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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "\n",
+ "\n",
+ "#import all libraries needed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " fixed acidity | \n",
+ " volatile acidity | \n",
+ " citric acid | \n",
+ " residual sugar | \n",
+ " chlorides | \n",
+ " free sulfur dioxide | \n",
+ " total sulfur dioxide | \n",
+ " density | \n",
+ " pH | \n",
+ " sulphates | \n",
+ " alcohol | \n",
+ " quality | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 7.4 | \n",
+ " 0.700 | \n",
+ " 0.00 | \n",
+ " 1.9 | \n",
+ " 0.076 | \n",
+ " 11.0 | \n",
+ " 34.0 | \n",
+ " 0.9978 | \n",
+ " 3.51 | \n",
+ " 0.56 | \n",
+ " 9.4 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 7.8 | \n",
+ " 0.880 | \n",
+ " 0.00 | \n",
+ " 2.6 | \n",
+ " 0.098 | \n",
+ " 25.0 | \n",
+ " 67.0 | \n",
+ " 0.9968 | \n",
+ " 3.20 | \n",
+ " 0.68 | \n",
+ " 9.8 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 7.8 | \n",
+ " 0.760 | \n",
+ " 0.04 | \n",
+ " 2.3 | \n",
+ " 0.092 | \n",
+ " 15.0 | \n",
+ " 54.0 | \n",
+ " 0.9970 | \n",
+ " 3.26 | \n",
+ " 0.65 | \n",
+ " 9.8 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 11.2 | \n",
+ " 0.280 | \n",
+ " 0.56 | \n",
+ " 1.9 | \n",
+ " 0.075 | \n",
+ " 17.0 | \n",
+ " 60.0 | \n",
+ " 0.9980 | \n",
+ " 3.16 | \n",
+ " 0.58 | \n",
+ " 9.8 | \n",
+ " 6 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 7.4 | \n",
+ " 0.700 | \n",
+ " 0.00 | \n",
+ " 1.9 | \n",
+ " 0.076 | \n",
+ " 11.0 | \n",
+ " 34.0 | \n",
+ " 0.9978 | \n",
+ " 3.51 | \n",
+ " 0.56 | \n",
+ " 9.4 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 7.4 | \n",
+ " 0.660 | \n",
+ " 0.00 | \n",
+ " 1.8 | \n",
+ " 0.075 | \n",
+ " 13.0 | \n",
+ " 40.0 | \n",
+ " 0.9978 | \n",
+ " 3.51 | \n",
+ " 0.56 | \n",
+ " 9.4 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 6 | \n",
+ " 7.9 | \n",
+ " 0.600 | \n",
+ " 0.06 | \n",
+ " 1.6 | \n",
+ " 0.069 | \n",
+ " 15.0 | \n",
+ " 59.0 | \n",
+ " 0.9964 | \n",
+ " 3.30 | \n",
+ " 0.46 | \n",
+ " 9.4 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 7 | \n",
+ " 7.3 | \n",
+ " 0.650 | \n",
+ " 0.00 | \n",
+ " 1.2 | \n",
+ " 0.065 | \n",
+ " 15.0 | \n",
+ " 21.0 | \n",
+ " 0.9946 | \n",
+ " 3.39 | \n",
+ " 0.47 | \n",
+ " 10.0 | \n",
+ " 7 | \n",
+ "
\n",
+ " \n",
+ " 8 | \n",
+ " 7.8 | \n",
+ " 0.580 | \n",
+ " 0.02 | \n",
+ " 2.0 | \n",
+ " 0.073 | \n",
+ " 9.0 | \n",
+ " 18.0 | \n",
+ " 0.9968 | \n",
+ " 3.36 | \n",
+ " 0.57 | \n",
+ " 9.5 | \n",
+ " 7 | \n",
+ "
\n",
+ " \n",
+ " 9 | \n",
+ " 7.5 | \n",
+ " 0.500 | \n",
+ " 0.36 | \n",
+ " 6.1 | \n",
+ " 0.071 | \n",
+ " 17.0 | \n",
+ " 102.0 | \n",
+ " 0.9978 | \n",
+ " 3.35 | \n",
+ " 0.80 | \n",
+ " 10.5 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 10 | \n",
+ " 6.7 | \n",
+ " 0.580 | \n",
+ " 0.08 | \n",
+ " 1.8 | \n",
+ " 0.097 | \n",
+ " 15.0 | \n",
+ " 65.0 | \n",
+ " 0.9959 | \n",
+ " 3.28 | \n",
+ " 0.54 | \n",
+ " 9.2 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 11 | \n",
+ " 7.5 | \n",
+ " 0.500 | \n",
+ " 0.36 | \n",
+ " 6.1 | \n",
+ " 0.071 | \n",
+ " 17.0 | \n",
+ " 102.0 | \n",
+ " 0.9978 | \n",
+ " 3.35 | \n",
+ " 0.80 | \n",
