttiecUh+QDAAAfNGTIEH344YeaM2eOgoKClJWVpaysLJ08eVLSuYWiAwcOVGJiolavXq2tW7fqoYceUqtWrUx3ujRs2FALFy6UJNlsNo0YMUL//Oc/9fnnn+uHH35Q//79FRkZqW7dupU6NqZdAACwmLsWnLpi+vTpkqQ2bdqY2mfNmqUBAwZIkiZNmiQ/Pz/df//9cjgciouL05tvvmnqn5aW5rxTRpKefvpp5efn69FHH1VOTo5at26t5cuXKyAgoNSx8ZwPXBV4zgeAknjiOR9f7WvslnHi6vzklnG8jWkXAADgUUy7AABgMVffy+LrSD4AALCYN9Z8lGUkHwAAWMxdz/nwFXwbAADAo6h8AABgsQLj8l4K52tIPgAAsBgLTs34NgAAgEdR+QAAwGKF3O1iQvIBAIDFmHYx49sAAAAeReUDAACLcbeLGckHAAAW4yFjZnwbAADAo6h8AABgMd7tYkbyAQCAxQrFmo8/Ivm4wt0SWlOPNrpNTapGKLxikP627hOt+L+fncefaHKn7qvVWDUqBulMYYF2/jdL/9qxVt8f/c2LUcPTujwep56juqhaRIjSvz+gacNnKu27vd4OC17Cz4PnUfkw49u4wlUsV167jh3SmK1fFXt83+9HNXbLV4pf9q56rfhAB/Nz9X6bPqpmr+jhSOEtsb1u198mJujDFxZocPQz+mXHASUv/7tCQqt4OzR4AT8PKAtIPq5wazN/0Ws/rNW/D/5c7PHPD/yk/2Tv16/5Odpz/IjGb1upoAoBahgS5uFI4S33j7xPX767Sl+lrFHGroOa/Njbcpw4rbiH7/Z2aPACfh68o0B+btl8hVenXY4cOaKZM2dq48aNysrKkiRFRETo9ttv14ABAxQaGurN8HxOeT8/9al/k46fPqVdx7K9HQ48oFz5cmoQXVdzX17obDMMQ9tW7lDj2xp4MTJ4Az8P3lPIcz5MvJZ8fPfdd4qLi1PFihXVvn17NWhw7gc/OztbU6ZM0csvv6yvvvpKLVu29FaIPuPuyPqafHs3BZYrr0Mn89R/9cc6dvqkt8OCBwRXD5J/OX8dy841tR87lKuaDa/1UlTwFn4eUFZ4LfkYNmyYevbsqRkzZshmM2eEhmHoscce07Bhw7Rx48YLjuNwOORwOMznnzkrW3nW0p63MfuA7lv+nqraA9WnXgu9cUd39fh3io46Tng7NAC4KvjSlIk7eO3b+P777zVy5MgiiYck2Ww2jRw5UqmpqRcdJzk5WcHBwaYtZ/FaCyK+cp0sOKMDeceUevQ3Pbt5mQqMQvWq19zbYcEDco/8roKzBaoaHmxqrxoWrGNZOd4JCl7Dz4P3FBp+btl8hdc+SUREhDZv3lzi8c2bNys8PPyi4yQlJSk3N9e0hXSNdWeoPscmmyr4URm6Gpw9c1Y/b/1FN7Vr6myz2Wy6qV1T/bSp+EXK8F38PKCs8NrfQKNGjdKjjz6qrVu3ql27ds5EIzs7W6tWrdI777yjf/3rXxcdx263y263m9qupimXiuXKq1blqs79mpWD1SgkTLmnT+mY46SG3Hi7Vv7fHh06madq9op6sEG0IioGaVnGLi9GDU/6dNISPZ0yRD9vSVfa5r3qPqKTAirZ9dWs1d4ODV7Az4N3FPCQMROv/S09ZMgQVa9eXZMmTdKbb76pgoICSZK/v7+io6OVkpKiXr16eSu8K0bTajX0cbu/Ovf/cXMHSdInv+zQP777UvWqVFePOs1U1R6oHMdJ7fhvpnqv/EB7jh/xVsjwsLXzNygktIoSxvVW1YgQpafu13Px45VzKPfiJ8Pn8PPgHb40ZeIONsMwDG8HcebMGR05cu4vw+rVq6t8+fKXNV7dj19yR1jwIfX6bfd2CADKqBWFCyy/xoSf4t0yztONv3TLON5WJuYnypcvrxo1ang7DAAALMG0i1mZSD4AAPBlTLuYkXwAAGAxXixnxrcBAAA8iuQDAACLFcrmls1V69atU+fOnRUZGSmbzaZFixaZjttstmK3V199tcQxx44dW6R/w4YNXYqLaRcAACzmrWmX/Px8NW/eXA8//LB69OhR5HhmZqZp/8svv9TAgQN1//33X3DcG2+8UStXrnTulyvnWjpB8gEAgI+Kj49XfHzJt/lGRESY9hcvXqy2bduqbt26Fxy3XLlyRc51BdMuAABYrNCwuWWzUnZ2tpYuXaqBAwdetO+ePXsUGRmpunXrql+/fsrIyHDpWlQ+AACwmLvealvcm9yLe83IpZg9e7aCgoKKnZ75o5iYGKWkpCgqKkqZmZkaN26c7rzzTu3cuVNBQUGluhaVDwAArhDFvck9OTnZLWPPnDlT/fr1U0BAwAX7xcfHq2fPnmrWrJni4uK0bNky5eTkaP78+aW+FpUPAAAs5q4pk6SkJCUmJpra3FH1+Oabb5SWlqZ58+a5fG5ISIgaNGigvXv3lvockg8AACxW6KaJBndNsfzZe++9p+joaDVv3tzlc/Py8pSenq4HH3yw1Ocw7QIAgI/Ky8tTamqqUlNTJUn79u1TamqqaYHo8ePHtWDBAg0aNKjYMdq1a6epU6c690eNGqW1a9dq//792rBhg7p37y5/f3/17du31HFR+QAAwGIFFt+pUpItW7aobdu2zv3zUzYJCQlKSUmRJM2dO1eGYZSYPKSnpzvfPC9JBw8eVN++fXX06FGFhoaqdevW2rRpk0JDQ0sdl80wDOMSPk+ZVvfjl7wdAsqYev22ezsEAGXUisIFll/jie2lrwpcyOSbPnbLON5G5QMAAIvxVlszvg0AAOBRVD4AALBYwSW8FM6XkXwAAGAxqx+NfqVh2gUAAHgUlQ8AACzGglMzkg8AACxWyJoPE1IxAADgUVQ+AACwmLeecFpWkXwAAGAx1nyY8W0AAACPovIBAIDFeM6HGckHAAAW424XM5IPAAAsRuXDjDUfAADAo6h8AABgMe52MSP5AADAYky7mJGKAQAAj6LyAQCAxbjbxYzkAwAAizHtYsa0CwAA8CgqHwAAWIzKhxnJBwAAFiP5MGPaBQAAeBSVDwAALEblw4zkAwAAi3GrrRnJBwAAFqPyYcaaDwAA4FFUPgAAsBiVDzOSDwAALEbyYca0CwAA8CiSDwAALFZo2NyyuWrdunXq3LmzIiMjZbPZtGjRItPxAQMGyGazmbZ77rnnouNOmzZNtWvXVkBAgGJiYrR582aX4iL5AADAYoZhc8vmqvz8fDVv3lzTpk0rsc8999yjzMxM5/bxxx9fcMx58+YpMTFRY8aM0bZt29S8eXPFxcXp0KFDpY6LNR8AAPio+Ph4xcfHX7CP3W5XREREqcd87bXX9Mgjj+ihhx6SJM2YMUNLly7VzJkz9eyzz5ZqDCofAABYrFA2t2xWWLNmjcLCwhQVFaXBgwfr6NGjJfY9ffq0tm7dqvbt2zvb/Pz81L59e23cuLHU16TyAQCAxdx1t4vD4ZDD4TC12e122e32SxrvnnvuUY8ePVSnTh2lp6frueeeU3x8vDZu3Ch/f/8i/Y8cOaKCggKFh4eb2sPDw7V79+5SX5fKBwAAV4jk5GQFBwebtuTk5Eser0+fPurSpYuaNm2qbt26acmSJfruu++0Zs0a9wVdDCofAABY7FIWixYnKSlJiYmJprZLrXoUp27duqpevbr27t2rdu3aFTlevXp1+fv7Kzs729SenZ3t0roRKh8AAFjMXbfa2u12ValSxbS5M/k4ePCgjh49qho1ahR7vEKFCoqOjtaqVav+99kKC7Vq1Sq1atWq1Nch+QAAwGLeutU2Ly9PqampSk1NlSTt27dPqampysjIUF5enp566ilt2rRJ+/fv16pVq9S1a1fVr19fcXFxzjHatWunqVOnOvcTExP1zjvvaPbs2dq1a5cGDx6s/Px8590vpcG0CwAAPmrLli1q27atc//8lE1CQoKmT5+uHTt2aPbs2crJyVFkZKQ6duyoF1980VRNSU9P15EjR5z7vXv31uHDhzV69