Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BeerConsumption.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Data</th>\n",
       "      <th>TemperatureMedium</th>\n",
       "      <th>TemperatureMin</th>\n",
       "      <th>TemperatureMax</th>\n",
       "      <th>Precipitacao (mm)</th>\n",
       "      <th>EndOfWeek</th>\n",
       "      <th>Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>27,3</td>\n",
       "      <td>23,9</td>\n",
       "      <td>32,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>27,02</td>\n",
       "      <td>24,5</td>\n",
       "      <td>33,5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>24,82</td>\n",
       "      <td>22,4</td>\n",
       "      <td>29,9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>30.814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>23,98</td>\n",
       "      <td>21,5</td>\n",
       "      <td>28,6</td>\n",
       "      <td>1,2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>23,82</td>\n",
       "      <td>21</td>\n",
       "      <td>28,3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Data TemperatureMedium TemperatureMin TemperatureMax  \\\n",
       "0  2015-01-01              27,3           23,9           32,5   \n",
       "1  2015-01-02             27,02           24,5           33,5   \n",
       "2  2015-01-03             24,82           22,4           29,9   \n",
       "3  2015-01-04             23,98           21,5           28,6   \n",
       "4  2015-01-05             23,82             21           28,3   \n",
       "\n",
       "  Precipitacao (mm)  EndOfWeek  Consumption  \n",
       "0                 0        0.0       25.461  \n",
       "1                 0        0.0       28.972  \n",
       "2                 0        1.0       30.814  \n",
       "3               1,2        1.0       29.799  \n",
       "4                 0        0.0       28.900  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 941 entries, 0 to 940\n",
      "Data columns (total 7 columns):\n",
      "Data                 365 non-null object\n",
      "TemperatureMedium    365 non-null object\n",
      "TemperatureMin       365 non-null object\n",
      "TemperatureMax       365 non-null object\n",
      "Precipitacao (mm)    365 non-null object\n",
      "EndOfWeek            365 non-null float64\n",
      "Consumption          365 non-null float64\n",
      "dtypes: float64(2), object(5)\n",
      "memory usage: 51.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[['TemperatureMedium', 'Consumption']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['TemperatureMedium']]\n",
    "Y=df[['Consumption']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '25,96'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-23-e8baebda8092>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mxTrain\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mxTest\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0myTrain\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0myTest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mlreg\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mlreg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxTrain\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myTrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\user1\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    456\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[1;32m--> 458\u001b[1;33m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[0;32m    459\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\user1\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    745\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    746\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 747\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    748\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    749\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[1;32mc:\\users\\user1\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    560\u001b[0m         \u001b[1;31m# make sure we actually converted to numeric:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    561\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdtype_numeric\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"O\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m             \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    563\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '25,96'"
     ]
    }
   ],
   "source": [
    "xTrain , xTest , yTrain , yTest = train_test_split(X,Y)\n",
    "lreg=LinearRegression()\n",
    "lreg.fit(xTrain,yTrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x212ffebe278>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "predictions = lreg.predict(xTest)\n",
    "plt.scatter(yTest,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}