Project_ML_Jeremy,_Elroy,_Joshua,_Michael.ipynb

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      "cell_type": "markdown",
      "source": [
        "Jeremy Ardhito Sulle – 2440031250\n"
      ],
      "metadata": {
        "id": "xdBaK_VSK91q"
      },
      "id": "xdBaK_VSK91q"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mike D.S. – 2440012182\n"
      ],
      "metadata": {
        "id": "sPjrhXgdLRQY"
      },
      "id": "sPjrhXgdLRQY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Vincentius Jonathan - 2440006822"
      ],
      "metadata": {
        "id": "wrPsjRTeLTb1"
      },
      "id": "wrPsjRTeLTb1"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Joshua Immanuel Tjandra – 2440032953"
      ],
      "metadata": {
        "id": "H_jW-vOHLSif"
      },
      "id": "H_jW-vOHLSif"
    },
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      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
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    },
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      "execution_count": null,
      "id": "fba6ca63",
      "metadata": {
        "id": "fba6ca63"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "154ecbc0",
      "metadata": {
        "id": "154ecbc0"
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      "outputs": [],
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/mike-ds30/ML-Project/main/housing.csv')"
      ]
    },
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      "source": [
        "df.head()"
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            "text/plain": [
              "  Location  Bedrooms  Bathrooms  Land (SquareMeters)  HouseType         Price\n",
              "0    Pusat         3        2.0                  261  Apartment  9.500000e+09\n",
              "1  Selatan         5        3.0                  200      House  1.080000e+10\n",
              "2    Pusat         2        1.0                   61  Apartment  2.100000e+09\n",
              "3    Timur         5        4.0                  175      House  2.250000e+09\n",
              "4    Timur         5        5.0                  400      House  4.000000e+09"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1906 entries, 0 to 1905\n",
            "Data columns (total 6 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Location             1906 non-null   object \n",
            " 1   Bedrooms             1906 non-null   int64  \n",
            " 2   Bathrooms            1906 non-null   float64\n",
            " 3   Land (SquareMeters)  1906 non-null   int64  \n",
            " 4   HouseType            1906 non-null   object \n",
            " 5   Price                1906 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(2)\n",
            "memory usage: 89.5+ KB\n"
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      "source": [
        "df.info()"
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          "data": {
            "text/plain": [
              "Location               0\n",
              "Bedrooms               0\n",
              "Bathrooms              0\n",
              "Land (SquareMeters)    0\n",
              "HouseType              0\n",
              "Price                  0\n",
              "dtype: int64"
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      "source": [
        "df.isnull().sum()"
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    {
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      "execution_count": null,
      "id": "759b633b",
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      "outputs": [],
      "source": [
        "Location = pd.get_dummies(df['Location'],drop_first=True)\n",
        "HouseType = pd.get_dummies(df['HouseType'],drop_first=True)\n",
        "\n",
        "df.drop(['Location','HouseType'],axis=1,inplace=True)\n",
        "\n",
        "df = pd.concat([df,Location, HouseType],axis=1)"
      ]
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          "data": {
            "text/plain": [
              "   Bedrooms  Bathrooms  Land (SquareMeters)         Price  Pusat  Selatan  \\\n",
              "0         3        2.0                  261  9.500000e+09      1        0   \n",
              "1         5        3.0                  200  1.080000e+10      0        1   \n",
              "2         2        1.0                   61  2.100000e+09      1        0   \n",
              "3         5        4.0                  175  2.250000e+09      0        0   \n",
              "4         5        5.0                  400  4.000000e+09      0        0   \n",
              "\n",
              "   Timur  Utara  Condo  House  \n",
              "0      0      0      0      0  \n",
              "1      0      0      0      1  \n",
              "2      0      0      0      0  \n",
              "3      1      0      0      1  \n",
              "4      1      0      0      1  "
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      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9962383d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "9962383d",
        "outputId": "848eba48-f3ce-4a05-f4be-cfd713f3bc24"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f21201fc390>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(np.absolute(df.corr()),annot=True, cmap='BrBG')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e3fb8d54",
      "metadata": {
        "id": "e3fb8d54"
      },
      "outputs": [],
      "source": [
        "df = df.drop('Bathrooms',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ec8fa0f6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ec8fa0f6",
        "outputId": "a5f7b8a7-a9fe-40e2-ef06-4d9c8bec6ce4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f211fd19cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.boxplot(x='Price', data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1ddb5e25",
      "metadata": {
        "id": "1ddb5e25"
      },
      "outputs": [],
      "source": [
        "Q1 = df['Price'].quantile(0.25)\n",
        "Q3 = df['Price'].quantile(0.75)\n",
        "iqr = Q3 - Q1\n",
        "\n",
        "upper = np.where(df['Price'] >= Q3 + 1.5 * iqr)\n",
        "lower = np.where(df['Price'] <= Q1 - 1.5 * iqr)\n",
        "\n",
        "df.drop(upper[0], inplace=True)\n",
        "df.drop(lower[0], inplace=True)\n",
        "\n",
