WoMakersCode · GABYper56 · Oct 18, 2019 · Oct 18, 2019 · Oct 18, 2019 · Oct 18, 2019
diff --git a/.DS_Store b/.DS_Store
diff --git a/2.1 Análise de Dados em Python/DataVisualizationAnswers.ipynb b/2.1 Análise de Dados em Python/DataVisualizationAnswers.ipynb
diff --git a/2.1 Análise de Dados em Python/PandasStructuredData-Answers.ipynb b/2.1 Análise de Dados em Python/PandasStructuredData-Answers.ipynb
diff --git a/3. Modelos regressivos/.DS_Store b/3. Modelos regressivos/.DS_Store
diff --git a/...gressivos/.ipynb_checkpoints/Exemplo Regressao Polinomial com Split Test-checkpoint.ipynb b/...gressivos/.ipynb_checkpoints/Exemplo Regressao Polinomial com Split Test-checkpoint.ipynb
diff --git a/3. Modelos regressivos/Exemplo Regressao Polinomial com Split Test.ipynb b/3. Modelos regressivos/Exemplo Regressao Polinomial com Split Test.ipynb
diff --git a/3. Modelos regressivos/Exercicio Diferenciação/tb-sales.xlsx b/3. Modelos regressivos/Exercicio Diferenciação/tb-sales.xlsx
diff --git a/...os/Exercicio_1/.ipynb_checkpoints/[Solução] Prevendo Expectativa de Vida-checkpoint.ipynb b/...os/Exercicio_1/.ipynb_checkpoints/[Solução] Prevendo Expectativa de Vida-checkpoint.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #Importe as bibliotecas que você irá usar\n",
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Carregue os dados\n",
+    "#Insira o data frame nessa váriavel\n",
+    "bmi_life_data = pd.read_csv(\"bmi_and_life_expectancy.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Execute o .fit do modelo e insira na variavel bmi_life_model\n",
+    "bmi_life_model = LinearRegression()\n",
+    "bmi_life_model.fit(bmi_life_data[['BMI']], bmi_life_data[['Life expectancy']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Faça predições usando o modelo para uma expectativa de vida de 21,07931\n",
+    "laos_life_exp = bmi_life_model.predict([[21.07931]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[60.31564716]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(laos_life_exp)"
+   ]
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/...ssivos/Prevendo Expectativa de Vida.ipynb → ...icio_1/Prevendo Expectativa de Vida.ipynb b/...ssivos/Prevendo Expectativa de Vida.ipynb → ...icio_1/Prevendo Expectativa de Vida.ipynb
diff --git a/...ção] Prevendo Expectativa de Vida.ipynb → ...ção] Prevendo Expectativa de Vida.ipynb b/...ção] Prevendo Expectativa de Vida.ipynb → ...ção] Prevendo Expectativa de Vida.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,24 +26,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Execute o .fit do modelo e insira na variavel bmi_life_model\n",
     "bmi_life_model = LinearRegression()\n",
-    "bmi_life_model.fit(bmi_life_data[['BMI']], bmi_life_data[['Life expectancy']])\n",
-    "\n"
+    "bmi_life_model.fit(bmi_life_data[['BMI']], bmi_life_data[['Life expectancy']])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Faça predições usando o modelo para uma expectativa de vida de 21,07931\n",
-    "laos_life_exp = bmi_life_model.predict(21.07931)"
+    "laos_life_exp = bmi_life_model.predict([[21.07931]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[60.31564716]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(laos_life_exp)"
    ]
   }
  ],

diff --git a/3. Modelos regressivos/Exercicio_1/[Solução] Prevendo Expectativa de Vida.ipynb b/3. Modelos regressivos/Exercicio_1/[Solução] Prevendo Expectativa de Vida.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# #Importe as bibliotecas que você irá usar\n",
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Carregue os dados\n",
+    "#Insira o data frame nessa váriavel\n",
+    "bmi_life_data = pd.read_csv(\"bmi_and_life_expectancy.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
+       "         normalize=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Execute o .fit do modelo e insira na variavel bmi_life_model\n",
+    "bmi_life_model = LinearRegression()\n",
+    "bmi_life_model.fit(bmi_life_data[['BMI']], bmi_life_data[['Life expectancy']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Faça predições usando o modelo para uma expectativa de vida de 21,07931\n",
+    "laos_life_exp = bmi_life_model.predict([[21.07931]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[60.31564716]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(laos_life_exp)"
+   ]
+  }
+ ],
+ "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/...s regressivos/bmi_and_life_expectancy.csv → ...s/Exercicio_1/bmi_and_life_expectancy.csv b/...s regressivos/bmi_and_life_expectancy.csv → ...s/Exercicio_1/bmi_and_life_expectancy.csv