cases/sell_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projeto Ciência de Dados - Previsão de Vendas\n",
    "\n",
    "- O desafio é conseguir prever as vendas de um determinado período com base nos gastos em anúncios nas 3 grandes redes que uma empresa fictícia investe: TV, jornal e rádio.\n",
    "- TV, Jornal e Rádio estão em milhares de reais\n",
    "- Vendas estão em milhões de reais"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passo a Passo de um Projeto de Ciência de Dados\n",
    "\n",
    "- Passo 1: Entendimento do Desafio\n",
    "- Passo 2: Entendimento da Área/Empresa\n",
    "- Passo 3: Extração/Obtenção de Dados\n",
    "- Passo 4: Ajuste de Dados (Tratamento/Limpeza)\n",
    "- Passo 5: Análise Exploratória\n",
    "- Passo 6: Modelagem + Algoritmos (Aqui que entra a Inteligência Artificial, se necessário)\n",
    "- Passo 7: Interpretação de Resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importando a Base de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Jornal</th>\n",
       "      <th>Vendas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Jornal  Vendas\n",
       "0    230.1   37.8    69.2    22.1\n",
       "1     44.5   39.3    45.1    10.4\n",
       "2     17.2   45.9    69.3    12.0\n",
       "3    151.5   41.3    58.5    16.5\n",
       "4    180.8   10.8    58.4    17.9\n",
       "..     ...    ...     ...     ...\n",
       "195   38.2    3.7    13.8     7.6\n",
       "196   94.2    4.9     8.1    14.0\n",
       "197  177.0    9.3     6.4    14.8\n",
       "198  283.6   42.0    66.2    25.5\n",
       "199  232.1    8.6     8.7    18.4\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "tabela = pd.read_csv(\"../data/advertising.csv\")\n",
    "display(tabela)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Análise Exploratória\n",
    "- Vamos tentar visualizar como as informações de cada item estão distribuídas\n",
    "- Vamos ver a correlação entre cada um dos itens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Jornal</th>\n",
       "      <th>Vendas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.901208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.349631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jornal</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vendas</th>\n",
       "      <td>0.901208</td>\n",
       "      <td>0.349631</td>\n",
       "      <td>0.157960</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              TV     Radio    Jornal    Vendas\n",
       "TV      1.000000  0.054809  0.056648  0.901208\n",
       "Radio   0.054809  1.000000  0.354104  0.349631\n",
       "Jornal  0.056648  0.354104  1.000000  0.157960\n",
       "Vendas  0.901208  0.349631  0.157960  1.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "display(tabela.corr())\n",
    "\n",
    "# Criar gráfico utilizando seaborn\n",
    "sbn.heatmap(tabela.corr(), cmap=\"Wistia\")\n",
    "\n",
    "# Utilizando o pyplot para exibir o gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Com isso, podemos partir para a preparação dos dados para treinarmos o Modelo de Machine Learning\n",
    "\n",
    "- Separando em dados de treino e dados de teste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x -> valores conhecidos\n",
    "# y -> valor a ser previsto\n",
    "\n",
    "x = tabela[[\"TV\", \"Radio\", \"Jornal\"]]\n",
    "y = tabela[\"Vendas\"]\n",
    "\n",
    "# Importando função que separa a base de dados em dados para treino e dados para teste\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temos um problema de regressão - Vamos escolher os modelos que vamos usar:\n",
    "\n",
    "- Regressão Linear\n",
    "- RandomForest (Árvore de Decisão)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importando modelos de predição\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Criando modelos (inteligência artificial) utilizando regressão linear e árvore de decisão\n",
    "modelo_regressao_linear = LinearRegression()\n",
    "modelo_arvore_decisao = RandomForestRegressor()\n",
    "\n",
    "# Treinando os modelos\n",
    "modelo_regressao_linear.fit(x_treino, y_treino)\n",
    "modelo_arvore_decisao.fit(x_treino, y_treino)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Teste da inteligência artificial e avaliação do melhor modelo\n",
    "\n",
    "- Vamos usar o R² -> diz o % que o nosso modelo consegue explicar o que acontece"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8900446717134075\n",
      "0.9561069622521304\n"
     ]
    }
   ],
   "source": [
    "# Realizando predições utilizando os modelos treinados\n",
    "previsao_regressao_linear = modelo_regressao_linear.predict(x_teste)\n",
    "previsao_arvore_decisao = modelo_arvore_decisao.predict(x_teste)\n",
    "\n",
    "# Importando o medidor de fator R^2 (fator de exatidão/acurácia do modelo)\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Mostrando em tela o fator R^2 obtido para cada modelo treinado\n",
    "print(r2_score(y_teste, previsao_regressao_linear))\n",
    "print(r2_score(y_teste, previsao_arvore_decisao))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualização gráfica das previsões"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criando tabela com o pandas\n",
    "tabela_auxiliar = pd.DataFrame()\n",
    "tabela_auxiliar[\"y_teste\"] = y_teste\n",
    "tabela_auxiliar[\"arvore decisao\"] = previsao_arvore_decisao\n",
    "tabela_auxiliar[\"regressao linear\"] = previsao_regressao_linear\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "sbn.lineplot(data=tabela_auxiliar)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fazendo uma nova previsão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      TV  Radio  Jornal\n",
      "0   23.1    3.8    69.2\n",
      "1   44.5    0.0     5.1\n",
      "2  170.2   45.9     0.0\n"
     ]
    }
   ],
   "source": [
    "# Lendo os novos valores da nova base de dados\n",
    "tabela_novos = pd.read_csv(\"../data/new_data.csv\")\n",
    "print(tabela_novos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 7.805  8.647 19.777]\n"
     ]
    }
   ],
   "source": [
    "# Mostrando os valores das novas predições\n",
    "print(modelo_arvore_decisao.predict(tabela_novos))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}