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Intro to Python for Data Science 9 & 10
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intro-to-python-for-data-science-9-10.ipynb
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"metadata": {
"colab": {
"name": "Intro to Python for Data Science 9 & 10",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"authorship_tag": "ABX9TyPno115TMMe9L7WSWVx0kr5",
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"kernelspec": {
"name": "python3",
"display_name": "Python 3"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/syamdev/23548f1c815877cbac025059213f5c9d/intro-to-python-for-data-science-9-10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JJt7-xVitEv1",
"colab_type": "text"
},
"source": [
"### Reviews"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3XHwzsy0qHAp",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UXXsEer7kMFj",
"colab_type": "code",
"colab": {}
},
"source": [
"import ast\n",
"\n",
"# dataset source: https://www.kaggle.com/tmdb/tmdb-movie-metadata#tmdb_5000_movies.csv\n",
"data = pd.read_csv('tmdb_5000_movies.csv')\n",
"\n",
"# 1. Define function\n",
"def get_genres(value):\n",
" combined_genres = ''\n",
" for i in ast.literal_eval(value):\n",
" combined_genres = combined_genres + i['name'] + ', '\n",
" return combined_genres\n",
"\n",
"# 2. Apply function\n",
"data['genres'] = data['genres'].apply(get_genres)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZZMzPaeIujn7",
"colab_type": "text"
},
"source": [
"----------"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xrvVhrZitIkX",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import sklearn"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fKCveqg7tN--",
"colab_type": "code",
"colab": {}
},
"source": [
"student = pd.read_csv('http://bit.ly/dwp-data-grade')\n",
"student_full = pd.read_csv('http://bit.ly/dwp-data-grade-full')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CjUTO1lotN6x",
"colab_type": "code",
"outputId": "20ec787d-51fc-4a3a-fccb-493ef7528845",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"student.head()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"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>Hour</th>\n",
" <th>Grade</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.5</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.6</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.3</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.0</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.3</td>\n",
" <td>48</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Hour Grade\n",
"0 2.5 21\n",
"1 4.6 51\n",
"2 3.3 48\n",
"3 5.0 30\n",
"4 6.3 48"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "J0-PmZSIAOL-",
"colab_type": "code",
"outputId": "72531e60-7fc4-43d5-ce0a-b1bfbbd4c12c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"student_full.head()"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"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>Hour</th>\n",
" <th>Previous</th>\n",
" <th>Grade</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2.5</td>\n",
" <td>25</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.6</td>\n",
" <td>58</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.3</td>\n",
" <td>41</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.0</td>\n",
" <td>42</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.3</td>\n",
" <td>46</td>\n",
" <td>48</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Hour Previous Grade\n",
"0 2.5 25 21\n",
"1 4.6 58 51\n",
"2 3.3 41 48\n",
"3 5.0 42 30\n",
"4 6.3 46 48"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gy0pjZi_vH1X",
"colab_type": "text"
},
"source": [
"# Machine Learning"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Z6_DAKTgvYdH",
"colab_type": "text"
},
"source": [
"Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.\n",
"\n",
"intinya: program yang dibuat untuk memprediksi sesuatu dari behaviour/kebiasaan model.\n",
"\n",
"1. supervised learning: **ada label**\n",
" - regression\n",
" - linear regression\n",
" - classification\n",
" - logistic regression\n",
"2. unsupervised: **tidak ada label**\n",
" - clustering\n",
" - k-means\n",
"\n",
"3. reinforcement learning (dipelajari saat level advance)\n",
"\n",
"ds: dpt insight dr dataset\n",
"\n",
"ml:\n",
"- buat model dr dataset\n",
"- model yg dibuat adalah rumusan/hitungan matematika, berdasarkan angka2/data numerik\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZUi9gCIpv1AY",
"colab_type": "text"
},
"source": [
"## Machine Learning Workflow"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ZIuxIvoE1IH8"
},
"source": [
"- **Question**\n",
"- **Wrangle**\n",
" - Data Acquisition\n",
" - Data Cleaning\n",
" - Handle Missing Values\n",
" - Handle Duplicate Data\n",
" - Data Transformation\n",
" - Categorical -> Numerical\n",
" - Normalization / Standardization\n",
"- **Explore**\n",
" - Training\n",
" - Testing\n",
" - Evaluation\n",
"- **Conclusion**\n",
"- **Communicate**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "mGyBJWQFv0cx",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
"\n",
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
"\n",
"from sklearn import metrics\n",
"\n",
"from sklearn import datasets"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "wFyKTz5L2Lgk",
"colab_type": "text"
},
"source": [
"## Supervised Learning (Linear Regression)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vfCtXh9LtN33",
"colab_type": "code",
"outputId": "727bc68c-0a04-411f-d54b-5b4662960289",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
}
},
"source": [
"viz = sns.scatterplot(data=student, x='Hour', y='Grade')"
],
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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eDeyOiL5kewswL+UY7CUqW2OyMn5KMctSaiN+SWcCOyJivaS3TOD4FcAKgAULFrQ5Oquy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\nIijyqwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7bwFagcy3Y77",
"colab_type": "text"
},
"source": [
"### 1. Linear Regression\n",
"\n",
"Model ML yg paling sederhana\n",
"\n",
"y = b0 + b1 x\n",
"\n",
"Cara dapetin MoError yg paling sedikit/mengurangi error\n",
"- otak-atik di independent variable (x)\n",
"- cari model yg baru"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uq0kVDkEtN0_",
"colab_type": "code",
"colab": {}
},
"source": [
"x = student[['Hour']]\n",
"y = student['Grade']"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5cH-yO75tNxQ",
