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udemy-ML/11-Logistic-Regression-Models/00-Logistic-Regression.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
"___\n",
"<center><em>Copyright by Pierian Data Inc.</em></center>\n",
"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"\n",
"An experiment was conducted on 5000 participants to study the effects of age and physical health on hearing loss, specifically the ability to hear high pitched tones. This data displays the result of the study in which participants were evaluated and scored for physical ability and then had to take an audio test (pass/no pass) which evaluated their ability to hear high frequencies. The age of the user was also noted. Is it possible to build a model that would predict someone's liklihood to hear the high frequency sound based solely on their features (age and physical score)?\n",
"\n",
"* Features\n",
"\n",
" * age - Age of participant in years\n",
" * physical_score - Score achieved during physical exam\n",
"\n",
"* Label/Target\n",
"\n",
" * test_result - 0 if no pass, 1 if test passed"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../DATA/hearing_test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>age</th>\n",
" <th>physical_score</th>\n",
" <th>test_result</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>33.0</td>\n",
" <td>40.7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>37.2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>52.0</td>\n",
" <td>24.7</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>56.0</td>\n",
" <td>31.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>35.0</td>\n",
" <td>42.9</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age physical_score test_result\n",
"0 33.0 40.7 1\n",
"1 50.0 37.2 1\n",
"2 52.0 24.7 0\n",
"3 56.0 31.0 0\n",
"4 35.0 42.9 1"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exploratory Data Analysis and Visualization\n",
"\n",
"Feel free to explore the data further on your own."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5000 entries, 0 to 4999\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 age 5000 non-null float64\n",
" 1 physical_score 5000 non-null float64\n",
" 2 test_result 5000 non-null int64 \n",
"dtypes: float64(2), int64(1)\n",
"memory usage: 117.3 KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>age</th>\n",
" <th>physical_score</th>\n",
" <th>test_result</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>5000.000000</td>\n",
" <td>5000.000000</td>\n",
" <td>5000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>51.609000</td>\n",
" <td>32.760260</td>\n",
" <td>0.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>11.287001</td>\n",
" <td>8.169802</td>\n",
" <td>0.489947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>18.000000</td>\n",
" <td>-0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>43.000000</td>\n",
" <td>26.700000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>51.000000</td>\n",
" <td>35.300000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>60.000000</td>\n",
" <td>38.900000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>90.000000</td>\n",
" <td>50.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age physical_score test_result\n",
"count 5000.000000 5000.000000 5000.000000\n",
"mean 51.609000 32.760260 0.600000\n",
"std 11.287001 8.169802 0.489947\n",
"min 18.000000 -0.000000 0.000000\n",
"25% 43.000000 26.700000 0.000000\n",
"50% 51.000000 35.300000 1.000000\n",
"75% 60.000000 38.900000 1.000000\n",
"max 90.000000 50.000000 1.000000"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 3000\n",
"0 2000\n",