GhlZWWpRYsWWr58eZFFqBdiMwzDcMPnK1M6+PX0dggoY7767Xtvh4AyJC6yubdDQBmyonCB5de4dflzbhln8z0vuWUcb6PyAQCAxXzvn/mXhzUfAADAo6h8AABgMaueTnqlIvkAAMBi7nrOh69g2gUAAHgUlQ8AACzmrne7+AqSDwAALMbdLmZMuwAAAI+i8gEAgMVYcGpG8gEAgMVIPsxIPgAAsBgLTs1Y8wEAADyKygcAABbjbhczkg8AACzGmg8zpl0AAIBHUfkAAMBiVD7MSD4AALAYSz7MmHYBAAAeReUDAACLMe1iRvIBAIDVmHcxIfkAAMBiVD7MWPMBAAA8qlSVjylTppR6wOHDh19yMAAA+CKecGpWquRj0qRJpRrMZrORfAAA8CdMu5iVKvnYt2+f1XEAAICrxCWv+Th9+rTS0tJ09uxZd8YDAIDvMWzu2XyEy8nHiRMnNHDgQFWsWFE33nijMjIyJEnDhg3Tyy+/7PYAAQC40hmGezZf4XLykZSUpO+//15r1qxRQECAs719+/aaN2+eW4MDAAC+x+XnfCxatEjz5s3TbbfdJpvtfyWgG2+8Uenp6W4NDgAAn+BDVQt3cDn5OHz4sMLCwoq05+fnm5IRAABwDne7mLk87dKyZUstXbrUuX8+4Xj33XfVqlUr90UGAAB8ksvJx0svvaTnnntOgwcP1tmzZzV58mR17NhRs2bN0vjx462IEQCAK5vhps1F69atU+fOnRUZGSmbzaZFixY5j505c0bPPPOMmjZtqkqVKikyMlL9+/fXb7/9dsExx44dK5vNZtoaNmzoUlwuJx+tW7dWamqqzp49q6ZNm+rf//63wsLCtHHjRkVHR7s6HAAAPs8wbG7ZXJWfn6/mzZtr2rRpRY6dOHFC27Zt0/PPP69t27bps88+U1pamrp06XLRcW+88UZlZmY6t/Xr17sU1yW9WK5evXp65513LuVUAACuPl5acBofH6/4+PhijwUHB2vFihWmtqlTp+rWW29VRkaGrr/++hLHLVeunCIiIi45rktKPgoKCrRw4ULt2rVLktS4cWN17dpV5crxklwAAK5Uubm5stlsCgkJuWC/PXv2KDIyUgEBAWrVqpWSk5MvmKz8mcvZwo8//qguXbooKytLUVFRkqRXXnlFoaGh+uKLL9SkSRNXhwQAwMe5524Xh8Mhh8NharPb7bLb7Zc99qlTp/TMM8+ob9++qlKlSon9YmJilJKSoqioKGVmZmrcuHG68847tXPnTgUFBZXqWi6v+Rg0aJBuvPFGHTx4UNu2bdO2bdv066+/qlmzZnr00UddHQ4AAN/npgWnycnJCg4ONm3JycmXHd6ZM2fUq1cvGYah6dOnX7BvfHy8evbsqWbNmikuLk7Lli1TTk6O5s+fX+rruVz5SE1N1ZYtW1S1alVnW9WqVTV+/Hjdcsstrg4HAABKKSkpSYmJiaa2y616nE88Dhw4oK+//vqCVY/ihISEqEGDBtq7d2+pz3G58tGgQQNlZ2cXaT906JDq16/v6nAAAPg+N1U+7Ha7qlSpYtouJ/k4n3js2bNHK1eu1DXXXOPyGHl5eUpPT1eNGjVKfU6pko/jx487t+TkZA0fPlyffPKJDh48qIMHD+qTTz7RiBEj9Morr7gcNAAAPs9Lb7XNy8tTamqqUlNTJUn79u1TamqqMjIydObMGT3wwAPasmWLPvroIxUUFCgrK0tZWVk6ffq0c4x27dpp6tSpzv1Ro0Zp7dq12r9/vzZs2KDu3bvL399fffv2LXVcpZp2CQkJMT063TAM9erVy9lm/P9X7XXu3FkFBQWlvjgAALDOli1b1LZtW+f++SmbhIQEjR07Vp9//rkkqUWLFqbzVq9erTZt2kiS0tPTdeTIEeexgwcPqm/fvjp69KhCQ0PVunVrbdq0SaGhoaWOq1TJx+rVq0s9IAAAMDO89JyPNm3aOAsExbnQsfP2799v2p87d+7lhlW65CM2NvayLwQAwFWLt9qaXPJTwU6cOKGMjAzTvJAkNWvW7LKDAgAAvsvl5OPw4cN66KGH9OWXXxZ7nDUfAAD8ySUsFvVlLt9qO2LECOXk5Ojbb79VYGCgli9frtmzZ+uGG25wLlwBAAD/YzPcs/kKlysfX3/9tRYvXqyWLVvKz89PtWrVUocOHVSlShUlJyerU6dOVsQJAMCVy4cSB3dwufKRn5+vsLAwSeeebHr48GFJUtOmTbVt2zb3RgcAAHyOy8lHVFSU0tLSJEnNmzfXW2+9pf/7v//TjBkzXHq6GQAAVw0vPWSsrHJ52uWJJ55QZmamJGnMmDG655579NFHH6lChQpKSUlxd3wAAFz5mHYxcTn5+Otf/+r8c3R0tA4cOKDdu3fr+uuvV/Xq1d0aHAAA8D2X/JyP8ypWrKibb77ZHbEAAOCbqHyYlCr5+PPrey/ktddeu+RgAADwSSQfJqVKPrZv316qwf748jkAAIDi8GI5AACs5kN3qrjDZa/5QNnU5fE49RzVRdUiQpT+/QFNGz5Tad/t9XZYsNjbH0or1km/ZEgBdummJtKTf5PqXH/ueM5xaepM6T9bpMxsqVqI1K61NHygFFTZq6HDg/j94Hm+9HRSd3D5OR8o+2J73a6/TUzQhy8s0ODoZ/TLjgNKXv53hYRW8XZosNh330t/6S7NnS69N1E6c1YaOEo6cfLc8UNHpENHpacHS5+nSC8lSd9slv4xwathw4P4/YCygOTDB90/8j59+e4qfZWyRhm7DmryY2/LceK04h6+29uhwWLvvCp1j5duqCM1rC8lJ0mZ2Tb9+PO54w3qSlNelNreIV1/rXTbzdKIQdLqDdLZs96NHZ7B7wcvMdy0+QiSDx9Trnw5NYiuq20rdzjbDMPQtpU71Pi2Bl6MDN7we965/w0OukCffKlyRakck7A+j98PKCtIPnxMcPUg+Zfz17HsXFP7sUO5qhoR4p2g4BWFhVLyVOnmpoYa1C2+z7Ecafr7Uq/OHg0NXsLvB+/hrbZmpfq3zueff17qAbt06XLJwfzZr7/+qjFjxmjmzJkl9nE4HHI4HKa2QqNAfjZ/t8UBXIlemCTt2Sd99Ebxx/PypceelerXkoY85NnYAFzdSpV8dOvWrVSD2Ww2FRQUXE48Jv/97381e/bsCyYfycnJGjdunKmtjhqpnm50WxxXktwjv6vgbIGqhgeb2quGBetYVo53goLHvfi6tHaj9MEbUkRY0eP5J6RHnpIqVpTe+KdUnimXqwK/H7yIW21NSvUrp7Cw0JKLX6yi8ssvv1x0jKSkpCJPYO0ePOBywrqinT1zVj9v/UU3tWuqDYu/k3QuKbypXVMtnrbcy9HBaoYh/XOytPIbafZk6bpiXjSdly8NGiVVqCC9+ZJkt3s+TngHvx+8yIemTNzBq//e6datm2w2mwyj5P9XLvbUVLvdLvuffnte7VMun05aoqdThujnLelK27xX3Ud0UkAlu76axcPifN0Lk6Slq6Sp46VKgdLho+fagyqfe+5HXv65W29PnZIm/OPcfl7+uT7VQiT/q/s/nasCvx9QFlxS8pGfn6+1a9cqIyNDp0+fNh0bPnx4qcepUaOG3nzzTXXt2rXY46mpqYqOjr6UEK9qa+dvUEhoFSWM662qESFKT92v5+LHK+dQ7sVPxhVt7uJzyXrCE+b2l5411D1e+ulnacdP5/rE/cXcZ+VcQ9cWUymBb+H3g5dQ+TBxOfnYvn277r33Xp04cUL5+fmqVq2ajhw5oooVKyosLMyl5CM6Olpbt24tMfm4WFUEJVs8bTll1KvQrrUX/u/l1psu3ge+j98PnudLd6q4g8u32o4cOVKdO3fWsWPHFBgYqE2bNunAgQOKjo7Wv/71L5fGeuqpp3T77beXeLx+/fq8VwYAAB/jcuUjNTVVb731lvz8/OTv7y+Hw6G6detqwoQJSkhIUI8ePUo91p133nnB45UqVVJsbKyrIQIAULZQ+TBxufJRvnx5+fmdOy0sLEwZGRmSpODgYP3666/ujQ4AAF/A49VNXK583HTTTfruu+90ww03KDY2VqNHj9aRI0f0wQcfqEmTJlbECAAAfIjLlY+XXnpJNWqcWxI/fvx4Va1aVYMHD9bhw4f19ttvuz1AAACudDxe3czlykfLli2dfw4LC9Py5ayYBgDggnjCqQkPVQYAwGo+VLVwB5enXerUqaO6deuWuAEAgLJh3bp16ty5syIjI2Wz2bRo0SLTccMwNHr0aNWoUUOBgYFq37699uzZc9Fxp02bptq1aysgIEAxMTHavHmzS3G5XPkYMWKEaf/MmTPavn27li9frqeeesrV4QAA8HneWq+Rn5+v5s2b6+GHHy72URgTJkzQlClTNHv2bNWpU0fPP/+84uLi9NNPPykgIKDYMefNm6fExETNmDFDMTExev311xUXF6e0tDSFhRXzJstiuJx8PPHEE8W2T5s2TVu2bHF1OAAAfJ+Xko/4+HjFx8cXe8wwDL3++uv6xz/+4XzS+Pvvv6/w8HAtWrRIffr0Kfa81157TY888ogeeughSdKMGTO0dOlSzZw5U88++2yp4nJ52qUk8fHx+vTTT901HAAAsNC+ffuUlZWl9u3bO9uCg4MVExOjjRs3FnvO6dOntXXrVtM5fn5+at++fYnnFMdtC04/+eQTVatWzV3DAQDgM9w17eJwOORwOExtxb3dvTSysrIkSeHh4ab28PBw57E/O3LkiAoKCoo9Z/fu3aW+9iU9ZOyPr7k3DENZWVk6fPiw3nzzTVeHAwDA97kp+UhOTta4ceNMbWPGjNHYsWPdcwEPcTn56Nq1qyn58PPzU2hoqNq0aaOGDRu6NTgAAPA/SUlJSkxMNLVdStVDkiIiIiRJ2dnZzoeHnt9v0aJFsedUr15d/v7+ys7ONrVnZ2c7xysNl5OPKy27AgDA69xU+bjUKZbi1KlTRxEREVq1apUz2Th+/Li+/fZbDR48uNhzKlSooOjoaK1atUrdunWTJBUWFmrVqlUaOnRoqa/t8oJTf39/HTp0qEj70aNH5e/v7+pwAAD4PG89Xj0vL0+pqalKTU2VdG6RaWpqqjIyMmSz2TRixAj985//1Oeff64ffvhB/fv3V2RkpDOxkKR27dpp6tSpzv3ExES98847mj17tnbt2qXBgwcrPz/fefdLabhc+TCM4j+9w+FQhQoVXB0OAABYZMuWLWrbtq1z//yUTUJCglJSUvT0008rPz9fjz76qHJyctS6dWstX77c9IyP9PR0HTlyxLnfu3dvHT58WKNHj1ZWVpZatGih5cuXF1mEeiE2o6Rs4k+mTJkiSRo5cqRefPFFVa5c2XmsoKBA69at0/79+7V9+/ZSX9wqHfx6ejsElDFf/fa9t0NAGRIX2dzbIaAMWVG4wPJrRL04yS3jpD0/0i3jeFupKx+TJp374gzD0IwZM0xTLBUqVFDt2rU1Y8YM90cIAMCVjne7mJQ6+di3b58kqW3btvrss89UtWpVy4ICAMCXeOvx6mWVy2s+Vq9ebUUcAADgKuHy3S7333+/XnnllSLtEyZMUM+erLUAAKAIw02bj3A5+Vi3bp3uvffeIu3x8fFat26dW4ICAMCnkHyYuJx85OXlFXtLbfny5XX8+HG3BAUAAHyXy8lH06ZNNW/evCLtc+fOVePGjd0SFAAAvsRbDxkrq1xecPr888+rR48eSk9P19133y1JWrVqlT7++GMtWGD9vdIAAFxxfChxcAeXk4/OnTtr0aJFeumll/TJJ58oMDBQzZo108qVKxUbG2tFjAAAwIe4nHxIUqdOndSpU6ci7Tt37lSTJk0uOygAAHyJL02ZuIPLaz7+7Pfff9fbb7+tW2+9Vc2b88hiAACK4G4Xk0tOPtatW6f+/furRo0a+te//qW7775bmzZtcmdsAADAB7k07ZKVlaWUlBS99957On78uHr16iWHw6FFixZxpwsAACXxoaqFO5S68tG5c2dFRUVpx44dev311/Xbb7/pjTfesDI2AAB8ArfampW68vHll19q+PDhGjx4sG644QYrYwIAwLf4UOLgDqWufKxfv16///67oqOjFRMTo6lTp+rIkSNWxgYAAHxQqZOP2267Te+8844yMzP1t7/9TXPnzlVkZKQKCwu1YsUK/f7771bGCQDAlYu7XUxcvtulUqVKevjhh7V+/Xr98MMPevLJJ/Xyyy8rLCxMXbp0sSJGAACuaKz5MLus53xERUVpwoQJOnjwoD7++GN3xQQAAHzYJT3h9M/8/f3VrVs3devWzR3DAQDgW3yoauEObkk+AABAyXxpysQdLvvx6gAAAK6g8gEAgNWofJiQfAAAYDWSDxOmXQAAgEdR+QAAwGI2bwdQxpB8AABgNaZdTEg+AACwGLfamrHmAwAAeBSVDwAArEblw4TkAwAAq5F8mDDtAgCAD6pdu7ZsNluRbciQIcX2T0lJKdI3ICDAktiofAAAYDFvLDj97rvvVFBQ4NzfuXOnOnTooJ49e5Z4TpUqVZSWlubct9msuUmY5AMAAKt5IfkIDQ017b/88suqV6+eYmNjSzzHZrMpIiLC6tCYdgEAwNedPn1aH374oR5++OELVjPy8vJUq1Yt1axZU127dtWPP/5oSTxUPgAAsJi7pl0cDoccDoepzW63y263X/C8RYsWKScnRwMGDCixT1RUlGbOnKlmzZopNzdX//rXv3T77bfrxx9/1HXXXeeO8J2ofAAAYDXDPVtycrKCg4NNW3Jy8kUv/9577yk+Pl6RkZEl9mnVqpX69++vFi1aKDY2Vp999plCQ0P11ltvXcYHLx6VDwAArhBJSUlKTEw0tV2s6nHgwAGtXLlSn332mUvXKl++vG666Sbt3bvX5TgvhuQDAACLuWvapTRTLH82a9YshYWFqVOnTi6dV1BQoB9++EH33nuvS+eVBskHrgr33tPH2yGgDPn57SBvh4CrjZceMlZYWKhZs2YpISFB5cqZ/8rv37+/rr32Wue0zQsvvKDbbrtN9evXV05Ojl599VUdOHBAgwYNcntcJB8AAFjNS8nHypUrlZGRoYcffrjIsYyMDPn5/W/p57Fjx/TII48oKytLVatWVXR0tDZs2KDGjRu7PS6SDwAAfFTHjh1lGMVnPmvWrDHtT5o0SZMmTfJAVCQfAABYzhtPOC3LSD4AALAayYcJz/kAAAAeReUDAACL2UpYd3G1IvkAAMBq5B4mTLsAAACPovIBAIDFuNvFjOQDAACrkXyYMO0CAAA8isoHAAAWY9rFjOQDAACrkXyYkHwAAGAxKh9mrPkAAAAeReUDAACrUfkwIfkAAMBiTLuYMe0CAAA8isoHAABW48VyJiQfAABYjGkXM6ZdAACAR1H5AADAalQ+TEg+AACwmK3Q2xGULUy7AAAAj6LyAQCA1Zh2MSH5AADAYtztYkbyAQCA1XjOhwlrPgAAgEdR+QAAwGJMu5iRfAAAYDWSDxOmXQAAgEdR+QAAwGJMu5iRfAAAYDXudjFh2gUAAB80duxY2Ww209awYcMLnrNgwQI1bNhQAQEBatq0qZYtW2ZJbCQfAABYzGa4Z3PVjTfeqMzMTOe2fv36Evtu2LBBffv21cCBA7V9+3Z169ZN3bp1086dOy/jkxeP5AMAAKsZbtpcVK5cOUVERDi36tWrl9h38uTJuueee/TUU0+pUaNGevHFF3XzzTdr6tSprl/4Ikg+AADwUXv27FFkZKTq1q2rfv36KSMjo8S+GzduVPv27U1tcXFx2rhxo9vjYsEpAAAWc9fdLg6HQw6Hw9Rmt9tlt9uL9I2JiVFKSoqioqKUmZmpcePG6c4779TOnTsVFBRUpH9WVpbCw8NNbeHh4crKynJP8H9A5QMAAKsVGm7ZkpOTFRwcbNqSk5OLvWR8fLx69uypZs2aKS4uTsuWLVNOTo7mz5/v4Q9fFJUPAACs5qbKR1JSkhITE01txVU9ihMSEqIGDRpo7969xR6PiIhQdna2qS07O1sRERGXFuwFUPkAAOAKYbfbVaVKFdNW2uQjLy9P6enpqlGjRrHHW7VqpVWrVpnaVqxYoVatWl123H9G8gEAgMW8cavtqFGjtHbtWu3fv18bNmxQ9+7d5e/vr759+0qS+vfvr6SkJGf/J554QsuXL9fEiRO1e/dujR07Vlu2bNHQoUPd+VVIYtoFAADreeEJpwcPHlTfvn119OhRhYaGqnXr1tq0aZNCQ0MlSRkZGfLz+18N4vbbb9ecOXP0j3/8Q88995xuuOEGLVq0SE2aNHF7bCQfAAD4oLlz517w+Jo1a4q09ezZUz179rQoov8h+QAAwGK8WM6M5AMAAKuRfJiw4BQAAHgUlQ8AACxm88KC07KM5AMAAKsVejuAsoVpFwAA4FFUPgAAsBjTLmYkHwAAWI3cw4TkAwAAq1H5MGHNBwAA8CgqHz6qy+Nx6jmqi6pFhCj9+wOaNnym0r4r/jXK8F2det6i+3reorDIEElSRvphffT2Gm35zx7vBgaPuTXiOv2t2S1qek2EwitV1iMrFurfB879Lihn89Oolq3VtmZdXR8UrN9Pn9b63w7o5e/W6tCJfC9H7lt4wqkZlQ8fFNvrdv1tYoI+fGGBBkc/o192HFDy8r8rJLSKt0ODhx3JPq6ZU1Zo2F9maPhf3lLqd79ozOt9VateqLdDg4dULFdeu44e1vMbVhY5FliunJpUD9eU7RvVadH7+tvKRaobXFXvdejhhUh9nGG4Z/MRVD580P0j79OX767SVylrJEmTH3tbMfferLiH79a8VxZ5NTZ41rfr0kz7s6eu0n09b1HDpjV1IP2wl6KCJ605uE9rDu4r9tjvZ07rr18uMLWN3rBKX3R7UJGVgvRb/u+eCBFXISofPqZc+XJqEF1X21bucLYZhqFtK3eo8W0NvBgZvM3Pz6bYuCayB1bQrh2/ejsclFFBFewqNAwdP+3wdig+xVbons1XUPnwMcHVg+Rfzl/HsnNN7ccO5apmw2u9FBW8qXb9ME16/xFVqFBOJ0+e1ouJHyvjF6oeKMru76+kW+/S5+m7lHfmtLfD8S0+NGXiDl6vfJw8eVLr16/XTz/9VOTYqVOn9P7771/wfIfDoePHj5u2QqPAqnCBK87B/Uf1eO/peuLBt7V0/nd68oUeur4uaz5gVs7mp2l3d5FNNv39Pyu8HQ58nFeTj59//lmNGjXSXXfdpaZNmyo2NlaZmZnO47m5uXrooYcuOEZycrKCg4NN2z7ttjr0Miv3yO8qOFugquHBpvaqYcE6lpXjnaDgVWfPFijz1/9q765MzXpjpfb9nKVuf7nN22GhDCln89O0dl10beUq6vflfKoeVjDctPkIryYfzzzzjJo0aaJDhw4pLS1NQUFBuuOOO5SRkVHqMZKSkpSbm2va6qihhVGXbWfPnNXPW3/RTe2aOttsNptuatdUP2362YuRoayw+dlUvgIzrjjnfOJRp0qI+n05XzmOU94OySfZDMMtm6/w6m+gDRs2aOXKlapevbqqV6+uL774Qo8//rjuvPNOrV69WpUqVbroGHa7XXa73dTmZ/O3KuQrwqeTlujplCH6eUu60jbvVfcRnRRQya6vZq32dmjwsIeGtdd3/9mjw1m5CqxYQW3jm6lZy9r6++MfeDs0eEjFcuVVu0pV537NoGA1rhamHMdJHTqRr+ntu6jJNeF6+N+fyd/mp9DAc793cxwndabQh1Y4okzxavJx8uRJlSv3vxBsNpumT5+uoUOHKjY2VnPmzPFidFeutfM3KCS0ihLG9VbViBClp+7Xc/HjlXMo9+Inw6eEVKukp/7ZQ1WrB+lE3int+zlbf3/8A23flO7t0OAhzUIjNK9TH+f+6NvuliQt+HmnXt/2H3WsdYMkaXmPAabzei+dq02Z3BXlNj5UtXAHryYfDRs21JYtW9SoUSNT+9SpUyVJXbp08UZYPmHxtOVaPG25t8OAl00at9jbIcDLNmX+qlrvvlri8QsdgxtRRDLx6pqP7t276+OPPy722NSpU9W3b18ZZIsAgCscaz7MvJp8JCUladmyZSUef/PNN1XInCMAAD6FJe8AAFjNh6oW7kDyAQCA1Ug+TLz+hFMAAHB1ofIBAIDVWL5oQvIBAIDFfOlOFXdg2gUAAHgUlQ8AAKxG5cOE5AMAAKuRfJgw7QIAgA9KTk7WLbfcoqCgIIWFhalbt25KS0u74DkpKSmy2WymLSAgwO2xkXwAAGA1w3DP5oK1a9dqyJAh2rRpk1asWKEzZ86oY8eOys/Pv+B5VapUUWZmpnM7cODA5XzyYjHtAgCA1bxwq+3y5eaXi6akpCgsLExbt27VXXfdVeJ5NptNERERlsZG5QMAAIuVhRfL5ebmSpKqVat2wX55eXmqVauWatasqa5du+rHH3+8rOsWh+QDAIArhMPh0PHjx02bw+G46HmFhYUaMWKE7rjjDjVp0qTEflFRUZo5c6YWL16sDz/8UIWFhbr99tt18OBBd34Mkg8AACznpjUfycnJCg4ONm3JyckXvfyQIUO0c+dOzZ0794L9WrVqpf79+6tFixaKjY3VZ599ptDQUL311lvu+iYkseYDAADrFbrnVtukpCQlJiaa2ux2+wXPGTp0qJYsWaJ169bpuuuuc+l65cuX10033aS9e/e6HOuFkHwAAHCFsNvtF002zjMMQ8OGDdPChQu1Zs0a1alTx+XrFRQU6IcfftC9997r8rkXQvIBAIDVvPCQsSFDhmjOnDlavHixgoKClJWVJUkKDg5WYGCgJKl///669tprnVM3L7zwgm677TbVr19fOTk5evXVV3XgwAENGjTIrbGRfAAAYDUvJB/Tp0+XJLVp08bUPmvWLA0YMECSlJGRIT+//y3/PHbsmB555BFlZWWpatWqio6O1oYNG9S4cWO3xkbyAQCADzJKkfCsWbPGtD9p0iRNmjTJooj+h+QDAACr8W4XE5IPAACs5qa7XXwFz/kAAAAeReUDAACrGV54uUsZRvIBAIDVWPNhQvIBAIDVWPNhwpoPAADgUVQ+AACwGtMuJiQfAABYjeTDhGkXAADgUVQ+AACwGpUPE5IPAACsVshzPv6IaRcAAOBRVD4AALAa0y4mJB8AAFiN5MOEaRcAAOBRVD4AALAaj1c3IfkAAMBiBm+1NSH5AADAalQ+TFjzAQAAPIrKBwAAVuNuFxOSDwAArMYTTk2YdgEAAB5F5QMAAKsx7WJC8gEAgMUMpl1MmHYBAAAeReUDAACrMe1iQvIBAIDVeMiYCdMuAADAo6h8AABgNd7tYkLyAQCAxQymXUyYdgEAwGpGoXu2SzBt2jTVrl1bAQEBiomJ0ebNmy/Yf8GCBWrYsKECAgLUtGlTLVu27JKueyEkHwAA+Kh58+YpMTFRY8aM0bZt29S8eXPFxcXp0KFDxfbfsGGD+vbtq4EDB2r79u3q1q2bunXrpp07d7o1Lpth+N79Px38eno7BJQx/s0aeTsElCG7hgZ5OwSUIQcGPWX5NTr493bLOCsK5rnUPyYmRrfccoumTp0qSSosLFTNmjU1bNgwPfvss0X69+7dW/n5+VqyZImz7bbbblOLFi00Y8aMywv+D6h8AABgNS9Mu5w+fVpbt25V+/btnW1+fn5q3769Nm7cWOw5GzduNPWXpLi4uBL7XyoWnAIAcIVwOBxyOBymNrvdLrvdXqTvkSNHVFBQoPDwcFN7eHi4du/eXez4WVlZxfbPysq6zMjNfDL5WFG4wNsheJ3D4VBycrKSkpKK/aHE1YefCfwRPw+e5a6/l8aOHatx48aZ2saMGaOxY8e6ZXxPYdrFRzkcDo0bN65IhoyrFz8T+CN+Hq5MSUlJys3NNW1JSUnF9q1evbr8/f2VnZ1tas/OzlZERESx50RERLjU/1KRfAAAcIWw2+2qUqWKaSupclWhQgVFR0dr1apVzrbCwkKtWrVKrVq1KvacVq1amfpL0ooVK0rsf6l8ctoFAABIiYmJSkhIUMuWLXXrrbfq9ddfV35+vh566CFJUv/+/XXttdcqOTlZkvTEE08oNjZWEydOVKdOnTR37lxt2bJFb7/9tlvjIvkAAMBH9e7dW4cPH9bo0aOVlZWlFi1aaPny5c5FpRkZGfLz+98kyO233645c+boH//4h5577jndcMMNWrRokZo0aeLWuHzyOR9gMRmK4mcCf8TPA7yJ5AMAAHgUC04BAIBHkXwAAACPIvkAAAAeRfIBAAA8iuTDR02bNk21a9dWQECAYmJitHnzZm+HBC9Zt26dOnfurMjISNlsNi1atMjbIcGLkpOTdcsttygoKEhhYWHq1q2b0tLSvB0WrjIkHz5o3rx5SkxM1JgxY7Rt2zY1b95ccXFxOnTokLdDgxfk5+erefPmmjZtmrdDQRmwdu1aDRkyRJs2bdKKFSt05swZdezYUfn5+d4ODVcRbrX1QTExMbrllls0depUSecep1uzZk0NGzZMzz77rJejgzfZbDYtXLhQ3bp183YoKCMOHz6ssLAwrV27VnfddZe3w8FVgsqHjzl9+rS2bt2q9u3bO9v8/PzUvn17bdy40YuRASiLcnNzJUnVqlXzciS4mpB8+JgjR46ooKDA+ejc88LDw5WVleWlqACURYWFhRoxYoTuuOMOtz8+G7gQ3u0CAFepIUOGaOfOnVq/fr23Q8FVhuTDx1SvXl3+/v7Kzs42tWdnZysiIsJLUQEoa4YOHaolS5Zo3bp1uu6667wdDq4yTLv4mAoVKig6OlqrVq1ythUWFmrVqlVq1aqVFyMDUBYYhqGhQ4dq4cKF+vrrr1WnTh1vh4SrEJUPH5SYmKiEhAS1bNlSt956q15//XXl5+froYce8nZo8IK8vDzt3bvXub9v3z6lpqaqWrVquv76670YGbxhyJAhmjNnjhYvXqygoCDnWrDg4GAFBgZ6OTpcLbjV1kdNnTpVr776qrKystSiRQtNmTJFMTEx3g4LXrBmzRq1bdu2SHtCQoJSUlI8HxC8ymazFds+a9YsDRgwwLPB4KpF8gEAADyKNR8AAMCjSD4AAIBHkXwAAACPIvkAAAAeRfIBAAA8iuQDAAB4FMkHAADwKJIPoAwZMGCAunXr5txv06aNRowY4fE41qxZI5vNppycnBL72Gw2LVq0qNRjjh07Vi1atLisuPbv3y+bzabU1NTLGgeAd5F8ABcxYMAA2Ww22Ww2VahQQfXr19cLL7ygs2fPWn7tzz77TC+++GKp+pYmYQCAsoB3uwClcM8992jWrFlyOBxatmyZhgwZovLlyyspKalI39OnT6tChQpuuW61atXcMg4AlCVUPoBSsNvtioiIUK1atTR48GC1b99en3/+uaT/TZWMHz9ekZGRioqKkiT9+uuv6tWrl0JCQlStWjV17dpV+/fvd45ZUFCgxMREhYSE6JprrtHTTz+tP7/t4M/TLg6HQ88884xq1qwpu92u+vXr67333tP+/fud72+pWrWqbDab8z0dhYWFSk5OVp06dRQYGKjmzZvrk08+MV1n2bJlatCggQIDA9W2bVtTnKX1zDPPqEGDBqpYsaLq1q2r559/XmfOnCnS76233lLNmjVVsWJF9erVS7m5uabj7777rho1aqSAgAA1bNhQb775psuxACjbSD6ASxAYGKjTp08791etWqW0tDStWLFCS5Ys0ZkzZxQXF6egoCB98803+s9//qPKlSvrnnvucZ43ceJEpaSkaObMmVq/fr3++9//auHChRe8bv/+/fXxxx9rypQp2rVrl9566y1VrlxZNWvW1KeffipJSktLU2ZmpiZPnixJSk5O1vvvv68ZM2boxx9/1MiRI/XXv/5Va9eulXQuSerRo4c6d+6s1NRUDRo0SM8++6zL30lQUJBSUlL0008/afLkyXrnnXc0adIkU5+9e/dq/vz5+uKLL7R8+XJt375djz/+uPP4Rx99pNGjR2v8+PHatWuXXnrpJT3//POaPXu2y/EAKMMMABeUkJBgdO3a1TAMwygsLDRWrFhh2O12Y9SoUc7j4eHhhsPhcJ7zwQcfGFFRUUZhYaGzzeFwGIGBgcZXX31lGIZh1KhRw5gwYYLz+JkzZ4zrrrvOeS3DMIzY2FjjiSeeMAzDMNLS0gxJxooVK4qNc/Xq1YYk49ixY862U6dOGRUrVjQ2bNhg6jtw4ECjb9++hmEYRlJSktG4cWPT8WeeeabIWH8myVi4cGGJx1999VUjOjrauT9mzBjD39/fOHjwoLPtyy+/NPz8/IzMzEzDMAyjXr16xpw5c0zjvPjii0arVq0MwzCMffv2GZKM7du3l3hdAGUfaz6AUliyZIkqV66sM2fOqLCwUH/5y180duxY5/GmTZua1nl8//332rt3r4KCgkzjnDp1Sunp6crNzVVmZqZiYmKcx8qVK6eWLVsWmXo5LzU1Vf7+/oqNjS113Hv37tWJEyfUoUMHU/vp06d10003SZJ27dplikOSWrVqVeprnDdv3jxNmTJF6enpysvL09mzZ1WlShVTn+uvv17XXnut6TqFhYVKS0tTUFCQ0tPTNXDgQD3yyCPOPmfPnlVwcLDL8QAou0g+gFJo27atpk+frgoVKigyMlLlypn/06lUqZJpPy8vT9HR0froo4+KjBUaGnpJMQQGBrp8Tl5eniRp6dKlpr/0pXPrWNxl48aN6tevn8aNG6e4uDgFBwdr7ty5mjhxosuxvvPOO0WSIX9/f7fFCsD7SD6AUqhUqZLq169f6v4333yz5s2bp7CwsCL/+j+vRo0a+vbbb3XXXXdJOvcv/K1bt+rmm28utn/Tpk1VWFiotWvXqn379kWOn6+8FBQUONsaN24su92ujIyMEismjRo1ci6ePW/Tpk0X/5B/sGHDBtWqVUt///vfnW0HDhwo0i8jI0O//fabIiMjndfx8/NTVFSUwsPDFRkZqV9++UX9+vVz6foAriwsOAUs0K9fP1WvXl1du3bVN998o3379mnNmjUaPny4Dh48KEl64okn9PLLL2vRokXavXu3Hn/88Qs+o6N27dpKSEjQww8/rEWLFjnHnD9/viSpVq1astlsWrJkiQ4fPqy8vDwFBQVp1KhRGjlypGbPnq309HRt27ZNb7zxhnMR52OPPaY9e/boqaeeUlpamubMmaOUlBSXPu8NN9ygjIwMzZ07V+np6ZoyZUqxi2cDAgKUkJCg77//Xt98842GDx+uXr16KSIiQpI0btw4JScna8qUKfr555/1ww8/aNasWXrttddcigdA2UbyAVigYsWKWrduna6//nr16NFDjRo10sCBA3Xq1ClnJeTJJ5/Ugw8+qISEBLVq1UpBQUHq3r37BcedPn26HnjgAT3++ONq2LChHnnkEeXn50uSrr32Wo0bN07PPvuswsPDNXToUEnSiy++qOeff17Jyclq1KiR7rnnHi1dulR16tSRdG4dxqeffqpFixapefPmmjFjhl566SWXPm+XLl00cuRIDR06VC1atNCGDRv0/PPPF+lXv3599ejRQ/fee686duyoZs2amW6lHTRokN59913NmjVLTZs2VWxsrFJSUpyxAvANNqOk1W0AAAAWoPIBAAA8iuQDAAB4FMkHAADwKJIPAADgUSQfAADAo0g+AACAR5F8AAAAjyL5AAAAHkXyAQAAPIrkAwAAeBTJBwAA8CiSDwAA4FH/D/+cWH4kmXhCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTPQOPStVtmI"
      },
      "source": [
        "## Section 2.4: Classification Report\n",
        "\n",
        "Precision, Recall, and F1-Score are other metrics besides accuracy that one might look for in an algorithm.  Depending on the use-case, one might consider one metric more important than the other.  \n",