        "df.drop(df.loc[df['Land (SquareMeters)'] == 0].index, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9bac3588",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "9bac3588",
        "outputId": "5cac9782-cac4-41c4-acba-30430ce3e727"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f211b6328d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.boxplot(x='Price', data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "02d08654",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "02d08654",
        "outputId": "68ad76c6-207b-4a5d-eb52-d3d33a4611d0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f211b5be410>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.heatmap(np.absolute(df.corr()),annot=True, cmap='BrBG')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelling with KNN"
      ],
      "metadata": {
        "id": "M990ypmJwJY4"
      },
      "id": "M990ypmJwJY4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e830110e",
      "metadata": {
        "id": "e830110e"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import numpy as np\n",
        "sc = StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5fc2d2f2",
      "metadata": {
        "id": "5fc2d2f2"
      },
      "outputs": [],
      "source": [
        "#X = pd.DataFrame(sc.fit_transform(df.drop(['Price','Pusat','Selatan','Timur','Utara', 'Condo','House'],axis=1))).values\n",
        "#X = df.drop(['Price','Pusat','Selatan','Timur','Utara', 'Condo','House'],axis=1).values\n",
        "\n",
        "X =pd.DataFrame(sc.fit_transform( df.drop(['Price'],axis=1))).values\n",
        "y = df['Price'].values\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state = 2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def euclidean(x1, x2):\n",
        "  return np.sqrt(np.sum((x1 - x2) ** 2))"
      ],
      "metadata": {
        "id": "nkpHjON6tQwf"
      },
      "id": "nkpHjON6tQwf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class KNNRegressionEuclidian:\n",
        "  def __init__(self, k):\n",
        "    self.k = k\n",
        "  \n",
        "  def fit(self, X_train, y_train):\n",
        "    self.X_train = X_train\n",
        "    self.y_train = y_train\n",
        "\n",
        "  def predict(self, X_test):\n",
        "    y_pred = [self._predict(x_test) for x_test in X_test]\n",
        "    return np.array(y_pred)\n",
        "\n",
        "  def _predict(self, x_test):\n",
        "    dist = [euclidean(x_test, x_train) for x_train in self.X_train]\n",
        "    k_nearest = np.argsort(dist)[:self.k]\n",
        "    k_nearest_values = [self.y_train[i] for i in k_nearest]\n",
        "    average = np.average(k_nearest_values)\n",
        "    return average"
      ],
      "metadata": {
        "id": "TVWh2Cq8tREv"
      },
      "id": "TVWh2Cq8tREv",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "e_ElSHtktzE-"
      },
      "id": "e_ElSHtktzE-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_values = []\n",
        "k_num = []\n",
        "for k in range(1, 51):\n",
        "  model = KNNRegressionEuclidian(k)\n",
        "  model.fit(X_train, y_train)\n",
        "  y_pred = model.predict(X_test)\n",
        "  k_num.append(k)\n",
        "  rmse_values.append(np.sqrt(mean_squared_error(y_test, y_pred)))\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.plot(k_num, rmse_values, \"o-\")\n",
        "plt.title(\"Graph of RMSE\")\n",
        "plt.xticks(np.arange(1, 51, 1.0))\n",
        "plt.xlabel(\"K\")\n",
        "plt.ylabel(\"RMSE\")\n",
        "plt.legend()\n",
        "plt.show"
      ],
      "metadata": {
        "id": "mx2SVKJatWBW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "9094e67a-732e-4ab1-9361-22d63a91c203"
      },
      "id": "mx2SVKJatWBW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "676b8b3f",
      "metadata": {
        "id": "676b8b3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "8dc31491-6d6e-468f-fec4-e3c7fe2bd74b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Predicted  Ground Truth\n",
              "0  6.450000e+09  2.500000e+09\n",
              "1  5.312500e+09  4.690000e+09\n",
              "2  9.687500e+09  1.000000e+10\n",
              "3  1.193125e+10  1.850000e+10\n",
              "4  2.662500e+09  2.700000e+09\n",
              "5  2.856250e+09  1.900000e+09\n",
              "6  4.837500e+09  5.450000e+09\n",
              "7  3.950000e+09  4.200000e+09\n",
              "8  1.350000e+09  1.450000e+09\n",
              "9  2.956250e+09  4.300000e+09"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6d9e29b2-958c-48ba-92ca-c6978d475bac\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Predicted</th>\n",
              "      <th>Ground Truth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.450000e+09</td>\n",
              "      <td>2.500000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5.312500e+09</td>\n",
              "      <td>4.690000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.687500e+09</td>\n",
              "      <td>1.000000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.193125e+10</td>\n",
              "      <td>1.850000e+10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.662500e+09</td>\n",
              "      <td>2.700000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.856250e+09</td>\n",
              "      <td>1.900000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>4.837500e+09</td>\n",
              "      <td>5.450000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>3.950000e+09</td>\n",
              "      <td>4.200000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.350000e+09</td>\n",
              "      <td>1.450000e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2.956250e+09</td>\n",
              "      <td>4.300000e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d9e29b2-958c-48ba-92ca-c6978d475bac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-6d9e29b2-958c-48ba-92ca-c6978d475bac 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-6d9e29b2-958c-48ba-92ca-c6978d475bac');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "KNN = KNNRegressionEuclidian(8)\n",
        "KNN.fit(X_train, y_train)\n",
        "y_pred = KNN.predict(X_test)\n",
        "res = pd.DataFrame({'Predicted': y_pred, 'Ground Truth': y_test})\n",
        "res.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('MAE = ', mean_absolute_error(y_test, y_pred))\n",
        "print('MSE = ', mean_squared_error(y_test, y_pred))\n",
