diff --git a/COMP3217.docx b/COMP3217.docx
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diff --git a/Report.pdf b/Report.pdf
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diff --git a/TestingResultsMulti.csv b/TestingResultsMulti.csv
index b733921374f9dcaf5b7cab3d436b004cfd8610ab..25643dba0c719c835d21918a9e612e7646888d6f 100644
--- a/TestingResultsMulti.csv
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diff --git a/part1.ipynb b/part1.ipynb
index 5e41726999a68751d75079df9f62d0966ed865a7..e6f8e86a9c3233b40bcc56ca6efd89c9a4deb011 100644
--- a/part1.ipynb
+++ b/part1.ipynb
@@ -150,7 +150,7 @@
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
- " 0 0.86 0.94 0.90 588\n",
+ " 0 0.86 0.93 0.90 588\n",
" 1 0.93 0.86 0.89 612\n",
"\n",
" accuracy 0.90 1200\n",
@@ -203,8 +203,8 @@
"output_type": "stream",
"text": [
"Confusion Matrix:\n",
- " [[550 38]\n",
- " [ 88 524]]\n"
+ " [[549 39]\n",
+ " [ 87 525]]\n"
]
}
],
@@ -371,21 +371,21 @@
"\n",
" warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:952: UserWarning: One or more of the test scores are non-finite: [ nan 0.87666667 0.92083333 nan nan 0.87416667\n",
- " nan 0.87666667 0.864375 0.87645833 0.78208333 0.87854167\n",
- " 0.72333333 0.87854167 0.85645833 nan nan nan\n",
- " 0.85083333 0.72333333 0.5025 0.92020833 0.78208333 0.918125\n",
- " 0.86458333 0.87666667 nan 0.9225 0.90375 nan\n",
- " 0.78208333 nan 0.5025 nan nan nan\n",
- " nan 0.78208333 nan 0.78208333 0.85645833 0.628125\n",
- " 0.918125 nan 0.49916667 0.85875 nan 0.49916667\n",
- " nan nan 0.87791667 0.86520833 nan 0.9225\n",
- " nan 0.918125 0.865625 0.84166667 nan 0.9225\n",
- " 0.90375 0.918125 0.87375 0.918125 0.864375 nan\n",
- " nan 0.87666667 nan 0.90375 0.85625 0.62895833\n",
+ " nan 0.87666667 0.864375 0.87625 0.921875 0.87854167\n",
+ " 0.72333333 0.87854167 0.85625 nan nan nan\n",
+ " 0.85083333 0.72333333 0.5025 0.92 0.921875 0.91604167\n",
+ " 0.864375 0.87666667 nan 0.9225 0.90354167 nan\n",
+ " 0.921875 nan 0.5025 nan nan nan\n",
+ " nan 0.921875 nan 0.921875 0.85625 0.62895833\n",
+ " 0.91666667 nan 0.50083333 0.85875 nan 0.50083333\n",
+ " nan nan 0.87791667 0.86479167 nan 0.9225\n",
+ " nan 0.91604167 0.865625 0.84166667 nan 0.9225\n",
+ " 0.90354167 0.91604167 0.87375 0.91604167 0.864375 nan\n",
+ " nan 0.87666667 nan 0.90354167 0.85625 0.62895833\n",
" nan nan 0.85625 nan nan 0.87854167\n",
- " 0.85645833 nan 0.87791667 0.90395833 0.87854167 nan\n",
- " nan 0.87375 0.78208333 0.87666667 nan nan\n",
- " 0.78208333 0.90270833 nan nan 0.85625 nan\n",
+ " 0.85625 nan 0.87791667 0.90416667 0.87854167 nan\n",
+ " nan 0.87354167 0.921875 0.87666667 nan nan\n",
+ " 0.921875 0.901875 nan nan 0.85666667 nan\n",
" 0.86583333 nan nan nan]\n",
" warnings.warn(\n",
"c:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1173: FutureWarning: `penalty='none'`has been deprecated in 1.2 and will be removed in 1.4. To keep the past behaviour, set `penalty=None`.\n",
@@ -400,15 +400,15 @@
"text": [
"Best Parameters: {'solver': 'newton-cg', 'penalty': 'none', 'max_iter': 100, 'C': 0.005994842503189409}\n",
"Best Score: 0.9225\n",
- "Accuracy: 0.9325\n",
+ "Accuracy: 0.9308333333333333\n",
"Confusion Matrix:\n",
- " [[557 31]\n",
+ " [[555 33]\n",
" [ 50 562]]\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
- " 0 0.92 0.95 0.93 588\n",
- " 1 0.95 0.92 0.93 612\n",
+ " 0 0.92 0.94 0.93 588\n",
+ " 1 0.94 0.92 0.93 612\n",
"\n",
" accuracy 0.93 1200\n",
" macro avg 0.93 0.93 0.93 1200\n",
@@ -467,7 +467,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -476,7 +476,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -496,13 +496,33 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 13,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Predicted Category: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1\n",
+ " 1 1 0 1 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0\n",
+ " 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
+ "Category Percentages:\n",
+ "1 50.0\n",
+ "0 50.0\n",
+ "dtype: float64\n"
+ ]
+ }
+ ],
"source": [
"# Use the best estimator for predictions on the new data\n",
"y_pred_new = best_model.predict(X_pca_new)\n",
"\n",
+ "print(\"Predicted Category: \", y_pred_new)\n",
+ "\n",
+ "category_counts = pd.Series(y_pred_new).value_counts(normalize=True) * 100\n",
+ "print('Category Percentages:')\n",
+ "print(category_counts)\n",
+ "\n",
"# Save the predictions to a new column in the DataFrame\n",
"test_data['predicted_marker'] = y_pred_new\n",
"\n",
diff --git a/part2.ipynb b/part2.ipynb
index 238267893f7aa260ed29da004db8976355d9823f..e4d8407d20d36bfcd844280eed1e8f216392766e 100644
--- a/part2.ipynb
+++ b/part2.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -37,7 +37,7 @@
},
{
"cell_type": "code",
- "execution_count": 68,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -47,7 +47,7 @@
