Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6f5474a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "0329f507",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the CSV file on which to work\n",
    "teams = pd.read_csv(\"teams.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "f5bbfea6",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
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       "      <th>athletes</th>\n",
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       "      <th>prev_3_medals</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1964</td>\n",
       "      <td>8</td>\n",
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       "      <td>AFG</td>\n",
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       "      <td>11</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>2139</th>\n",
       "      <td>ZIM</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>25.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>71.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>14</td>\n",
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       "      <td>177.8</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>ZIM</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>174.4</td>\n",
       "      <td>65.2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "     team      country  year  events  athletes   age  height  weight  medals  \\\n",
       "0     AFG  Afghanistan  1964       8         8  22.0   161.0    64.2       0   \n",
       "1     AFG  Afghanistan  1968       5         5  23.2   170.2    70.0       0   \n",
       "2     AFG  Afghanistan  1972       8         8  29.0   168.3    63.8       0   \n",
       "3     AFG  Afghanistan  1980      11        11  23.6   168.4    63.2       0   \n",
       "4     AFG  Afghanistan  2004       5         5  18.6   170.8    64.8       0   \n",
       "...   ...          ...   ...     ...       ...   ...     ...     ...     ...   \n",
       "2139  ZIM     Zimbabwe  2000      19        26  25.0   179.0    71.1       0   \n",
       "2140  ZIM     Zimbabwe  2004      11        14  25.1   177.8    70.5       3   \n",
       "2141  ZIM     Zimbabwe  2008      15        16  26.1   171.9    63.7       4   \n",
       "2142  ZIM     Zimbabwe  2012       8         9  27.3   174.4    65.2       0   \n",
       "2143  ZIM     Zimbabwe  2016      13        31  27.5   167.8    62.2       0   \n",
       "\n",
       "      prev_medals  prev_3_medals  \n",
       "0             0.0            0.0  \n",
       "1             0.0            0.0  \n",
       "2             0.0            0.0  \n",
       "3             0.0            0.0  \n",
       "4             0.0            0.0  \n",
       "...           ...            ...  \n",
       "2139          0.0            0.0  \n",
       "2140          0.0            0.0  \n",
       "2141          3.0            1.0  \n",
       "2142          4.0            2.3  \n",
       "2143          0.0            2.3  \n",
       "\n",
       "[2144 rows x 11 columns]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "800a3397",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     team      country  year  athletes   age  prev_medals  medals\n",
       "0     AFG  Afghanistan  1964         8  22.0          0.0       0\n",
       "1     AFG  Afghanistan  1968         5  23.2          0.0       0\n",
       "2     AFG  Afghanistan  1972         8  29.0          0.0       0\n",
       "3     AFG  Afghanistan  1980        11  23.6          0.0       0\n",
       "4     AFG  Afghanistan  2004         5  18.6          0.0       0\n",
       "...   ...          ...   ...       ...   ...          ...     ...\n",
       "2139  ZIM     Zimbabwe  2000        26  25.0          0.0       0\n",
       "2140  ZIM     Zimbabwe  2004        14  25.1          0.0       3\n",
       "2141  ZIM     Zimbabwe  2008        16  26.1          3.0       4\n",
       "2142  ZIM     Zimbabwe  2012         9  27.3          4.0       0\n",
       "2143  ZIM     Zimbabwe  2016        31  27.5          0.0       0\n",
       "\n",
       "[2144 rows x 7 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering out the columns that are relevant to us for this project\n",
    "\n",
    "teams = teams[[\"team\", \"country\", \"year\", \"athletes\", \"age\", \"prev_medals\", \"medals\"]]\n",
    "teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "2fc04684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year          -0.021603\n",
       "athletes       0.840817\n",
       "age            0.025096\n",
       "prev_medals    0.920048\n",
       "medals         1.000000\n",
       "Name: medals, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking correlation between 'Medals' and other columns, to figure out the best pair for parameters\n",
    "# The closest to 1, the better the parameter will be\n",
    "\n",
    "teams.corr()[\"medals\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "be405a92",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can plot the data on graph for visualisation using MatPlotLib\n",
    "# 'athletes' and 'prev_medals' are plotted against 'medals'\n",
    "\n",
    "x = teams[\"athletes\"]\n",
    "y = teams[\"medals\"]\n",
    "\n",
    "''' 1st implementation with matplotlib\n",
    "\n",
    "plt.scatter(x, y)\n",
    "'''\n",
    "# 2nd implementation with matplotlib\n",
    "\n",
    "# plt.xlabel('athletes')\n",
