Final Assignment 2.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Analyzing Where Do People Drink?\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "-   Be confident about your data analysis skills\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Dataset is from the story <a href=https://fivethirtyeight.com/features/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/>  Dear Mona Followup: Where Do People Drink The Most Beer, Wine And Spirits? </a>  The dataset contains Average serving sizes per person such as average wine, spirit, beer servings. As well as several other metrics. You will be asked to analyze the data and predict the total liters served given the servings. See how to share your lab at the end.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will need the following libraries:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Importing the Data</b>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the csv:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DA0101EN/edx/project/drinks.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the method  <code>head()</code>  to display the first 5 columns of the dataframe:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>country</th>\n",
       "      <th>beer_servings</th>\n",
       "      <th>spirit_servings</th>\n",
       "      <th>wine_servings</th>\n",
       "      <th>total_litres_of_pure_alcohol</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>89</td>\n",
       "      <td>132</td>\n",
       "      <td>54</td>\n",
       "      <td>4.9</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>245</td>\n",
       "      <td>138</td>\n",
       "      <td>312</td>\n",
       "      <td>12.4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>217</td>\n",
       "      <td>57</td>\n",
       "      <td>45</td>\n",
       "      <td>5.9</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  beer_servings  spirit_servings  wine_servings  \\\n",
       "0  Afghanistan              0                0              0   \n",
       "1      Albania             89              132             54   \n",
       "2      Algeria             25                0             14   \n",
       "3      Andorra            245              138            312   \n",
       "4       Angola            217               57             45   \n",
       "\n",
       "   total_litres_of_pure_alcohol continent  \n",
       "0                           0.0      Asia  \n",
       "1                           4.9    Europe  \n",
       "2                           0.7    Africa  \n",
       "3                          12.4    Europe  \n",
       "4                           5.9    Africa  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 1</b>:  Display the data types of each column using the attribute dtype.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country                          object\n",
       "beer_servings                     int64\n",
       "spirit_servings                   int64\n",
       "wine_servings                     int64\n",
       "total_litres_of_pure_alcohol    float64\n",
       "continent                        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 2</b> Use the method <code>groupby</code> to get the number of wine servings per continent:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wine_servings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oceania</th>\n",
       "      <td>570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               wine_servings\n",
       "continent                   \n",
       "Africa                   862\n",
       "Asia                     399\n",
       "Europe                  6400\n",
       "North America            564\n",
       "Oceania                  570\n",
       "South America            749"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2 = df[['wine_servings','continent']]\n",
    "q2.groupby('continent').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 3:</b> Perform a statistical summary and analysis of beer servings for each continent:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_servings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>193.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106.160622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>101.143103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>376.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_servings\n",
       "count     193.000000\n",
       "mean      106.160622\n",
       "std       101.143103\n",
       "min         0.000000\n",
       "25%        20.000000\n",
       "50%        76.000000\n",
       "75%       188.000000\n",
       "max       376.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = df[['beer_servings','continent']]\n",
    "q3.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 4:</b> Use the function boxplot in the seaborn library to produce a plot that can be used to show the number of beer servings on each continent.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"continent\", y=\"beer_servings\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 5</b>: Use the function <code> regplot</code> in the seaborn library to determine if the number of wine servings is\n",
    "negatively or positively correlated with the number of beer servings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='wine_servings', y=\"beer_servings\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Question 6:</b> Fit a linear regression model to predict the <code>'total_litres_of_pure_alcohol'</code> using the number of <code>'wine_servings'</code> then calculate $R^{2}$:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of R^2 = 0.4456875459787605\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "X = df[['wine_servings']]\n",
    "Y = df['total_litres_of_pure_alcohol']\n",
    "lm.fit(X,Y)\n",
    "\n",
    "print('The value of R^2 =',lm.score(X, Y))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<b>Note:</b> Please use <code>test_size = 0.10</code> in the following questions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 7: </b>Use list of features to predict the <code>'total_litres_of_pure_alcohol'</code>, split the data into training and testing and determine the $R^2$ on the test data, using the provided code:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of R^2 with 10% of training data = 0.6990304512837944\n"
     ]
    }
   ],
   "source": [
    "features = ['beer_servings','spirit_servings','wine_servings']\n",
    "X = df[features]\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.1, random_state=0)\n",
    "\n",
    "lre=LinearRegression()\n",
    "lre.fit(x_train, y_train)\n",
    "print('The value of R^2 with 10% of training data =',lre.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 8 :</b> Create a pipeline object that scales the data, performs a polynomial transform and fits a linear regression model. Fit the object using the training data in the question above, then calculate the R^2 using. the test data. Take a screenshot of your code and the $R^{2}$. There are some hints in the notebook:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>'scale'</code>\n",
    "\n",
    "<code>'polynomial'</code>\n",
    "\n",
    "<code>'model'</code>\n",
    "\n",
    "The second element in the tuple contains the model constructor\n",
    "\n",
    "<code>StandardScaler()</code>\n",
    "\n",
    "<code>PolynomialFeatures(include_bias=False)</code>\n",
    "\n",
    "<code>LinearRegression()</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7076376133886035"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
    "\n",
    "Input=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model',LinearRegression())]\n",
    "\n",
    "pipe=Pipeline(Input)\n",
    "pipe.fit(x_train,y_train)\n",
    "pipe.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 9</b>: Create and fit a Ridge regression object using the training data, setting the regularization parameter to 0.1 and calculate the $R^{2}$ using the test data. Take a screenshot of your code and the $R^{2}$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.699030454901918"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "RigeModel=Ridge(alpha=0.1)\n",
    "RigeModel.fit(x_train, y_train)\n",
    "RigeModel.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Question 10 </b>: Perform a 2nd order polynomial transform on both the training data and testing data.  Create and fit a Ridge regression object using the training data, setting the regularization parameter to 0.1. Calculate the $R^{2}$ utilizing the test data provided. Take a screen-shot of your code and the $R^{2}$. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7076376228095799"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr=PolynomialFeatures(degree=2)\n",
    "x_train_pr=pr.fit_transform(x_train)\n",
    "x_test_pr=pr.fit_transform(x_test)\n",
    "\n",
    "RigeModel=Ridge(alpha=0.1)\n",
    "\n",
    "RigeModel.fit(x_train_pr, y_train)\n",
    "RigeModel.score(x_test_pr, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/share-notebooks.html\\\" > CLICK HERE </a>  to see how to share your notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Sources</b>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=https://fivethirtyeight.com/features/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/> Dear Mona Followup: Where Do People Drink The Most Beer, Wine And Spirits?</a> by By Mona Chalabi , you can download the dataset <a href=https://github.com/fivethirtyeight/data/tree/master/alcohol-consumption>here</a>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                 |\n",
    "| ----------------- | ------- | ---------- | ---------------------------------- |\n",
    "| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
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