        "\n",
        "Note: *T-> True, F->False, P->Positive, N->Negative*\n",
        "    \n",
        "Mathematically, Accuracy is :  \n",
        "\n",
        "$Accuracy = \\frac{TP+TN}{TP+TN+FP+FN}$  \n",
        "\n",
        "**Precision**: The accuracy of positive predictions\n",
        "\n",
        "\n",
        "$Precision = \\frac{TP}{TP+FP}$\n",
        "\n",
        "**Recall**:Fraction of positives that were correctly identified\n",
        "\n",
        "\n",
        "$Recall = \\frac{TP}{TP+FN}$\n",
        "\n",
        "\n",
        "**F1-score**: Harmonic mean of precision and recall  \n",
        "\n",
        "\n",
        "$F1 = \\frac{2*Precision*Recall}{Precision+Recall} = \\frac{2*TP}{2*TP+FP+FN}$  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VH3KEfEYW190",
        "outputId": "0ea9eefa-ba01-4331-c958-764965a9fbf6"
      },
      "source": [
        "#import classification_report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        13\n",
            "           1       0.88      1.00      0.94        22\n",
            "           2       1.00      0.80      0.89        15\n",
            "\n",
            "    accuracy                           0.94        50\n",
            "   macro avg       0.96      0.93      0.94        50\n",
            "weighted avg       0.95      0.94      0.94        50\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtYNFoJh0LU-"
      },
      "source": [
        "### **Car Evaluation Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Upload the Car evaluation data CSV file that has been shared with you.\n",
        "# Run this cell, click on the 'Choose files' button and upload the file.\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "8BLg02qJ0Oo0",
        "outputId": "54c77ba5-5ee8-4f05-b4b7-bf125a995963"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-01c12cca-5d9f-4511-8a7c-d53daaef8cbf\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-01c12cca-5d9f-4511-8a7c-d53daaef8cbf\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving car_evaluation.csv to car_evaluation (1).csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0T5yvzVH4jrQ",
        "outputId": "c312d93e-9b0c-4f07-b36c-9348e55aa7cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "car_df = pd.read_csv('car_evaluation.csv')\n",
        "car_df"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      vhigh vhigh.1      2   2.1  small   low  unacc\n",
              "0     vhigh   vhigh      2     2  small   med  unacc\n",
              "1     vhigh   vhigh      2     2  small  high  unacc\n",
              "2     vhigh   vhigh      2     2    med   low  unacc\n",
              "3     vhigh   vhigh      2     2    med   med  unacc\n",
              "4     vhigh   vhigh      2     2    med  high  unacc\n",
              "...     ...     ...    ...   ...    ...   ...    ...\n",
              "1722    low     low  5more  more    med   med   good\n",
              "1723    low     low  5more  more    med  high  vgood\n",
              "1724    low     low  5more  more    big   low  unacc\n",
              "1725    low     low  5more  more    big   med   good\n",
              "1726    low     low  5more  more    big  high  vgood\n",
              "\n",
              "[1727 rows x 7 columns]"
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vhigh</th>\n",
              "      <th>vhigh.1</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>2</td>\n",
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              "      <td>med</td>\n",
              "      <td>unacc</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>vhigh</td>\n",
              "      <td>vhigh</td>\n",
              "      <td>2</td>\n",
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              "      <td>high</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>1722</th>\n",
              "      <td>low</td>\n",
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              "      <td>more</td>\n",
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              "      <th>1724</th>\n",
              "      <td>low</td>\n",
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              "      <td>more</td>\n",
              "      <td>big</td>\n",
              "      <td>low</td>\n",
              "      <td>unacc</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>low</td>\n",
              "      <td>low</td>\n",
              "      <td>5more</td>\n",
              "      <td>more</td>\n",
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              "      <td>med</td>\n",
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              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>low</td>\n",
              "      <td>low</td>\n",
              "      <td>5more</td>\n",
              "      <td>more</td>\n",
              "      <td>big</td>\n",
              "      <td>high</td>\n",
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              "  </tbody>\n",
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              "<p>1727 rows × 7 columns</p>\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5a7915cc-4084-47fe-a303-7fcbaf7a5f02 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwjBankjB9Os",
        "outputId": "2c8ac389-246b-4638-a8fd-badf57242ac5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "for x in car_df.columns:\n",
        "  # print(x)\n",
        "  le = preprocessing.LabelEncoder()\n",
        "  le.fit(car_df[x])\n",
        "  car_df[x]=le.transform(car_df[x])\n",
        "\n",
        "car_df"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      vhigh  vhigh.1  2  2.1  small  low  unacc\n",
              "0         3        3  0    0      2    2      2\n",
              "1         3        3  0    0      2    0      2\n",
              "2         3        3  0    0      1    1      2\n",
              "3         3        3  0    0      1    2      2\n",
              "4         3        3  0    0      1    0      2\n",
              "...     ...      ... ..  ...    ...  ...    ...\n",
              "1722      1        1  3    2      1    2      1\n",
              "1723      1        1  3    2      1    0      3\n",
              "1724      1        1  3    2      0    1      2\n",
              "1725      1        1  3    2      0    2      1\n",
              "1726      1        1  3    2      0    0      3\n",
              "\n",
              "[1727 rows x 7 columns]"
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>vhigh</th>\n",
              "      <th>vhigh.1</th>\n",
              "      <th>2</th>\n",
              "      <th>2.1</th>\n",
              "      <th>small</th>\n",
              "      <th>low</th>\n",
              "      <th>unacc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1722</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1723</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1724</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1727 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e8a27c7a-565f-4df5-afa0-f3df8a023787 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e8a27c7a-565f-4df5-afa0-f3df8a023787');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a245617c-e9d3-412d-a202-4448424920e0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a245617c-e9d3-412d-a202-4448424920e0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a245617c-e9d3-412d-a202-4448424920e0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5Klx4yMCgKa"
      },
      "source": [
        "dataset = car_df.values\n",
        "X = dataset[:,0:6]\n",
        "y = np.array(dataset[:,6])\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=19)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HL5ufCHDANh",
        "outputId": "e8a2d9c0-5364-4826-d20c-34196aa2f520",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "predictions = knn.predict(X_test)\n",
