diff --git a/Winequality/WineQT.csv b/Winequality/WineQT.csv
new file mode 100644
index 0000000..215185e
--- /dev/null
+++ b/Winequality/WineQT.csv
@@ -0,0 +1,1144 @@
+fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality,Id
+7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,0
+7.8,0.88,0.0,2.6,0.098,25.0,67.0,0.9968,3.2,0.68,9.8,5,1
+7.8,0.76,0.04,2.3,0.092,15.0,54.0,0.997,3.26,0.65,9.8,5,2
+11.2,0.28,0.56,1.9,0.075,17.0,60.0,0.998,3.16,0.58,9.8,6,3
+7.4,0.7,0.0,1.9,0.076,11.0,34.0,0.9978,3.51,0.56,9.4,5,4
+7.4,0.66,0.0,1.8,0.075,13.0,40.0,0.9978,3.51,0.56,9.4,5,5
+7.9,0.6,0.06,1.6,0.069,15.0,59.0,0.9964,3.3,0.46,9.4,5,6
+7.3,0.65,0.0,1.2,0.065,15.0,21.0,0.9946,3.39,0.47,10.0,7,7
+7.8,0.58,0.02,2.0,0.073,9.0,18.0,0.9968,3.36,0.57,9.5,7,8
+6.7,0.58,0.08,1.8,0.09699999999999999,15.0,65.0,0.9959,3.28,0.54,9.2,5,10
+5.6,0.615,0.0,1.6,0.08900000000000001,16.0,59.0,0.9943,3.58,0.52,9.9,5,12
+7.8,0.61,0.29,1.6,0.114,9.0,29.0,0.9974,3.26,1.56,9.1,5,13
+8.5,0.28,0.56,1.8,0.092,35.0,103.0,0.9969,3.3,0.75,10.5,7,16
+7.9,0.32,0.51,1.8,0.341,17.0,56.0,0.9969,3.04,1.08,9.2,6,19
+7.6,0.39,0.31,2.3,0.08199999999999999,23.0,71.0,0.9982,3.52,0.65,9.7,5,21
+7.9,0.43,0.21,1.6,0.106,10.0,37.0,0.9966,3.17,0.91,9.5,5,22
+8.5,0.49,0.11,2.3,0.084,9.0,67.0,0.9968,3.17,0.53,9.4,5,23
+6.9,0.4,0.14,2.4,0.085,21.0,40.0,0.9968,3.43,0.63,9.7,6,24
+6.3,0.39,0.16,1.4,0.08,11.0,23.0,0.9955,3.34,0.56,9.3,5,25
+7.6,0.41,0.24,1.8,0.08,4.0,11.0,0.9962,3.28,0.59,9.5,5,26
+7.1,0.71,0.0,1.9,0.08,14.0,35.0,0.9972,3.47,0.55,9.4,5,28
+7.8,0.645,0.0,2.0,0.08199999999999999,8.0,16.0,0.9964,3.38,0.59,9.8,6,29
+6.7,0.675,0.07,2.4,0.08900000000000001,17.0,82.0,0.9958,3.35,0.54,10.1,5,30
+8.3,0.655,0.12,2.3,0.083,15.0,113.0,0.9966,3.17,0.66,9.8,5,32
+5.2,0.32,0.25,1.8,0.10300000000000001,13.0,50.0,0.9957,3.38,0.55,9.2,5,34
+7.8,0.645,0.0,5.5,0.086,5.0,18.0,0.9986,3.4,0.55,9.6,6,35
+7.8,0.6,0.14,2.4,0.086,3.0,15.0,0.9975,3.42,0.6,10.8,6,36
+8.1,0.38,0.28,2.1,0.066,13.0,30.0,0.9968,3.23,0.73,9.7,7,37
+7.3,0.45,0.36,5.9,0.07400000000000001,12.0,87.0,0.9978,3.33,0.83,10.5,5,40
+8.8,0.61,0.3,2.8,0.08800000000000001,17.0,46.0,0.9976,3.26,0.51,9.3,4,41
+7.5,0.49,0.2,2.6,0.332,8.0,14.0,0.9968,3.21,0.9,10.5,6,42
+8.1,0.66,0.22,2.2,0.069,9.0,23.0,0.9968,3.3,1.2,10.3,5,43
+4.6,0.52,0.15,2.1,0.054000000000000006,8.0,65.0,0.9934,3.9,0.56,13.1,4,45
+7.7,0.935,0.43,2.2,0.114,22.0,114.0,0.997,3.25,0.73,9.2,5,46
+8.8,0.66,0.26,1.7,0.07400000000000001,4.0,23.0,0.9971,3.15,0.74,9.2,5,50
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+6.6,0.5,0.04,2.1,0.068,6.0,14.0,0.9955,3.39,0.64,9.4,6,52
+8.6,0.38,0.36,3.0,0.081,30.0,119.0,0.997,3.2,0.56,9.4,5,53
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+7.3,0.39,0.31,2.4,0.07400000000000001,9.0,46.0,0.9962,3.41,0.54,9.4,6,59
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+8.3,0.675,0.26,2.1,0.084,11.0,43.0,0.9976,3.31,0.53,9.2,4,73
+8.8,0.41,0.64,2.2,0.09300000000000001,9.0,42.0,0.9986,3.54,0.66,10.5,5,76
+6.8,0.785,0.0,2.4,0.10400000000000001,14.0,30.0,0.9966,3.52,0.55,10.7,6,77
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+6.2,0.45,0.2,1.6,0.069,3.0,15.0,0.9958,3.41,0.56,9.2,5,80
+7.4,0.5,0.47,2.0,0.086,21.0,73.0,0.997,3.36,0.57,9.1,5,82
+6.3,0.3,0.48,1.8,0.069,18.0,61.0,0.9959,3.44,0.78,10.3,6,84
+6.9,0.55,0.15,2.2,0.076,19.0,40.0,0.9961,3.41,0.59,10.1,5,85
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+7.7,0.49,0.26,1.9,0.062,9.0,31.0,0.9966,3.39,0.64,9.6,5,87
+9.3,0.39,0.44,2.1,0.107,34.0,125.0,0.9978,3.14,1.22,9.5,5,88
+7.0,0.62,0.08,1.8,0.076,8.0,24.0,0.9978,3.48,0.53,9.0,5,89
+7.9,0.52,0.26,1.9,0.079,42.0,140.0,0.9964,3.23,0.54,9.5,5,90
+8.6,0.49,0.28,1.9,0.11,20.0,136.0,0.9972,2.93,1.95,9.9,6,91
+7.7,0.49,0.26,1.9,0.062,9.0,31.0,0.9966,3.39,0.64,9.6,5,93
+5.0,1.02,0.04,1.4,0.045,41.0,85.0,0.9938,3.75,0.48,10.5,4,94
+6.8,0.775,0.0,3.0,0.102,8.0,23.0,0.9965,3.45,0.56,10.7,5,96
