Multiple_Variable_Linear_Regression.ipynb

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        "# 1.\tLoad the basic libraries and packages\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
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      "source": [
        "# Importing the dataset\n",
        "\n",
        "dataset = pd.read_csv(\"https://raw.githubusercontent.com/nishithkotak/machine-learning/master/ex1data2.txt\" , names = ['Area' , \"Rooms\" , \"Prices\"])\n",
        "dataset.head()"
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          "data": {
            "text/plain": [
              "   Area  Rooms  Prices\n",
              "0  2104      3  399900\n",
              "1  1600      3  329900\n",
              "2  2400      3  369000\n",
              "3  1416      2  232000\n",
              "4  3000      4  539900"
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              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 47,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 794,\n        \"min\": 852,\n        \"max\": 4478,\n        \"num_unique_values\": 47,\n        \"samples\": [\n          2526,\n          2162,\n          1458\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 125039,\n        \"min\": 169900,\n        \"max\": 699900,\n        \"num_unique_values\": 40,\n        \"samples\": [\n          252900,\n          199900,\n          449900\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "source": [
        "# 3.\tAnalyse the dataset\n",
        "\n",
        "dataset.describe()"
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            "text/plain": [
              "              Area      Rooms         Prices\n",
              "count    47.000000  47.000000      47.000000\n",
              "mean   2000.680851   3.170213  340412.659574\n",
              "std     794.702354   0.760982  125039.899586\n",
              "min     852.000000   1.000000  169900.000000\n",
              "25%    1432.000000   3.000000  249900.000000\n",
              "50%    1888.000000   3.000000  299900.000000\n",
              "75%    2269.000000   4.000000  384450.000000\n",
              "max    4478.000000   5.000000  699900.000000"
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              "        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-e3b36e9f-8576-462d-9942-93b28cb3b2f4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e3b36e9f-8576-462d-9942-93b28cb3b2f4')\"\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-e3b36e9f-8576-462d-9942-93b28cb3b2f4 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\": \"dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Area\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1336.6505706190044,\n        \"min\": 47.0,\n        \"max\": 4478.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2000.6808510638298,\n          1888.0,\n          47.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.673709745066358,\n        \"min\": 0.7609818867800998,\n        \"max\": 47.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          47.0,\n          3.1702127659574466,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prices\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 209276.54288395809,\n        \"min\": 47.0,\n        \"max\": 699900.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          340412.6595744681,\n          299900.0,\n          47.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Feature_Normalization(X):\n",
        "  X = (X - np.mean(X , axis = 0)) / np.std(X , axis = 0)\n",
        "  return X , np.mean(X , axis = 0) ,  np.std(X , axis = 0)"
      ],
      "metadata": {
        "id": "vh_Fgv5eGbDD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_norm = dataset.values\n",
        "m = len(dataset_norm[:,0])"
      ],
      "metadata": {
        "id": "R6OPBcK-G4NN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_in = dataset_norm[:,0:2].reshape(m,2)\n",
        "X2 , mean_x2 , std_x2 = Feature_Normalization(X_in)"
      ],
      "metadata": {
        "id": "sU7G8o8bG89i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y2 = dataset_norm[:,-1].reshape(m,1)"
      ],
      "metadata": {
        "id": "u-fNrEvGHkVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "theta_Array = np.zeros((3,1))\n",
        "theta_Array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "agfXcRRhHsNf",
        "outputId": "f91211c6-39b9-45b1-c1b7-73eab554b83b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.],\n",
              "       [0.],\n",
              "       [0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Hypothesis(theta_array , x1 , x2) :\n",
        "  return theta_array[0] + theta_array[1]*x1 + theta_array[2]*x2"
      ],
      "metadata": {
        "id": "-WeZb4ZzH584"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def  Cost_Function(theta_array,x1,x2,y , m):\n",
        "  total_cost = 0\n",
        "  for i in range(m):\n",
        "    total_cost += (Hypothesis(theta_array,x1[i] , x2[i]) - y[i])**2\n",
        "  return total_cost/(2*m)"
      ],
      "metadata": {
        "id": "mxdIrHDEIRRT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Gradient_Descent(theta_array , x1, x2, y , m ,alpha) :\n",
        "  summation_0 = 0\n",
        "  summation_1 = 0\n",
        "  summation_2 = 0\n",
        "\n",
        "  for i in range(m):\n",
        "    summation_0 += (Hypothesis(theta_array,x1[i] , x2[i]) - y[i])\n",
        "    summation_1 += ((Hypothesis(theta_array,x1[i] , x2[i]) - y[i])*x1[i])\n",
        "    summation_2 += ((Hypothesis(theta_array,x1[i] , x2[i]) - y[i])*x2[i])\n",
        "  new_theta0 = theta_array[0] - (alpha/m)*summation_0\n",
        "  new_theta1 = theta_array[1] - (alpha/m)*summation_1\n",
        "  new_theta2 = theta_array[2] - (alpha/m)*summation_2\n",
        "\n",
        "  new_theta = [new_theta0 , new_theta1 , new_theta2]\n",
        "\n",
        "  return new_theta"
      ],
      "metadata": {
        "id": "ScfO5WvdI902"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Training(x1, x2, y, alpha, epochs):\n",
        "    theta_0 = 0\n",
        "    theta_1 = 0\n",
        "    theta_2 = 0\n",
        "    theta_array = [theta_0, theta_1  ,theta_2]\n",
        "    m = len(x1)\n",
        "    cost_values = []\n",
        "\n",
        "    for i in range(epochs):\n",
        "        theta_array = Gradient_Descent(theta_array, x1  ,x2, y, m, alpha)\n",
        "        loss = Cost_Function(theta_array, x1  ,x2, y, m)\n",
        "        cost_values.append(loss)\n",
        "        y_new = theta_array[0] + theta_array[1]*x1 + theta_array[2]*x2\n",
        "\n",
        "\n",
        "\n",
        "    x = np.arange(0, epochs)\n",
        "    plt.plot(x, cost_values)\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Cost')\n",
        "    plt.show()\n",
        "\n",
        "    return theta_array"
      ],
      "metadata": {
        "id": "nBxHkTRRKn_M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = 0.01\n",
        "epochs = 500\n",
        "area = dataset.iloc[:,0].values\n",
        "room = dataset.iloc[:,1].values\n",
        "price = dataset.iloc[:,2].values\n",
        "theta_array = Training(X2[:,0:1]  , X2[: , 1:2] ,Y2 , alpha , epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "EK-sY1qBLUzp",
        "outputId": "9f9f548e-ce5e-4fe3-ed1a-8afb2c0d834e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(theta_array)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1FLSAg1SQHK",
        "outputId": "e00e0943-b227-4bc0-dae9-a60af2575b5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([338175.98396731]), array([103032.12432974]), array([-202.3252331])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Hypothesis(theta_array , 4 , 2100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9LHZ43ScSW8s",
        "outputId": "5282a4ba-5c79-4eac-f9f5-4c5b0d55f6c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([325421.49176854])"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    }
  ]
}