LinearRegressionModels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. pip 21.0 will drop support for Python 2.7 in January 2021. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support\u001b[0m\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting pandas\n",
      "  Downloading pandas-0.24.2-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (16.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 16.7 MB 16.2 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: python-dateutil>=2.5.0 in /Users/ramyanagarajan/Library/Python/2.7/lib/python/site-packages (from pandas) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2011k in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from pandas) (2013.7)\n",
      "Collecting numpy>=1.12.0\n",
      "  Downloading numpy-1.16.6-cp27-cp27m-macosx_10_9_x86_64.whl (13.9 MB)\n",
      "\u001b[K     |████████████████████████████████| 13.9 MB 1.0 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: six>=1.5 in /System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
      "\u001b[31mERROR: matplotlib 1.3.1 requires nose, which is not installed.\u001b[0m\n",
      "Installing collected packages: numpy, pandas\n",
      "Successfully installed numpy-1.16.6 pandas-0.24.2\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Python 2.7 reached the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 is no longer maintained. pip 21.0 will drop support for Python 2.7 in January 2021. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support\u001b[0m\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting scikit-learn\n",
      "  Downloading scikit_learn-0.20.4-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (8.3 MB)\n",
      "\u001b[K     |████████████████████████████████| 8.3 MB 2.8 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy>=1.8.2 in /Users/ramyanagarajan/Library/Python/2.7/lib/python/site-packages (from scikit-learn) (1.16.6)\n",
      "Collecting scipy>=0.13.3\n",
      "  Downloading scipy-1.2.3-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (27.4 MB)\n",
      "\u001b[K     |████████████████████████████████| 27.4 MB 65 kB/s s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: scipy, scikit-learn\n",
      "Successfully installed scikit-learn-0.20.4 scipy-1.2.3\n"
     ]
    }
   ],
   "source": [
    "!pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>year</th>\n",
       "      <th>VAA</th>\n",
       "      <th>PSNV</th>\n",
       "      <th>GE</th>\n",
       "      <th>RQ</th>\n",
       "      <th>RL</th>\n",
       "      <th>CC</th>\n",
       "      <th>GDPgrowth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>1.44</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.42</td>\n",
       "      <td>1.47</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.35</td>\n",
       "      <td>6.471014671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002</td>\n",
       "      <td>-1.43</td>\n",
       "      <td>-2.04</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>-1.26</td>\n",
       "      <td>..</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>-1.46</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>13.66568652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>-0.53</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>4.53996064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.43345681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   VAA  PSNV    GE    RQ    RL    CC    GDPgrowth\n",
       "0  2002  1.44  1.28  1.42  1.47  1.25  1.35  6.471014671\n",
       "1  2002 -1.43 -2.04 -1.57 -1.80 -1.67 -1.26           ..\n",
       "2  2002 -1.24 -1.58 -1.24 -1.46 -1.62 -1.18  13.66568652\n",
       "3  2002 -0.01 -0.29 -0.53 -0.22 -0.76 -0.87   4.53996064\n",
       "4  2002 -0.63  0.93  0.85  1.12  0.73  1.15   2.43345681"
      ]
     },
     "execution_count": 91,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_csv(\"dataset.csv\")\n",
    "df = df.drop(['Tier', 'country', 'country_abb'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
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     "text": [
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      "done!\n",
      "[1, 34, 39, 46, 71, 112, 119, 139, 154, 155, 160, 172, 224, 229, 236, 262, 306, 313, 333, 348, 349, 354, 368, 390, 392, 424, 429, 436, 462, 507, 514, 534, 538, 550, 551, 556, 570, 593, 595, 627, 632, 639, 665, 710, 717, 737, 741, 753, 754, 759, 773, 796, 798, 830, 835, 842, 868, 913, 921, 941, 945, 957, 958, 963, 977, 1000, 1002, 1034, 1039, 1046, 1071, 1071, 1071, 1071, 1071, 1071, 1073, 1118, 1126, 1137, 1147, 1151, 1163, 1164, 1169, 1183, 1206, 1208, 1240, 1245, 1252, 1278, 1324, 1332, 1354, 1358, 1370, 1371, 1376, 1378, 1390, 1413, 1415, 1447, 1450, 1453, 1460, 1487, 1533, 1541, 1549, 1564, 1568, 1580, 1581, 1586, 1588, 1600, 1623, 1625, 1657, 1660, 1663, 1670, 1697, 1729, 1743, 1751, 1759, 1774, 1778, 1790, 1791, 1796, 1798, 1810, 1833, 1835, 1868, 1873, 1880, 1907, 1939, 1953, 1961, 1983, 1987, 1999, 2000, 2005, 2007, 2019, 2032, 2043, 2045, 2078, 2083, 2090, 2097, 2117, 2149, 2163, 2171, 2193, 2197, 2209, 2210, 2215, 2217, 2229, 2242, 2253, 2255, 2288, 2293, 2300, 2307, 2327, 2359, 2373, 2381, 2403, 2407, 2419, 2420, 2425, 2427, 2439, 2452, 2494, 2499, 2513, 2533, 2564, 2578, 2607, 2622, 2623, 2628, 2630, 2642, 2654, 2696, 2701, 2715, 2735, 2766, 2780, 2809, 2824, 2825, 2830, 2832, 2844, 2852, 2856, 2891, 2898, 2903, 2917, 2937, 2968, 2982, 3011, 3026, 3027, 3032, 3034, 3046, 3054, 3058, 3093, 3100, 3105, 3119, 3139, 3170, 3184, 3213, 3228, 3229, 3234, 3236, 3248, 3256, 3260, 3267, 3295, 3302, 3307, 3310, 3321, 3341, 3351, 3372, 3386, 3415, 3430, 3431, 3436, 3438, 3450, 3458, 3462]\n"
     ]
    }
   ],
   "source": [
    "# # some of the rows have 'NaN' as labels, so gotta drop those\n",
    "drop_row_indices = []\n",
    "for i, row in df.iterrows():\n",
    "    for j, col in row.iteritems():\n",
    "        try:\n",
    "            row[j] = float(col)\n",
    "        except ValueError:\n",
    "            print(\"ValueError\")\n",
    "            drop_row_indices.append(i)\n",
    "            continue\n",
    "        if math.isnan(row[j]):\n",
    "            drop_row_indices.append(i)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(drop_row_indices, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.119534040338607"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = ['year', 'VAA', 'PSNV', 'GE', 'RQ', 'RL', 'CC']\n",
    "\n",
    "X = df.loc[:, features]\n",
    "y = df.GDPgrowth\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.15)\n",
    "\n",
    "regression_model = LinearRegression()\n",
    "regression_model.fit(X_train, y_train)\n",
    "predicted_y = regression_model.predict(X_test)\n",
    "mean_squared_error(y_test, predicted_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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