        "print('RMSE = ', np.sqrt(mean_squared_error(y_test, y_pred)))\n",
        "print('R2 SCORE = ', r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "HjxfswY_vfBz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a41d795-4e10-4f6e-a906-a9c0c9bd0e5c"
      },
      "id": "HjxfswY_vfBz",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE =  1639825819.672131\n",
            "MSE =  8.784029841188524e+18\n",
            "RMSE =  2963786402.7605844\n",
            "R2 SCORE =  0.740353230611412\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "862a32a1",
      "metadata": {
        "id": "862a32a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "608918cf-7481-47c1-f1c3-68c88ced9b75"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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QaARgZjsAdYBvY/z8w8655s655vXq1Ut1uSIiIpKFvv0W2rSBt9+G55+H7t3DrSfdszLrmVl+cDkPaAt8UOq0CUDf4PKpwCznXOlxaCIiIiJVsmoVtGoFS5bAuHFw8slhVwQ7pPn+GgAjzKw6PhSOcs5NMrMbgfnOuQnAI8CTZvYx8B3QM801ioiISJZbudK3lC1bBpMmwQknhF2Rl9Zg5px7F2gW4/rrS1zeCPRIZ10iIiKSOwoL/UD/wkKYMgWOPz7sin6R7hYzERERkdB8/rkPZatXw7Rp0KJF2BVtT8FMREREcsKnn/oxZevWwYwZcNRRYVf0awpmIiIikvU+/NC3lBUVwaxZcOSRYVcUm4KZiIiIZLX33/ehbOtWmD0bfve7sCsqW2jrmImIiIik2uLFvwzunzMn2qEMFMxEREQkSy1a5MeU7bgjvPwyHHJI2BVVTMFMREREss5bb/nuy5139qHsoIPCrig+CmYiIiKSVV57zS8Yu9tuMHcu7L9/2BXFT8FMREREssbcuXDiiVC/vr/cuHHYFSVGwUxERESywsyZ0L497LOP777ce++wK0qcgpmIiIhkvKlToVMnOOAAP/uyQYOwK6ocBTMRERHJaBMnQpcu8Nvf+nXK9twz7IoqT8FMREREMtbzz0O3bnD44b4rc/fdw66oahTMREREJCM9+yycfrrf83L6dD8LM9MpmImIiEjGeeIJOPNMaNHCjy+rUyfsipJDwUxEREQyyiOPwFln+VX9J0+G2rXDrih5FMxEREQkYzzwAPztb9CunR/0v/POYVeUXApmIiIikhHuvhvOPx86d4Zx4yAvL+yKkk/BTERERCLvttvg0kuhe3cYMwZq1gy7otRQMBMREZFIu+kmuPpq6NnTz8TcccewK0odBTMRERGJJOdg8GC4/nro3Rueegp22CHsqlJLwUxEREQixzm45hq4+WY4+2x47DGoXj3sqlIvy3OniIiIZBrn4LLL/GD/886De++FajnSlJQjD1NEREQywbZtcMEFPpRdeincd1/uhDJQMBMREZGI2LoV+vf3a5VddRXccQeYhV1VeimYiYiISOi2bIF+/fyq/oMHw9ChuRfKQGPMREREJGSbN0OfPn4pjJtugkGDwq4oPApmIiIiEpqffoJevWDsWL+I7IABYVcULgUzERERCcWmTdCjh9/z8q674JJLwq4ofApmIiIiknZFRXDKKTBtGtx/v18WQxTMREREJM02bICTT4bZs2H4cL+ArHgKZiIiIpI2338PnTrBq6/CiBF+qyX5hYKZiIiIpMW6ddChA7z1FowcCaefHnZF0aNgJiIiIim3Zg20aweLFsGoUdCtW9gVRZOCmYiIiKTUN99A27awZIlfFqNz57Arii4FMxEREUmZVaugTRv4+GMYPx7atw+7omhTMBMREZGUWLnSh7Jly2DSJH9ZyqdgJiIiIkm3fDm0bu3D2dSpcNxxYVeUGRTMREREJKmWLfOh7Ntv/QKyf/pT2BVlDgUzERERSZpPPvGhbP16mDED/vCHsCvKLApmIiIikhRLl/pxZBs3wqxZ0KxZ2BVlHgUzERERqbIlS3xLmXN+q6WmTcOuKDNVC7sAERERyWzvvgstW0K1ajBnjkJZVSiYiYiISKUtXAitWkHNmvDyy/Db34ZdUWZTMBMREZFKefNN331Zu7YPZQceGHZFmU/BTERERBI2b57fZmn33X0o22+/sCvKDgpmIiIikpA5c/yG5A0awNy50Lhx2BVlDwUzERERiduMGdCxow9jL78MBQVhV5RdFMxEREQkLlOmQKdOcMABfkmM+vXDrij7KJiJiIhIhSZMgK5d4dBDfSjbc8+wK8pOCmYiIiJSrjFjoHt3OOIImDnTD/iX1FAwExERkTKNHAk9e8LRR8P06ZCfH3ZF2U3BTERERGIaMQL+8hf4859h6lTYddewK8p+CmYiIiLyK//5D/Tr5zclnzwZdtkl7Ipyg4KZiIiIbOe++6B/f2jfHiZOhJ12Crui3KFgJiIiIj+780648ELo0gVeeAFq1Qq7otyiYCYiIiIADB0Kl18Op54Ko0f7jcklvRTMREREhBtvhGuvhV694JlnoEaNsCvKTQpmIiIiOcw5GDQIhgyBvn3hySdhhx3Crip36akXERHJUc7BVVfB7bfDOefAgw9CNTXZhEpPv4iISA5yDi691IeyCy5QKIsKtZiJiIjkmG3b4Pzz4aGH4LLL4F//ArOwqxJQi5mIiEhO2boV/vY3H8quuUahLGoUzERERHLEli1w1lnw2GN+sP8//qFQFjXqyhQREckBmzf7fS9HjYJbboGBA8OuSGJRMBMREclyP/0EPXv6lfyHDYMrrwy7IimLgpmIiEgW27jRr+T/4otw991w8cVhVyTlUTATERHJUkVF0LUrvPSSXw7j3HPDrkgqomAmIiKShTZsgM6dYc4cePRR6Ncv7IokHgpmIiIiWeb77+Gkk2DePHjiCT/oXzKDgpmIiEgWWbsWOnSA//7Xb0Z+2mlhVySJUDATERHJEt99B+3awTvvwOjRcMopYVckiVIwExERyQLffANt28KSJTB2LHTqFHZFUhkKZiIiIhnu66+hTRv45BOYOBFOPDHsiqSyFMxEREQy2IoVPpR98YVfq6x167ArkqpQMBMREclQX37pg9hXX8HUqXDssWFXJFWV1k3MzayRmc02syVm9j8zuyTGOS3NbJ2ZvR18XZ/OGkVERDLBsmVw3HGwapVfQFahLDuku8VsC3CFc26hmdUGFpjZdOfcklLnveKc07BFERGRGD7+2LeU/fADzJwJzZuHXZEkS1pbzJxzK51zC4PL3wPvAwXprEFERCSTffABHH+8325p1iyFsmyT1mBWkpk1AZoBb8Y4/Ecze8fMppjZoWktTEREJKLeew9atoQtW2D2bDjiiLArkmQLZfC/me0CPA9c6pxbX+rwQqCxc+4HM+sIjAMOjHEb/YH+APvss0+KKxYREQnXO+/ACSdAjRq+pew3vwm7IkmFtLeYmVkNfCh72jk3tvRx59x659wPweXJQA0z2yPGeQ8755o755rXq1cv5XWLiIiEZcECaNUKatWCl19WKMtm6Z6VacAjwPvOuTvKOKd+cB5mdhS+xm/TV6WIiEh0vPGGX6esTh2YOxcO/FUfkmSTdHdltgB6A4vN7O3guoHAPgDOuQeBU4HzzGwLUAT0dM65NNcpIiISuldf9RuS77WX777UyJ3sl9Zg5px7FbAKzrkXuDc9FYmIiETT7Nl+v8tGjfySGAVawyAnhDYrU0RERGJ76SXo2BGaNIE5cxTKcomCmYiISIRMngwnnwwHH+xDWf36YVck6aRgJiIiEhHjxkHXrnDYYX5MmRYdyD0KZiIiIhEwejT06AFHHgkzZkDdumFXJGFQMBMREQnZ009Dz55wzDF+fFl+ftgVSVgUzEREREL0+OPQu7ff/3LKFNh117ArkjApmImIiITk4YehXz+/1dKkSbDLLmFXJGFTMBMREQnBvffCuefCSSfBhAmw005hVyRRoGAmIiKSZv/6F1x0kZ+BOXas3wNTBBTMRERE0urWW+HKK/0MzFGjYMcdw65IokTBTEREJA2cg7//HQYOhDPPhJEjoUaNsKuSqEn3JuYiIiI5xzm47jrfWnbWWTB8OFSvHnZVEkUKZiIiIinknO+6vOMO6N8fHngAqqm/Ssqgl4aIiEiKbNsGF1/sQ9mFF8KDDyqUSfn08hAREUmBbdvgvPP8shhXXAH33ANmYVclUadgJiIikmRbt8LZZ/sFZAcOhGHDFMokPhpjJiIikkRbtkDfvn7W5d//DoMHK5RJ/BTMREREkmTzZr8UxujR8I9/wLXXhl2RZBoFMxERkSTYtAlOPx3Gj/cr+19+edgVSSZSMBMREamijRuhe3eYPBn+/W8/A1OkMhTMREREquDHH/2elzNmwEMP+bXKRCpLwUxERKSSfvgBOneGl1+GRx/1q/qLVIWCmYiISCWsXw8dO8Lrr8OTT/pB/yJVpWAmIiKSoLVroX17WLAAnn0WevQIuyLJFgpmIiIiCfjuOzjxRHj3XRgzBrp0CbsiySYKZiIiInFavRpOOAGWLoVx43xXpkgyKZiJiIjE4auvoE0b+PRTmDgR2rYNuyLJRgpmIiIiFSgshNatYflyv1ZZq1ZhVyTZSsFMRESkHF984UPZqlUwbRr8+c9hVyTZTMFMRESkDJ995kPZmjUwfTocfXTYFUm2UzATERGJ4aOPfCjbsAFmzoTf/z7siiQXKJiJiIiU8v77fqD/5s0wezYcfnjYFUmuUDATEREp4b33fCgzgzlz4NBDw65Ickm1sAsQERGJirffhpYtYYcd/P6XCmWSbgpmIiIiwPz5fkzZTjv5UHbwwWFXJLlIwUxERHLe66/77ss6dWDuXDjggLArklylYCYiIjntlVf83pd77ulDWZMmYVckuUzBTEREctasWdC+Pey9t+++bNQo7Iok1ymYiYhITpo2DU46Cfbbz8++bNgw7IpEFMxERCQHTZoEJ58Mv/mNX6dsr73CrkjEUzATEZGc8sIL0K0b/O53fkX/PfYIuyKRXyiYiYhIznjuOejRw2+vNGMG1K0bdkUi21MwExGRnPDUU3DGGfCnP8FLL/mlMUSiRsFMRESy3qOPQp8+flX/KVOgdu2wKxKJTcFMRESy2oMPwtlnQ9u2ftD/zjuHXZFI2RTMREQka91zD5x3nl8WY/x4yMsLuyKR8imYiYhIVrr9drjkEjjlFBg7FmrVCrsikYopmImISNa55RYYMABOP93PxNxxx7ArEomPgpmIiGQN52DIEBg0CHr39jMxa9QIuyqR+O0QdgEiIiLJ4Bxcey3885/w17/Cww9D9ephVyWSGAUzERHJeM7BFVfAnXfC//0f3HcfVFOfkGQgvWxFRCSjbdsGF13kQ9nFF8P99yuUSebSS1dERDLWtm1w7rm+hWzAALjrLjALuyqRylMwExGRjLR1qx9LNnw4XHedH1umUCaZTmPMREQk42zZ4rdYeuYZuPFGGDw47IpEkkPBTEREMsrmzdCrFzz/PAwdCldfHXZFIsmjYCYiIhlj0yY47TSYMAHuuAMuuyzsikSSS8FMREQyQlERdO8OU6b4wf7nnx92RSLJp2AmIiKR9+OP0KULzJwJ//kP/O1vYVckkhoKZiIiEmk//ACdOsErr8Bjj0HfvmFXJJI6CmYiIhJZ69dDx47wxht+38tevcKuSCS1FMxERCSS1qyB9u1h4UJ47jk/vkwk2ymYiYhI5Hz7LbRtC++955fFOPnksCsSSQ8FMxERiZRVq+CEE+DDD2H8eOjQIeyKRNJHwUxERCJj5Upo0waWLYNJk3xAE8klCmYiIhIJhYXQurX/d8oUOP74sCsSST8FMxERCd3nn/tQtno1TJsGLVqEXZFIOBTMREQkVJ9+Cq1awbp1MGMGHHVU2BWJhEfBTEREQvPRRz6UFRXBrFlw5JFhVyQSLgUzEREJxfvv++7LrVth9mz43e/CrkgkfNXCLkBERHLP4sW/DO6fM0ehTKSYgpmIiKTVokW++3LHHeHll+GQQ8KuSCQ6FMxERCRt3nrLd1/uvLMPZQcdFHZFItGiYCYiImnx2mt+wdjddoO5c2H//cOuSCR6FMxERCTl5s6FE0+E+vX95caNw65IJJoUzEREJKVmzoT27WGffXz35d57h12RSHSlNZiZWSMzm21mS8zsf2Z2SYxzzMzuMbOPzexdM9OqNiIiGWrqVOjUCQ44wM++bNAg7IqSb9yiQloMncW+17xIi6GzGLeoMOySJIOlex2zLcAVzrmFZlYbWGBm051zS0qc0wE4MPg6Gngg+FdERDLIxIlw6ql+1uX06bDHHmFXlHzjFhVy7djFFG3eCkDh2iKuHbsYgK7NCsIsTTJUWlvMnHMrnXMLg8vfA+8DpV+5XYAnnPcGkG9mWfgZS0Qke40dC926weGH+xX9szGUAQybtvTnUFasaPNWhk1bGlJFkulCG2NmZk2AZsCbpQ4VAF+W+H45vw5vIiISUc8+C6ed5ve8nD7dz8LMVivWFiV0vUhFQglmZrYL8DxwqXNufSVvo8snXeQAACAASURBVL+ZzTez+atXr05ugSIiUilPPAFnngktWvjxZXXqhF1RajXMz0voepGKpD2YmVkNfCh72jk3NsYphUCjEt/vHVy3Hefcw8655s655vXq1UtNsSIiErdHHoGzzvKr+k+eDLVrh11R6g1odzB5Napvd11ejeoMaHdwSBVJpkv3rEwDHgHed87dUcZpE4A+wezMY4B1zrmVaStSREQS9sAD8Le/Qbt2ftD/zjuHXVF6dG1WwK3dmlKQn4cBBfl53NqtqQb+S6Wle1ZmC6A3sNjM3g6uGwjsA+CcexCYDHQEPgZ+BPqluUYREUnA3XfDpZdC584wejTUrBl2RenVtVmBgpgkTVqDmXPuVcAqOMcBF6SnIhERqYrbboOrr4bu3WHkSL8xuYhUnlb+FxGRSrnpJh/Kevb0MzEVykSqTsFMREQS4hwMHgzXXw+9e8NTT8EO6R4YI5KlFMxERCRuzsE118DNN8PZZ8Njj0H16hX/nIjER59xREQkLs7BZZf5wf7nnQf33gvV9PFeJKn0lhIRkQpt2wYXXPDLDMz77lMoE0kFva1ERKRcW7dC//5+rbKrroI77gArd369iFSWgpmIiJRp61bo18+v6j94MAwdqlAmkkoaYyYiIjFt3gx9+vilMG66CQYNCrsikeynYCYiOWXcokKGTVvKirVFNMzPY0C7g7Vqeww//QS9esHYsX4R2QEDwq5IJDcomIlIzhi3qJBrxy6maPNWAArXFnHt2MUACmclbNoEPXr4PS/vugsuuSTsikRyh8aYiUjOGDZt6c+hrFjR5q0Mm7Y0pIqip6gIunTxoez++xXKRNJNLWYikjNWrC1K6Ppcs2EDnHwyzJ4Nw4f7BWRFJL3UYiYiOaNhfl5C1+eS77+Hjh1hzhwYMUKhTCQsCmYikjMGtDuYvBrb7x+UV6M6A9odHFJF0bBuHbRrB/PmwciRfv9LEQmHujJFJGcUD/DXrMxfrFnjQ9miRTBqFHTrVrXb06xXkapRMBORnNK1WYGCQuCbb6BtW1iyxC+L0blz1W5Ps15Fqk5dmSIiOWjVKmjdGj74AMaPr3ooA816FUkGtZiJiOSYlSuhTRtYtgwmTfKXk0GzXkWqTsFMRCRFojjeavly31K2YgVMnQrHHZe8226Yn0dhjBCmWa8i8VNXpohIChSPtypcW4Tjl/FW4xYVhlbTsmU+iH39Nbz0UnJDGWjWq0gyKJiJiKRA1MZbffIJHH+8n4U5Ywb86U/Jv4+uzQq4tVtTCvLzMKAgP49buzUNvZVQJJOoK1NEJAWiNN5q6VI/jmzjRpg1C5o1S919adarSNWoxUxEJAWissvAkiW+pWzzZr/VUipDmYhUnYKZiEgKRGG81bvvQsuWYOa3WmraNG13LSKVpK5MEZEUCHuXgYUL/eKxeXm++/Kgg9JytyJSRQpmIiIpEtZ4q7fe8tss1anjQ9l++6W9BBGpJHVliohkkXnz4IQToG5dePllhTKRTKNgJiKSJebM8S1lDRrA3LnQuHHYFYlIohTMRESywIwZ0LGjD2MvvwwFWrFCJCMpmImIZLgpU6BTJzjgAL8kRv36YVckIpWlwf8iklOiuH9lVUyYAD16wGGH+W2Wdt897IpEpCoUzEQkZxTvX1m8VVLx/pVARoazMWOgVy848kiYNg3y88OuSHJFtn3AiRJ1ZYpIzoja/pVVMXIk9OwJRx0F06crlEn6FH/AKVxbhOOXDzjjFhWGXVpWUIuZiOSMKO1fWRUjRsBf/wrHHguTJsEuu1T9Ns/8z+vM++S7n79vsX9dnj7nj1W/Yck65X3AUatZ1anFTERyRlT2r6yK4cOhXz9o3RomT05NKAOY98l3nPmf16t+45J1suUDTlQpmIlIzojC/pVVcd99cM450L49TJwIO+2UnNstHcoqul5yWzZ8wIkyBTMRyRldmxVwa7emFOTnYUBBfh63dmuaEd0vd94JF14IXbrACy9ArVphVyS5KtM/4ESdxpiJSE4Ja//Kqhg6FK69Fk491Q/6r1Ej7IoklxW/fzQrMzUUzEREIuzGG2HIEL8sxhNPwA4p+F+7xf51Y3Zbtti/bvLvTLJCJn7AyRTqyhQRiSDnYNAgH8r69oUnn0xNKAN4+pw//iqEaVamSDgqfJubWZ9EbtA590TlyxEREefgqqvg9tv9YP8HH4RqKf4YrRAmEg3xfP56vNT3LvjXYlwHoGAmIlJJzsGll8I998AFF/h/Ux3KRCQ64nm71y7x9QdgGTAYOATYI/j3+uD6o1JRpIhILti2Dc47z4exyy6Df/9boUwk11TYYuac21B82cz+BdzvnPtXiVO+A24xs43AHcDxSa9SRCTLbd3quy0fewyuuQb+8Q8wq/jnRCS7JPpZ7CjgvTKOvYdvURMRkQRs2QJnneVD2ZAhCmUiuSzROT5fAv2AaTGOnQ0sr3JFIiI5ZPNm+MtfYNQouOUWGDgw7IpEctO4RYWRWJst0WA2EHjWzN4DJgCrgD2Bk4HfAKcntzwRkez100/Qs6dfyX/YMLjyyrArEslN4xYVcu3YxT9vzl64tohrxy4GSHs4S6gr0zn3PHA0sAToBdwa/LsEODo4LiIiFdi4Ebp186Hs7rsVykTCNGza0p9DWbGizVsZNm1p2mtJeLlC59xC4LQU1CIikhOKiqBrV3jpJb9G2bnnhl2RSG5bsbYooetTqVITsc1sNzM71szOMLPdgutqmZkmdouIlGPDBjjpJJg+HR59VKFMJAoa5ucldH0qJRSkzKy6md2GH+T/MvAksG9w+HlgSHLLExHJHt9/Dx06wMsv+30v+/ULuyIRARjQ7mDyalTf7rq8GtUZ0O7gtNeSaAvXP4BzgAuB/dh+9f/xQOck1SUiklXWroUTT4TXXoORI/1MTBGJhq7NCri1W1MK8vMwoCA/j1u7Nc2IWZl9gGucc4+ZWfVSxz7BhzURESnhu++gXTt45x0YPRpOOSXsikSktK7NCkIJYqUlGszy8QEslh2B0mFNRCSnffMNtG0LS5bA2LHQqVPYFYlIlCXalfke0KWMYx2AhVUrR0Qke3z9NbRsCR98ABMnKpSJSMUSbTG7GXjezPKA0YADjjCzU4Bz8QvNiojkvBUroE0b+OILePFFaN067IpEJBMkFMycc+PN7AzgNuCvwdXDgUKgt3Mu1lZNIlkjXVt2RGVrEKmcL7/0Qeyrr2DqVDj22LArEpFMUZkFZkcBo8zsIGAP4DtgqXPOJbs4kShJ15YdUdoaRBK3bJkPZd9+6xeQ/eMfw65IRDJJouuYXW9mDQGccx86515zzn3gnHNm1sDMrk9NmSLhS9eWHVHaGkQS8/HHcNxxfmmMmTMVykQkcYkO/h8C7F3GsYZogVnJYunasiNKW4NI/D74AI4/Hn78EWbNgubNw65IRDJRosHM8AP+Y9kbWFO1ckSiK11bdkRpaxCJz3vv+dmXW7bAnDlwxBFhVyQimarCYGZmfc1slpnNwoeyB4q/L/H1GvAUfpsmkayUri07orQ1iFTsnXegVSuoVs1vtXTYYWFXJCKZLJ7B/z8C3waXDViHH/Bf0k/AFOD+5JUmEi3FA+9TPVsyXfcjVbdggV88duedffflgQeGXZGIZDpLZDKlmT0G3Oic+yx1JSWuefPmbv78+WGXISI55I03oH172G03H8r23TfsikQkU5jZAudczJGoiS6XcQmwcxl30gD43jn3Q4K3KSJZaNC4xTzz5pdsdY7qZvQ6uhE3d20adllxqWgduVdfhQ4dYK+9fCjbZ58QixWRrJJoMBuO78o8J8axG4A6QM8q1iQiGW7QuMU89cYXP3+/1bmfv69KOEvHwrsVrSM3Zw6cdBI0auSXxChQD7OIJFGiszKPA14s49jk4LiI5Lhn3vwyoevjURyYCtcW4fglMI1bVFjp24ylvHXkpk+Hjh2hSRM/+1KhTESSLdFgVgc/GSCWjcBuVStHRLLB1jLGrpZ1fTzStfBuWevFfbxgFzp3hoMO8qGsfv2k3q2ICJB4MPsIOKmMYx2BT6pWjohkg+pmCV0fj3QtvBtrvbgfP9yL1S8057DD/JiyevWSepciIj9LNJj9G7jQzIaZ2aFmVjf49zbgAuDu5JcoIpmm19GNEro+HulaeLf0OnIbPqjP6vFHcuBvtzBjBtStm9S7ExHZTkLBzDn3H/y2S+cD7wKrg38vAAYFx0Ukx93ctSkt9t8+wbTYv26VBv6na+Hdrs0KuLVbUwry89jwv4Z8M+FIfvu7zfx33o7k5yf1rkREfiXRWZk45242s38DfwR2xy8++7pzbl2yixORzDRuUSELv9j+v4SFX6xj3KLCSs+iTOfCu12bFbD2nQL++iK0agkTJtRkl12SfjdJk47ZqiKSHgktMBtVWmBWJFpaDJ1FYYyxXwX5ecy7pnUIFSXm4Yfh3HP9qv7jxsFOO4VdUdlKL+8BviXx1m5NFc5EIqpKC8yaWUfgVefc+uByuZxzkytRo4hkkXQN1K9IZVqS7r0XLrrIL4vx/PNQq1aaiq2k8marKpiJZJ54ujInAccAbwWXHX7PzFgcUL2MYyKSIxrm58VsMUv2QP3yVLRQbCx33AFXXAFdusBzz0HNmmkrt9KiEoJFJDniGfy/L/B2icv7Bf/G+tovBTWKSIZJ10D98iS67tmtt/pQ1qMHjB6dGaEM0jdbVUTSo8IWM+fc57Eui4iUJZ0D9csSb0uSc3DjjXDDDXDmmfD447BDwtOiwjOg3cExx5ilMwSLSPLEM8Ysoe15nXNflHXMzB4FOgGrnHOHxTjeEhgPfBZcNdY5d2Mi9y8i0dC1WUGoY5zi6U51Dq67zreWnXUWDB8O1TNsMEYUQrCIJE88nwuX4ceOxau8/9YeB+4FnijnnFecc50SuD8RkV+pqCXJObjySj+urH9/eOABqJbokttVlKxlLsIOwSLZICrLzsQTzDqXuLwrcBvwPjAWWAXsCXQHfgMMKO+GnHNzzaxJZQoVEUlEeS1JzsHFF/sZmBdeCPfcA1XYLapSKjM5QURSI0rvx4TWMTOzx4Ei59x5MY49COzsnOtdwW00ASaV05X5PLAcWAFc6Zz7X0V1aR2z3BOVTzaSebZtg/PO82uVXXEFDBuW/lAGmb/Wm0g2Sff7sUrrmJXSDd86FsvzwJgEb6+0hUBj59wPwZpp44ADY51oZv2B/gD77JPQMDjJcFH6ZCOZZetW+Nvf/AD/gQPh5pvDCWWgZS5EoiRK78dER1QUAX8u49ixwMaqFOOcW++c+yG4PBmoYWZ7lHHuw8655s655vXq1avK3UqGSXQZBBGALVugTx8fym64IdxQBlrmQiRKovR+TDSYPQAMMrN7zexEMzsi+Pc+YCDwYFWKMbP6Zv6/SjM7Kqjv26rcpmSfKH2ykcyweTOccQaMHAn/+AcMGRJuKINorPUmIl6U3o8JdWU6524wszXAVcD5/LILwFf48WB3lffzZvYM0BLYw8yWA0OAGsFtPwicCpxnZlvwrXM9XTZs5ilJFYVV5SVzbNoEp58O48fDv/4Fl18edkWelrkQiY4ovR8rtYm5mVUD9gH2woeyL51z25JcW9w0+D+3aNNmidfGjdC9O0yeDP/+t5+BKSIStmQO/gfAObfNzD4HfsIvFhtaKJPcE6VPNhJdP/4IXbvCjBnw0EN+rTIRkahLOJgFsyWHAEfgF5M9ClhoZg8Dc51zTyW3RJFf04KaUp4ffoDOneHll+HRR/2q/iIimSChwf9m1geYAHyAX6qi5M9/BJydvNJERBK3fj20bw9z58KTTyqUiUhmSXRW5nXAMOdcX6B0y9j/gEOSUpWISCWsXQsnnghvvgnPPus3JRcRySSJdmU2BqaXcWwjfssmEZG0++47H8refRfGjIEuXcKuSEQkcYm2mH0JNCvjWHPg46qVIyKSuNWroVUreO89GDdOoUxEMleiwewRYIiZ/QUoXjTKzKwNfm2z/ySzOBGRinz1FbRsCR9+CBMmQMeOYVckIlJ5iXZl/hNoBIwAiheReg0/O/Mh59w9SaxNRKRchYXQujUsX+7XKmvVKuyKRESqJtGV/x1wgZndAbQB9gC+A2Y55z5MQX0iIjF98YUPZatWwbRp8OeydvEVEckgcQczM6sFrANOd86NAz5JWVUiIuX47DMfytasgenT4eijw65IkmncosLILCAdpVokN8QdzJxzG81sFbAlhfWIiJTro498KNuwAWbOhN//PuyKJJlKb7lWuLaIa8cuBkh7IIpSLZI7Eh38/xBwsZnVSEUxIiLl+eADOP54vwfm7NkKZdlo2LSl2+2DC1C0eSvDpi3N6VokdyQ6+D8fOAxYZmYzga+BkrugO+fc1ckqTkRSK55umqh05bz3HrRpA2YwZw4cemjaS5A0WLG2KKHrUylKtUjuSDSYdQc2BZePjXHcAQpmIhkgnm6aqHTlvP02nHAC1KwJs2bBwQen7a4lzRrm51EYI/g0zM+LcXbu1CK5I66uTDPLM7PuwL3AIOAY59y+Mb72S2m1IpI08XTTRKErZ/58P6Zsp538puQKZdltQLuDyatRfbvr8mpUZ0C79P/io1SL5I4KW8zMbD9gBtCkxNXrzOx059xLqSpMRFIrnm6asLtyXn/db0het64fU9akSVruVkJU3BIbhe7zKNUiuSOerszbgG34rssFwL7A/fiJAPumrjQRSaV4umnC7Mp55RW/in/9+r77slGjlN+lRETXZgWRCT9RqkVyQzxdmX8EBjnn5jnnNjrn3gfOBfYxswapLU9EUiWebpqwunJmzfItZXvv7bsvFcpEJFfE02LWAPi01HWfAAbUB1YmuygRSb14umnC6MqZNg26doUDDoAZM2CvvVJ2VyIikRPvrExX8Skikmni6aZJZ1fOpEnQvTsccohf0X+PPdJytyIikRFvMJtmZrFW/J9Z+nrn3J5VL0tEcs0LL8Dpp8Phh/tWs7p1w65IRCT94glmf095FSKS00aNgjPOgD/8AaZOhTp1wq5IRCQcFQYz55yCmYikzFNPQd++0KIFvPgi1K4ddkUiIuFJdK9MEZGkefRR6NMHWraEKVMUykREFMxEJBQPPghnnw1t2/pB/zvvHHZFIiLhS3SvTBHJYunasPyee+CSS+Ckk2DMGKhVK+l3ISKSkRTMRJIoXcEmFcrasHz+598x+4PVSXtMt98OAwbAKafAs8/Cjjsm6xGIiGQ+BTORJCkr2AAZEc7K2rD86Te++Hkhw6o+pltugUGD/LIYTz4JNWpUtWoRkeyiMWYiSVJWsBk2bWlIFSWmrI3JS68uXZnH5BwMGeJD2V/+4mdiKpSJiPyagplIkpQVbMq6PmoS2Zg8kcfkHAwcCDfeCP36weOPww5qqxcRiUnBTCRJygo2iQSeMMXasNzKODfex+QcXHEFDB0K//d/MHw4VK9e8c+JiOQqBTORJIkVbPJqVGdAu4NDqWfcokJaDJ3Fvte8SIuhsxi3qLDc87s2K+DWbk0pyM/DgIL8PM48Zp9KP6Zt2+Cii+DOO+GkXj/wzj6z2H9gfLWIiOQqdSiIJEnxYPgozMqs7ESEWBuWN29cN+HHtG0bnHuubyHr2ud73m80j43rMnNShIhIOplzpYf2Zp7mzZu7+fPnh12GSFqVtzRHi6GzKIwxDqwgP49517ROaV1bt/qFY0eMgOuug1k7z2LFunBqERGJIjNb4JxrHuuYujJFMlBxi1jh2iIcv7RCFXcRhjURYcsW6N3bh7Ibb4Sbb4aVMUJZOmoREclECmYiGaiipTnCmIiweTP06gXPPOMH+w8eXP59ZsqkCBGRdFIwE8lAFbWIpXsiwqZNcOqpfnulO+6Aq6/+5VjUJkWIiESZBv+LZKCG+Xkxx5AVt0KlcyJCURF07w5TpsC998IFF2x/PEqTIkREok6D/0UiJN69NkvPugTfCnVrt6ZpDTw//ghdusDMmfDQQ3DOOWm7axGRjFXe4H+1mIlERCJLXEShFeqHH6BTJ3jlFXjsMejbN213LSKStRTMRCKivAH9sQJXrDXH0mX9eujYEd54w+972atXKGWIiGQdBTORiMiUvTbXrIH27WHhQnjuOT++TEREkkOzMkUiIhOWlfj2W2jTBhYtguefVygTEUk2BTORiIj6shKrVkGrVrBkCYwfDyefHHZFIiLZR12ZIhERhQH9ZVm5Ek44AT77DCZN8pcls8Q741dEwqVgJhIhYQ7oL0thIbRu7f+dMgWOPz7siiRRld3UXhRoJf3UlSkiZfr8c/j9MVv45PMt1O76GgNfn/XzfpySOSrawktiq2hPWpFUUDATkZg+/RT+cMwWVq921DvtTWruvUZ/mDJUpsz4jRoFWgmDgpmI/MpHH8Fxx8GadY49e75BzYZrfz6mP0yZJxNm/EaRAq2EQWPMJK00XiP63n/fjynbuhX27Pk6Nfb8/lfn6A9T+iTjPTOg3cExt/CKyozfqKpoT1qRVFCLmaSNxmtE3+LFvwzunzMHmhy0JeZ5+sOUHsl6z3RtVsCt3ZpSkJ+HAQX5eWnfVzUTRX0JG8lOajGTtEl0yyFJr0WLoG1bqFULZs2Cgw6CAZvU0hKmZL5nojjjN+qivISNZC8FM0kbjdeIrv/+F048EXbd1Yey/ff31+sPU7j0ngmfAq2km4KZpI3Ga0TTa69Bhw6w++4wezY0brz9cf1hCo/eMyK5R2PMJG00XiN65s71LWV77eUvlw5lEi69Z0Ryj1rMJG3ULRYtM2dC587QpIm/3KBB2BVJaXrPiOQec86FXUOVNW/e3M2fPz/sMkQyxtSpcMopcOCBMGMG7Lln2BVJqmXSUjWZVKtIZZjZAudc81jH1GImkmMmToRTT4VDDoHp02GPPcKuSFItk/bKzKRaRVJBY8xEcsjYsdCtGxx+uJ99qVCWGzJpa6FMqlUkFRTMRHLEs8/CaafBUUf5lrLddgu7IkmXTFp2I5NqFUkFBTORHPDEE3DmmdCihR9fVqdO2BVJOmXSXpmZVKtIKiiYZZBxiwppMXQW+17zIi2GztJWRhKXRx6Bs86Cli1h8mSoXTvsiiTdMmnZjUyqVSQVNPg/Q2hArFTGAw/A+edD+/Z+fFmeGh1yUiYtu5FJtYqkgpbLyBAths6KuQJ4QX4e865pHUJFEnV33w2XXurXKhs9GmrWDLsiERGB8pfLUFdmhtCAWEnEbbf5UNa9O4wZo1AmIpIpFMwyhAbESrxuugmuvhp69vQzMXfcMeyKREQkXgpmGUIDYqUizsHgwXD99dC7Nzz1FOygUaQiIhlF/21nCA2IlfI4B9dc47swzz4bHnoIqlev+OfSQdvriIjET8Esg3RtVqA/aPIrzsFll/nB/uedB/feC9Ui0hau2cQiIomJyH/fIlIZ27bBBRf4UHbJJXDffdEJZaDtdUREEhWh/8JFJBHbtkH//n6tsquugjvvBLOwq9qeZhOLiCRGwUwkA23dCv36+VX9Bw+GoUOjF8pAs4lFRBKlYCaSYTZvhr/8xe9/edNNcOONqQ1lVdkKTLOJRUQSo8H/Ihnkp5+gVy+/vdJtt8GAAam9v6oO3tdsYhGRxCiYiWSITZugRw+YOBHuussP9k+18gbvxxuuNJtYRCR+CmYSk9aeil86nquiIujWDaZOhfvv98tipIMG74uIpFdax5iZ2aNmtsrM3ivjuJnZPWb2sZm9a2ZHprM+8Yq7rwrXFuH4pfsqkbFFuSIdz9WGDX4j8mnTYPjw9IUy0OB9EZF0S/fg/8eB9uUc7wAcGHz1Bx5IQ01Sitaeil+qn6vvv4eOHWH2bBgxwq/qn04avC8ikl5p7cp0zs01syblnNIFeMI554A3zCzfzBo451ampUAB1H2ViFQ+V+vWQYcO8NZb8PTTflPydNPgfRGR9IraGLMC4MsS3y8PrlMwS6HSY6Tq5NVgbdHmX52n7qtfa5ifR2GMEFbV52rNGmjXDhYtgueeg+7dq3RzVRKlwfsa+yhRpNelJFPGrmNmZv3NbL6ZzV+9enXY5WSsWGOkNvy0hRrVtl8YS91XsaWiq++bb6B1a3jnHb8sRpihLEo09lGiSK9LSbaoBbNCoFGJ7/cOrvsV59zDzrnmzrnm9erVS0tx2SjWGKnNWx271NqBgvw8DCjIz+PWbk31CTCGrs0KuLVb06Q9V6tW+VD2wQcwfrwf9C+exj5KFOl1KckWta7MCcCFZvYscDSwTuPLUqussVBrf9zMoutPTHM1mSlZXX0rV0KbNrBsGVx75zfc9Pa7nDdHXSPFNPZRokivS0m2tAYzM3sGaAnsYWbLgSFADQDn3IPAZKAj8DHwI9AvnfXlolSNkZLELF/uW8pWrIDr7lnNU58vqPRq+9lKr1WJIr0uJdnS2pXpnOvlnGvgnKvhnNvbOfeIc+7BIJThvAucc/s755o65+ans75cpOUQwrdsGRx3HHz9Nbz0Ekz+ZnGkukaqsldmMum1KlGk16UkW9S6MiXNtBxCuD75xLeUrV8PM2bAH/4AKyYkp2skGTPFqrpXZjLptSpRpNelJJv5JcMyW/Pmzd38+WpcSzdNEa+apUv9mLKNG2H6dGjWzF/fYuismF0jBfl5zLumdVy3XTpQgf8Un+jEhGTUIiIi2zOzBc655rGORW1WpmQITRGvmiVL4Pjj4aef/Kr+xaEMktM1kqyZYhrYLCKSXgpmUimaIl55774LLVuCGcyZA02bbn88GUtwJCtQaa9MEZH00hgzqRS1pFTOwoXQti3k5cGsWXDQQbHPq+oSHMmaKTag3cExu0Q1sFlEJDXUYiaVopaUxL31lh9TVrs2zJ1bdihLhmTNFEv2ArqSO6Iym1ck06jFTCpFLSmJmTfPb0her55vKWvcOLX3l8yZYlHaK1MyQ5Rm84pkGgUzqRRNEY/fnDnQqRMUkirPdgAAIABJREFUFPhQVpCmp0iBSsJS3hhUvSZFyqdgliNSsbSF/vBXbMYMOPlk2Hdff7lBg7ArEkk9jUEVqTwFsxygboVwTJkCp5zix5LNmAF77hl2RRI12boWoLYpEqk8Df7PAVraIv0mTICuXeHQQ/06ZQplUlo2rwWobYpEKk/BLAeoWyG9xoyB7t3hiCNg5kzYffewK5IoyuYPTJrNK1J56srMAdnYrRDVLqCRI6FPHzj6aN+VueuuYVckUZXtH5g0BlWkctRilgOyrVshql1AI0ZA797w5z/DtGkKZVI+rQUoIrEomOWAbOtWiGIX0PDh0K8ftG4NkyfDLruEVopkiGz7wCQiyaGuzByRTd0KUesCuu8+uPBCv4Ds2LFQq1b550epGzZKteQarQUoIrEomEnGidKYuTvvhMsv92uVjRoFNWuWf36Uli6JUi25Kps+MIlIcqgrUzJOVLqA/vlPH8q6d4fRoysOZRCtbtgo1SIiIp5azHJItnRbRaEL6Kab4PrroVcveOIJ2CHOd1KUumGjVIuIiHgKZjki27qtwuoCcg4GD4ZbboG+feGRR6B69Yp/rliUumGjVIuIiHjqyswR6raqOufgqqt8KDvnHP6/vXuPrrK+8z3+/uYCJCCEOxhBqVooNAUEAYczVbEV6o0MXhDrTJ3V0fYcHUdrOYMtS9G6BqaM1XamU8flOLbVIiI0olDRY2jrMIKEchMLiiiXiBAIAYEAufzOH8/eYWdn3+9JPq+1ssjez7P388vjXvGT3+X749ln4wtlkDvDsLnWFhER8ajHrJPQsFVynIP77oOf/Qzuvtv7Ny+BP2tyYRg2F9siIiIeBbNOQsNWiWtu9sLYU0/B/ffD44+DWbZblRpaFSgikls0lNlJaNgqMU1N3rDlU0/BnDnJh7Jc3bVARERyg3rMOol0DFtlc5VnvNdOpK2NjV41/+efh4cf9r6S7SmLNNdPPVciIqJg1omkctgqm6s84712Im1taIDbb/eKxj72GPzwh6lpu+b6iYhIJBrKlIRkc5VnvNeO9/wzZ2DmTC+ULVyYulAG2rhaREQiUzCThITr4amuq2fYnBVMXlCZtnlTka4d6prx9FKdOgUzZsBvfws//Sl8//vJtTWY5vqJiEgkCmaSkEg9POme1B7p2qGuGWsvVX09TJ8OK1Z4k/3vvTf5tgYrH1vK/BlllJYUYUBpSRHzZ5RpfpmIiAAKZpKgUD0/wdI1tBnp2qGuGUsv1YkTcO218OabXuHY73wn5c1uUT62lDVzpvDxgmtZM2eKQpmIiLTQ5H9JSPAqTxfmvHRMavdf+77Fm2K6ZrQVqZ9/7oWyNWu8fS9vvz3lTRYREYmJgpkkLHCV5+QFlWkrYBuu1MXCVTtivma4FalHj8K0abB+PfzmN96kfxERkWzRUKakRLomtUcqyJrsNWtr4Wtfgw0bYMkShTIREck+9ZhJSqRr38VIpS7WzJmS8DUPHYKvfx3efx+WLYPrrkuqmSIiIimhYCYpk459F6OVukjkmgcOwFVXwUcfwfLlMHVq0s0UERFJCQUzyWmp3nz900+9ULZnj1cWY8qU5NqXrm2psrndlYiIZI/mmElOS+Xctb174fLLYd8+eP311ISydGxIro3ORUQ6L/WYScxS1YsTz/ukau7aJ594QezwYXjjDbjssrib3Ua6NiTviBudqwdQRCQ2CmYSk1RtWp7I+yQ7d23nTi+UHT8Ob70F48cn/FatpGtD8o620Xk2N7wXEWlvNJQpMUnVpuWZ3vx8+3Zv+PLkSaisTF0og/RtSN7RNjrP5ob3IiLtjYKZxCRVvTiZ7A3atg2uuAIaG+H3v4cxY1L7/umq3dbRNjrvaD2AIiLppKFMiUmqVkemepWlX/AcppuGjeKf7hlIYaHXUzZiRFJvH1K6areVjy2lancti9btpck58s24cVzqS5FkSrr+m4uIdETqMZOYhNs4/MTpxrhWC6ajN6hiYzWzX97csopx1/ZCvn9Hb1x+I3/4Q3pCmV86NiSv2FjN0g3VNDlvB9Im51i6obrdrsrsaD2AIiLppB4ziYk/cDzy6jaOnGxoeb6uviGuidzp6GV65NVtNDR5IeZ0dQkHlkwgr2sDA2Zt4OKL/zLh9w0lE6sLO9qqzHT1LIqIdEQKZhIz/8bhgcEM4g8Nqd4hwN+eU/t6c3DJBPKLTzNw1lpOdj2VsmtA5lYXdsQ5WenYFUJEpCPSUKbEJVdCQ8XGaiYvqGTYnBUAnNrTh4MvTSC/xykG3vYOBT1TG8ogc6sLO9qqTBERiZ2CmcQlF0JDcGX8+o/7cXDJBAp61jNo1loKzjkNQElRYUqvm6lQqjlZIiKdl4KZxCUXQkNgz1X9R/05uHQ8BX2OM/C2teT38EJZYZ4x74ZRKb1upkJp+dhS5s8oo7SkCANKS4qYP6NMQ4EiIp2A5phJXHJhIre/h+rkBwOpeeUSugw4xoBb3iW/qAGDtLVp9tThreaYQfpCqeZkiYh0TgpmErdsh4ZzS4r4YG0vDr06li4DjzLwlnfJ69ZIaUkRa+YkuTN5BLkQSkVEpGNTMJOsSqT8xMTmMfzP8t50LT3CgJvWk9e1MWPDqdkOpSIi0rEpmEnWJFJ+4rnn4Mm5ffjyuNN0v24rB+obk+65ykRtMhERkVgomOWQzhYQ4i2k+vTT8J3vwNe/DhUVXSkuvjzpNmSqNpmIiEgstCozRwSXgPAHhPa6DU8s4ik/8W//5oWya66B5cuhuDg1bchUbTIREZFYKJjliM4YEGItP/GTn8Df/z1Mnw7LlkG3bqlrQ64UzBUREQEFs5zRGQNCLDXR5s+HBx6Am2+GJUuga9fUtiEXCuaKiIj4aY5Zjji3pIjqECGsvQaEuRVbWbRuL03OkW/GrIlDeKy8rNU5kcpPOAePPgrz5sE3v+lN+i9Iw6c1k7XJREREolEwyxEdKSDMrdjK82v3tDxucq7lcahwFjzJ3jn44Q+93rI77oBnnoH81h1rKaPaZCIikkvMOZftNiRt/PjxrqqqKtvNSFpHWZV54YMraQrxuco346P510R8rXMwezY8/jjcdRf84heQpwF3ERHpQMxsg3NufKhj6jHLIR2leGmoUBbpeT/n4B/+Af71X+Gee+BnPwOzdLRQREQkN6kvQlIuP0yaCvc8QHMzfPe7Xih74AGFMhER6ZwUzATwhlEnL6hk2JwVTF5QmVT9tFkTh8T1fFMTfPvbXgHZH/wAFi5UKBMRkc5JQ5mS8ur3/gn+kVZl+ufTVdee4sQbl3Bo8yDmzYOHHoJXNnWMuXYiIiLx0uR/YfKCypClOkpLilgzZ0rKr+cPgidPNXPo1TGc3HEu/a/8gKcf7w4QcnXq/BllCmciItIhaPK/RJTp4rYLV+3gZL2jZvkl1H84iN5Xvk/xhI9ZuMqr2RbP/pkiIiIdiYKZZLy47b5Dp6n57Tjqdw2g99feo+e43UDkINiRd0AQERHx0+R/iWlrpFQ5eRKOvjKR+l0D6DN1S0soAy8IaoskERHpzNRjJhmrfn/8OFx/PRz7qDeDrttK11F7W44FBsGOsgOCiIhIvBTMBEh/cdtjx+Caa+Cdd+DXvza6j+zDwlU1YYOgVmWKiEhnpFWZKdBRtlKKVbw/b10dTJsGGzbAb34DN9+cwcaKiIjkGK3KTKNU1wBLti3pDojx/ry1tXD11bBlC7z8MkyfntLmiIiIdCia/J+khat2hC3vkEn+wFRdV4/DC0z3L97E3IqtKb1OPD9vTQ1ceSW89x5UVCiUiYiIRKMesyRlugZYOKECkwNeWLuH8ef3SarnLLAnLtzAd/DP+9lncNVVsGsXLF/u9Zql4vqdYahYREQ6L/WYJSlXyjuEC4IOkuq9C+6JC6db4dmPUnU1XHEFfPIJrFyZfCibvWRzq57A2Us2J7WXp4iISK5SMEtSJmuARRIpCCbTexeqJy6U+oZm5lZsZc8euPxy+PRTWLXKG8pMxrzl22hobh0JG5od85ZvS+6NRUREcpCGMpOUqRpggUIN7c2eOpz7F28K2auVTO9dPKHuV6sO8cL9cOQIvPEGTJqU8GVb1NU3xPW8iIhIe5bxYGZm04CfAvnAM865BUHH7wAWAv6xqn9zzj2T0UbGKd01wAKFWxU5f0YZ35w0lBfW7mkVzpLtvQu3XVOwhtpiDrw4iZ4F8NZbMG5cwpcUERHptDI6lGlm+cDPgW8AI4FZZjYyxKmLnXNjfF85HcoyLdKqyMfKy3hi5hhKS4owoLSkiPkzypIKjaGGaoM1HO7OgUWX4RrzWL06eiir2FjN5AWVDJuzgskLKiPOF+tdXBjX8yIiIu1ZpnvMJgA7nXO7AMzsRWA68H6G29FuRVsFmureu1BDtRf0LWLNR7UAnKnpwYEXJ4E5/m7BbkaP/mLE94u3DtrD149i9subaWg62w9YmG88fP2olPx8IiIiuSTTwawU2BvweB8wMcR5N5rZV4EPgPudc3tDnNMphRtaTOcq0FBhb27FVp5bXseBFydi+c3cuWA3v7g7+pBppB6/UMEsG3P4REREsiUXJ/+/Cixyzp02s+8AvwSmBJ9kZncBdwEMHTo0sy3MotlTh+fEJt/l55Xx7xVQ2g8qK+Gii1pff27FVhat20uTc+SbMWviEB4rL0uo7lsm5/CJiIhkU6aDWTUwJODxeZyd5A+Ac+5wwMNngB+HeiPn3NPA0+DtlZnaZmZHLIVU09WDFE8R13fe8fa+7NMHVq+GCy5ofXxuxVaeX7un5XGTcy2Ps9HjJyIi0l5kOpitBy42s2F4gexW4LbAE8xssHNuv+/hDcCfM9vE7Ihn7lWqe5Diufbbb8M118A5vRvpO3MdVz5V1ybILVoXeuR50bq9PH7L6Jzo8RMREclFGV2V6ZxrBO4BVuEFrpecc9vM7FEzu8F32r1mts3MNgP3Andkso3Zks09N2O9dmWl11PWq18DxTPe5hB1LdX4H1y2tWV1ZZML3YHZ5BzlY0uZP6MspStHRUREOoqMzzFzzq0EVgY991DA9w8CD2a6XdmWzT03w9UpC7z2qlVQXg4XXQTdblhLTdPJVucGTuDPNwsZzvLNAM0ZExERCUdbMuWIbO25WbGxGgtzzH/t116DG26AESO8OWWHmo6FPN8f5GZNHBLyeLjnRURExKNgliOytefmwlU7Qm7jZL42zXn8MDeUN0OfOgqv/yP/vbc6aoh8rLyM2ycNbekhyzfj9klDeay8LE0/hYiISMeQi+UyOqVs1esKN1TqgDVvFPEvD5bQZfBRBt7yLgfPNPLgsq3cOK6UpRuqI07gf6y8TEFMREQkTgpmOSTWuVfxlLaIJlz5ii4fX8C//Lg3XUtrGXDTevK6eiGsvqGJ1dtrmD+jTEVfRUREUkzBrJ2JVtoi3tAWqmDt6W1D2bNiJN2GHKb/jVXkdWm9YvPTunpN4BcREUkDBbN2Jlppi3j2oQx83h/m8nZcyGevjeDqq+Ho5Pf47GRTm9eoGKyIiEh6aPJ/OxOprEaitdDKx5ayZs4Uvld6LbsqRnDttfDKKzDn+ospzGu7ZvPKEf0T/wHSpGJjNZMXVDJszgomL6hsqakmIiLSniiYtTORVkQmWgutYmM1F1zzIffeC31GHeRbD1XTrZt3rDnE+Yvf3ZtTwcc/vFtdVx+y4K2IiEh7oWDWzoQqq1GYZ5w80xiy7AVAnlnYkFKxsZo7v3eC3b+7mOIRn9LjG1U89OrWlrlqTc1t37Wh2WVkR4JYZXPXBBERkVTSHLN2JnhOWK+iQk6caeTIyYawr2lyjgeXbaVqdy2rt9dQXVdPvhmNzY5ja75I3Zov0n3UPvpeswXLc9Q3NDFv+Tbq6sO/56d19SldHZqMbO6aICIikkoKZlkQLtDEEnSCzzl5ppGGpnB9ZWfVNzTxwto9Lb1qjc2Ouj8O59jai+hetpe+07ZgAf2nkUIZQK+iwrgXGqRLuJIfWqQgIiLtjYJZhoUrd1G1u7ZV0dZQQSfUa+PhD2XOwZHKL/F51RfoMWY3fa5+Dwu3L1MIhXmGGWGHD5MNZqko+ZGJXRNERERSTXPMMizcfKhF6/ZGnScV6rXxcg6O/L9RfF71Bc4Z93HcoaykqJCFN4+mLszQabLDh4lM5C8fW8r8GWWUlhRhQGlJEfNnlKnOmoiItDvqMcuwcMGlyYUejgw8P5nQY0Czg9rXyzi+ZSg9J3xEyRXb4wplpSVFrJkzBfBCYjqGDyNN5I8UtFTwVkREOgL1mGVYuOCSHyYhBZ4f7rUlRYWURglEsy4dSt3rY7xQdtmHUUNZ8KHgocF0bbquifwiItKZKZhl2JUj+rcJPQZM+kLvqEEnXBiad8MoZk8dTvcurY/53XbpUHYvK+PYllKGfG0Xvb/6QUsoy7O2H4Kiwny+OWloxKHBdA0fRqrTJiIi0tFpKDODKjZWs3RDdZt6Yw74056j3DiulNXba8JOeg8uleE/B2gz+R28wHfr+KF8+GIZL78MCxbA8Ku78uCy/JZzmx0U5hs9uxRwtL4hrrIXqRg+DJ7of+WI/q0WQYAm8ouISOdhLszcpvZk/PjxrqqqKtvNiGrygsqIKykD53Cl4n0H9yhmwPorWb4cfvITuP/+8Ocmeu1kBK8yBS+ERQuoIiIi7ZmZbXDOjQ91TD1mGRStvEV1XT2TF1TGHUhCvW9zQx6b/msUp3bBnf9Yx/33l1CxsTpsG7IxhyvcRP/V22syHhJFRERygYJZGgUP0xmE3TYJvKFHf3CKp2BrnnlDkn7NDXnULB3Pqd396DN1C6vz9jG3YghLN4QvOZGNOVya6C8iItKaglmaxFsMNlRoi6VMRMXG6tah7Ew+B5deyum9feh7zWZ6lFXT0AyL1u0NW5Ij0hyudG67pIr9IiIirWlVZprEUww23yxsT1p1XT3D5qxg8oLKkEVWAwvQNp8u4OBLEzi9tzf9rttEj7Kz54cLZUDY1ZSJFHuNR7pKboiIiLRXCmZpEutwXFFhPo/fMprexYVhz4kUivzXaTpVwIHFEzi9v4R+N2yk+8hPW50Xrk5aaUlR2B6wSMVeU0EV+0VERFrTUGaahBum611cSHGXgjblLsJtcRQo1NDmuSVF7NnfyMHFEzlTcw79y/9E8cUHWr2uMM+YOWFI3GUoMjEHTBX7RUREzlIwS5NwG2s/fP2oVpuSz16yiYbm2N83OOzdeemX+O5tPThzuJgBM6oourCm1fGiwjzmz/gKAK9t3t/Snt7Fha3aEormgImIiGSWhjLTJNowXcXGar73UnyhDFoPSe7fDwvvHYw71p2Rd2yh6MKaNrsKgFG1u5YHl22lrv5sr9ypGC6sOWAiIiKZpR6zNIo0TLdw1Y5Wqylj5Z/EX10NU6Z4/77xeh6XXz6WyQuOtOnhqm9oCrkiM9aNwf1tVbFXERGR9FMwy5JE52nlm/H0is+49296ceZ4AYNvqeJbv6ul9J3Qw44QfkVmYBvClcXQHDAREZHMUTBLs3CBp1dRYauhxVidOtKNu7/Zi6ZTBQyc+S6F59YB3tyzcAVs881ChjP/XLFQNddiLW4rIiIiqaNgliYVG6t55NVtHAlYbekPPFW7a/n8dGPc79lQW8yBFyfhGvIZeOtaug461up4qFBWmG/MvDT0iswrR/QPu3dmLEOdIiIikloKZmngn9gfag5ZfUMTL6zbQ7x7xzcc6sGBFyfinDFw1lq6DPg8thc6GH9+H8af36el565XUSFnGpt4fu2eiC/V1kgiIiKZpVWZafCDZVsiTuyPN5SdqTmHzxZNwkF8oQxoaHY88uo2yseWsmbOFJ6YOYYTZxo5GcOqTJXFEBERySwFszSIJfTE6syBnhxYNAnLcwyatZYu/Y7H/R5HTja07BjwyKvbaGiKngxVFkNERCTzFMwyrKgwP0StsdBO7+/lhbLCRgbe9g6FfU8kfF3/NkpHYthhQFsjiYiIZIfmmKVA8MrLcKsjwdsw/L7Fm6K+56l9vTm45FLyixoYOGstBb3azvcqKsyPeaP0WOaLFRXmZzyQhVu1KiIi0hmpxyxJ/lIT1XX1LZuNh+sRu33SUMrHllIaZe7Wqb19OPjSBPK7n2bgbe+0CmXBOwmE25w8mH++WElR6M3SzchKKAu+d6E2ahcREeksFMyStHDVjja9Vs14e1T6Q1O+GbdPGspj5WVA6K2O/Oo/6euFsnNOMfC2tRT0PNXq+Jo5U/h4wbWsmTOF8rGlzJo4JGobDS/0TF5QyXWjB1OY1zrMFeYZT9wyJuM9VaHunb9Mh4iISGekYJakcNX26xuaGdSrGwYM6tWN8ef3aTlWPraUG8eVtulZq9/Vn5qll1JQcoJBt71DQY/Tbd73gjkrmLygsqVXafz5fUL+R/T3jAUOq1bX1bN0QzUzJwxp1fO28ObRrfbwnLygkmFB10mHcMOrKtMhIiKdleaYJSlcVX04G9qq6+pb5pVV7a4NuXflyZ0DqKm4hMK+xxk4cx35xeEn6QdW5l+4ageh1oB271pA964FIffOXL29hjVzprR5TaZ3ADi3JPQ2UirTISIinZV6zJIULpSFct/iTTy/dk/bULZjEDW/HUeXAZ8zcNbaiKHMzz/kF67H7tO6+rh7pDI9tBhqSFdlOkREpDNTj1mSSsP0+sTqxPuDOfTaGLqeW8eAm9eT1zX2rZoi7Y/p73WKp0cq00OL/l44rcoUERHxKJglafbU4a2G/+Jx/L1SDq8cTdfzahlw43ryusb3HuGGUc3XLqBN2yL1SGVjaLF8bKmCmIiIiI+GMlOgW+HZ2xiuHEWwzzcP4fCK0XQbepgBN8UfyooK88MOozrOBp75M8ralNgIF4Q0tCgiIpJdCmZJ8E+WD6ymf7qxmZ5dQ5fC8Pt841BqX/8K3YbV0P/G9eR1iR7KehcXtglY4eqhBT5fPraU2VOHc25JEZ/W1bNw1Y6wKy3jDXIiIiKSWhrKTEK4yfKRZmQdq7qAI2+NouiiA/Sf/iesIPq+moX5xsPXjwoZkKINVca70lJDiyIiItmjHrMkxDsp/ui6L3DkrVEUf3E//cs3hAxlhflGUcDQaO/iQhbeNDpsiIrWw6UiriIiIu2HesySEG6yfCh1/3MRR98eTvGXqul37WYsv+38sN7FhTgHR+sbKI1xhWK0Hi4VcRUREWk/1GMWg7kVW7nwwZVcMGcFFz64krkV3lBgLJPinYO6t7/I0beH033UPvpdt6kllHXvks+TM8fwyYJreXLmGE41NFNX39Cyb+R9izcx9tE3kqq+H25FpYq4ioiI5B71mEUxt2Irz6/d0/K4yTmeX7uHj2uO88nhyL1OzkHdH0ZwbN2F9PjKHvpM3YoFROETZ5paVfAPVXLjyMmGpKrvhyrnoZWWIiIiuUnBLIpF6/aGfH7NR7URX+ccHHlrJJ9vGEaPsbvp8/X3sODNMfHme/m3awrHPycskWCmIq4iIiLth4JZFPFsueTnHNS++WWObzyfc8Z9TO+r3g8ZyuKRzJwwrbQUERFpHxTMooi0SXkozkHt62Uc3zKUXhM/otfl25MOZaA5YSIiIp2BJv9HMekLvWM/uRl6vfsXXij7iw85f9pHdClIPpVpTpiIiEjnoGAWRbQJ/n6uyah5bQxbf9+bXn+5g5K//ICjpxrAeWUw/HXGbp80NGzFfnznPDlzjKrvi4iIdEIayowiljplrsk4tHwsJz8YTMkVf6bXxF0txxqaHcVdCtj40NWtXhNckR/O9oxpTpiIiEjnpGAWRbQ5Zq4xj5pXLqF+50B6T9lGz0s/aXNOqIn7Wi0pIiIiwRTMoogUypob8qj57ThOfTyAPldv5Zyxe0KeF27ivnrGREREJJDmmEURbj5Y85l8apZeyqmP+9Nn2pawoUwT90VERCRWCmZRzJ46nLyghZXNp/M5+PKlnNrTl77Xbuac0W2L0GrivoiIiMRLQ5lRlI8tZd7ybdTVNwDQfLqAgy9N4PT+XvS7biPdR+5v85rJF/bhhTsvy3RTRUREpJ1TMIvBUV8oazpVwMGXJnLmQE/6Td9I9+GftTlXoUxEREQSpaHMGJxbUkTTyUIOvjiJMwfPof9fbQgZykpLihTKREREJGEKZjG489IvcfDFSTQc7sGAGRsovuhgm3MK80yT/EVERCQpGsqMwYTBg+mV38g5t22kaXBNm+MlRYXMu2GUJvmLiIhIUhTMYjByJOz9pICuXcdnuykiIiLSgWkoM0Zdu2a7BSIiItLRKZiJiIiI5AgFMxEREZEcoWAmIiIikiMUzERERERyhIKZiIiISI5QMBMRERHJEQpmIiIiIjlCwUxEREQkRyiYiYiIiOQIBTMRERGRHJHxYGZm08xsh5ntNLM5IY53NbPFvuPrzOyCTLdRREREJBsyGszMLB/4OfANYCQwy8xGBp32beCIc+4i4AngnzPZRhEREZFsyXSP2QRgp3Nul3PuDPAiMD3onOnAL33fvwxcZWaWwTaKiIiIZEWmg1kpsDfg8T7fcyHPcc41AkeBvhlpnYiIiEgWtdvJ/2Z2l5lVmVlVTU1NtpsjIiIikrRMB7NqYEjA4/N8z4U8x8wKgF7A4eA3cs497Zwb75wb379//zQ1V0RERCRzMh3M1gMXm9kwM+sC3AosDzpnOfAt3/c3AZXOOZfBNoqIiIhkRUEmL+acazSze4BVQD7wrHNum5k9ClQ555YD/wn82sx2ArV44U1ERESkw8toMANwzq0EVgY991DA96eAmzPdLhEREZFsa7eT/0VEREQ6GgUzERERkRyhYCYiIiKSI6wjLHg0sxpgd4RT+gGHMtSczkj3N310b9NH9za9dH/TR/c2vTJxf893zoWs9dUhglk0ZlblnBuf7XZ0VLq/6aN7mz66t+ml+5s+urfple37q6FMERERkRyhYCYiIiKSIzqFlr6gAAAJYElEQVRLMHs62w3o4HR/00f3Nn10b9NL9zd9dG/TK6v3t1PMMRMRERFpDzpLj5mIiIhIzutQwczMppnZDjPbaWZzQhzvamaLfcfXmdkFmW9l+xTDvb3DzGrMbJPv6++y0c72yMyeNbODZvZemONmZj/z3fstZnZJptvYnsVwf68ws6MBn92HQp0nbZnZEDNbbWbvm9k2M/uHEOfo85uAGO+tPrsJMrNuZvaumW323d9HQpyTlczQYYKZmeUDPwe+AYwEZpnZyKDTvg0ccc5dBDwB/HNmW9k+xXhvARY758b4vp7JaCPbt+eAaRGOfwO42Pd1F/CLDLSpI3mOyPcX4O2Az+6jGWhTR9EIPOCcGwlMAu4O8btBn9/ExHJvQZ/dRJ0GpjjnRgNjgGlmNinonKxkhg4TzIAJwE7n3C7n3BngRWB60DnTgV/6vn8ZuMrMLINtbK9iubeSIOfcH4HaCKdMB37lPGuBEjMbnJnWtX8x3F9JkHNuv3PuT77vPwf+DJQGnabPbwJivLeSIN/n8bjvYaHvK3jSfVYyQ0cKZqXA3oDH+2j7IW45xznXCBwF+makde1bLPcW4EbfUMXLZjYkM03rFGK9/5K4y3xDGr8zs1HZbkx75BvmGQusCzqkz2+SItxb0Gc3YWaWb2abgIPAm865sJ/dTGaGjhTMJLteBS5wzn0FeJOzf2WI5Lo/4W2PMhr4V6Aiy+1pd8ysB7AUuM85dyzb7elIotxbfXaT4Jxrcs6NAc4DJpjZl7PdJuhYwawaCOylOc/3XMhzzKwA6AUczkjr2reo99Y5d9g5d9r38BlgXIba1hnE8tmWBDnnjvmHNJxzK4FCM+uX5Wa1G2ZWiBccXnDOLQtxij6/CYp2b/XZTQ3nXB2wmrZzUbOSGTpSMFsPXGxmw8ysC3ArsDzonOXAt3zf3wRUOhVyi0XUexs0Z+QGvPkQkhrLgb/xrW6bBBx1zu3PdqM6CjMb5J83YmYT8H4v6g+2GPju238Cf3bO/STMafr8JiCWe6vPbuLMrL+Zlfi+LwK+DmwPOi0rmaEg3RfIFOdco5ndA6wC8oFnnXPbzOxRoMo5txzvQ/5rM9uJNxn41uy1uP2I8d7ea2Y34K0kqgXuyFqD2xkzWwRcAfQzs33Aw3gTUXHOPQWsBK4BdgIngb/NTkvbpxju703A/zazRqAeuFV/sMVsMvDXwFbfXB2AHwBDQZ/fJMVyb/XZTdxg4Je+qgN5wEvOuddyITOo8r+IiIhIjuhIQ5kiIiIi7ZqCmYiIiEiOUDATERERyREKZiIiIiI5QsFMREREJICZPWtmB83svRjO/aqZ/cnMGs3spqBj3zKzD31f3wr3HoEUzEQk7cys3MzeMLPDZnbGzKp9W3dF21w8Z5jZdWbmfNvjhDr+e9/xSF/zkrj+LWZ2R5jrvpzo+4pISM/RtuBsOHvwSkT9JvBJM+uDV55nIt6e0w+bWe9ob9Zh6piJSG4ysyeAe4FfAb/AK4B5Pl5NoN+Z2UXOuY+y2MRU+T9Az4DH/wXsAn4U8Ny+JN7/FqAf3v8wRCSNnHN/DP4jzMwuBH4O9MeryXenc267c+4T3/HmoLeZircHZ63v+Jt4YW9RpGsrmIlI2pjZdOA+4G+dc88FHf61mV2PVxgz3OuLnHNhj+cS59z7gY/N7ARQ45xbG+41ZtbNOXcq7Y0TkVR4Gviuc+5DM5sI/DswJcL5LZug++zzPReRhjJFJJ3uA9aHCGUAOOdedc596n/sG+77npk9aWY1wFbf8/3M7Je+odCTvuG78YHv5XvtPUHPzTOzQwGP7/CdV2Zmb5rZCTPbbmYzgl5nvtceNLPPzexXtO4Ni1vAtSf42l8PzDazK3zPfzno/JYhSjN7DrgRuDzcsKiZ3WZmO83smJn9zszOS6a9InKWeZvJ/wWwxLcTw3/g7R6QcgpmIpIW5m36exnwRpwvnY33C++v8YZAASrwhgW+D8zE+9212swuSrB5v8HbB++vgA+BF4OCzL3AQ3h/Id+E16v34wSvFWwR8CreNkWvxfiaH+FtsrwR755eBjwTcHwicA/wAHAXcAle20UkNfKAOufcmICvL0V5Tcsm6D7n+Z6LSEOZIpIufYGutO7K92/OnB/wVFPQ/n77nXMzA86fhrdv4BXOuT/4nqsEPsELcd9JoG1POOee9b3XBuAAcB3wlG/vvH8E/sM5N9d3/irf/JCowxAx+Jlz7qf+B2Z2RbQXOOc+MrNaIC/M0GhP4Frn3BHfew4CnmhPQ8Eiucw5d8zMPjazm51zS3y/x77inNsc4WWrgH8KmPB/NfBgtGupx0xE0i14Q94HgIaAr7uDjq8MejwBOOgPZQDOuRN4vU3/K8E2tfTiOecOAwfx/poF7y/cwcArQa9ZluC1gq1I0fsEWu8PZT7++W6pCJIinY6ZLQLeAYab2T4z+zbwTeDbZrYZ2AZM9517qZntA24G/sPMtgH4Jv3/CFjv+3rUvxAgEvWYiUi6HAZOczbw+P0a+L3v+/UhXncg6PFgvOAU6rw+CbatLujxGaCb7/tBvn+DrxmqDYkI/vlSIdTPA2d/JhGJg3NuVphDbUpoOOfW0/b3nP/Ys8Cz8VxbPWYikhbOuUa8vzivDnr+gHOuyjlXFe6lQY/3AwNCnDcQCPzr8zTQJeicqDWDQvjM92/wNUO1IRHBP59/VWYq2i4i7ZyCmYik05PARDP76yTeYx0wwMy+6n/CzIqBa4H/DjhvH/ClgHPygKsSuN5evHA2Pej5GSHOTQV/bbPAtg8BRgSdF9irJyIdlIYyRSRtnHOvmNmTwHNmdiXeasRDeAsD/D1px6O8xyoz+x9gsZnNwRsi/T5QBCwMOPW3wN1mthGvsOvfkUCJC+dck5n9GPgXX6mNt/FKVURbgZUQ59w+M6sCfmRmJ/H+YP4BrXsDAbYD082sHC/MfRpYakREOgb1mIlIWjnn7scrOTEE+E+gEq8w40DgmnA1zoKUA2/i9cAtAQyY4pzbGXDOI75jj+FVx9+EV30/EU8C/wR8F1gK9AD+b4LvFYtZeNu6PO+77qPAjqBz/h1v0cKzeHPz7kpje0QkS6z1KnURERERyRb1mImIiIjkCAUzERERkRyhYCYiIiKSIxTMRERERHKEgpmIiIhIjlAwExEREckRCmYiIiIiOULBTERERCRHKJiJiIiI5Ij/D1lezJciAvv3AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.scatter(y_test, y_pred)\n",
        "p1 = max(max(y_test), max(y_pred))\n",
        "p2 = min(min(y_test), min(y_pred))\n",
        "plt.plot([p1, p2], [p1, p2], 'b-')\n",
        "plt.title(\"Comparison of Predicted Value Vs Actual Value\")\n",