+7.6,0.9,0.06,2.5,0.079,5.0,10.0,0.9967,3.39,0.56,9.8,5,98
+8.1,0.545,0.18,1.9,0.08,13.0,35.0,0.9972,3.3,0.59,9.0,6,99
+8.3,0.61,0.3,2.1,0.084,11.0,50.0,0.9972,3.4,0.61,10.2,6,100
+8.1,0.545,0.18,1.9,0.08,13.0,35.0,0.9972,3.3,0.59,9.0,6,102
+8.1,0.575,0.22,2.1,0.077,12.0,65.0,0.9967,3.29,0.51,9.2,5,103
+7.2,0.49,0.24,2.2,0.07,5.0,36.0,0.996,3.33,0.48,9.4,5,104
+8.1,0.575,0.22,2.1,0.077,12.0,65.0,0.9967,3.29,0.51,9.2,5,105
+7.8,0.41,0.68,1.7,0.467,18.0,69.0,0.9973,3.08,1.31,9.3,5,106
+6.2,0.63,0.31,1.7,0.08800000000000001,15.0,64.0,0.9969,3.46,0.79,9.3,5,107
+7.8,0.56,0.19,1.8,0.10400000000000001,12.0,47.0,0.9964,3.19,0.93,9.5,5,110
+8.4,0.62,0.09,2.2,0.084,11.0,108.0,0.9964,3.15,0.66,9.8,5,111
+10.1,0.31,0.44,2.3,0.08,22.0,46.0,0.9988,3.32,0.67,9.7,6,113
+7.8,0.56,0.19,1.8,0.10400000000000001,12.0,47.0,0.9964,3.19,0.93,9.5,5,114
+9.4,0.4,0.31,2.2,0.09,13.0,62.0,0.9966,3.07,0.63,10.5,6,115
+8.3,0.54,0.28,1.9,0.077,11.0,40.0,0.9978,3.39,0.61,10.0,6,116
+7.3,1.07,0.09,1.7,0.17800000000000002,10.0,89.0,0.9962,3.3,0.57,9.0,5,120
+8.8,0.55,0.04,2.2,0.11900000000000001,14.0,56.0,0.9962,3.21,0.6,10.9,6,121
+7.3,0.695,0.0,2.5,0.075,3.0,13.0,0.998,3.49,0.52,9.2,5,122
+7.8,0.5,0.17,1.6,0.08199999999999999,21.0,102.0,0.996,3.39,0.48,9.5,5,124
+8.2,1.33,0.0,1.7,0.081,3.0,12.0,0.9964,3.53,0.49,10.9,5,126
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+8.0,0.59,0.16,1.8,0.065,3.0,16.0,0.9962,3.42,0.92,10.5,7,128
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+8.1,0.67,0.55,1.8,0.11699999999999999,32.0,141.0,0.9968,3.17,0.62,9.4,5,145
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diff --git a/Winequality/wine quality data set.ipynb b/Winequality/wine quality data set.ipynb
new file mode 100644
index 0000000..ce2e154
--- /dev/null
+++ b/Winequality/wine quality data set.ipynb
@@ -0,0 +1,1097 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "aebed2f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import seaborn as sns\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.metrics import accuracy_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "f711b454",
+ "metadata": {},
+ "outputs": [
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+ "metadata": {},
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+ "df"
+ ]
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\n",
+ " \n",
+ " 25% | \n",
+ " 7.100000 | \n",
+ " 0.392500 | \n",
+ " 0.090000 | \n",
+ " 1.900000 | \n",
+ " 0.070000 | \n",
+ " 7.000000 | \n",
+ " 21.000000 | \n",
+ " 0.995570 | \n",
+ " 3.205000 | \n",
+ " 0.550000 | \n",
+ " 9.500000 | \n",
+ " 5.000000 | \n",
+ " 411.000000 | \n",
+ "
\n",
+ " \n",
+ " 50% | \n",
+ " 7.900000 | \n",
+ " 0.520000 | \n",
+ " 0.250000 | \n",
+ " 2.200000 | \n",
+ " 0.079000 | \n",
+ " 13.000000 | \n",
+ " 37.000000 | \n",
+ " 0.996680 | \n",
+ " 3.310000 | \n",
+ " 0.620000 | \n",
+ " 10.200000 | \n",
+ " 6.000000 | \n",
+ " 794.000000 | \n",
+ "
\n",
+ " \n",
+ " 75% | \n",
+ " 9.100000 | \n",
+ " 0.640000 | \n",
+ " 0.420000 | \n",
+ " 2.600000 | \n",
+ " 0.090000 | \n",
+ " 21.000000 | \n",
+ " 61.000000 | \n",
+ " 0.997845 | \n",
+ " 3.400000 | \n",
+ " 0.730000 | \n",
+ " 11.100000 | \n",
+ " 6.000000 | \n",
+ " 1209.500000 | \n",
+ "
\n",
+ " \n",
+ " max | \n",
+ " 15.900000 | \n",
+ " 1.580000 | \n",
+ " 1.000000 | \n",
+ " 15.500000 | \n",
+ " 0.611000 | \n",
+ " 68.000000 | \n",
+ " 289.000000 | \n",
+ " 1.003690 | \n",
+ " 4.010000 | \n",
+ " 2.000000 | \n",
+ " 14.900000 | \n",
+ " 8.000000 | \n",
+ " 1597.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " fixed acidity volatile acidity citric acid residual sugar \\\n",
+ "count 1143.000000 1143.000000 1143.000000 1143.000000 \n",
+ "mean 8.311111 0.531339 0.268364 2.532152 \n",
+ "std 1.747595 0.179633 0.196686 1.355917 \n",
+ "min 4.600000 0.120000 0.000000 0.900000 \n",