    "# plt.ylabel('medals')\n",
    "# plt.scatter(x = 'athletes', y = 'medals', data = teams)\n",
    "# m, b = np.polyfit(x, y, 1)\n",
    "# plt.plot(x, m*x+b, color = 'red')\n",
    "\n",
    "# Implementation with Seaborn\n",
    "sns.lmplot(x = \"athletes\", y = \"medals\", data = teams, fit_reg = True, ci = None)\n",
    "\n",
    "# Data Cleaning - identify missing values\n",
    "teams[teams.isnull().any(axis=1)]\n",
    "\n",
    "# Drop missing values\n",
    "teams = teams.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "b0fe37c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1609, 7)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the data for Training Data and Testing Data\n",
    "# Last two years to be made testing data\n",
    "\n",
    "train = teams[teams[\"year\"] < 2012].copy()\n",
    "test = teams[teams[\"year\"] >= 2012].copy()\n",
    "\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "0d3831b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(405, 7)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3eab36ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "72f9aff9",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = [\"athletes\", \"prev_medals\"]\n",
    "target =  [\"medals\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "79931f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pass in the predictors from training dataset and target\n",
    "reg.fit(train[predictors], train[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "bf331f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on predictors from test dataset\n",
    "\n",
    "predictions = reg.predict(test[predictors])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "2b636b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tabular view\n",
    "test[\"predictions\"] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a9a32357",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>athletes</th>\n",
       "      <th>age</th>\n",
       "      <th>prev_medals</th>\n",
       "      <th>medals</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.961221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.176333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.425032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.711847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ALG</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>39</td>\n",
       "      <td>24.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.155629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.926958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>ZAM</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.640143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>ZAM</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>24.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.640143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>ZIM</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.505767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2143</th>\n",
       "      <td>ZIM</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2016</td>\n",
       "      <td>31</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.080748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>405 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team      country  year  athletes   age  prev_medals  medals  predictions\n",
       "6     AFG  Afghanistan  2012         6  24.8          1.0       1    -0.961221\n",
       "7     AFG  Afghanistan  2016         3  24.7          1.0       0    -1.176333\n",
       "24    ALB      Albania  2012        10  25.7          0.0       0    -1.425032\n",
       "25    ALB      Albania  2016         6  23.7          0.0       0    -1.711847\n",
       "37    ALG      Algeria  2012        39  24.8          2.0       1     2.155629\n",
       "...   ...          ...   ...       ...   ...          ...     ...          ...\n",
       "2111  YEM        Yemen  2016         3  19.3          0.0       0    -1.926958\n",
       "2131  ZAM       Zambia  2012         7  22.6          0.0       0    -1.640143\n",
       "2132  ZAM       Zambia  2016         7  24.1          0.0       0    -1.640143\n",
       "2142  ZIM     Zimbabwe  2012         9  27.3          4.0       0     1.505767\n",
       "2143  ZIM     Zimbabwe  2016        31  27.5          0.0       0     0.080748\n",
       "\n",
       "[405 rows x 8 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "db06ed54",
   "metadata": {},
   "outputs": [],
   "source": [
    "test.loc[test[\"predictions\"] < 0, \"predictions\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "8ba158c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test[\"predictions\"] = test[\"predictions\"].round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "e2cbee78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>athletes</th>\n",
       "      <th>age</th>\n",
       "      <th>prev_medals</th>\n",
       "      <th>medals</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>24.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ALB</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>23.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ALG</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2012</td>\n",
       "      <td>39</td>\n",
       "      <td>24.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>YEM</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2131</th>\n",