        "plt.xlabel('Ground Truth', fontsize=15)\n",
        "plt.ylabel('Predicted', fontsize=15)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Regression\n"
      ],
      "metadata": {
        "id": "XH7n6sM26iLL"
      },
      "id": "XH7n6sM26iLL"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        " \n",
        "def MSE_count(y, y_predicted):\n",
        "    length = len(y)\n",
        "    sum_value = 0\n",
        "    for i in range(length):\n",
        "        sum_value += (y[i] - y_predicted[i])**2\n",
        "    return sum_value/length\n",
        "\n",
        "class LinearRegression:\n",
        "    def __init__(self, lr=0.001, n_iters=1000):\n",
        "        self.lr = lr\n",
        "        self.n_iters = n_iters\n",
        "        self.weights = None\n",
        "        self.bias = None\n",
        "\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        n_samples, n_features = X.shape\n",
        "        self.weights = np.zeros(n_features)\n",
        "        self.bias = 0\n",
        "        n_iters = self.n_iters\n",
        "        loss = []\n",
        "\n",
        "        for _ in range(self.n_iters):\n",
        "            y_predicted = np.dot(X, self.weights) + self.bias\n",
        "            dw =  ( 2 * np.dot(X.T, (y_predicted - y))) / n_samples\n",
        "            db = ( 2 * np.sum(y_predicted - y)) / n_samples\n",
        "            loss.append(MSE_count(y, y_predicted))\n",
        "            \n",
        "            self.weights -= self.lr * dw\n",
        "            self.bias -= self.lr * db\n",
        "        \n",
        "        plt.plot(np.arange(1, n_iters), loss[1:])\n",
        "        plt.xlabel(\"Iteration\")\n",
        "        plt.ylabel(\"Loss\")\n",
        "        \n",
        "        return self.weights, self.bias\n",
        "    \n",
        "    def predict(self, X):\n",
        "        y_predicted = np.dot(X, self.weights) + self.bias\n",
        "        return y_predicted"
      ],
      "metadata": {
        "id": "wKSzCBui6sYt"
      },
      "id": "wKSzCBui6sYt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LR = LinearRegression(lr=0.01, n_iters=250)\n",
        "weights,bias = LR.fit(X_train,y_train)\n",
        "\n",
        "weights = {'Features':[['Bedrooms'],['Land'],['Pusat'],['Selatan'],['Timur'],['Utara'],['Condo'],['House']], 'Weights':weights}  \n",
        "weight_list = pd.DataFrame(weights)\n",
        "\n",
        "predictions = LR.predict(X_test)\n",
        "\n",
        "from sklearn import metrics\n",
        "print('MAE =',metrics.mean_absolute_error(y_test,predictions))\n",
        "print('MSE =',metrics.mean_squared_error(y_test,predictions))\n",
        "print('RMSE =',np.sqrt(metrics.mean_squared_error(y_test,predictions)))\n",
        "print('R2 SCORE =',metrics.r2_score(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "id": "VblgWCK57ANq",
        "outputId": "41032746-d147-402e-991c-d50bf071d73e"
      },
      "id": "VblgWCK57ANq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE = 1991003903.485234\n",
            "MSE = 9.997068435655272e+18\n",
            "RMSE = 3161814105.1705227\n",
            "R2 SCORE = 0.7044970737117504\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " weight_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "_kJC6j2V8Kje",
        "outputId": "0b902646-e613-4242-b8a5-795921d8d5e0"
      },
      "id": "_kJC6j2V8Kje",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Features       Weights\n",
              "0  [Bedrooms]  6.231043e+08\n",
              "1      [Land]  4.053130e+09\n",
              "2     [Pusat]  3.253127e+08\n",
              "3   [Selatan]  8.767919e+08\n",
              "4     [Timur] -4.019764e+08\n",
              "5     [Utara]  9.023110e+08\n",
              "6     [Condo]  2.410422e+07\n",
              "7     [House] -1.316215e+08"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-985c90e2-ee92-43ca-a909-8e5a134f9f57\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Features</th>\n",
              "      <th>Weights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[Bedrooms]</td>\n",
              "      <td>6.231043e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[Land]</td>\n",
              "      <td>4.053130e+09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[Pusat]</td>\n",
              "      <td>3.253127e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[Selatan]</td>\n",
              "      <td>8.767919e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[Timur]</td>\n",
              "      <td>-4.019764e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>[Utara]</td>\n",
              "      <td>9.023110e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>[Condo]</td>\n",
              "      <td>2.410422e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>[House]</td>\n",
              "      <td>-1.316215e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-985c90e2-ee92-43ca-a909-8e5a134f9f57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-985c90e2-ee92-43ca-a909-8e5a134f9f57 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-985c90e2-ee92-43ca-a909-8e5a134f9f57');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Bias =',bias)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eYz9WE3h8Njx",
        "outputId": "a02d1b3b-1325-4eb9-9648-1a8bf890280d"
      },
      "id": "eYz9WE3h8Njx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bias = 6637531310.037721\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.scatter(y_test, predictions)\n",
        "p1 = max(max(y_test), max(predictions))\n",
        "p2 = min(min(y_test), min(predictions))\n",
        "plt.plot([p1, p2], [p1, p2], 'b-')\n",
        "plt.title(\"Comparison of Predicted Value Vs Actual Value\")\n",
        "plt.xlabel('Ground Truth', fontsize=15)\n",
        "plt.ylabel('Predicted', fontsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "PKLCflJQ8C0T",
        "outputId": "ca8abf35-b6a3-4748-8e39-b986c6fe6bd5"
      },
      "id": "PKLCflJQ8C0T",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
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        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [
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        "M990ypmJwJY4",
        "XH7n6sM26iLL"
      ]
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}