+ " 10.5 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 12 | \n",
+ " 5.6 | \n",
+ " 0.615 | \n",
+ " 0.00 | \n",
+ " 1.6 | \n",
+ " 0.089 | \n",
+ " 16.0 | \n",
+ " 59.0 | \n",
+ " 0.9943 | \n",
+ " 3.58 | \n",
+ " 0.52 | \n",
+ " 9.9 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 13 | \n",
+ " 7.8 | \n",
+ " 0.610 | \n",
+ " 0.29 | \n",
+ " 1.6 | \n",
+ " 0.114 | \n",
+ " 9.0 | \n",
+ " 29.0 | \n",
+ " 0.9974 | \n",
+ " 3.26 | \n",
+ " 1.56 | \n",
+ " 9.1 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " 14 | \n",
+ " 8.9 | \n",
+ " 0.620 | \n",
+ " 0.18 | \n",
+ " 3.8 | \n",
+ " 0.176 | \n",
+ " 52.0 | \n",
+ " 145.0 | \n",
+ " 0.9986 | \n",
+ " 3.16 | \n",
+ " 0.88 | \n",
+ " 9.2 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
+ "0 7.4 0.700 0.00 1.9 0.076 \n",
+ "1 7.8 0.880 0.00 2.6 0.098 \n",
+ "2 7.8 0.760 0.04 2.3 0.092 \n",
+ "3 11.2 0.280 0.56 1.9 0.075 \n",
+ "4 7.4 0.700 0.00 1.9 0.076 \n",
+ "5 7.4 0.660 0.00 1.8 0.075 \n",
+ "6 7.9 0.600 0.06 1.6 0.069 \n",
+ "7 7.3 0.650 0.00 1.2 0.065 \n",
+ "8 7.8 0.580 0.02 2.0 0.073 \n",
+ "9 7.5 0.500 0.36 6.1 0.071 \n",
+ "10 6.7 0.580 0.08 1.8 0.097 \n",
+ "11 7.5 0.500 0.36 6.1 0.071 \n",
+ "12 5.6 0.615 0.00 1.6 0.089 \n",
+ "13 7.8 0.610 0.29 1.6 0.114 \n",
+ "14 8.9 0.620 0.18 3.8 0.176 \n",
+ "\n",
+ " free sulfur dioxide total sulfur dioxide density pH sulphates \\\n",
+ "0 11.0 34.0 0.9978 3.51 0.56 \n",
+ "1 25.0 67.0 0.9968 3.20 0.68 \n",
+ "2 15.0 54.0 0.9970 3.26 0.65 \n",
+ "3 17.0 60.0 0.9980 3.16 0.58 \n",
+ "4 11.0 34.0 0.9978 3.51 0.56 \n",
+ "5 13.0 40.0 0.9978 3.51 0.56 \n",
+ "6 15.0 59.0 0.9964 3.30 0.46 \n",
+ "7 15.0 21.0 0.9946 3.39 0.47 \n",
+ "8 9.0 18.0 0.9968 3.36 0.57 \n",
+ "9 17.0 102.0 0.9978 3.35 0.80 \n",
+ "10 15.0 65.0 0.9959 3.28 0.54 \n",
+ "11 17.0 102.0 0.9978 3.35 0.80 \n",
+ "12 16.0 59.0 0.9943 3.58 0.52 \n",
+ "13 9.0 29.0 0.9974 3.26 1.56 \n",
+ "14 52.0 145.0 0.9986 3.16 0.88 \n",
+ "\n",
+ " alcohol quality \n",
+ "0 9.4 5 \n",
+ "1 9.8 5 \n",
+ "2 9.8 5 \n",
+ "3 9.8 6 \n",
+ "4 9.4 5 \n",
+ "5 9.4 5 \n",
+ "6 9.4 5 \n",
+ "7 10.0 7 \n",
+ "8 9.5 7 \n",
+ "9 10.5 5 \n",
+ "10 9.2 5 \n",
+ "11 10.5 5 \n",
+ "12 9.9 5 \n",
+ "13 9.1 5 \n",
+ "14 9.2 5 "
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv('Dataset/winequality-red.csv')\n",
+ "#delimiter used since thee file uses \";\" to seperate values\n",
+ "df.head(15)\n",
+ "#i have created a dataframe with the white wine dataset and thne used the head func to show the first 5 entries\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "fixed acidity 0\n",
+ "volatile acidity 0\n",
+ "citric acid 0\n",
+ "residual sugar 0\n",
+ "chlorides 0\n",
+ "free sulfur dioxide 0\n",
+ "total sulfur dioxide 0\n",
+ "density 0\n",
+ "pH 0\n",
+ "sulphates 0\n",
+ "alcohol 0\n",
+ "quality 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 94,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#pre-processing goes here\n",
+ "df.isnull().sum() #checks if there are any missing values.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 95,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
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