"colab_type": "code",
"colab": {}
},
"source": [
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n",
"\n",
"#x_train"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KMi7napGtNs4",
"colab_type": "code",
"colab": {}
},
"source": [
"model = LinearRegression()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zCQ4MuNPtNeY",
"colab_type": "code",
"outputId": "49f9804c-de5c-4c88-b948-690bd5594999",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# proses training\n",
"model.fit(x_train, y_train)"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Yos0CXaYtNOq",
"colab_type": "code",
"colab": {}
},
"source": [
"# nilai hasil prediksi\n",
"y_pred = model.predict(x_test)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "lwDZXLGNtMhe",
"colab_type": "code",
"outputId": "cd896e9b-e76c-4a42-960e-188f6e008e25",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"error = metrics.mean_absolute_error(y_test, y_pred)\n",
"print('MAE: {}'.format(error))"
],
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": [
"MAE: 8.069006998289032\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NtvY0_Wn6wyp",
"colab_type": "code",
"outputId": "cffc6200-cd44-40c6-8b78-c39ff08d832a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 266
}
},
"source": [
"x_test"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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>Hour</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>7.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>6.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>5.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" Hour\n",
"14 1.5\n",
"19 7.7\n",
"3 5.0\n",
"27 6.1\n",
"32 6.9\n",
"26 1.9\n",
"20 5.9"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LBB-HXRf7sBP",
"colab_type": "code",
"outputId": "44156fff-fd75-4b37-ca32-3c5517c8a9dd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"y_pred"
],
"execution_count": 23,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([17.91185474, 73.87400328, 49.5033902 , 59.43215849, 66.65308088,\n",
" 21.52231593, 57.62692789])"
]
},
"metadata": {
"tags": []
},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TlYeTvq97ygk",
"colab_type": "code",
"outputId": "f7f9b6e0-4ee6-4714-95f3-7308ea124a35",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
}
},
"source": [
"y_test"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"14 20\n",
"19 85\n",
"3 30\n",
"27 67\n",
"32 76\n",
"26 24\n",
"20 62\n",
"Name: Grade, dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5_eBWtNA72af",
"colab_type": "code",
"outputId": "858846d8-5317-4f4c-cab2-a1ea62239bc6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 266
}
},
"source": [
"result = pd.DataFrame({'hour': list(x_test.values),\n",
" 'actual': y_test.tolist(),\n",
" 'prediction': y_pred.tolist()})\n",
"\n",
"result['error'] = abs(result['actual'] - result['prediction'])\n",
"\n",
"result"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>hour</th>\n",
" <th>actual</th>\n",
" <th>prediction</th>\n",
" <th>error</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>[1.5]</td>\n",
" <td>20</td>\n",
" <td>17.911855</td>\n",
" <td>2.088145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>[7.7]</td>\n",
" <td>85</td>\n",
" <td>73.874003</td>\n",
" <td>11.125997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>[5.0]</td>\n",
" <td>30</td>\n",
" <td>49.503390</td>\n",
" <td>19.503390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>[6.1]</td>\n",
" <td>67</td>\n",
" <td>59.432158</td>\n",
" <td>7.567842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>[6.9]</td>\n",
" <td>76</td>\n",
" <td>66.653081</td>\n",
" <td>9.346919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>[1.9]</td>\n",
" <td>24</td>\n",
" <td>21.522316</td>\n",
" <td>2.477684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>[5.9]</td>\n",
" <td>62</td>\n",
" <td>57.626928</td>\n",
" <td>4.373072</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" hour actual prediction error\n",
"0 [1.5] 20 17.911855 2.088145\n",
"1 [7.7] 85 73.874003 11.125997\n",
"2 [5.0] 30 49.503390 19.503390\n",
"3 [6.1] 67 59.432158 7.567842\n",
"4 [6.9] 76 66.653081 9.346919\n",
"5 [1.9] 24 21.522316 2.477684\n",
"6 [5.9] 62 57.626928 4.373072"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "u4K_Olgb-tIn",
"colab_type": "code",
"outputId": "0b91f6c7-45a3-41ed-a490-551bc058b46e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"model.intercept_ + model.coef_[0]*7.7"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"73.87400327514422"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "S84yVHXH8Ujn",
"colab_type": "code",
"outputId": "5df9cc99-959a-44a8-c6f8-e39ae568d7b3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# nilai b1\n",
"model.coef_"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([9.02615299])"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "V4VBf1Yo9g9T",
"colab_type": "code",
"outputId": "4a428ccb-45b9-4462-be45-8966b4f2a484",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# nilai b0\n",
"model.intercept_"
],
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"4.372625252860395"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_VZToNi-Ac9C",
"colab_type": "text"
},
"source": [
"### Multiple Linear Regression\n",
"\n",
"- untuk lebih 2 dependent variable\n",
"\n",
"y = b0 + b1 x1 + b2 x2\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1JIwHyBR9mnW",
"colab_type": "code",
"outputId": "6c6f13c8-9cec-4bef-dc06-33ec645f3aba",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Separate independent var & dependent var\n",
"x = student_full[['Hour', 'Previous']]\n",
"y = student_full['Grade']\n",
"\n",
"# Split the data into training set & testing set\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n",
"\n",
"# Initiate the model & do the training\n",
"model = LinearRegression()\n",
"model.fit(x_train, y_train)\n",
"\n",
"# Testing & evaluation\n",
"y_pred = model.predict(x_test)\n",
"error = metrics.mean_absolute_error(y_test, y_pred)\n",
"print('MAE: {}'.format(error))"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": [
"MAE: 4.922453857571768\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MnTwMoOUCAbd",
"colab_type": "code",
"outputId": "5033ab2b-e0a1-41de-f1be-c328bef550d8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"x.columns"
],
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Hour', 'Previous'], dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "J40vhR94BUmD",
