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projjal1/wine-class-prediction.ipynb

Created November 16, 2020 04:46
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Wine Class Prediction.ipynb
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wine-class-prediction.ipynb
{
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"metadata": {
"colab": {
"name": "Wine Class Prediction.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyOYWOhy9JCKZKvDLDEtnark",
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},
"kernelspec": {
"name": "python3",
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/projjal1/a5a941dc664f292bcf641bebc4e82edd/wine-class-prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AlPpQ6_dLpW1"
},
"source": [
"Importing all the required modules for dataset analysis"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vKRxyyIxG_JS"
},
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.preprocessing import StandardScaler"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "7Ue0P3B2LvJ2"
},
"source": [
"Importing dataset from github repo"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NWUUg3HXHrvU"
},
"source": [
"file_path='https://raw.githubusercontent.com/projjal1/datasets/master/wine-k-means.csv'"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "yzx_FgoYLzc0"
},
"source": [
"Loading the dataset and looking at few rows "
]
},
{
"cell_type": "code",
"metadata": {
"id": "rxA6VwloHtzZ",
"outputId": "393afb1f-fa1c-46b6-fa7d-1e61347cbddb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 212
}
},
"source": [
"data=pd.read_csv(file_path)\n",
"data.head()"
],
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Class</th>\n",
" <th>Alcohol</th>\n",
" <th>Malic Acid</th>\n",
" <th>Ash</th>\n",
" <th>Alkalinity of Ash</th>\n",
" <th>Magnesium</th>\n",
" <th>Total Phenols</th>\n",
" <th>Flavanoids</th>\n",
" <th>Non-flavanoid phenols</th>\n",
" <th>Proanthocyanins</th>\n",
" <th>Color Intensity</th>\n",
" <th>Hue</th>\n",
" <th>OD280/OD315 of diluted wines</th>\n",
" <th>Proline</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>14.23</td>\n",
" <td>1.71</td>\n",
" <td>2.43</td>\n",
" <td>15.6</td>\n",
" <td>127</td>\n",
" <td>2.80</td>\n",
" <td>3.06</td>\n",
" <td>0.28</td>\n",
" <td>2.29</td>\n",
" <td>5.64</td>\n",
" <td>1.04</td>\n",
" <td>3.92</td>\n",
" <td>1065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>13.20</td>\n",
" <td>1.78</td>\n",
" <td>2.14</td>\n",
" <td>11.2</td>\n",
" <td>100</td>\n",
" <td>2.65</td>\n",
" <td>2.76</td>\n",
" <td>0.26</td>\n",
" <td>1.28</td>\n",
" <td>4.38</td>\n",
" <td>1.05</td>\n",
" <td>3.40</td>\n",
" <td>1050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>13.16</td>\n",
" <td>2.36</td>\n",
" <td>2.67</td>\n",
" <td>18.6</td>\n",
" <td>101</td>\n",
" <td>2.80</td>\n",
" <td>3.24</td>\n",
" <td>0.30</td>\n",
" <td>2.81</td>\n",
" <td>5.68</td>\n",
" <td>1.03</td>\n",
" <td>3.17</td>\n",
" <td>1185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>14.37</td>\n",
" <td>1.95</td>\n",
" <td>2.50</td>\n",
" <td>16.8</td>\n",
" <td>113</td>\n",
" <td>3.85</td>\n",
" <td>3.49</td>\n",
" <td>0.24</td>\n",
" <td>2.18</td>\n",
" <td>7.80</td>\n",
" <td>0.86</td>\n",
" <td>3.45</td>\n",
" <td>1480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>13.24</td>\n",
" <td>2.59</td>\n",
" <td>2.87</td>\n",
" <td>21.0</td>\n",
" <td>118</td>\n",
" <td>2.80</td>\n",
