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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "auHU5AhD0eMt"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LassoRegression():\n",
        "    def __init__(self, learning_rate, iterations, l1_penalty):\n",
        "        self.learning_rate = learning_rate\n",
        "        self.iterations = iterations\n",
        "        self.l1_penalty = l1_penalty\n",
        "\n",
        "    def fit(self, X, Y):\n",
        "        self.m, self.n = X.shape\n",
        "        self.W = np.zeros(self.n)\n",
        "        self.b = 0\n",
        "        self.X = X\n",
        "        self.Y = Y\n",
        "\n",
        "        for i in range(self.iterations):\n",
        "            self.update_weights()\n",
        "        return self\n",
        "\n",
        "    def update_weights(self):\n",
        "        Y_pred = self.predict(self.X)\n",
        "\n",
        "        dW = np.zeros(self.n)\n",
        "        for j in range(self.n):\n",
        "            if self.W[j] > 0:\n",
        "                dW[j] = (-2 * (self.X[:, j]).dot(self.Y - Y_pred) +\n",
        "                         self.l1_penalty) / self.m\n",
        "            else:\n",
        "                dW[j] = (-2 * (self.X[:, j]).dot(self.Y - Y_pred) -\n",
        "                         self.l1_penalty) / self.m\n",
        "\n",
        "        db = -2 * np.sum(self.Y - Y_pred) / self.m\n",
        "\n",
        "        self.W = self.W - self.learning_rate * dW\n",
        "        self.b = self.b - self.learning_rate * db\n",
        "        return self\n",
        "\n",
        "    def predict(self, X):\n",
        "        return X.dot(self.W) + self.b"
      ],
      "metadata": {
        "id": "s3T5gIp80ePV"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"Experience-Salary.csv\")\n",
        "df.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "outputId": "16d5dfdf-5195-45aa-cb4c-1bbcf7a87c7a"
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      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Years of Experience        Salary\n",
              "0             4.941931   9372.711912\n",
              "1             9.069024  23015.300694\n",
              "2             8.109230  17039.178915\n",
              "3             9.807933  19905.398181\n",
              "4             6.274945  17033.827082"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Years of Experience</th>\n",
              "      <th>Salary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.941931</td>\n",
              "      <td>9372.711912</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.069024</td>\n",
              "      <td>23015.300694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8.109230</td>\n",
              "      <td>17039.178915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.807933</td>\n",
              "      <td>19905.398181</td>\n",
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              "      <th>4</th>\n",
              "      <td>6.274945</td>\n",
              "      <td>17033.827082</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2000,\n  \"fields\": [\n    {\n      \"column\": \"Years of Experience\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.605348080522848,\n        \"min\": 1.0014002892290157,\n        \"max\": 19.99636298422234,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          6.558846092004417,\n          2.138941666741892,\n          5.349244394763487\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Salary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12445.535465069075,\n        \"min\": -8481.560830042528,\n        \"max\": 61177.63606459599,\n        \"num_unique_values\": 2000,\n        \"samples\": [\n          11477.669047518284,\n          7500.409460441524,\n          20694.2380405138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "Y = df.iloc[:, 1].values"
      ],
      "metadata": {
        "id": "UX2bvHxG1OtM"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(\n",
        "        X, Y, test_size=1/3, random_state=0)"
      ],
      "metadata": {
        "id": "tXezZzfn0od6"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LassoRegression(iterations=1000, learning_rate=0.01, l1_penalty=500)\n",
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZNGjni80u8B",
        "outputId": "680fcb4f-acee-4982-9b23-4e12a938817a"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<__main__.LassoRegression at 0x78c4430516a0>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "J7aCVaXf0u-l"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Predicted values: \", np.round(Y_pred[:3], 2))\n",
        "print(\"Real values:      \", Y_test[:3])\n",
        "print(\"Trained W:        \", round(model.W[0], 2))\n",
        "print(\"Trained b:        \", round(model.b, 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gR83Di2O03Fh",
        "outputId": "97800458-a615-4af2-9d9d-4d3fe7fc0695"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted values:  [35539.41 18099.76 43796.5 ]\n",
            "Real values:       [42328.57198221 16443.83637617 44375.48684823]\n",
            "Trained W:         11516.31\n",
            "Trained b:         26129.99\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(X_test, Y_test, color='blue', label='Actual Data')\n",
        "plt.plot(X_test, Y_pred, color='yellow', label='Lasso Regression Line')\n",
        "plt.title('Salary vs Experience (Lasso Regression)')\n",
        "plt.xlabel('Years of Experience (Standardized)')\n",
        "plt.ylabel('Salary')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "-f0Bx1xz0eR7",
        "outputId": "38a0bbaf-3476-4ef7-88b6-b2ef6746dd89"
      },
      "execution_count": 52,
      "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": [],
      "metadata": {
        "id": "0wEBC_IM0eYx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}