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Scikit-Learn Tutorial | Machine Learning With Scikit-Learn | Sklearn | Python Tutorial | Simplilearn (https://youtu.be/0Lt9w-BxKFQ)
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scikit-learn-tut.ipynb
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{
"source": [
"# Scikit-Learn Tutorial | Machine Learning With Scikit-Learn | Sklearn | Python Tutorial | Simplilearn\n",
"\n",
"https://youtu.be/0Lt9w-BxKFQ"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Importing required packages:\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn import svm\n",
"from sklearn.neural_network import MLPClassifier\n",
"# from sklearn.linear_model import SGDClassifier\n",
"from sklearn.metrics import confusion_matrix, classification_report\n",
"from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Loading dataset\n",
"wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv', sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.4 0.70 0.00 1.9 0.076 \n",
"1 7.8 0.88 0.00 2.6 0.098 \n",
"2 7.8 0.76 0.04 2.3 0.092 \n",
"3 11.2 0.28 0.56 1.9 0.075 \n",
"4 7.4 0.70 0.00 1.9 0.076 \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",
"\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 "
],
"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>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>7.4</td>\n <td>0.70</td>\n <td>0.00</td>\n <td>1.9</td>\n <td>0.076</td>\n <td>11.0</td>\n <td>34.0</td>\n <td>0.9978</td>\n <td>3.51</td>\n <td>0.56</td>\n <td>9.4</td>\n <td>5</td>\n </tr>\n <tr>\n <th>1</th>\n <td>7.8</td>\n <td>0.88</td>\n <td>0.00</td>\n <td>2.6</td>\n <td>0.098</td>\n <td>25.0</td>\n <td>67.0</td>\n <td>0.9968</td>\n <td>3.20</td>\n <td>0.68</td>\n <td>9.8</td>\n <td>5</td>\n </tr>\n <tr>\n <th>2</th>\n <td>7.8</td>\n <td>0.76</td>\n <td>0.04</td>\n <td>2.3</td>\n <td>0.092</td>\n <td>15.0</td>\n <td>54.0</td>\n <td>0.9970</td>\n <td>3.26</td>\n <td>0.65</td>\n <td>9.8</td>\n <td>5</td>\n </tr>\n <tr>\n <th>3</th>\n <td>11.2</td>\n <td>0.28</td>\n <td>0.56</td>\n <td>1.9</td>\n <td>0.075</td>\n <td>17.0</td>\n <td>60.0</td>\n <td>0.9980</td>\n <td>3.16</td>\n <td>0.58</td>\n <td>9.8</td>\n <td>6</td>\n </tr>\n <tr>\n <th>4</th>\n <td>7.4</td>\n <td>0.70</td>\n <td>0.00</td>\n <td>1.9</td>\n <td>0.076</td>\n <td>11.0</td>\n <td>34.0</td>\n <td>0.9978</td>\n <td>3.51</td>\n <td>0.56</td>\n <td>9.4</td>\n <td>5</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"wine.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1599 entries, 0 to 1598\nData columns (total 12 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 fixed acidity 1599 non-null float64\n 1 volatile acidity 1599 non-null float64\n 2 citric acid 1599 non-null float64\n 3 residual sugar 1599 non-null float64\n 4 chlorides 1599 non-null float64\n 5 free sulfur dioxide 1599 non-null float64\n 6 total sulfur dioxide 1599 non-null float64\n 7 density 1599 non-null float64\n 8 pH 1599 non-null float64\n 9 sulphates 1599 non-null float64\n 10 alcohol 1599 non-null float64\n 11 quality 1599 non-null int64 \ndtypes: float64(11), int64(1)\nmemory usage: 150.0 KB\n"
]
}
],
"source": [
"wine.info()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"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"
]
},
"metadata": {},
"execution_count": 5
}
],
"source": [
"wine.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['bad', 'good']\n",
"Categories (2, object): ['bad' < 'good']"
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"# Preprocessing Data\n",
"bins = (2, 6.5, 8)\n",
"group_names = ['bad','good']\n",
"wine['quality'] = pd.cut(wine['quality'], bins=bins, labels=group_names)\n",
"wine['quality'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"label_quality = LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"wine['quality'] = label_quality.fit_transform(wine['quality'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" fixed acidity volatile acidity citric acid residual sugar chlorides \\\n",
"0 7.4 0.70 0.00 1.9 0.076 \n",
"1 7.8 0.88 0.00 2.6 0.098 \n",
"2 7.8 0.76 0.04 2.3 0.092 \n",
"3 11.2 0.28 0.56 1.9 0.075 \n",
"4 7.4 0.70 0.00 1.9 0.076 \n",
"5 7.4 0.66 0.00 1.8 0.075 \n",
"6 7.9 0.60 0.06 1.6 0.069 \n",
"7 7.3 0.65 0.00 1.2 0.065 \n",
"8 7.8 0.58 0.02 2.0 0.073 \n",
"9 7.5 0.50 0.36 6.1 0.071 \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",
"\n",
" alcohol quality \n",
"0 9.4 0 \n",
"1 9.8 0 \n",
"2 9.8 0 \n",
"3 9.8 0 \n",
"4 9.4 0 \n",
"5 9.4 0 \n",
"6 9.4 0 \n",
"7 10.0 1 \n",
"8 9.5 1 \n",
"9 10.5 0 "
],
"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>fixed acidity</th>\n <th>volatile acidity</th>\n <th>citric acid</th>\n <th>residual sugar</th>\n <th>chlorides</th>\n <th>free sulfur dioxide</th>\n <th>total sulfur dioxide</th>\n <th>density</th>\n <th>pH</th>\n <th>sulphates</th>\n <th>alcohol</th>\n <th>quality</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>7.4</td>\n <td>0.70</td>\n <td>0.00</td>\n <td>1.9</td>\n <td>0.076</td>\n <td>11.0</td>\n <td>34.0</td>\n <td>0.9978</td>\n <td>3.51</td>\n <td>0.56</td>\n <td>9.4</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>7.8</td>\n <td>0.88</td>\n <td>0.00</td>\n <td>2.6</td>\n <td>0.098</td>\n <td>25.0</td>\n <td>67.0</td>\n <td>0.9968</td>\n <td>3.20</td>\n <td>0.68</td>\n <td>9.8</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>7.8</td>\n <td>0.76</td>\n <td>0.04</td>\n <td>2.3</td>\n <td>0.092</td>\n <td>15.0</td>\n <td>54.0</td>\n <td>0.9970</td>\n <td>3.26</td>\n <td>0.65</td>\n <td>9.8</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>11.2</td>\n <td>0.28</td>\n <td>0.56</td>\n <td>1.9</td>\n <td>0.075</td>\n <td>17.0</td>\n <td>60.0</td>\n <td>0.9980</td>\n <td>3.16</td>\n <td>0.58</td>\n <td>9.8</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>7.4</td>\n <td>0.70</td>\n <td>0.00</td>\n <td>1.9</td>\n <td>0.076</td>\n <td>11.0</td>\n <td>34.0</td>\n <td>0.9978</td>\n <td>3.51</td>\n <td>0.56</td>\n <td>9.4</td>\n <td>0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>7.4</td>\n <td>0.66</td>\n <td>0.00</td>\n <td>1.8</td>\n <td>0.075</td>\n <td>13.0</td>\n <td>40.0</td>\n <td>0.9978</td>\n <td>3.51</td>\n <td>0.56</td>\n <td>9.4</td>\n <td>0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>7.9</td>\n <td>0.60</td>\n <td>0.06</td>\n <td>1.6</td>\n <td>0.069</td>\n <td>15.0</td>\n <td>59.0</td>\n <td>0.9964</td>\n <td>3.30</td>\n <td>0.46</td>\n <td>9.4</td>\n <td>0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>7.3</td>\n <td>0.65</td>\n <td>0.00</td>\n <td>1.2</td>\n <td>0.065</td>\n <td>15.0</td>\n <td>21.0</td>\n <td>0.9946</td>\n <td>3.39</td>\n <td>0.47</td>\n <td>10.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>8</th>\n <td>7.8</td>\n <td>0.58</td>\n <td>0.02</td>\n <td>2.0</td>\n <td>0.073</td>\n <td>9.0</td>\n <td>18.0</td>\n <td>0.9968</td>\n <td>3.36</td>\n <td>0.57</td>\n <td>9.5</td>\n <td>1</td>\n </tr>\n <tr>\n <th>9</th>\n <td>7.5</td>\n <td>0.50</td>\n <td>0.36</td>\n <td>6.1</td>\n <td>0.071</td>\n <td>17.0</td>\n <td>102.0</td>\n <td>0.9978</td>\n <td>3.35</td>\n <td>0.80</td>\n <td>10.5</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"wine.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 1382\n",
"1 217\n",
"Name: quality, dtype: int64"
]
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"wine['quality'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
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]
},
"metadata": {},
"execution_count": 11
},
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"output_type": "display_data",
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}
],
"source": [
"sns.displot(wine['fixed acidity'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/home/bruce/.local/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n warnings.warn(\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:xlabel='quality', ylabel='count'>"
]
},
"metadata": {},
"execution_count": 12
},
{
"output_type": "display_data",
"data": {
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"sns.countplot(wine['quality'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Now separate the dataset as reponse variable and feature variables\n",
"X = wine.drop('quality', axis=1)\n",
"y = wine['quality']"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Train and test splitting of data\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Applying standard scaling to get optimized result\n",
"sc = StandardScaler()\n",
"X_train = sc.fit_transform(X_train)\n",
"X_test = sc.transform(X_test)"
]
},
{
"source": [
"# Random Forest Classifier"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"rfc = RandomForestClassifier(n_estimators=200)\n",
"rfc.fit(X_train, y_train)\n",
"pred_rfc = rfc.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n\n 0 0.93 0.96 0.94 273\n 1 0.72 0.55 0.63 47\n\n accuracy 0.90 320\n macro avg 0.82 0.76 0.79 320\nweighted avg 0.90 0.90 0.90 320\n\n[[263 10]\n [ 21 26]]\n"
]
}
],
"source": [
"# Let's see how out model performed\n",
"print(classification_report(y_test, pred_rfc))\n",
"print(confusion_matrix(y_test, pred_rfc))"
]
},
{
"source": [
"# SVM Classifier"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"clf = svm.SVC()\n",
"clf.fit(X_train, y_train)\n",
"pred_clf = clf.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n\n 0 0.88 0.98 0.93 273\n 1 0.71 0.26 0.37 47\n\n accuracy 0.88 320\n macro avg 0.80 0.62 0.65 320\nweighted avg 0.86 0.88 0.85 320\n\n[[268 5]\n [ 35 12]]\n"
]
}
],
"source": [
"# Let's see how out model performed\n",
"print(classification_report(y_test, pred_clf))\n",
"print(confusion_matrix(y_test, pred_clf))"
]
},
{
"source": [
"# Neural Network"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/home/bruce/.local/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:614: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n warnings.warn(\n"
]
}
],
"source": [
"mlpc = MLPClassifier(hidden_layer_sizes=(11,11,11), max_iter=500)\n",
"mlpc.fit(X_train, y_train)\n",
"pred_mlpc = mlpc.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n\n 0 0.92 0.94 0.93 273\n 1 0.61 0.53 0.57 47\n\n accuracy 0.88 320\n macro avg 0.77 0.74 0.75 320\nweighted avg 0.88 0.88 0.88 320\n\n[[257 16]\n [ 22 25]]\n"
]
}
],
"source": [
"# Let's see how out model performed\n",
"print(classification_report(y_test, pred_mlpc))\n",
"print(confusion_matrix(y_test, pred_mlpc))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.903125"
]
},
"metadata": {},
"execution_count": 29
}
],
"source": [
"accuracy_score(y_test, pred_rfc)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0])"
]
},
"metadata": {},
"execution_count": 30
}
],
"source": [
"Xnew = [[7.3, 0.58, 0.00, 2.0, 0.065, 15.0, 21.0, 0.9946, 3.36, 0.47, 10.0]]\n",
"Xnew = sc.transform(Xnew)\n",
"rfc.predict(Xnew)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}
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