Analise de investimentos.ipynb

{"nbformat_minor": 1, "cells": [{"source": "# Watson Studio Lab", "cell_type": "markdown", "metadata": {"collapsed": true}}, {"source": "Carregando a base de dados", "cell_type": "markdown", "metadata": {}}, {"source": "\nimport types\nimport pandas as pd\nfrom botocore.client import Config\nimport ibm_boto3\n\ndef __iter__(self): return 0\n\n# @hidden_cell\n# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n# You might want to remove those credentials before you share the notebook.\nclient_58b2fff1efe64e29abda9172ee977f54 = ibm_boto3.client(service_name='s3',\n    ibm_api_key_id='Cq0DGH0_Bi0teBGbG_8ZyxO8VzVHwyKZEdhypc4uieI5',\n    ibm_auth_endpoint=\"https://iam.ng.bluemix.net/oidc/token\",\n    config=Config(signature_version='oauth'),\n    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n\nbody = client_58b2fff1efe64e29abda9172ee977f54.get_object(Bucket='talknlabs-donotdelete-pr-66gvqnq2wkq7pm',Key='PublicidadeI.csv')['Body']\n# add missing __iter__ method, so pandas accepts body as file-like object\nif not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n\ndf_IPub = pd.read_csv(body)\ndf_IPub.head()\n", "cell_type": "code", "execution_count": 1, "outputs": [{"output_type": "execute_result", "metadata": {}, "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>Periodo</th>\n      <th>Propaganda</th>\n      <th>Desconto</th>\n      <th>Vendas</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>16-Feb</td>\n      <td>8.33</td>\n      <td>12.0</td>\n      <td>1528.5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>16-Mar</td>\n      <td>83.33</td>\n      <td>5.5</td>\n      <td>1560.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>16-Apr</td>\n      <td>25.00</td>\n      <td>8.0</td>\n      <td>1419.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>16-May</td>\n      <td>41.67</td>\n      <td>4.0</td>\n      <td>1335.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>16-Jun</td>\n      <td>16.67</td>\n      <td>9.5</td>\n      <td>1462.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>", "text/plain": "  Periodo  Propaganda  Desconto  Vendas\n0  16-Feb        8.33      12.0  1528.5\n1  16-Mar       83.33       5.5  1560.0\n2  16-Apr       25.00       8.0  1419.0\n3  16-May       41.67       4.0  1335.0\n4  16-Jun       16.67       9.5  1462.5"}, "execution_count": 1}], "metadata": {}}, {"source": "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy as sp\nimport seaborn as sns", "cell_type": "code", "execution_count": 2, "outputs": [], "metadata": {}}, {"source": "sns.heatmap(df_IPub.corr(), annot= True, cmap=\"YlGnBu\")", "cell_type": "code", "execution_count": 3, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f525b4b5b00>"}, "execution_count": 3}, {"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<Figure size 432x288 with 2 Axes>"}, "metadata": {"needs_background": "light"}}], "metadata": {}}, {"source": "# Vamos criar um modelo simples", "cell_type": "markdown", "metadata": {}}, {"source": "Carregando bibliotecas de ML", "cell_type": "markdown", "metadata": {}}, {"source": "from sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_score, cross_val_predict\nfrom sklearn.linear_model import Lasso\nfrom sklearn.ensemble import RandomForestRegressor\n\nfrom xgboost import XGBRegressor\n\nfrom sklearn.preprocessing import RobustScaler, StandardScaler, MaxAbsScaler\nfrom sklearn.metrics import mean_squared_error\n\nfrom scipy import stats\nfrom scipy.stats import norm, skew \n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split\nimport seaborn as sns\n\n\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_score, cross_val_predict\nfrom sklearn.linear_model import Lasso\nfrom sklearn.ensemble import RandomForestRegressor\nfrom xgboost import XGBRegressor\n\nfrom sklearn.preprocessing import RobustScaler, StandardScaler, MaxAbsScaler\nfrom sklearn.metrics import mean_squared_error\n\n", "cell_type": "code", "execution_count": 