+ "25% 7.100000 0.392500 0.090000 1.900000 \n",
+ "50% 7.900000 0.520000 0.250000 2.200000 \n",
+ "75% 9.100000 0.640000 0.420000 2.600000 \n",
+ "max 15.900000 1.580000 1.000000 15.500000 \n",
+ "\n",
+ " chlorides free sulfur dioxide total sulfur dioxide density \\\n",
+ "count 1143.000000 1143.000000 1143.000000 1143.000000 \n",
+ "mean 0.086933 15.615486 45.914698 0.996730 \n",
+ "std 0.047267 10.250486 32.782130 0.001925 \n",
+ "min 0.012000 1.000000 6.000000 0.990070 \n",
+ "25% 0.070000 7.000000 21.000000 0.995570 \n",
+ "50% 0.079000 13.000000 37.000000 0.996680 \n",
+ "75% 0.090000 21.000000 61.000000 0.997845 \n",
+ "max 0.611000 68.000000 289.000000 1.003690 \n",
+ "\n",
+ " pH sulphates alcohol quality Id \n",
+ "count 1143.000000 1143.000000 1143.000000 1143.000000 1143.000000 \n",
+ "mean 3.311015 0.657708 10.442111 5.657043 804.969379 \n",
+ "std 0.156664 0.170399 1.082196 0.805824 463.997116 \n",
+ "min 2.740000 0.330000 8.400000 3.000000 0.000000 \n",
+ "25% 3.205000 0.550000 9.500000 5.000000 411.000000 \n",
+ "50% 3.310000 0.620000 10.200000 6.000000 794.000000 \n",
+ "75% 3.400000 0.730000 11.100000 6.000000 1209.500000 \n",
+ "max 4.010000 2.000000 14.900000 8.000000 1597.000000 "
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6b3eec60",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([5, 6, 7, 4, 8, 3], dtype=int64)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df['quality'].unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "ef766c06",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.catplot(x='quality',data=df,kind='count')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "5a77da68",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.bar(df['quality'], df['alcohol'], color = 'blue')\n",
+ "plt.title('relation of alcohol with quality')\n",
+ "plt.xlabel('quality')\n",
+ "plt.ylabel('alcohol')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "dc55261d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.bar(df['quality'], df['fixed acidity'], color = 'pink')\n",
+ "plt.title('relation of alcohol with quality')\n",
+ "plt.xlabel('quality')\n",
+ "plt.ylabel('alcohol')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "878927d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.bar(df['quality'], df['volatile acidity'], color = 'yellow')\n",
+ "plt.title('relation of alcohol with quality')\n",
+ "plt.xlabel('quality')\n",
+ "plt.ylabel('alcohol')\n",
+ "plt.show() #inversely proportional"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "41ee05be",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.bar(df['quality'], df['citric acid'], color = 'maroon')\n",
+ "plt.title('relation of alcohol with quality')\n",
+ "plt.xlabel('quality')\n",
+ "plt.ylabel('alcohol')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "83ae3232",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "correl=df.corr()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "207a0fbb",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10,10))\n",
+ "sns.heatmap(correl,cmap='Blues',fmt='.1f',annot=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "0413c086",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y=df.drop('quality',axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "c178a162",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 0\n",
+ "1 0\n",
+ "2 0\n",
+ "3 0\n",
+ "4 0\n",
+ " ..\n",
+ "1138 0\n",
+ "1139 0\n",
+ "1140 0\n",
+ "1141 0\n",
+ "1142 0\n",
+ "Name: quality, Length: 1143, dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "#label binarisation\n",
+ "Z=df['quality'].apply(lambda z_value:1 if z_value>=7 else 0)\n",