"Name: test_result, dtype: int64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['test_result'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='test_result', ylabel='count'>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(data=df,x='test_result')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='test_result', ylabel='age'>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEHCAYAAACp9y31AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWZklEQVR4nO3df5Bd9V3/8edrd0MJoW2SzTYDm6ZLvZkCwwhtV6YW26GQxRWsQYfBOrW5WjSjbZcFHAUdporDVLD9FkLmq99miroZbQVpK1hlZRNB1KmRBQLhZ3PFDWWFZFl+Ewv74+0f9yRskk264bufezb7eT1mdu49595z7ith89oPZ8/5HEUEZmaWj6ayA5iZWWO5+M3MMuPiNzPLjIvfzCwzLn4zs8y0lB1gJpYtWxYdHR1lxzAzO6rcf//9z0dE24Hrj4ri7+joYHBwsOwYZmZHFUk7p1vvQz1mZplx8ZuZZcbFb2aWGRe/mVlmXPyZGR0d5dJLL2V0dLTsKGZWkqTFL6lX0iOSHpV0WbFuqaQBSTuKxyUpM9j++vr62L59O5s2bSo7ipmVJFnxSzoN+HXgTOB04GclVYCrgC0RsQrYUixbA4yOjtLf309E0N/f71G/WaZSjvhPAbZGxJ6IGAf+GfgFYA3QV7ynD7gwYQaboq+vj8nJSQAmJiY86jfLVMrifwT4mKRWSccB5wPvBZZHxLPFe54Dlk+3saR1kgYlDY6MjCSMmY/NmzczPj4OwPj4OAMDAyUnMrMyJCv+iHgcuB64C+gHtgETB7wngGnvBBMRGyOiMyI629oOuuLY3obVq1fT0lK/WLulpYWurq6SE5lZGZL+cjcibo6ID0fEx4EXge8DuySdAFA87k6Zwd5SrVZpaqr/J29ubmbt2rUlJzKzMqQ+q+c9xeNK6sf3vwHcAVSLt1SB21NmsLe0trbS3d2NJLq7u2ltbS07kpmVIPUkbd+S1AqMAZ+PiJckXQfcKukSYCdwceIMNkW1WmVoaMijfbOMJS3+iPjYNOtGgXNTfq6ZmR2ar9zNjC/gMjMXf0ZGR0e58847iQjuvPNOX8BllikXf0b6+vr2ncc/NjbmUb9Zplz8GRkYGKB+6QREBHfddVfJicysDC7+jCxfvvywy2aWBxd/Rnbt2nXYZTPLg4s/I11dXUgCQBLnnXdeyYnMrAypL+CywoYNG6jVaqVmGBsb23eMH2DHjh309vaWkqVSqdDT01PKZ5vlziP+jCxYsGDfJG1Lly5lwYIFJScyszJ4xN8gc2V0+7nPfY6dO3eyceNGz9VjlimP+DOzYMECKpWKS98sYy5+M7PMuPjNzDLj4jczy4yL38wsMy5+M7PMuPjNzDLj4jczy4yL38wsM0mLX9Llkh6V9Iikb0o6VtJJkrZKqkm6RdIxKTOYmdn+khW/pHbgUqAzIk4DmoFPAdcDN0REBXgRuCRVBjMzO1jqQz0twEJJLcBxwLPAOcBtxet9wIWJM5iZ2RTJij8ihoGvAE9TL/yXgfuBlyJivHjbM0D7dNtLWidpUNLgyMhIqphmZtlJeahnCbAGOAk4EVgEdM90+4jYGBGdEdHZ1taWKKWZWX5SHupZDfxXRIxExBjwbeAsYHFx6AdgBTCcMIOZmR0gZfE/DXxE0nGq3+/vXOAx4G7gouI9VeD2hBnMzOwAKY/xb6X+S9wHgO3FZ20ErgSukFQDWoGbU2UwM7ODJb0DV0T8PvD7B6x+Cjgz5eeamdmh+cpdM7PMuPjNzDLj4jczy4yL38wsMy5+M7PMuPjNzDLj4jczy4yL38wsMy5+M7PMuPjNzDLj4jczy4yL38wsMy5+M7PMuPjNzDLj4jczy4yL38zmhFqtxgUXXECtVis7yrzn4jezOeHaa6/l9ddf59prry07yrzn4jez0tVqNYaGhgAYGhryqD+xZMUv6QOStk35ekXSZZKWShqQtKN4XJIqg5kdHQ4c5XvUn1bKm60/GRFnRMQZwIeBPcB3gKuALRGxCthSLJtZxvaO9g+1bLOrUYd6zgX+MyJ2AmuAvmJ9H3BhgzKY2RzV0dFx2GWbXY0q/k8B3yyeL4+IZ4vnzwHLp9tA0jpJg5IGR0ZGGpHRzEpy9dVXH3bZZlfy4pd0DPBzwN8c+FpEBBDTbRcRGyOiMyI629raEqc0szItWbIESQBIYskS/+ovpUaM+H8GeCAidhXLuySdAFA87m5ABjObw/r6+vYr/k2bNpWcaH5rRPH/Em8d5gG4A6gWz6vA7Q3IYGZz2ObNm5mcnARgcnKSgYGBkhPNb0mLX9IioAv49pTV1wFdknYAq4tlM8tYpVLZb3nVqlUlJclDS8qdR8TrQOsB60apn+VjZgbAww8/vN/yQw89VFKSPPjKXTOzzCQd8c8VGzZs8CXghb1/D729vSUnKV+lUqGnp6fsGEb9F7r1k/zeWrZ0sij+Wq3GtkceZ+K4pWVHKV3Tm/V/XPc/tetHvHN+a97zQtkRbIrLLruMG264Yd/yFVdcUWKa+S+L4geYOG4p/3Py+WXHsDli4RP/UHYEm2LNmjXceOONRASS+OQnP1l2pHktm+I3s+nNlUOhCxcuZM+ePZx44omlHorM4RCgf7lrZnOCJBYtWsSyZcvKjjLvecRvlrm5MrrdO8pfv359yUnmP4/4zcwy4+I3M8uMi9/MLDMufjOzzLj4zcwy4+I3M8uMi9/MLDMufjOzzLj4zcwy4+I3M8uMi9/MLDNJ5+qRtBj4OnAaEMBngSeBW4AOYAi4OCJeTJljeHiY5j0veype26d5zyjDw+NlxzArReoR/3qgPyJOBk4HHgeuArZExCpgS7FsZmYNkmzEL+ndwMeBXwGIiDeBNyWtAc4u3tYH3ANcmSoHQHt7O8+90eIbsdg+C5/4B9rbl5cdw6wUKUf8JwEjwJ9LelDS1yUtApZHxLPFe54D/K/PzKyBUhZ/C/Ah4E8j4oPA6xxwWCfqd1eOabZF0jpJg5IGR0ZGEsY0M8tLyuJ/BngmIrYWy7dR/0GwS9IJAMXj7uk2joiNEdEZEZ1tbW0JY5qZ5SVZ8UfEc8APJH2gWHUu8BhwB1At1lWB21NlMDOzg6W+9WIP8FeSjgGeAn6V+g+bWyVdAuwELk6cwczMpkha/BGxDeic5qVzU36umZkdmq/cNTPLjIvfzCwzLn4zs8y4+M3MMnNExS/puFRBzMysMWZU/JI+Kukx4Ili+XRJf5I0mZmZJTHTEf8NwE8DowAR8RD1CdjMzOwoM+Pz+CPiB5KmrpqY/TjpNO95wfPxA00/fAWAyWPfVXKScjXveQHPD2i5mmnx/0DSR4GQtADopT63/lGhUqmUHWHOqNVeBaDy/txLb7m/LyxbMy3+36B+U5V2YBi4C/h8qlCzraenp+wIc0Zvby8A69evLzmJmZVlRsUfEc8Dn06cxczMGmBGxS/ppmlWvwwMRoRn1zQzO4rM9KyeY4EzgB3F148DK4BLJN2YJJmZmSUx02P8Pw6cFRETAJL+FPgX4KeA7YmymZlZAjMd8S8Bjp+yvAhYWvwgeGPWU5mZWTIzHfH/MbBN0j2AqF+89aXi5umbE2UzM7MEZnpWz82S7gQ+Q/38/buo30/3deC3E+YzM7NZNtOzen6N+kVbK4BtwEeA7wHnJEtmZmZJzPRQTy/wE8C/R8QnJJ0MfCldLLM8bNiwgVqtVnaMOWHv38PeiwxzV6lUkl18OtPi/2FE/FASkt4REU9I+sCP2kjSEPAq9Xl9xiOiU9JS4BagAxgCLo6IF99WerOjXK1WY8ejD7Ly+KNq6qskjhmrn2vyxs7BkpOU7+nXmpPuf6bF/4ykxcDfAgOSXgR2znDbTxRX/u51FbAlIq6TdFWxfOUM92U276w8foLf+9ArZcewOeRLD6SdRHGmv9z9+eLpH0i6G3g30P82P3MNcHbxvA+4Bxe/mVnDzHha5r0i4p+P5O3AXZIC+FpEbASWR8SzxevPcYi5cSWtA9YBrFy58khjmpnZIRxx8R+hn4qIYUnvoX6I6ImpL0ZEFD8UDlL8kNgI0NnZOe17zMzsyCW92XpEDBePu4H
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x='test_result',y='age',data=df)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='test_result', ylabel='physical_score'>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x='test_result',y='physical_score',data=df)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='age', ylabel='physical_score'>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(x='age',y='physical_score',data=df,hue='test_result')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x19ceae2fd08>"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 420.5x360 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.pairplot(df,hue='test_result')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(df.corr(),annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='physical_score', ylabel='test_result'>"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(x='physical_score',y='test_result',data=df)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='age', ylabel='test_result'>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(x='age',y='test_result',data=df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Easily discover new plot types with a google search! Searching for \"3d matplotlib scatter plot\" quickly takes you to: https://matplotlib.org/3.1.1/gallery/mplot3d/scatter3d.html"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x19ceaf878c8>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.mplot3d import Axes3D \n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"ax.scatter(df['age'],df['physical_score'],df['test_result'],c=df['test_result'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train | Test Split and Scaling"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"X = df.drop('test_result',axis=1)\n",
"y = df['test_result']"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=101)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"scaler = StandardScaler()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"scaled_X_train = scaler.fit_transform(X_train)\n",
"scaled_X_test = scaler.transform(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic Regression Model"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# help(LogisticRegression)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# help(LogisticRegressionCV)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"log_model = LogisticRegression()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_model.fit(scaled_X_train,y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Coefficient Interpretation\n",
"\n",
"Things to remember:\n",
"\n",
"* These coeffecients relate to the *odds* and can not be directly interpreted as in linear regression.\n",
"* We trained on a *scaled* version of the data \n",
"* It is much easier to understand and interpret the relationship between the coefficients than it is to interpret the coefficients relationship with the probability of the target/label class.\n",
"\n",
"Make sure to watch the video explanation, also check out the links below:\n",
"\n",
"* https://stats.idre.ucla.edu/stata/faq/how-do-i-interpret-odds-ratios-in-logistic-regression/\n",