" <td>2.69</td>\n",
" <td>0.39</td>\n",
" <td>1.82</td>\n",
" <td>4.32</td>\n",
" <td>1.04</td>\n",
" <td>2.93</td>\n",
" <td>735</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Class Alcohol Malic Acid ... Hue OD280/OD315 of diluted wines Proline\n",
"0 1 14.23 1.71 ... 1.04 3.92 1065\n",
"1 1 13.20 1.78 ... 1.05 3.40 1050\n",
"2 1 13.16 2.36 ... 1.03 3.17 1185\n",
"3 1 14.37 1.95 ... 0.86 3.45 1480\n",
"4 1 13.24 2.59 ... 1.04 2.93 735\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-BRyRO2sL4-p"
},
"source": [
"Shuffling the dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VKJ382DGH-ie",
"outputId": "7af5810a-5b68-4a5c-c242-ac2c9d72cc70",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 212
}
},
"source": [
"df=data.sample(frac=1).reset_index(drop=True)\n",
"df.head()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Class</th>\n",
" <th>Alcohol</th>\n",
" <th>Malic Acid</th>\n",
" <th>Ash</th>\n",
" <th>Alkalinity of Ash</th>\n",
" <th>Magnesium</th>\n",
" <th>Total Phenols</th>\n",
" <th>Flavanoids</th>\n",
" <th>Non-flavanoid phenols</th>\n",
" <th>Proanthocyanins</th>\n",
" <th>Color Intensity</th>\n",
" <th>Hue</th>\n",
" <th>OD280/OD315 of diluted wines</th>\n",
" <th>Proline</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>11.81</td>\n",
" <td>2.12</td>\n",
" <td>2.74</td>\n",
" <td>21.5</td>\n",
" <td>134</td>\n",
" <td>1.60</td>\n",
" <td>0.99</td>\n",
" <td>0.14</td>\n",
" <td>1.56</td>\n",
" <td>2.50</td>\n",
" <td>0.95</td>\n",
" <td>2.26</td>\n",
" <td>625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>13.05</td>\n",
" <td>2.05</td>\n",
" <td>3.22</td>\n",
" <td>25.0</td>\n",
" <td>124</td>\n",
" <td>2.63</td>\n",
" <td>2.68</td>\n",
" <td>0.47</td>\n",
" <td>1.92</td>\n",
" <td>3.58</td>\n",
" <td>1.13</td>\n",
" <td>3.20</td>\n",
" <td>830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>13.05</td>\n",
" <td>1.77</td>\n",
" <td>2.10</td>\n",
" <td>17.0</td>\n",
" <td>107</td>\n",
" <td>3.00</td>\n",
" <td>3.00</td>\n",
" <td>0.28</td>\n",
" <td>2.03</td>\n",
" <td>5.04</td>\n",
" <td>0.88</td>\n",
" <td>3.35</td>\n",
" <td>885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2</td>\n",
" <td>11.61</td>\n",
" <td>1.35</td>\n",
" <td>2.70</td>\n",
" <td>20.0</td>\n",
" <td>94</td>\n",
" <td>2.74</td>\n",
" <td>2.92</td>\n",
" <td>0.29</td>\n",
" <td>2.49</td>\n",
" <td>2.65</td>\n",
" <td>0.96</td>\n",
" <td>3.26</td>\n",
" <td>680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>14.38</td>\n",
" <td>3.59</td>\n",
" <td>2.28</td>\n",
" <td>16.0</td>\n",
" <td>102</td>\n",
" <td>3.25</td>\n",
" <td>3.17</td>\n",
" <td>0.27</td>\n",
" <td>2.19</td>\n",
" <td>4.90</td>\n",
" <td>1.04</td>\n",
" <td>3.44</td>\n",
" <td>1065</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Class Alcohol Malic Acid ... Hue OD280/OD315 of diluted wines Proline\n",
"0 2 11.81 2.12 ... 0.95 2.26 625\n",
"1 1 13.05 2.05 ... 1.13 3.20 830\n",
"2 1 13.05 1.77 ... 0.88 3.35 885\n",
"3 2 11.61 1.35 ... 0.96 3.26 680\n",
"4 1 14.38 3.59 ... 1.04 3.44 1065\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jeGo2YBaJ8Ev",
"outputId": "77383776-3ef8-4394-9daa-9eb299fee086",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"df.shape"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(178, 14)"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qjMaNXJGL_OG"
},
"source": [