        "score = accuracy_score(y_test, predictions)\n",
        "print(\"The accuracy of the classifier on Car evaluation dataset is {:.2f} %\".format(100*score))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the classifier on Car evaluation dataset is 89.88 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYCimUC_A56C"
      },
      "source": [
        "## TASK-3\n",
        "## Plot a Confusion Matrix for the results of the Car evaluation dataset"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Confusion Matrix for the Car evaluation dataset\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, predictions)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"viridis\" ,fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "UaWo4jC7sFRM",
        "outputId": "a7bccd23-7130-4748-a7fc-8401c43a502a"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 23.52222222222222, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8TRknp0XTqJ"
      },
      "source": [
        "## TASK-4\n",
        "## Print a Classification Report for the results of the Car evaluation dataset"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Classification Report for the results of the Car evaluation dataset\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64mELMTxsM0U",
        "outputId": "427be5a0-085d-4ce7-c633-c6f5c62a07e9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.78      0.77        72\n",
            "           1       0.75      0.40      0.52        15\n",
            "           2       0.94      0.99      0.96       242\n",
            "           3       1.00      0.59      0.74        17\n",
            "\n",
            "    accuracy                           0.90       346\n",
            "   macro avg       0.86      0.69      0.75       346\n",
            "weighted avg       0.90      0.90      0.89       346\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xr9dI1Kqsprz"
      },
      "source": [
        "### Questions to Think About and Answer\n",
        "1. In the section on Decision boundaries, you must have seen that we ran the KNN algorithm twice: first with the _weights_ set to 'uniform' and then set to 'distance'. Find out the difference between these two.  \n",
        "2. What do you think could be the drawbacks of using KNN ?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Answer for question1**\n",
        "\n",
        "Here, in the first case when weights = \"uniform\" means all the datapoints are distinguished with the help of just weights\n",
        "\n",
        "In the second case when weights = \"distance\" means the data points are dependent on weighted knn algorithm as it suggests that weigth is inverse of the distance and also depends on the k value to predict whether k is too low or k is too high.\n",
        "\n",
        "**Answer for question2**\n",
        "\n",
        "1.It needs feature scaling either standardization or normalization before applying KNN algorithm to any dayaset, if it not happens then it may leads to wrong predictions.\n",
        "\n",
        "2.KNN is also known as lazy learning.It is called lazy not because of its apparent simplicity, but because it doesn't learn a discriminative function from the training data but memorizes the training dataset instead.\n",
        "\n",
        "3.In large datasets, the cost of calculating the distance between the new point and each existing points is huge which degrades the performance of the algorithm.\n",
        "\n",
        "4.KNN is sensitive to noise in the dataset. We need to manually impute missing values and remove outliers."
      ],
      "metadata": {
        "id": "hogBzL-MsSur"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRlrn4EctZrC"
      },
      "source": [
        "### Useful Resources for further reading\n",
        "1. Interactive KNN visualization, with class boundaries: http://vision.stanford.edu/teaching/cs231n-demos/knn/  \n"
      ]
    }
  ]
}