Proj_13_Costumer_Segmentation.ipynb

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        "Import dependencies"
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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans\n"
      ]
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      "source": [
        "data Collection and analysis"
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      "source": [
        "# loading the dataset from csv to Pandas dataframe\n",
        "mall_dataset = pd.read_csv('Mall_Customers.csv')"
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        "id": "HvfnqlFIIhVN"
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      "execution_count": 2,
      "outputs": []
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      "source": [
        "# print 1st five rows\n",
        "mall_dataset.head()"
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            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
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              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "195         196  Female   35                 120                      79\n",
              "196         197  Female   45                 126                      28\n",
              "197         198    Male   32                 126                      74\n",
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              "        const element = document.querySelector('#df-696daec6-d3b2-47af-a114-8cb7c1f2b259');\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-c6a52c97-8d17-4206-b881-0b731c50af9e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"mall_dataset\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 196,\n        \"max\": 200,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          197,\n          200,\n          198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 30,\n        \"max\": 45,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          45,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 120,\n        \"max\": 137,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          120,\n          126\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 18,\n        \"max\": 83,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#finding the number of rows and columns\n",
        "mall_dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T31ex6h_JJuS",
        "outputId": "8f5b1eef-a529-434f-e049-fdb86be19d26"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting some information about the data set\n",
        "mall_dataset.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5zRyFqLJQi3",
        "outputId": "5963d547-8171-429c-b108-e22e227e1747"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# chercking the any null value dataset have?\n",
        "mall_dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "sHjTosXZJWJn",
        "outputId": "847ef6c3-595b-41d8-f2bb-10575c20dcf4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID                0\n",
              "Gender                    0\n",
              "Age                       0\n",
              "Annual Income (k$)        0\n",
              "Spending Score (1-100)    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "the dataset not contain any null values because its small dataset."
      ],
      "metadata": {
        "id": "WX0jpriuJgCS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "chossing the annul income column and spending score column"
      ],
      "metadata": {
        "id": "IUzucSQwWZA1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = mall_dataset.iloc[:,[3,4]].values #iloc = licate the column in tha dataset"
      ],
      "metadata": {
        "id": "0RJ2OvbVWV0H"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "IVwRYPE5XM1t",
        "outputId": "7306a9a6-80c4-4b45-f6f8-cd7d46236748"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 15  39]\n",
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            " [137  83]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "choosing the number of clusters"
      ],
      "metadata": {
        "id": "7H5Z8ic6Xhrt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "WCSS - Within cluster sum of squred"
      ],
      "metadata": {
        "id": "mtppbhy9YBoM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# finding the wcss value for different number of cluster\n",
        "wcss = []\n",
        "for i in range(1,11):\n",
        "  kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
        "  kmeans.fit(X)\n",
        "  wcss.append(kmeans.inertia_)"
      ],
      "metadata": {
        "id": "i4hZzZQEXhJb"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the elbow graph\n",
        "sns.set()\n",
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The Elbow Point Graph')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "rJonMEloaAqb",
        "outputId": "b1e8273e-9d88-4a6b-bea6-54adfa0c9036"
      },
      "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",
      "source": [
        "Optimum Number of cluster = 5"
      ],
      "metadata": {
        "id": "cW0WJAJJajd6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "training the k-means clustering model"
      ],
      "metadata": {
        "id": "HTZCmoknaqRJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans = KMeans(n_clusters=5, init='k-means++', random_state=0)\n",
        "\n",
        "# return a label fro each data point based on thier cluster\n",
        "Y = kmeans.fit_predict(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7FBQenxaSsc",
        "outputId": "b0ab966c-425c-4a99-eb76-e48cd9c5d873"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3 4 3\n",
            " 4 3 4 3 4 3 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 1 2 1 0 1 2 1 2 1 0 1 2 1 2 1 2 1 2 1 0 1 2 1 2 1\n",
            " 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2\n",
            " 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5 clusters - 0,1,2,,3,4"
      ],
      "metadata": {
        "id": "NDEGY350dgZz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visulaize all clusters"
      ],
      "metadata": {
        "id": "SwzW6gpRcNao"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotung the all the clusterts and their Centroids\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "plt.scatter(X[Y==0,0], X[Y==0,1],s=50, c='green', label = 'Cluster1')\n",
        "plt.scatter(X[Y==1,0], X[Y==1,1],s=50, c='yellow', label = 'Cluster2')\n",
        "plt.scatter(X[Y==2,0], X[Y==2,1],s=50, c='red', label = 'Cluster3')\n",
        "plt.scatter(X[Y==3,0], X[Y==3,1],s=50, c='orange', label = 'Cluster4')\n",
        "plt.scatter(X[Y==4,0], X[Y==4,1],s=50, c='blue', label = 'Cluster5')\n",
        "\n",
        "# plot the centroid\n",
        "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=100, c='black', label = 'Centroids')\n",
        "\n",
        "plt.title('Cluster of costumer')\n",
        "plt.xlabel('Annual Income')\n",
        "plt.ylabel('Spending Score')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "id": "w5jYzPWZbcwZ",
        "outputId": "90fb2ef6-b66e-41db-e9c0-bf168551ed1e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9bdhW2g3dp5o"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}