4, "outputs": [], "metadata": {}}, {"source": "df_IPub.info()", "cell_type": "code", "execution_count": 5, "outputs": [{"output_type": "stream", "name": "stdout", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 17 entries, 0 to 16\nData columns (total 4 columns):\nPeriodo       17 non-null object\nPropaganda    17 non-null float64\nDesconto      17 non-null float64\nVendas        17 non-null float64\ndtypes: float64(3), object(1)\nmemory usage: 624.0+ bytes\n"}], "metadata": {}}, {"source": "from sklearn.model_selection import train_test_split\n\nvariaveis = ['Propaganda','Desconto']\nalvo = ['Vendas']\n        \nX = df_IPub[variaveis].values\ny = df_IPub[alvo].values\nsplit_test_size = 0.30\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split_test_size, random_state=42)", "cell_type": "code", "execution_count": 6, "outputs": [], "metadata": {}}, {"source": "print(\"{0:0.2f}% in training set\".format((len(X_train)/len(df_IPub.index)) * 100))\nprint(\"{0:0.2f}% in test set\".format((len(X_test)/len(df_IPub.index)) * 100))", "cell_type": "code", "execution_count": 7, "outputs": [{"output_type": "stream", "name": "stdout", "text": "64.71% in training set\n35.29% in test set\n"}], "metadata": {}}, {"source": "from sklearn import linear_model\n\nlr_model = linear_model.LinearRegression()\nlr_model.fit(X_train, y_train.ravel())\n", "cell_type": "code", "execution_count": 8, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n         normalize=False)"}, "execution_count": 8}], "metadata": {}}, {"source": "print(\"Vendas = \",lr_model.intercept_,\" + \",lr_model.coef_)", "cell_type": "code", "execution_count": 9, "outputs": [{"output_type": "stream", "name": "stdout", "text": "Vendas =  1085.0344489465701  +  [ 2.83193412 34.56946124]\n"}], "metadata": {}}, {"source": "from sklearn.metrics import mean_squared_error, r2_score\n\ny_pred = lr_model.predict(X_test)\n\nprint('R2 score: %.2f' % r2_score(y_test, y_pred))\nprint('Mean Squared Error: %.2f' % mean_squared_error(y_test, y_pred) )", "cell_type": "code", "execution_count": 10, "outputs": [{"output_type": "stream", "name": "stdout", "text": "R2 score: 0.96\nMean Squared Error: 774.68\n"}], "metadata": {"scrolled": true}}, {"source": "plt.hist(df_IPub.Vendas, color=\"blue\", alpha=0.5, bins=20, density=True)", "cell_type": "code", "execution_count": 11, "outputs": [{"output_type": "execute_result", "metadata": {}, "data": {"text/plain": "(array([0.00504382, 0.00252191, 0.00252191, 0.        , 0.00252191,\n        0.00504382, 0.00252191, 0.00252191, 0.        , 0.        ,\n        0.00252191, 0.00756573, 0.00252191, 0.        , 0.00252191,\n        0.        , 0.        , 0.        , 0.00252191, 0.00252191]),\n array([1281.   , 1304.325, 1327.65 , 1350.975, 1374.3  , 1397.625,\n        1420.95 , 1444.275, 1467.6  , 1490.925, 1514.25 , 1537.575,\n        1560.9  , 1584.225, 1607.55 , 1630.875, 1654.2  , 1677.525,\n        1700.85 , 1724.175, 1747.5  ]),\n <a list of 20 Patch objects>)"}, "execution_count": 11}, {"output_type": "display_data", "data": {"image/png": 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\n", "text/plain": "<Figure size 432x288 with 1 Axes>"}, "metadata": {"needs_background": "light"}}], "metadata": {"scrolled": true}}, {"source": "", "cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}}, {"source": "", "cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}}, {"source": "", "cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}}, {"source": "", "cell_type": "code", "execution_count": null, "outputs": [], "metadata": {}}], "nbformat": 4, "metadata": {"kernelspec": {"display_name": "Python 3.6", "name": "python3", "language": "python"}, "language_info": {"mimetype": "text/x-python", "nbconvert_exporter": "python", "version": "3.6.8", "name": "python", "pygments_lexer": "ipython3", "file_extension": ".py", "codemirror_mode": {"version": 3, "name": "ipython"}}}}