"colab_type": "code",
"outputId": "51ec9f46-b2df-417e-da2a-4022e15ad824",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"model.coef_"
],
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([5.65866542, 0.44569096])"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NZgyJ-qwB4iw",
"colab_type": "code",
"outputId": "95ee375c-1d73-4637-c147-3da0f508fdcb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"model.intercept_"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"-1.6925886779140313"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iTIblUjhB97G",
"colab_type": "code",
"outputId": "771807ec-5790-4ceb-b035-06b6a5139117",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
}
},
"source": [
"coef = pd.DataFrame({'columns': x.columns.tolist(),\n",
" 'coefficient': model.coef_.tolist()})\n",
"\n",
"coef = coef.sort_values('coefficient')\n",
"\n",
"coef.plot(x='columns', y='coefficient', kind='barh')"
],
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3581c703c8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ne-iZwLqHLKD",
"colab_type": "text"
},
"source": [
"### Boston Housing Datasets\n",
"\n",
"https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fvM5Qj7iHA8-",
"colab_type": "code",
"colab": {}
},
"source": [
"boston_dataset = datasets.load_boston()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WE6WYjdvHRjE",
"colab_type": "code",
"outputId": "71c3282c-d548-4c02-8cef-bf0c71100b49",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"# data = x ([[dataframe]])\n",
"# target = y ([series])\n",
"# feature = column name\n",
"boston_dataset"
],
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:** \\n\\n :Number of Instances: 506 \\n\\n :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n :Attribute Information (in order):\\n - CRIM per capita crime rate by town\\n - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\\n - INDUS proportion of non-retail business acres per town\\n - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n - NOX nitric oxides concentration (parts per 10 million)\\n - RM average number of rooms per dwelling\\n - AGE proportion of owner-occupied units built prior to 1940\\n - DIS weighted distances to five Boston employment centres\\n - RAD index of accessibility to radial highways\\n - TAX full-value property-tax rate per $10,000\\n - PTRATIO pupil-teacher ratio by town\\n - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n - LSTAT % lower status of the population\\n - MEDV Median value of owner-occupied homes in $1000's\\n\\n :Missing Attribute Values: None\\n\\n :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980. N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems. \\n \\n.. topic:: References\\n\\n - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
" 'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
" 4.9800e+00],\n",
" [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
" 9.1400e+00],\n",
" [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
" 4.0300e+00],\n",
" ...,\n",
" [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
" 5.6400e+00],\n",
" [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
" 6.4800e+00],\n",
" [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
" 7.8800e+00]]),\n",
" 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
" 'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
" 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/boston_house_prices.csv',\n",
" 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
" 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
" 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
" 13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
" 21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
" 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
" 19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
" 20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
" 23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
" 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
" 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
" 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
" 23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
" 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
" 17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
" 25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
" 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
" 32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
" 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
" 20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
" 26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
" 31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
" 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
" 42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
" 36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
" 32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
" 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
" 20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
" 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
" 21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
" 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
" 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
" 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
" 16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
" 13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8,\n",
" 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,\n",
" 12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5. , 11.9,\n",
" 27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3, 7. , 7.2, 7.5, 10.4,\n",
" 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11. ,\n",
" 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8,\n",
" 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
" 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
" 19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
" 29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
" 20.6, 21.2, 19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,\n",
" 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])}"
]
},
"metadata": {
"tags": []
},
"execution_count": 35
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nQd7JVcxHsVH",
"colab_type": "code",
"colab": {}
},
"source": [
"x = pd.DataFrame(boston_dataset['data'],\n",
" columns = boston_dataset['feature_names'])\n",
"\n",
"y = pd.DataFrame(boston_dataset['target'],\n",