},
{
"cell_type": "code",
- "execution_count": 69,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -69,7 +69,7 @@
},
{
"cell_type": "code",
- "execution_count": 70,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -125,7 +125,7 @@
},
{
"cell_type": "code",
- "execution_count": 71,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -137,7 +137,7 @@
" <BarContainer object of 10 artists>)"
]
},
- "execution_count": 71,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
@@ -159,7 +159,7 @@
},
{
"cell_type": "code",
- "execution_count": 72,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
@@ -170,19 +170,19 @@
},
{
"cell_type": "code",
- "execution_count": 73,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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- "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
],
"text/plain": [
"RandomForestClassifier()"
]
},
- "execution_count": 73,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -194,7 +194,7 @@
},
{
"cell_type": "code",
- "execution_count": 74,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -205,7 +205,7 @@
},
{
"cell_type": "code",
- "execution_count": 75,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -215,7 +215,7 @@
},
{
"cell_type": "code",
- "execution_count": 76,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -223,11 +223,11 @@
"output_type": "stream",
"text": [
" Feature Importance\n",
- "90 91 0.050203\n",
- "100 101 0.024118\n",
- "50 51 0.022545\n",
- "81 82 0.021537\n",
- "3 4 0.020457\n"
+ "90 91 0.048144\n",
+ "100 101 0.029083\n",
+ "88 89 0.020978\n",
+ "50 51 0.020799\n",
+ "79 80 0.019228\n"
]
}
],
@@ -237,7 +237,7 @@
},
{
"cell_type": "code",
- "execution_count": 89,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -247,7 +247,7 @@
},
{
"cell_type": "code",
- "execution_count": 90,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -258,7 +258,7 @@
},
{
"cell_type": "code",
- "execution_count": 91,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -268,19 +268,19 @@
},
{
"cell_type": "code",
- "execution_count": 92,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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- "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
+ "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
],
"text/plain": [
"RandomForestClassifier()"
]
},
- "execution_count": 92,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -293,7 +293,7 @@
},
{
"cell_type": "code",
- "execution_count": 81,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -303,7 +303,7 @@
},
{
"cell_type": "code",
- "execution_count": 93,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -313,7 +313,7 @@
},
{
"cell_type": "code",
- "execution_count": 94,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -322,13 +322,13 @@
"text": [
" precision recall f1-score support\n",
"\n",
- " 0 0.99 0.98 0.98 602\n",
- " 1 0.90 0.90 0.90 277\n",
- " 2 0.91 0.91 0.91 321\n",
+ " 0 0.99 0.99 0.99 602\n",
+ " 1 0.90 0.92 0.91 277\n",
+ " 2 0.94 0.92 0.93 321\n",
"\n",
- " accuracy 0.94 1200\n",
- " macro avg 0.93 0.93 0.93 1200\n",
- "weighted avg 0.95 0.94 0.95 1200\n",
+ " accuracy 0.95 1200\n",
+ " macro avg 0.94 0.94 0.94 1200\n",
+ "weighted avg 0.95 0.95 0.95 1200\n",
"\n"
]
}
@@ -342,12 +342,12 @@
},
{
"cell_type": "code",
- "execution_count": 95,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -393,7 +393,7 @@
},
{
"cell_type": "code",
- "execution_count": 85,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -416,16 +416,21 @@
},
{
"cell_type": "code",
- "execution_count": 96,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Predicted Category: [2 2 2 2 2 2 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1 1 2 2 2 2 2 2 2 2 0 0 0 1 1 1 1\n",
+ "Predicted Category: [2 2 2 2 2 2 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 2 2 2 0 2 2 1 1 1 1\n",
" 1 1 1 1 2 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0\n",
- " 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0]\n"
+ " 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0]\n",
+ "Category Percentages:\n",
+ "1 38.0\n",
+ "2 34.0\n",
+ "0 28.0\n",
+ "dtype: float64\n"
]
}
],
@@ -433,27 +438,8 @@
"#Split data to classify based on those same n-features\n",
"multi_test = test_df[n_features]\n",
"predicted_category = rfc.predict(multi_test)\n",
- "print(\"Predicted Category: \", predicted_category)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Category Percentages:\n",
- "1 39.0\n",
- "2 31.0\n",
- "0 30.0\n",
- "dtype: float64\n"
- ]
- }
- ],
- "source": [
+ "print(\"Predicted Category: \", predicted_category)\n",
+ "\n",
"#Calculate distribution of predicted classification\n",
"category_counts = pd.Series(predicted_category).value_counts(normalize=True) * 100\n",
"print('Category Percentages:')\n",
@@ -462,7 +448,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [