examples/ch14/snippets_ipynb/.ipynb_checkpoints/16_04-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 16.4 Case Study: Time Series and Simple Linear Regression \n",
    "### Loading the Average High Temperatures into a `DataFrame` \n",
    "\n",
    "**We added `%matplotlib inline` to enable Matplotlib in this notebook.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc = pd.read_csv('ave_hi_nyc_jan_1895-2018.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc.columns = ['Date', 'Temperature', 'Anomaly']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc.Date = nyc.Date.floordiv(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nyc.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the Data for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "     nyc.Date.values.reshape(-1, 1), nyc.Temperature.values, \n",
    "     random_state=11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regression = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regression.fit(X=X_train, y=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regression.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_regression.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = linear_regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "expected = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p, e in zip(predicted[::5], expected[::5]):\n",
    "     print(f'predicted: {p:.2f}, expected: {e:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting Future Temperatures and Estimating Past Temperatures "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = (lambda x: linear_regression.coef_ * x + \n",
    "                      linear_regression.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict(2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict(1890)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Dataset with the Regression Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "axes = sns.scatterplot(data=nyc, x='Date', y='Temperature',\n",
    "     hue='Temperature', palette='winter', legend=False)\n",
    "\n",
    "axes.set_ylim(10, 70)\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "x = np.array([min(nyc.Date.values), max(nyc.Date.values)])\n",
    "\n",
    "y = predict(x)\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "line = plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This placeholder cell was added because we had to combine \n",
    "# the sections snippets 22-28 for the visualization to work in Jupyter\n",
    "# and want the subsequent snippet numbers to match the book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder cell "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder cell "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder cell "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder cell "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Placeholder cell "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Self Check Exercises check mark image](files/art/check.png)\n",
    "## 16.4 Self Check\n",
    "**1. _(Fill-In)_** A `LinearRegression` object’s `________` and `________` attributes can be used as _m_ and _b_, respectively, in the equation _y = mx + b_ to make predictions. \n",
    "\n",
    "**Answer:** `coeff_`, `intercept_`.\n",
    "\n",
    "**2. _(True/False)_** By default, the `LinearRegression` estimator performs simple linear regression.\n",
    "\n",
    "**Answer:** False. By default, the `LinearRegression` estimator uses all the numeric features in a dataset, performing a multiple linear regression.\n",
    "\n",
    "**3. _(IPython Session)_** Use the predict lambda to estimate what the average January high temperature was in `1800` and to predict what it will be in `2100`.\n",
    "\n",
    "**Answer:** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict(1800)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict(2100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################################\n",
    "# (C) Copyright 2019 by Deitel & Associates, Inc. and                    #\n",
    "# Pearson Education, Inc. All Rights Reserved.                           #\n",
    "#                                                                        #\n",
    "# DISCLAIMER: The authors and publisher of this book have used their     #\n",
    "# best efforts in preparing the book. These efforts include the          #\n",
    "# development, research, and testing of the theories and programs        #\n",
    "# to determine their effectiveness. The authors and publisher make       #\n",
    "# no warranty of any kind, expressed or implied, with regard to these    #\n",
    "# programs or to the documentation contained in these books. The authors #\n",
    "# and publisher shall not be liable in any event for incidental or       #\n",
    "# consequential damages in connection with, or arising out of, the       #\n",
    "# furnishing, performance, or use of these programs.                     #\n",
    "##########################################################################\n"
   ]
  }
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