" columns = ['MEDV'])\n",
"y = y['MEDV']"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "fSHSZh-0IIMF",
"colab_type": "code",
"outputId": "d7db1e6b-decd-4dd9-8a18-a0686a522aba",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 80
}
},
"source": [
"x.head(1)"
],
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"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>CRIM</th>\n",
" <th>ZN</th>\n",
" <th>INDUS</th>\n",
" <th>CHAS</th>\n",
" <th>NOX</th>\n",
" <th>RM</th>\n",
" <th>AGE</th>\n",
" <th>DIS</th>\n",
" <th>RAD</th>\n",
" <th>TAX</th>\n",
" <th>PTRATIO</th>\n",
" <th>B</th>\n",
" <th>LSTAT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0.0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.09</td>\n",
" <td>1.0</td>\n",
" <td>296.0</td>\n",
" <td>15.3</td>\n",
" <td>396.9</td>\n",
" <td>4.98</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CRIM ZN INDUS CHAS NOX ... RAD TAX PTRATIO B LSTAT\n",
"0 0.00632 18.0 2.31 0.0 0.538 ... 1.0 296.0 15.3 396.9 4.98\n",
"\n",
"[1 rows x 13 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "m7B__62uO4r7",
"colab_type": "code",
"outputId": "1bda0c42-da3d-4da5-c468-4b44ef61f71b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
}
},
"source": [
"y"
],
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 24.0\n",
"1 21.6\n",
"2 34.7\n",
"3 33.4\n",
"4 36.2\n",
" ... \n",
"501 22.4\n",
"502 20.6\n",
"503 23.9\n",
"504 22.0\n",
"505 11.9\n",
"Name: MEDV, Length: 506, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 38
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "oE65K-S3IIH0",
"colab_type": "code",
"outputId": "2422b1ee-3294-4222-b074-617c3c36b1a1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"y.head(1)"
],
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0 24.0\n",
"Name: MEDV, dtype: float64"
]
},
"metadata": {
"tags": []
},
"execution_count": 39
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ui9NnV0xIIFk",
"colab_type": "code",
"colab": {}
},
"source": [
"# Split the data into training set & testing set\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "R3Q0fgGMIIDF",
"colab_type": "code",
"outputId": "3982806c-5cd9-4dac-f3b6-bcaee59762f2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Initiate the model & do the training\n",
"model = LinearRegression()\n",
"model.fit(x_train, y_train)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
]
},
"metadata": {
"tags": []
},
"execution_count": 41
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "b0ORZvpzIH_a",
"colab_type": "code",
"outputId": "b6cac803-746d-490c-f5c3-88c9ed789b9a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# Testing & evaluation\n",
"y_pred = model.predict(x_test)\n",
"error = metrics.mean_absolute_error(y_test, y_pred)\n",
"print('MAE: {}'.format(error))"
],
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"text": [
"MAE: 3.7507121808389092\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_upjVjqYLJkL",
"colab_type": "code",
"outputId": "8fc4341d-729c-4f4b-c8de-85f11d142a88",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
}
},
"source": [
"x.columns"
],
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
" 'PTRATIO', 'B', 'LSTAT'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "F_Z-PGe8IH8X",
"colab_type": "code",
"outputId": "4e0c268d-ea16-47e7-ce45-1d7bedb8eb32",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"model.coef_"
],
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([-1.12386867e-01, 5.80587074e-02, 1.83593559e-02, 2.12997760e+00,\n",
" -1.95811012e+01, 3.09546166e+00, 4.45265228e-03, -1.50047624e+00,\n",
" 3.05358969e-01, -1.11230879e-02, -9.89007562e-01, 7.32130017e-03,\n",
" -5.44644997e-01])"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "f4YW10KVIH5f",
"colab_type": "code",
"outputId": "bac9daa5-cde2-4389-8731-686ad344c734",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"model.intercept_"
],
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"42.93352585337652"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "y7PNQKmPIHrh",
"colab_type": "code",
"outputId": "793a99f1-8b7a-466b-cf94-d5b8a8eaff89",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
}
},
"source": [
"coef = pd.DataFrame({'columns': x.columns.tolist(),\n",
" 'coefficient': model.coef_.tolist()})\n",
"\n",
"coef = coef.sort_values('coefficient')\n",
"\n",
"coef.plot(x='columns', y='coefficient', kind='barh')"
],
"execution_count": 46,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f358098ecc0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 46
},
{
"output_type": "display_data",
"data": {
"image/png": 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EnJtpJWZmVtfKfbTNrZK+IuljkrYovDKtrAuSviAp0kfUFMbtJOkBSS9KminpUUkHpdNO\nk7QofVp04bV7peo3M2tE5QbOCuBKYDrJzZ8zgbasiipDK/Cf6V8k9QUeBCZGxI4RMRT4N6D4SQh3\npE+LLrzm5V61mVkDK/eQ2nnAJyJicZbFlENSP+AA4LPA/cB3gJOA6RFxX2G+iHgWeLYiRZqZ2fuU\nGzgvAG9nWcg6OBb4bUQskLRE0lBgD6C7m1C/KOmAouH9ImJZ6UySzgDOANhuu+02VM1mZg2v3MB5\nC5gt6VHe+6SBSlwW3Qr8OH0/OR1+D0lTgJ2ABRFxfDr6jog4q7uVR8REkq6mtLS0xAap2MzMyg6c\ne9JXRaUXKgwH9pQUJD1tAvgucFBhvog4TlILcFVFCjUzs/cp90kDP8+6kDKdCNwaEV8tjJD0e5JD\nfmMlHVN0HmfTShRoZmYdK/dJAy+R7Em8RwX64bTy/sZvdwMjgaOAayT9CPgf4A3gkqL5Ss/h/GtE\nPJFlsWZmtla5h9SKH9rZFxgB5H4fTkfPdIuIa4sGj+hkuZuBm7OpyszMylHWfTgRsaTo9UpE/Ag4\nMuPazMysjpR7SG2fosEPkOzxlLt3ZGZmVnZoXF30fiXQDvzjBq/GzMzqlvvhmJlZLroMHEldPiE6\nIq7ZsOWYmVm96m4PZ7NcqjAzs7rXZeBExHfzKsTMzOpbWZdFSxokaYqk19LX3ZIGZV2cmVk9abrw\nwUqXUFHl9sO5CbgP2Dp93Z+Oy52k/kVN1F6V9ErR8EadNGdrkTRX0kbp8I6S/izpQ5X4DmZmjajc\nwBkYETdFxMr0dTMwMMO6OpXefNocEc3ABGBcUVO1FZQ0Z0uXaQN+D4xJR/0EuCgiXs+5fDOzhlXu\nfThLJJ0M3J4OtwJLsimp5zppzlbwDWCWpJVA74i4vYNVmJlZRsrdw/knkhs9XwX+RvLU5tMyqml9\nrGnORhKSQwsTIuL/gMuBy4AzO1uBpDMktUlqW7RoUeYFm5k1inID53vAqRExMCI+ShJA1XgFWytJ\nUzbouDnb50meJL17ZyuIiIkR0RIRLQMHVuSooZlZXSr3kNqQiPjfwkBELJW0d0Y19UhnzdkknR8R\nIeko4MPAYcAUSQ9FRLW0zTYzq3vl7uF8QNJHCgPpP+7V9vDOQnO27SOiKSK2BV4CDpS0CXANcGZE\nPAPcC1xUwVrNzBrOujy8c7qku9LhEcCl2ZTUY501Z2sFDgemRMS8dPzFwBxJN0fEf+VXopk1svbL\nG7uriyLe18iz4xml3UkOWQE8UvSPd91qaWmJtra2SpdhZlYzJM2MiJaOppV9WCwNmLoPGTMzy0a5\n53DMzMzWiwPHzMxy4cAxM7NcOHDMzCwXDhwzM8uFA8fMLAdNFz7ofjiVLqA7kraSNFnSi5JmSvq1\npJ0lLUt74MyTdIukPun8wyQ9kL4/Le2Nc0jR+gr9ck6s1HcyM2tEVR04kgRMAaZFxI4RMRQYC2wJ\nvJj2xNkTGETyNOuOPAOMLBpuBeZkV7WZmXWkqgOHpK/NuxExoTAiIuYA/100vAr4E7BNJ+t4DPik\npD5pv5xPALOzK9nMzDpS7YEzGJjZ1QyS+gL/APy2k1kCmErylOhjSVplm5lZzqo9cLqyo6TZJP1t\n/hYRT3cx72SSw2ojWdu1tENuwGZmlo1qD5y5wNBOphXO4ewIDJV0TGcriYg/kZzrGZB2A+2UG7CZ\nmWWj2gPnEWBjSWcURkgaAmxbGI6IxcCFJBcTdOVC4BtZFGlmZt2r6sCJpHfCccAh6WXRc4HLgFdL\nZr0H2FTSgV2s6zcR8Wh21ZqZWVfK7ofTiNwPx8xs3XTVD6eq93DMzKx+OHDMzCwXDhwzM8uFA8fM\nzHLhwDEzs1w4cMzMLBcOHDMzy0XvShdgZlZPumuy1n75kTlVUn2qZg9H0psdjNtF0rS00dp8SRMl\nHZYOz5b0pqTn0/e3FC33I0mvSPpAOvzlomVWSHomfX95nt/RzKyRVfsezrXAuIi4F0DSnhHxDPBQ\nOjwNGBMRax4HkIbMcSQ9cz4DPBoRNwE3pdPbgc+mz2AzM7OcVM0eTic+BiwsDKRh051hJE+Z/ilJ\nd08zM6sC1R4444BHJP1G0jmSNi9jmVaSnjdTgCMl9VmXD3Q/HDOzbFR14KSHwnYD7iLZc5khaePO\n5pe0EXAEcE9EvA78kaTT57p8pvvhmJlloKoDByAi/hoRN0bEscBKkrbTnTkM2Bx4Jj1XcwA+rGZm\nVhWqOnAkHV44JCZpK6A/8EoXi7QCp0dEU0Q0ATsAh0raNPNizcysS9V0ldqmkhYWDV8DDAJ+LOmd\ndNz5EVHafA2ANFQOB0YVxkXEW5L+EzgauCObss3M1mrk+2y6UzWBExGd7W2d28Uyw4revw1s0cE8\nx5cMN/WsQjMzWx9VfUjNzMzqhwPHzMxy4cAxM7NcOHDMzCwXDhwzM8uFA8fMzHLhwDEzs1xUzX04\nZma1pLtGa51p5BtDM9vDkbQqbXL2rKS7JG1T1ATt1bRBWmF4o5L57y99MrSkr0l6R9KH0+FOG7FJ\nGibpgaJlvyDp6bSJ2zOSvpDV9zYzs45leUhtWUQ0R8RgYAXwxXS4GZhA0litOX2tKJl/KXBmyfpa\ngSeB4wEi4qGi9bUBJ6XDXypeSNJewFXAsRGxG3AMcJWkIdl9dTMzK5XXOZzHgE+sw/zTgW0KA5J2\nBPoB32Tdn/48BvhBRLwEkP69DDh/HddjZmbrIfPAkdQb+DxQTrdOJPUCDgbuKxo9EphMEly7SNpy\nHUrYA5hZMq4tHd/R57sBm5lZBrIMnE0kzSb5x/1l4Gdlzv8qsCXwcNG0VmByRKwG7gZGZFAv4AZs\nZmZZyfIqtWXp+ZV1mj9tM/AQyTmcayXtCewEPCwJYCPgJeC6Mtc7DxgKzCkaNxSYuw61mZnZeqq6\n+3DSNgOjgfPSw3GtwMWFpmoRsTWwtaTty1zlVcBYSU0A6d9vAFdv4NLNzKwLVXkfTkTMkvQ0SdiM\nBI4omWVKOv6KMtY1W9LXgfvT7qHvAhdExOwNXLaZNZBGvp+mpxQRla6harW0tERbW1ulyzAzqxmS\nZkZES0fTqu6QmpmZ1ScHjpmZ5cKBY2ZmuXDgmJlZLhw4ZmaWCweOmZnlwoFjZma5qMobP83MqkFP\nm6x1pZFvGK35PZyixm1zJc2RdJ6kD6TT1jRik7SlpAfSeeZJ+nVlKzczayz1sIez5iGhkj4K3AZ8\nCPhOyXzfAx6OiB+n87oBm5lZjmp+D6dYRLwGnAGcpfTR0kU+BiwsmvfpPGszM2t0dRU4ABHxZ6AX\n8NGSST8BfibpUUkXSdq6o+XdgM3MLBt1FzidiYiHgI8D/w7sCsyS9L4Oa27AZmaWjboLHEkfB1YB\nr5VOi4ilEXFbRJwCPAkclHd9ZmaNqq4CJ91jmQBcFyV9FyQNT7uJImkzYEeS1tdmZpaDerhKbRNJ\ns4E+wErgVuCaDuYbClwnaSVJ0E6KiCfzK9PMak0j3zOThZoPnIjo1cW0acC09P2VwJX5VGVmZqXq\n6pCamZlVLweOmZnlwoFjZma5cOCYmVkuHDhmZpYLB46ZmeWi5i+LrlZZ9NEws9rXyPf21MwejqSQ\ndHXR8BhJFxcNnyHpufT1J0kHpON7SZop6aCieX8naUSuX8DMrMHVTOAAy4HjJQ0onSDpKOCrwAER\nsSswCrhN0lYRsQr4V5KnDPSR1Aqsjoi78izezKzR1VLgrAQmAud0MO3rwPkRsRggIp4Cfg6cmQ7/\nEZgOXAz8ADgrh3rNzKxILQUOJD1tTpL04ZLxewAzS8a1peMLxgJfA26LiBc6+wD3wzEzy0ZNBU5E\nvA7cAozuweIHAX8HBnfzGe6HY2aWgZoKnNSPgH8GPlg0bh7J06CLDQXmAkj6IPBDYDjwUUlH5FCn\nmZkVqbnAiYilwJ0koVPwQ+AKSf0BJDUDpwHXp9O/DdwZEc+RXEAwTlLf3Io2M7OavQ/naopO/EfE\nfZK2AZ6QFMAbwMkR8TdJewDHAXul886S9BDJhQbfzb90M7PGpJLGmFakpaUl2traKl2GmVnNkDQz\nIlo6mlZzh9TMzKw2OXDMzCwXDhwzM8uFz+F0QdIi4C/dzDYAWJxDObXA22Itb4u1vC3eq963x/YR\n0eFNjA6c9SSprbMTZI3G22Itb4u1vC3eq5G3hw+pmZlZLhw4ZmaWCwfO+ptY6QKqiLfFWt4Wa3lb\nvFfDbg+fwzEzs1x4D8fMzHLhwDEzs1w4cHpI0pWSnpP0tKQpkjYvmjZW0guSnpd0WCXrzIOkEZLm\nSlotqaVofJOkZZJmp68JlawzD51ti3RaQ/0uikm6WNIrRb+FhmsRIunw9L/9C5IurHQ9leDA6bmH\ngcERMQRYQNJRFEm7AyNJuo0eDlwvqVfFqszHs8DxwB86mPZiRDSnr1E511UJHW6LBv1dlBpX9Fv4\ndaWLyVP63/onwOeB3YHW9DfRUBw4PRQRv4uIlengDGBQ+v5YYHJELI+Il4AXgE9Wosa8RMT8iHi+\n0nVUgy62RcP9Luw9Pgm8EBF/jogVwGSS30RDceBsGP8E/CZ9vw3w30XTFqbjGtUOkmZJ+r2kAytd\nTAX5dwFnpYegb5T0kUoXkzP/96d2G7DlQtJUYKsOJl0UEfem81wErAR+kWdteStnW3Tgb8B2EbFE\n0lDgHkl7RMTrmRWagx5ui7rX1XYBfgp8H4j079Uk/6NmDcSB04WIOKSr6ZJOA44CDo61NzS9Amxb\nNNugdFxN625bdLLMcmB5+n6mpBeBnYGa7mrXk21Bnf4uipW7XST9O/BAxuVUm7r/718OH1LrIUmH\nAxcAx0TE20WT7gNGStpY0g7ATsCfKlFjpUkaWDgxLunjJNviz5WtqmIa+nch6WNFg8eRXFzRSJ4E\ndpK0g6SNSC4gua/CNeXOezg9dx2wMfCwJIAZETEqIuZKuhOYR3Ko7cyIWFXBOjMn6ThgPDAQeFDS\n7Ig4DDgI+J6kd4HVwKiIWFrBUjPX2bZoxN9FiR9KaiY5pNYOfLWy5eQrIlZKOgt4COgF3BgRcytc\nVu78aBszM8uFD6mZmVkuHDhmZpYLB46ZmeXCgWNmZrlw4JiZWS4cOGZmlgsHjpmZ5eL/A9+ViarN\ngSygAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yV5uv87ZgiKL",
"colab_type": "text"
},
"source": [
"## Coefficient Correlation"
]
},
{
"cell_type": "code",
"metadata": {
"id": "JjHdRnOxghI7",
"colab_type": "code",
"colab": {}
},
"source": [
"full_data = pd.concat ([x, y], axis=1)\n",
"\n",
"#full_data.corr()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mnRdTaA-POnR",
"colab_type": "code",
"outputId": "e15a668f-b9b2-4ac3-8006-1137fc27621c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 504
}
},
"source": [
"plt.figure(figsize=(12,8))\n",
"sns.heatmap(full_data.corr(), annot=True, fmt='.2f')"
],
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f35808f9ac8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
},
{
"output_type": "display_data",
"data": {
"image/png": 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38esy6zVP9cSmZx8wGpB1OrI+eBfD1Vgkewcc5s5H3aABedu2kL3iQ4vkKW3m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OmJN7zoYsGUWDNg2J/WMnVSOqU7dFPca0Hoox24inr5f9ohQSlWb25NCb09HHJRO+fgZJ6/eT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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ROMTA_LWksN0",
"colab_type": "text"
},
"source": [
"## Encoding"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SnQmorDHkv3M",
"colab_type": "text"
},
"source": [
"### One Hot Encoding\n",
"\n",
"untuk 1 kategori dgn 2 nilai (1=yes, 0=no)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9ZXZUzRZmXH_",
"colab_type": "code",
"outputId": "58d9169f-ad70-422a-d1c7-70634e4df515",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"university = pd.read_csv('http://bit.ly/dwp-data-university')\n",
"\n",
"university = pd.get_dummies(university, columns=['Research'])\n",
"\n",
"university.head()"
],
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"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>GRE</th>\n",
" <th>TOEFL</th>\n",
" <th>GPA</th>\n",
" <th>University Class</th>\n",
" <th>Research_N</th>\n",
" <th>Research_Y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>330</td>\n",
" <td>115</td>\n",
" <td>9.34</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>302</td>\n",
" <td>102</td>\n",
" <td>8.00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>328</td>\n",
" <td>116</td>\n",
" <td>9.50</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>334</td>\n",
" <td>119</td>\n",
" <td>9.70</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>336</td>\n",
" <td>119</td>\n",
" <td>9.80</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GRE TOEFL GPA University Class Research_N Research_Y\n",
"0 330 115 9.34 1 0 1\n",
"1 302 102 8.00 0 1 0\n",
"2 328 116 9.50 1 0 1\n",
"3 334 119 9.70 1 0 1\n",
"4 336 119 9.80 1 0 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ucLZ3DqLkvkK",
"colab_type": "text"
},
"source": [
"### Label Encoding\n",
"\n",
"memberi nilai dalam 1 kategori\n",
"\n",
"bila data memiliki klasifikasi"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PvWj_SxSnFxT",
"colab_type": "code",