"* https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-how-do-i-interpret-odds-ratios-in-logistic-regression/\n",
"\n",
"### The odds ratio\n",
"\n",
"For a continuous independent variable the odds ratio can be defined as:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://wikimedia.org/api/rest_v1/media/math/render/svg/18dc1087bc50b9c1afee6820aad1858704b43ea3\" >"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This exponential relationship provides an interpretation for $$\\beta _{1}$$ "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The odds multiply by $${e^\\beta _{1}}$$ for every 1-unit increase in x."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.94953524, 3.45991194]])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_model.coef_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This means:\n",
"* We can expect the **odds** of passing the test to **decrease** (the original coeff was negative) per unit increase of the age.\n",
"* We can expect the **odds** of passing the test to **increase** (the original coeff was positive) per unit increase of the physical score.\n",
"* Based on the ratios with each other, the physical_score indicator is a stronger predictor than age."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Performance on Classification Tasks"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,plot_confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"y_pred = log_model.predict(scaled_X_test)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.93"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_score(y_test,y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[172, 21],\n",
" [ 14, 293]], dtype=int64)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"confusion_matrix(y_test,y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x19ceb65e588>"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_confusion_matrix(log_model,scaled_X_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x19ceb691b88>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Scaled so highest value=1\n",
"plot_confusion_matrix(log_model,scaled_X_test,y_test,normalize='true')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.92 0.89 0.91 193\n",
" 1 0.93 0.95 0.94 307\n",
"\n",
" accuracy 0.93 500\n",
" macro avg 0.93 0.92 0.93 500\n",
"weighted avg 0.93 0.93 0.93 500\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test,y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"age 32.0\n",
"physical_score 43.0\n",
"Name: 141, dtype: float64"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 1.]])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 0% probability of 0 class\n",
"# 100% probability of 1 class\n",
"log_model.predict_proba(X_train.iloc[0].values.reshape(1, -1))"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1], dtype=int64)"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"log_model.predict(X_train.iloc[0].values.reshape(1, -1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Curves and AUC\n",
"\n",
"**Make sure to watch the video on this!**"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import precision_recall_curve,plot_precision_recall_curve,plot_roc_curve"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<sklearn.metrics._plot.precision_recall_curve.PrecisionRecallDisplay at 0x19cec76dac8>"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_precision_recall_curve(log_model,scaled_X_test,y_test)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x19ceb5c4288>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plot_roc_curve(log_model,scaled_X_test,y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------\n",
"------"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}