Regression Linear Simple --Bruno Casaca

LinearRegressionSimple.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "code",
            "source": "#Import required libraries\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn import datasets, linear_model\nfrom sklearn.metrics import mean_squared_error, r2_score\n",
            "execution_count": 1,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Split the data into training/testing sets\nX_train =np.array([1,2,4,3,5,6,7,8,8])\nX_test = np.array([3,9,7,5])\n# Split the targets into training/testing sets\ny_train = np.array([1,3,6,4,5,5,6,7,6])\ny_test = np.array([5,7,5,3])\n#Plot the data to see how it looks\nplt.scatter(X_train, y_train,  color='blue')\n",
            "execution_count": 3,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 3,
                    "data": {
                        "text/plain": "<matplotlib.collections.PathCollection at 0x7f380a2a90d0>"
                    },
                    "metadata": {}
                },
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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E3aF2cJOkZ21/T9K/qpijTn/bW0P8iqTnbb8s6UVJfx8R36450zx/Imk4PR5uk/SXNefZke2WpN9TcXaa2vSnlMckHZf0iopuLe1x8jS35wEAZktzRg0AmI2iBoDkKGoASI6iBoDkKGoASI6iBoDkKGoASO7/Aep5AfWfuAyDAAAAAElFTkSuQmCC\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Create linear regression object\nregr = linear_model.LinearRegression()\n\n# Train the model using the training sets\nregr.fit(X_train[:, np.newaxis], y_train[:, np.newaxis])\n\n# Make predictions using the testing set\ny_pred = regr.predict(X_test[:, np.newaxis])\n\n# The coefficients\nprint('Coefficients: \\n', regr.coef_)\nprint('Intercept: \\n',regr.intercept_ )\n# The mean squared error\nprint(\"Mean squared error: %.2f\"\n      % mean_squared_error(y_test, y_pred))",
            "execution_count": null,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Plot outputs\nallXvalues = np.concatenate((X_train,X_test), axis=0)\nallYvalues = np.concatenate((y_train,y_test), axis=0)\nplt.scatter(allXvalues, allYvalues,  color='black')\nplt.scatter(X_test,y_pred,  color='red')\nplt.plot(X_test, y_pred, color='blue', linewidth=3)\n\nplt.xticks(())\nplt.yticks(())\n\nplt.show()",
            "execution_count": null,
            "outputs": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.7",
            "language": "python"
        },
        "language_info": {
            "name": "python",
            "version": "3.7.10",
            "mimetype": "text/x-python",
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "pygments_lexer": "ipython3",
            "nbconvert_exporter": "python",
            "file_extension": ".py"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 1
}