qodatecnologia

qodatecnologia

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qodatecnologia

library(tidyverse) # pacote com divdersas funções úteis para Data Analysis
install.packages('pacman')
library(pacman)
pacman::p_load(pacman,dplyr, ggplot2, rio, gridExtra, scales, ggcorrplot, caret, e1071)

# Função de plotagem
fig <- function(width, heigth){
     options(repr.plot.width = width, repr.plot.height = heigth)
}

qodatecnologia

df <- read.csv("https://raw.githubusercontent.com/qodatecnologia/mobileprice/main/train.csv")
head(df)

qodatecnologia

longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income,median_house_value,ocean_proximity
-122.23,37.88,41.0,880.0,129.0,322.0,126.0,8.3252,452600.0,NEAR BAY
-122.22,37.86,21.0,7099.0,1106.0,2401.0,1138.0,8.3014,358500.0,NEAR BAY
-122.24,37.85,52.0,1467.0,190.0,496.0,177.0,7.2574,352100.0,NEAR BAY
-122.25,37.85,52.0,1274.0,235.0,558.0,219.0,5.6431,341300.0,NEAR BAY
-122.25,37.85,52.0,1627.0,280.0,565.0,259.0,3.8462,342200.0,NEAR BAY
-122.25,37.85,52.0,919.0,213.0,413.0,193.0,4.0368,269700.0,NEAR BAY
-122.25,37.84,52.0,2535.0,489.0,1094.0,514.0,3.6591,299200.0,NEAR BAY
-122.25,37.84,52.0,3104.0,687.0,1157.0,647.0,3.12,241400.0,NEAR BAY
-122.26,37.84,42.0,2555.0,665.0,1206.0,595.0,2.0804,226700.0,NEAR BAY

qodatecnologia

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('ViewingActivity-sample.csv')"

qodatecnologia

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Este é um título de nível 1\n",
    "\n",
    "## Este é um título de nível 2\n",
    "\n",

qodatecnologia

import requests

class vdocipher() :
    values = {}

    def __init__(self, csk):
        self.values[ 'clientSecretKey' ] = csk

    def getOtp(self, video, annotate=None, forcedBitrate=None):
        url = 'https://api.vdocipher.com/v2/otp?video='+video

qodatecnologia

# Using linear regression becasuse we want a QUANTITY VALUE: the price
import matplotlib.pyplot as plt
import numpy as np
import scipy
from sklearn.linear_model import LinearRegression
size = [[7],[10],[15],[30],[45]] #diameter of pizza
price = [[8],[11],[16],[38.5],[52]] #price in BRAZILIAN REALS, off course! R$
#seeing this in graph
plt.figure()
plt.xlabel('Diameter(cm)')