+ "print(Z)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "a95e7c05",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.ensemble import RandomForestClassifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "05104c9e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_train,x_test,y_train,y_test=train_test_split(y,Z,test_size=0.2,random_state=3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "20c0deed",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(914,) (229,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y_train.shape,y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "dce034d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Random forest model\n",
+ "model=RandomForestClassifier()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "f1b0e447",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "RandomForestClassifier()"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(x_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "37a29bca",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "x_test_predict=model.predict(x_test)\n",
+ "test_accuracy=accuracy_score(x_test_predict,y_test)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "88de3362",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9170305676855895\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(test_accuracy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "a468b77a",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1]\n",
+ "good quality wine\n"
+ ]
+ }
+ ],
+ "source": [
+ "inputdata=(8.5,0.28,0.56,1.8,0.092,35,103,0.9969,3.3,0.75,10.5,16)\n",
+ "idas_numpyarr=np.asarray(inputdata)\n",
+ "inputdata_reshape=idas_numpyarr.reshape(1,-1)\n",
+ "prediction=model.predict(inputdata_reshape)\n",
+ "print(prediction)\n",
+ "if (prediction[0]==0):\n",
+ " print(\"bad quality wine\")\n",
+ "else:\n",
+ " print(\"good quality wine\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "17587fd9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#using logistic regression\n",
+ "from sklearn.linear_model import LogisticRegression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "b1bddbc2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = LogisticRegression()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "f2818e28",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\THANMAI\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+ "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+ "\n",
+ "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+ " https://scikit-learn.org/stable/modules/preprocessing.html\n",
+ "Please also refer to the documentation for alternative solver options:\n",
+ " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+ " n_iter_i = _check_optimize_result(\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "LogisticRegression()"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(x_train, y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "81296cd2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "test accuracy is 0.8733624454148472\n"
+ ]
+ }
+ ],
+ "source": [
+ "x_test_pre = model.predict(x_test)\n",
+ "testaccuracy=accuracy_score(x_test_pre,y_test)\n",
+ "print(\"test accuracy is\",testaccuracy)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "50d8a227",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#using support vector machines\n",
+ "from sklearn.svm import SVC\n",
+ "model = SVC()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "45586ce2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "SVC()"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(x_train,y_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "16692a50",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Testing accuracy : 0.834061135371179\n"
+ ]
+ }
+ ],
+ "source": [
+ "x_test_pred = model.predict(x_test)\n",
+ "print(\"Testing accuracy :\",accuracy_score(x_test_pred, y_test))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "796751ea",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.8.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}