"outputId": "c96485ff-47be-4e24-b99c-582ff38ebb6a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"university = pd.read_csv('http://bit.ly/dwp-data-university')\n",
"\n",
"def convert_research(value):\n",
" if value == 'Y':\n",
" return 1\n",
" return 0\n",
"\n",
"university['Research'] = university['Research'].apply(convert_research)\n",
"\n",
"university.head()"
],
"execution_count": 50,
"outputs": [
{
"output_type": "execute_result",
"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>GRE</th>\n",
" <th>TOEFL</th>\n",
" <th>GPA</th>\n",
" <th>Research</th>\n",
" <th>University Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>330</td>\n",
" <td>115</td>\n",
" <td>9.34</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>302</td>\n",
" <td>102</td>\n",
" <td>8.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>328</td>\n",
" <td>116</td>\n",
" <td>9.50</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>334</td>\n",
" <td>119</td>\n",
" <td>9.70</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>336</td>\n",
" <td>119</td>\n",
" <td>9.80</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GRE TOEFL GPA Research University Class\n",
"0 330 115 9.34 1 1\n",
"1 302 102 8.00 0 0\n",
"2 328 116 9.50 1 1\n",
"3 334 119 9.70 1 1\n",
"4 336 119 9.80 1 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 50
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lmlw1fxxjbC8",
"colab_type": "text"
},
"source": [
"## Supervised Learning (Classification)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "biQ8pbndjx9k",
"colab_type": "text"
},
"source": [
"### Logistic Regression"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VRW5tIwCjKz9",
"colab_type": "code",
"outputId": "44c63100-36f9-406d-a4d1-73a82599291d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"# Import the data\n",
"university = pd.read_csv('http://bit.ly/dwp-data-university')\n",
"\n",
"# Seperate the independent variables (x) and dependent variables (y)\n",
"x = university.drop(columns={'University Class'})\n",
"y = university['University Class']\n",
"\n",
"# Encode the research column using One Hot Encoding\n",
"x = pd.get_dummies(x, columns=['Research'])\n",
"\n",
"# Split the data into training data & testing data\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n",
"\n",
"# Scale the data\n",
"# Yg di standarisasi hanya variable x saja\n",
"scaler = StandardScaler()\n",
"x_train = scaler.fit_transform(x_train)\n",
"x_test = scaler.fit_transform(x_test)\n",
"\n",
"# Initiate the model & train it\n",
"model = LogisticRegression()\n",
"model.fit(x_train, y_train)\n",
"\n",
"# Testing & evaluation\n",
"y_pred = model.predict(x_test)\n",
"\n",
"accuracy = metrics.accuracy_score(y_test, y_pred)\n",
"print('Accuracy Score: {}'.format(accuracy))\n",
"\n",
"accuracy = metrics.precision_score(y_test, y_pred)\n",
"print('Precision Score: {}'.format(accuracy))\n",
"\n",
"accuracy = metrics.recall_score(y_test, y_pred)\n",
"print('Recall Score: {}'.format(accuracy))\n",
"\n",
"accuracy = metrics.f1_score(y_test, y_pred)\n",
"print('F1 Score: {}'.format(accuracy))\n"
],
"execution_count": 51,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy Score: 0.8636363636363636\n",
"Precision Score: 0.9375\n",
"Recall Score: 0.8823529411764706\n",
"F1 Score: 0.9090909090909091\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-4EKDztp1F6_",
"colab_type": "code",
"outputId": "f75d2658-58fd-4fbf-fe38-e40fef1cf42c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
}
},
"source": [
"tn, fp, fn, tp = metrics.confusion_matrix(y_test, y_pred).ravel()\n",
"\n",
"print('TN: {}'.format(tn))\n",
"print('FP: {}'.format(fp))\n",
"print('FN: {}'.format(fn))\n",
"print('TP: {}'.format(tp))"
],
"execution_count": 52,
"outputs": [
{
"output_type": "stream",
"text": [
"TN: 4\n",
"FP: 1\n",
"FN: 2\n",
"TP: 15\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fh2HCKb8048w",
"colab_type": "code",
"outputId": "96f96b33-ddb0-4299-c58c-2c6b5bd457ac",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 731
}
},
"source": [
"result = pd.DataFrame({'actual': y_test.tolist(),\n",
" 'prediction': y_pred.tolist()})\n",
"\n",
"result"
],
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"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>actual</th>\n",
" <th>prediction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" actual prediction\n",
"0 1 1\n",
"1 1 1\n",
"2 0 0\n",
"3 1 1\n",
"4 1 1\n",
"5 1 1\n",
"6 1 1\n",
"7 1 1\n",
"8 1 1\n",
"9 1 1\n",
"10 1 0\n",
"11 1 1\n",
"12 0 1\n",
"13 1 1\n",
"14 1 0\n",
"15 1 1\n",
"16 1 1\n",
"17 0 0\n",
"18 0 0\n",
"19 0 0\n",
"20 1 1\n",
"21 1 1"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "o2sLQ_YYyPPy",
"colab_type": "code",
"outputId": "453de781-1f25-47c1-ee94-f6aabd02a646",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
}
},
"source": [
"coef = pd.DataFrame({'columns': x.columns.tolist(),\n",
" 'coefficient': model.coef_[0].tolist()})\n",
"\n",
"coef = coef.sort_values('coefficient')\n",
"\n",
"coef.plot(x='columns', y='coefficient', kind='barh')"
],
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f35806659b0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 54
},
{
"output_type": "display_data",
"data": {
"image/png": 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aleKJGOsiYjCApNuBicDVJdjvZpJ6RsTGbdlGRMyW9C3gW8AM4NtAXUQ83RE1mll5+uij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\nbz9Gqf5jruH2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "OWKi5YrIuYJT",
"colab_type": "code",
"outputId": "9f2e02f7-2e10-4025-9da1-580ed6f78ef7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"model.predict(x_test)"
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1])"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "u5iJY6fLuX7Y",
"colab_type": "code",
"outputId": "9f652b57-960e-40b2-fbaa-80544edd779c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 391
}
},
"source": [
"model.predict_proba(x_test)"
],
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[0.02709135, 0.97290865],\n",
" [0.04368304, 0.95631696],\n",
" [0.96340988, 0.03659012],\n",
" [0.0308711 , 0.9691289 ],\n",
" [0.10841722, 0.89158278],\n",
" [0.04296713, 0.95703287],\n",
" [0.02160933, 0.97839067],\n",
" [0.0072037 , 0.9927963 ],\n",
" [0.00993304, 0.99006696],\n",
" [0.04845717, 0.95154283],\n",
" [0.68885818, 0.31114182],\n",
" [0.09783686, 0.90216314],\n",
" [0.41235492, 0.58764508],\n",
" [0.00260157, 0.99739843],\n",
" [0.60397465, 0.39602535],\n",
" [0.04485289, 0.95514711],\n",
