aprendizado-de-maquina-i/regressao/regressao_linear_multipla.ipynb

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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
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   "name": "python3",
   "display_name": "Python 3.8.5 64-bit",
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 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ],
      "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>R&amp;D Spend</th>\n      <th>Administration</th>\n      <th>Marketing Spend</th>\n      <th>State</th>\n      <th>Profit</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>165349.20</td>\n      <td>136897.80</td>\n      <td>471784.10</td>\n      <td>New York</td>\n      <td>192261.83</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>162597.70</td>\n      <td>151377.59</td>\n      <td>443898.53</td>\n      <td>California</td>\n      <td>191792.06</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>153441.51</td>\n      <td>101145.55</td>\n      <td>407934.54</td>\n      <td>Florida</td>\n      <td>191050.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>144372.41</td>\n      <td>118671.85</td>\n      <td>383199.62</td>\n      <td>New York</td>\n      <td>182901.99</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>142107.34</td>\n      <td>91391.77</td>\n      <td>366168.42</td>\n      <td>Florida</td>\n      <td>166187.94</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "investimentos = pd.read_csv('50_startups.csv')\n",
    "investimentos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = investimentos.iloc[:, :-1].values\n",
    "y = investimentos.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "California    17\n",
       "New York      17\n",
       "Florida       16\n",
       "Name: State, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "investimentos['State'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "oHe = OneHotEncoder()\n",
    "ct = ColumnTransformer(transformers=[\n",
    "    ('encoder', oHe, [3])],\n",
    "    remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_encoded = ct.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([126187.39411504,  85788.82259513,  99777.02815178,  45706.12238329,\n",
       "       127062.20722771,  51891.8388446 , 109114.62977495, 100600.61123702,\n",
       "        97953.99874716, 111730.57706809, 128818.49200668, 174195.35772632,\n",
       "        93736.2853844 , 148381.04097159, 172313.87139388])"
      ]
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([134307.35,  81005.76,  99937.59,  64926.08, 125370.37,  35673.41,\n",
       "       105733.54, 107404.34,  97427.84, 122776.86, 141585.52, 166187.94,\n",
       "        89949.14, 152211.77, 182901.99])"
      ]
     },
     "metadata": {},
     "execution_count": 36
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "MSE: 9210.154995186738\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print('MSE:', np.sqrt(mean_squared_error(y_test, y_pred)))"
   ]
  }
 ]
}