"Extracting labels and values from the dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rRlqbAmVISUE"
},
"source": [
"params=df.values\n",
"label=params[:,0]\n",
"values=params[:,1:]"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Egq-COo5JIcK",
"outputId": "023a08b9-c784-4c86-eb8d-6c5a1121fbd1",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"print(values[:3])\n",
"print(label[:3])"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"text": [
"[[1.181e+01 2.120e+00 2.740e+00 2.150e+01 1.340e+02 1.600e+00 9.900e-01\n",
" 1.400e-01 1.560e+00 2.500e+00 9.500e-01 2.260e+00 6.250e+02]\n",
" [1.305e+01 2.050e+00 3.220e+00 2.500e+01 1.240e+02 2.630e+00 2.680e+00\n",
" 4.700e-01 1.920e+00 3.580e+00 1.130e+00 3.200e+00 8.300e+02]\n",
" [1.305e+01 1.770e+00 2.100e+00 1.700e+01 1.070e+02 3.000e+00 3.000e+00\n",
" 2.800e-01 2.030e+00 5.040e+00 8.800e-01 3.350e+00 8.850e+02]]\n",
"[2. 1. 1.]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AoHgtju7MCfk"
},
"source": [
"Importing StandardScaler preprocessing library and normalizing the dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cmqkK0NpJTit",
"outputId": "bf7b2554-19d9-4ea2-df34-0669769c0a41",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"scaler=StandardScaler()\n",
"scaler.fit(values)\n",
"norm_values=scaler.transform(values)\n",
"print(norm_values[:2])"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"[[-1.47072867 -0.19420791 1.36520822 0.60208828 2.4053986 -1.11380046\n",
" -1.04339216 -1.78765596 -0.05413743 -1.1065529 -0.03268321 -0.49673551\n",
" -0.38816832]\n",
" [ 0.06099988 -0.25704433 3.11977186 1.65308575 1.7032652 0.53660103\n",
" 0.6533116 0.87142004 0.57661286 -0.63937732 0.75703776 0.83096074\n",
" 0.2646529 ]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QiYb1JapMJPt"
},
"source": [
"Splitting the dataset into test and train data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NQAthTEgKIjf"
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train,X_test,y_train,y_test=train_test_split(norm_values,label,test_size=0.33, random_state=42)"
],
"execution_count": 51,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "qwDCOxirMNI4"
},
"source": [
"Loading the Kmeans clustering model and training it"
]
},
{
"cell_type": "code",
"metadata": {
"id": "o-jTJp7IJv9w",
"outputId": "94e47a8d-c8e7-4afe-c0c6-3310a939a016",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"model=KMeans(max_iter=1000,n_clusters=5)\n",
"model.fit(X_train,y_train)"
],
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=1000,\n",
" n_clusters=5, n_init=10, n_jobs=None, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fMzG9CO1MSUc"
},
"source": [
"Perform predictions on the test data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Bl9pgTUcJ5wM"
},
"source": [
"predictions=model.predict(X_test)"
],
"execution_count": 62,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "epKZF80FMWMb"
},
"source": [
"Looking at the accuracy score metric. Yohoo! we got 93%"
]
},
{
"cell_type": "code",
"metadata": {
"id": "282gUE6kKq3Z",
"outputId": "d95ffe7b-b953-4d69-d011-32f009122a50",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"from sklearn.metrics import accuracy_score\n",
"score=accuracy_score(y_test,predictions)\n",
"print(score)"
],
"execution_count": 63,
"outputs": [
{
"output_type": "stream",
"text": [
"0.9322033898305084\n"
],
"name": "stdout"
}
]
}
]
}
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