" [0.02531703, 0.97468297],\n",
" [0.9586995 , 0.0413005 ],\n",
" [0.99850361, 0.00149639],\n",
" [0.98885855, 0.01114145],\n",
" [0.19139893, 0.80860107],\n",
" [0.02717612, 0.97282388]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "czeUmAcsuvrn",
"colab_type": "code",
"colab": {}
},
"source": [
"import math\n",
"\n",
"def sigmoid(x):\n",
" return 1 / (1 + math.exp(-x))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8UEdLcVpqViG",
"colab_type": "code",
"outputId": "602dab78-5f40-405f-c9af-ecfc13551537",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"x_test[0]"
],
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 0.55013376, 0.8347188 , 0.48095854, -0.47140452, 0.47140452])"
]
},
"metadata": {
"tags": []
},
"execution_count": 58
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QSWOHHThw3rS",
"colab_type": "code",
"outputId": "db744325-5b51-4e71-ca1e-f6d51b238794",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"x.columns"
],
"execution_count": 59,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['GRE', 'TOEFL', 'GPA', 'Research_N', 'Research_Y'], dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nSVXajxkqVeb",
"colab_type": "code",
"outputId": "0d53c197-e904-4457-c504-8fba505b5f11",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"model.coef_"
],
"execution_count": 60,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[ 0.23979327, 1.3141394 , 1.86730635, 0.06184949, -0.06184949]])"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ALedx9G5qVax",
"colab_type": "code",
"outputId": "bd888374-66ce-40e1-bf91-4e7a37c51ea1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"model.intercept_"
],
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1.51243569])"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rgRMtHLAqVYF",
"colab_type": "code",
"outputId": "5c6c41d7-ebb3-4202-f2c8-033fe865a429",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"sigmoid((x_test[0] @ model.coef_[0]) + model.intercept_)"
],
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.9729086476053764"
]
},
"metadata": {
"tags": []
},
"execution_count": 62
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "L8zQf0AMqVSA",
"colab_type": "code",
"outputId": "e421a3a2-4602-4085-8524-4d4f39a9f56b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"sigmoid(1)"
],
"execution_count": 63,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.7310585786300049"
]
},
"metadata": {
"tags": []
},
"execution_count": 63
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cg38HpZXqVOi",
"colab_type": "code",
"outputId": "3960cc0d-4bac-4a00-b880-3cb847b893ab",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"sigmoid(2)"
],
"execution_count": 64,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.8807970779778823"
]
},
"metadata": {
"tags": []
},
"execution_count": 64
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CMSu6wtYqVLP",
"colab_type": "code",
"outputId": "9e535199-4cf0-424d-d7d6-2e1b747de8db",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"sigmoid(3)"
],
"execution_count": 65,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.9525741268224334"
]
},
"metadata": {
"tags": []
},
"execution_count": 65
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XmmQvWMVqVHW",
"colab_type": "code",
"outputId": "62347cee-8f61-4308-8374-95e36bb5c720",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"sigmoid(4)"
],
"execution_count": 66,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.9820137900379085"
]
},
"metadata": {
"tags": []
},
"execution_count": 66
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h9uDfXIPkn3O",
"colab_type": "text"
},
"source": [
"### Diabetes Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "t7_9XLqmA4uC",
"colab_type": "code",
"outputId": "6f7cd190-9c49-4fc9-a7f3-2550b869fe00",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 292
}
},
"source": [
"!pip install pandasql"
],
"execution_count": 67,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting pandasql\n",
" Downloading https://files.pythonhosted.org/packages/6b/c4/ee4096ffa2eeeca0c749b26f0371bd26aa5c8b611c43de99a4f86d3de0a7/pandasql-0.7.3.tar.gz\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.17.5)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pandasql) (0.25.3)\n",
"Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.6/dist-packages (from pandasql) (1.3.13)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->pandasql) (2.6.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->pandasql) (1.12.0)\n",
"Building wheels for collected packages: pandasql\n",
" Building wheel for pandasql (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pandasql: filename=pandasql-0.7.3-cp36-none-any.whl size=26819 sha256=c211ff2753c86c4c54d57a74011c77fc3b4a936ea58dbdf6d21db9ad5b731232\n",
" Stored in directory: /root/.cache/pip/wheels/53/6c/18/b87a2e5fa8a82e9c026311de56210b8d1c01846e18a9607fc9\n",
"Successfully built pandasql\n",
"Installing collected packages: pandasql\n",
"Successfully installed pandasql-0.7.3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "4n4YPCXLBC32",
"colab": {}
},
"source": [
"import pandasql"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "StcBwzfn8JQn",
"colab_type": "text"
},
"source": [
"## Exploratory Data Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "B_e3sgnL8ecj",
"colab_type": "text"
},
"source": [
"### Questions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kfZoTCxL89dl",
"colab_type": "text"
},
"source": [
"1. Melihat korelasi dari setiap variable / feature ?\n",
"2. Apakah wanita yang lebih sering mengalami kehamilan memiliki BMI lebih tinggi?\n",
"3. Apakah wanita berumur (diatas 50 tahun) memiliki skin thickness yang lebih tebal?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_OcrQiQr80--",
"colab_type": "text"
},
"source": [
"### Wrangle"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EZWl073583wT",
"colab_type": "code",
"colab": {}
},
"source": [
"# Import data\n",
"diabetes = pd.read_csv('http://bit.ly/dwp-data-diabetes')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ugMPVHJ3_CXI",
"colab_type": "text"
},
"source": [
"### Explore"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "t8EW5N_BC-pv"
},
"source": [
"**1. Correlation**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "3avTTirj_E7U",
"colab_type": "code",
"outputId": "96f31d75-b15a-4875-9bf7-9e9e6d30cadc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 625
}
},
"source": [
"plt.figure(figsize=(12,8))\n",
