{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Title\n", "\n", "**Exercise: A.1 - Guesstimate the β values**\n", "\n", "# Description\n", "\n", "The goal of this exercise is to **guess a model** based on the plot below and also calculate the MSE for your guess model.\n", "\n", "\n", "\n", "# Instructions:\n", "\n", "We are trying to predict sales as a function of the advertising budget for TV using the data.\n", "To do so we need 1) a model and 2) a method to estimate how good the model is.\n", "\n", "- Guess the values of the coefficients $\\beta_0$ and $\\beta_1$ by visually inspecting the graph above;\n", "- Plot your model's prediction (use the formula of a simple linear regression, no package allowed) ;\n", "- Change the values of the coefficients $\\beta_0$ and $\\beta_1$ to improve the fit;\n", "- Calculate the **Mean Squared Error (MSE)** for the model.\n", "\n", "\n", "# Hints:\n", "\n", "- Recall the formula for the linear regression model $\\hat{y}= \\beta_0 + \\beta_1*x$\n", "- Recall the formula for Mean Squared Error $MSE =\\frac{1}{n}\\sum_{i=1}^{n}(y_i-\\hat{y})^2$\n", "- Guess $\\beta_0$ by observing the intercept of your model and $\\beta_1$ looking at the slope\n", "\n", "np.mean() : Computes the arithmetic mean along the specified axis\n", "\n", "plt.plot() : Plots x versus y as lines and/or markers\n", "\n", "plt.xlabel() : Sets the label for the x-axis.\n", "\n", "plt.ylabel() : Sets the label for the y-axis.\n", "\n", "plt.legend() : Places a legend on the axes\n", "\n", "Note: This exercise is auto-graded and you can try multiple attempts. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reading the dataset" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "# Data set used in this exercise :Advertising.csv\n", "data_filename = 'Advertising.csv'\n", "\n", "# Read the data using pandas libraries\n", "df = pd.read_csv(data_filename)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Create a new dataframe called `df_new` witch the columns 'TV' and 'sales'\n", "df_new = df[['TV', 'sales']]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot the data\n", "\n", "plt.plot(df_new.TV, df_new.sales, '*', label='data')\n", "plt.xlabel('TV')\n", "plt.ylabel('Sales')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Beta Estimation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "### edTest(test_betas) ###\n", "# Estimate beta0 by observing the value of y when x = 0\n", "beta0 = ___\n", "\n", "# Estimate beta1 - Check the slope for guidance\n", "beta1 = ___" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Calculate prediction of x using beta0 and beta1\n", "y_predict = ___" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the graph" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot the predicted values as well as the data\n", "plt.plot(df_new.TV, df_new.sales, '*', label='data')\n", "plt.plot(df_new.TV, y_predict, label='model')\n", "plt.xlabel('TV')\n", "plt.ylabel('Sales')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MSE Computation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "### edTest(test_mse) ###\n", "# Calculate the MSE\n", "MSE = ___\n", "\n", "# Print the results\n", "print(\"My MSE is: {0}\".format(MSE))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }