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iamdeepakram/Iris_data_set_analysis.ipynb

Created February 14, 2019 19:32
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Iris_data_set_analysis.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import libraries "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd \n",
"from pandas.plotting import scatter_matrix\n",
"import matplotlib.pyplot as plt \n",
"from sklearn import model_selection\n",
"from sklearn.metrics import classification_report \n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import accuracy_score \n",
"from sklearn.linear_model import LogisticRegression \n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.svm import SVC"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset = pd.read_csv(\"../Data Science Projects/iris.csv\")\n",
"dataset.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summarize the Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dimensions of Datset"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(150, 5)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Peek at the data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" <th>species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.7</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.6</td>\n",
" <td>3.4</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>5.0</td>\n",
" <td>3.4</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4.4</td>\n",
" <td>2.9</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4.9</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>5.4</td>\n",
" <td>3.7</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>4.8</td>\n",
" <td>3.4</td>\n",
" <td>1.6</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4.8</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4.3</td>\n",
" <td>3.0</td>\n",
" <td>1.1</td>\n",
" <td>0.1</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>5.8</td>\n",
" <td>4.0</td>\n",
" <td>1.2</td>\n",
" <td>0.2</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>5.7</td>\n",
" <td>4.4</td>\n",
" <td>1.5</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>5.4</td>\n",
" <td>3.9</td>\n",
" <td>1.3</td>\n",
" <td>0.4</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5.7</td>\n",
" <td>3.8</td>\n",
" <td>1.7</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5.1</td>\n",
" <td>3.8</td>\n",
" <td>1.5</td>\n",
" <td>0.3</td>\n",
" <td>setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width species\n",
"0 5.1 3.5 1.4 0.2 setosa\n",
"1 4.9 3.0 1.4 0.2 setosa\n",
"2 4.7 3.2 1.3 0.2 setosa\n",
"3 4.6 3.1 1.5 0.2 setosa\n",
"4 5.0 3.6 1.4 0.2 setosa\n",
"5 5.4 3.9 1.7 0.4 setosa\n",
"6 4.6 3.4 1.4 0.3 setosa\n",
"7 5.0 3.4 1.5 0.2 setosa\n",
"8 4.4 2.9 1.4 0.2 setosa\n",
"9 4.9 3.1 1.5 0.1 setosa\n",
"10 5.4 3.7 1.5 0.2 setosa\n",
"11 4.8 3.4 1.6 0.2 setosa\n",
"12 4.8 3.0 1.4 0.1 setosa\n",
"13 4.3 3.0 1.1 0.1 setosa\n",
"14 5.8 4.0 1.2 0.2 setosa\n",
"15 5.7 4.4 1.5 0.4 setosa\n",
"16 5.4 3.9 1.3 0.4 setosa\n",
"17 5.1 3.5 1.4 0.3 setosa\n",
"18 5.7 3.8 1.7 0.3 setosa\n",
"19 5.1 3.8 1.5 0.3 setosa"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stasticial Summary"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"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>sepal_length</th>\n",
" <th>sepal_width</th>\n",
" <th>petal_length</th>\n",
" <th>petal_width</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.843333</td>\n",
" <td>3.054000</td>\n",
" <td>3.758667</td>\n",
" <td>1.198667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.828066</td>\n",
" <td>0.433594</td>\n",
" <td>1.764420</td>\n",
" <td>0.763161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>4.300000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.100000</td>\n",
" <td>2.800000</td>\n",
" <td>1.600000</td>\n",
" <td>0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.800000</td>\n",
" <td>3.000000</td>\n",
" <td>4.350000</td>\n",
" <td>1.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.400000</td>\n",
" <td>3.300000</td>\n",
" <td>5.100000</td>\n",
" <td>1.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>7.900000</td>\n",
" <td>4.400000</td>\n",
" <td>6.900000</td>\n",
" <td>2.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sepal_length sepal_width petal_length petal_width\n",
"count 150.000000 150.000000 150.000000 150.000000\n",
"mean 5.843333 3.054000 3.758667 1.198667\n",
"std 0.828066 0.433594 1.764420 0.763161\n",
"min 4.300000 2.000000 1.000000 0.100000\n",
"25% 5.100000 2.800000 1.600000 0.300000\n",
"50% 5.800000 3.000000 4.350000 1.300000\n",
"75% 6.400000 3.300000 5.100000 1.800000\n",
"max 7.900000 4.400000 6.900000 2.500000"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Class Distribution"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"species\n",
"setosa 50\n",
"versicolor 50\n",
"virginica 50\n",
"dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.groupby('species').size()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Univariate Plots "
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dataset.plot(kind = 'box', subplots = True, layout = (2,2), sharex=False, sharey=False)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Histogram"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dataset.hist()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Multivariate Plots"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 16 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"scatter_matrix(dataset)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate Some Algorithms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a validation Dataset"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"array = dataset.values\n",
"X = array[:,0:4]\n",
"Y = array[:,4]\n",
"validation_size = 0.20\n",
"seed = 7\n",
"X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X,Y,test_size = validation_size, random_state = seed)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test Harness"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"seed = 7 \n",
"scoring = 'accuracy'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Build Models"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Spot check algorithms\n",
"models = []\n",
"models.append(('LR', LogisticRegression(solver = 'liblinear', multi_class= 'ovr')))\n",
"models.append(('LDA', LinearDiscriminantAnalysis()))\n",
"models.append(('KNN', KNeighborsClassifier()))\n",
"models.append(('CART', DecisionTreeClassifier()))\n",
"models.append(('NB', GaussianNB()))\n",
"models.append(('SVM', SVC(gamma = 'auto')))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LR: 0.966667 (0.040825)\n",
"LDA: 0.975000 (0.038188)\n",
"KNN: 0.983333 (0.033333)\n",
"CART: 0.975000 (0.038188)\n",
"NB: 0.975000 (0.053359)\n",
"SVM: 0.991667 (0.025000)\n"
]
}
],
"source": [
"# evaluate each model in turn\n",
"results = []\n",
"names = []\n",
"for name, model in models:\n",
" kfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
" cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
" results.append(cv_results)\n",
" names.append(name)\n",
" msg = \"%s: %f (%f)\" %(name, cv_results.mean(), cv_results.std())\n",
" print(msg)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Compare Algorithms\n",
"fig = plt.figure()\n",
"fig.suptitle('Algorithm Comparison')\n",
"ax = fig.add_subplot(111)\n",
"plt.boxplot(results)\n",
"ax.set_xticklabels(names)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9\n"
]
}
],
"source": [
"# Make predictions on validation dataset \n",
"knn = KNeighborsClassifier()\n",
"knn.fit(X_train, Y_train)\n",
"predictions = knn.predict(X_validation)\n",
"print(accuracy_score(Y_validation, predictions))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 7 0 0]\n",
" [ 0 11 1]\n",
" [ 0 2 9]]\n"
]
}
],
"source": [
"print(confusion_matrix(Y_validation, predictions))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" setosa 1.00 1.00 1.00 7\n",
" versicolor 0.85 0.92 0.88 12\n",
" virginica 0.90 0.82 0.86 11\n",
"\n",
" micro avg 0.90 0.90 0.90 30\n",
" macro avg 0.92 0.91 0.91 30\n",
"weighted avg 0.90 0.90 0.90 30\n",
"\n"
]
}
],
"source": [
"print(classification_report(Y_validation, predictions))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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