       "      <td>ZAM</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2132</th>\n",
       "      <td>ZAM</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>2016</td>\n",
       "      <td>7</td>\n",
       "      <td>24.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>ZIM</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2143</th>\n",
       "      <td>ZIM</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2016</td>\n",
       "      <td>31</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>405 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     team      country  year  athletes   age  prev_medals  medals  predictions\n",
       "6     AFG  Afghanistan  2012         6  24.8          1.0       1          0.0\n",
       "7     AFG  Afghanistan  2016         3  24.7          1.0       0          0.0\n",
       "24    ALB      Albania  2012        10  25.7          0.0       0          0.0\n",
       "25    ALB      Albania  2016         6  23.7          0.0       0          0.0\n",
       "37    ALG      Algeria  2012        39  24.8          2.0       1          2.0\n",
       "...   ...          ...   ...       ...   ...          ...     ...          ...\n",
       "2111  YEM        Yemen  2016         3  19.3          0.0       0          0.0\n",
       "2131  ZAM       Zambia  2012         7  22.6          0.0       0          0.0\n",
       "2132  ZAM       Zambia  2016         7  24.1          0.0       0          0.0\n",
       "2142  ZIM     Zimbabwe  2012         9  27.3          4.0       0          2.0\n",
       "2143  ZIM     Zimbabwe  2016        31  27.5          0.0       0          0.0\n",
       "\n",
       "[405 rows x 8 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4e49fae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Absolute error\n",
    "\n",
    "error = mean_absolute_error(test[\"medals\"], test[\"predictions\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "7a9c2062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2987654320987656"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value of mean absolute error\n",
    "error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5d9a6776",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2014.000000\n",
       "mean       10.990070\n",
       "std        33.627528\n",
       "min         0.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         5.000000\n",
       "max       442.000000\n",
       "Name: medals, dtype: float64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To determine if the mean absolute error value is reasonable or not\n",
    "# The mean absolute error should be lower than std (Standard Deviation)\n",
    "# In this case, the mean absolute error has a reasonable value\n",
    "\n",
    "teams.describe()[\"medals\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "eab569f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>team</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>athletes</th>\n",
       "      <th>age</th>\n",
       "      <th>prev_medals</th>\n",
       "      <th>medals</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2012</td>\n",
       "      <td>689</td>\n",
       "      <td>26.7</td>\n",
       "      <td>317.0</td>\n",
       "      <td>248</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2054</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>2016</td>\n",
       "      <td>719</td>\n",
       "      <td>26.4</td>\n",
       "      <td>248.0</td>\n",
       "      <td>264</td>\n",
       "      <td>236.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     team        country  year  athletes   age  prev_medals  medals  \\\n",
       "2053  USA  United States  2012       689  26.7        317.0     248   \n",
       "2054  USA  United States  2016       719  26.4        248.0     264   \n",
       "\n",
       "      predictions  \n",
       "2053        285.0  \n",
       "2054        236.0  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see the predictions of a certain country\n",
    "\n",
    "test[test[\"team\"] == \"USA\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "541d2390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team</th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>athletes</th>\n",
       "      <th>age</th>\n",
       "      <th>prev_medals</th>\n",
       "      <th>medals</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1666</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2012</td>\n",
       "      <td>549</td>\n",
       "      <td>25.4</td>\n",
       "      <td>142.0</td>\n",
       "      <td>140</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>RUS</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2016</td>\n",
       "      <td>406</td>\n",
       "      <td>25.4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>115</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     team country  year  athletes   age  prev_medals  medals  predictions\n",
       "1666  RUS  Russia  2012       549  25.4        142.0     140        144.0\n",
       "1667  RUS  Russia  2016       406  25.4        140.0     115        132.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[test[\"team\"] == \"RUS\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ce664b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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