"sns.heatmap(diabetes.corr(), annot=True, fmt='.2f')"
],
"execution_count": 70,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f357e039c18>"
]
},
"metadata": {
"tags": []
},
"execution_count": 70
},
{
"output_type": "display_data",
"data": {
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SILAAW5f95qmUr0nRmmU5uj+SY4ecr+U/Z6+hQqv0r+WDq/8kyXVswzbvJiDU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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xoTmKKRsDHoi",
"colab_type": "text"
},
"source": [
"**2. Pregnancies & BMI**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "i_QHf-v7Ajnc",
"colab_type": "code",
"outputId": "a4bda65a-d100-4875-f77d-ceb662c4a218",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"import pandasql\n",
"\n",
"query = \"\"\"\n",
" SELECT\n",
" Pregnancies,\n",
" AVG(BMI) AS average_BMI\n",
" FROM\n",
" diabetes\n",
" GROUP BY\n",
" Pregnancies\n",
" \"\"\"\n",
"\n",
"result = pandasql.sqldf(query)\n",
"\n",
"result.head()"
],
"execution_count": 71,
"outputs": [
{
"output_type": "execute_result",
"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>Pregnancies</th>\n",
" <th>average_BMI</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>34.290090</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>31.372593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>30.583495</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>30.425333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>32.141176</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pregnancies average_BMI\n",
"0 0 34.290090\n",
"1 1 31.372593\n",
"2 2 30.583495\n",
"3 3 30.425333\n",
"4 4 32.141176"
]
},
"metadata": {
"tags": []
},
"execution_count": 71
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cpiY1jC8EpzR",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"outputId": "95757682-d905-4cd0-a4c2-2076f06a6cdf"
},
"source": [
"plt.figure(figsize=(13,4))\n",
"viz = sns.barplot(data=result, x='Pregnancies', y='average_BMI')"
],
"execution_count": 72,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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gvE3c71FJXg8sA95VVXeMtVKSRcAigJ122mkTdyVJkiRpYzblPgZU1feq6qyquncTNj8e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\nD1S9xqNxqHzDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 936x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fHwzS5cxmIh3",
"colab_type": "text"
},
"source": [
"**3. Age and skin thickness**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tvJvPyXMl1LK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "969da8cc-b15c-4d10-a601-deb7c0fe2130"
},
"source": [
"query = \"\"\"\n",
" SELECT\n",
" Age,\n",
" AVG(SkinThickness) AS average_skin_thickness\n",
" FROM\n",
" diabetes\n",
" GROUP BY\n",
" Age\n",
" \"\"\"\n",
"\n",
"result = pandasql.sqldf(query)\n",
"\n",
"result.head()"
],
"execution_count": 73,
"outputs": [
{
"output_type": "execute_result",
"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>average_skin_thickness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21</td>\n",
" <td>19.349206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>22</td>\n",
" <td>20.486111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>23</td>\n",
" <td>22.368421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>24</td>\n",
" <td>25.934783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>25</td>\n",
" <td>23.958333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age average_skin_thickness\n",
"0 21 19.349206\n",
"1 22 20.486111\n",
"2 23 22.368421\n",
"3 24 25.934783\n",
"4 25 23.958333"
]
},
"metadata": {
"tags": []
},
"execution_count": 73
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "G3xYeliRmYkN",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"outputId": "6e499b47-869a-4255-b2e8-36a885f6b76c"
},
"source": [
"plt.figure(figsize=(15,4))\n",
"viz = sns.barplot(data=result, x='Age', y='average_skin_thickness')"
],
"execution_count": 74,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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Vq1bNcFWSJEmStG4rbuwy87rMPCoz/70d/q/M/PT09Ij4ZuGiPgr8AbAzsBL4x551Hp2Z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\n",
"text/plain": [
"<Figure size 1080x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7C2bCURTmfjt",
"colab_type": "text"
},
"source": [
"### Conclusion"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JfFjaO9fmjrC",
"colab_type": "text"
},
"source": [
"Isi sendiri"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GA6I9GEHmuLR",
"colab_type": "text"
},
"source": [
"## Building Machine Learning Model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "_zGy3tlmmbvB",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 85
},
"outputId": "c3185685-5900-438b-8f73-8f865f950e86"
},
"source": [
"# Import the data\n",
"diabetes = pd.read_csv('http://bit.ly/dwp-data-diabetes')\n",
"\n",
"# Separate the independent variables (x) and dependent variables (y)\n",
"x = diabetes.drop(columns={'Outcome'})\n",
"y = diabetes['Outcome']\n",
"\n",
"# Split the data into training data and testing data\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)\n",
"\n",
"# Scale the data\n",
"scaler = StandardScaler()\n",
"x_train = scaler.fit_transform(x_train)\n",
"x_test = scaler.fit_transform(x_test)\n",
"\n",
"# Initiate the model and train it\n",
"model = LogisticRegression()\n",
"model.fit(x_train, y_train)\n",
"\n",
"# Testing and evaluation\n",
"y_pred = model.predict(x_test)\n",
"\n",
"accuracy = metrics.accuracy_score(y_test, y_pred)\n",
"print('Accuracy Score: {}'.format(accuracy))\n",
"\n",
"accuracy = metrics.precision_score(y_test, y_pred)\n",
"print('Precision Score: {}'.format(accuracy))\n",
"\n",
"accuracy = metrics.recall_score(y_test, y_pred)\n",
"print('Recall Score: {}'.format(accuracy))\n",
"\n",
"accuracy = metrics.f1_score(y_test, y_pred)\n",
"print('F1 Score: {}'.format(accuracy))"
],
"execution_count": 75,
"outputs": [
{
"output_type": "stream",
"text": [
"Accuracy Score: 0.7792207792207793\n",
"Precision Score: 0.7560975609756098\n",
"Recall Score: 0.5636363636363636\n",
"F1 Score: 0.6458333333333333\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "prBiGYonm4Za",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "85af3fe6-aabe-4e91-e15d-f0b357f8db14"
},
"source": [
"len(x_test)"
],
"execution_count": 76,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"154"
]
},
"metadata": {
"tags": []
},
"execution_count": 76
}
]
}
]
}
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