├── Classification
├── ML0101EN-Clas-Decision-Trees-drug.jupyterlite.ipynb
├── Module 3_ML0101EN-Clas-K-Nearest-neighbors-CustCat.jupyterlite.ipynb
└── classification_tree_svm.ipynb
├── LICENSE
├── README.md
└── Regression
├── 01_LR.ipynb
├── Module 2_ML0101EN-Reg-Mulitple-Linear-Regression-Co2.jupyterlite.ipynb
└── Module 2_ML0101EN-Reg-Simple-Linear-Regression-Co2.jupyterlite.ipynb
/Classification/ML0101EN-Clas-Decision-Trees-drug.jupyterlite.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "metadata": {
3 | "kernelspec": {
4 | "name": "python",
5 | "display_name": "Python (Pyodide)",
6 | "language": "python"
7 | },
8 | "language_info": {
9 | "codemirror_mode": {
10 | "name": "python",
11 | "version": 3
12 | },
13 | "file_extension": ".py",
14 | "mimetype": "text/x-python",
15 | "name": "python",
16 | "nbconvert_exporter": "python",
17 | "pygments_lexer": "ipython3",
18 | "version": "3.8"
19 | }
20 | },
21 | "nbformat_minor": 4,
22 | "nbformat": 4,
23 | "cells": [
24 | {
25 | "cell_type": "markdown",
26 | "source": "\n \n ![\"Skills](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\")
\n \n
\n\n\n# Decision Trees\n\n\nEstimated time needed: **15** minutes\n \n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n* Develop a classification model using Decision Tree Algorithm\n",
27 | "metadata": {}
28 | },
29 | {
30 | "cell_type": "markdown",
31 | "source": "In this lab exercise, you will learn a popular machine learning algorithm, Decision Trees. You will use this classification algorithm to build a model from the historical data of patients, and their response to different medications. Then you will use the trained decision tree to predict the class of an unknown patient, or to find a proper drug for a new patient.\n",
32 | "metadata": {}
33 | },
34 | {
35 | "cell_type": "markdown",
36 | "source": "Table of contents
\n\n\n
\n
\n",
37 | "metadata": {}
38 | },
39 | {
40 | "cell_type": "markdown",
41 | "source": "Import the Following Libraries:\n\n - numpy (as np)
\n - pandas
\n - DecisionTreeClassifier from sklearn.tree
\n
\n",
42 | "metadata": {}
43 | },
44 | {
45 | "cell_type": "markdown",
46 | "source": "if you uisng you own version comment out\n",
47 | "metadata": {}
48 | },
49 | {
50 | "cell_type": "code",
51 | "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])\n\n",
52 | "metadata": {},
53 | "outputs": [],
54 | "execution_count": null
55 | },
56 | {
57 | "cell_type": "code",
58 | "source": "import numpy as np \nimport pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\nimport sklearn.tree as tree",
59 | "metadata": {},
60 | "outputs": [],
61 | "execution_count": null
62 | },
63 | {
64 | "cell_type": "code",
65 | "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n response = await pyfetch(url)\n if response.status == 200:\n with open(filename, \"wb\") as f:\n f.write(await response.bytes())",
66 | "metadata": {},
67 | "outputs": [],
68 | "execution_count": null
69 | },
70 | {
71 | "cell_type": "markdown",
72 | "source": "\n
About the dataset
\n Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. \n
\n
\n Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.\n
\n
\n It is a sample of multiclass classifier, and you can use the training part of the dataset \n to build a decision tree, and then use it to predict the class of an unknown patient, or to prescribe a drug to a new patient.\n\n",
73 | "metadata": {}
74 | },
75 | {
76 | "cell_type": "markdown",
77 | "source": " \n
Downloading the Data
\n To download the data, we will use !wget to download it from IBM Object Storage.\n\n",
78 | "metadata": {}
79 | },
80 | {
81 | "cell_type": "code",
82 | "source": "path= 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv'\nawait download(path,\"drug200.csv\")\npath=\"drug200.csv\"",
83 | "metadata": {},
84 | "outputs": [],
85 | "execution_count": null
86 | },
87 | {
88 | "cell_type": "markdown",
89 | "source": "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n",
90 | "metadata": {}
91 | },
92 | {
93 | "cell_type": "markdown",
94 | "source": "Now, read the data using pandas dataframe:\n",
95 | "metadata": {}
96 | },
97 | {
98 | "cell_type": "code",
99 | "source": "my_data = pd.read_csv(\"drug200.csv\", delimiter=\",\")\nmy_data[0:5]",
100 | "metadata": {},
101 | "outputs": [],
102 | "execution_count": null
103 | },
104 | {
105 | "cell_type": "markdown",
106 | "source": " \n
Practice
\n What is the size of data? \n\n",
107 | "metadata": {}
108 | },
109 | {
110 | "cell_type": "code",
111 | "source": "# write your code here\n\n\n",
112 | "metadata": {},
113 | "outputs": [],
114 | "execution_count": null
115 | },
116 | {
117 | "cell_type": "markdown",
118 | "source": "Click here for the solution
\n\n```python\nmy_data.shape\n\n```\n\n\n
Pre-processing
\n\n",
124 | "metadata": {}
125 | },
126 | {
127 | "cell_type": "markdown",
128 | "source": "Using my_data as the Drug.csv data read by pandas, declare the following variables:
\n\n\n - X as the Feature Matrix (data of my_data)
\n - y as the response vector (target)
\n
\n",
129 | "metadata": {}
130 | },
131 | {
132 | "cell_type": "markdown",
133 | "source": "Remove the column containing the target name since it doesn't contain numeric values.\n",
134 | "metadata": {}
135 | },
136 | {
137 | "cell_type": "code",
138 | "source": "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\nX[0:5]\n",
139 | "metadata": {},
140 | "outputs": [],
141 | "execution_count": null
142 | },
143 | {
144 | "cell_type": "markdown",
145 | "source": "As you may figure out, some features in this dataset are categorical, such as __Sex__ or __BP__. Unfortunately, Sklearn Decision Trees does not handle categorical variables. We can still convert these features to numerical values using __LabelEncoder__\nto convert the categorical variable into numerical variables.\n",
146 | "metadata": {}
147 | },
148 | {
149 | "cell_type": "code",
150 | "source": "from sklearn import preprocessing\nle_sex = preprocessing.LabelEncoder()\nle_sex.fit(['F','M'])\nX[:,1] = le_sex.transform(X[:,1]) \n\n\nle_BP = preprocessing.LabelEncoder()\nle_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])\nX[:,2] = le_BP.transform(X[:,2])\n\n\nle_Chol = preprocessing.LabelEncoder()\nle_Chol.fit([ 'NORMAL', 'HIGH'])\nX[:,3] = le_Chol.transform(X[:,3]) \n\nX[0:5]\n",
151 | "metadata": {},
152 | "outputs": [],
153 | "execution_count": null
154 | },
155 | {
156 | "cell_type": "markdown",
157 | "source": "Now we can fill the target variable.\n",
158 | "metadata": {}
159 | },
160 | {
161 | "cell_type": "code",
162 | "source": "y = my_data[\"Drug\"]\ny[0:5]",
163 | "metadata": {},
164 | "outputs": [],
165 | "execution_count": null
166 | },
167 | {
168 | "cell_type": "markdown",
169 | "source": "
\n\n\n
Setting up the Decision Tree
\n We will be using train/test split on our decision tree. Let's import train_test_split from sklearn.cross_validation.\n\n",
170 | "metadata": {}
171 | },
172 | {
173 | "cell_type": "code",
174 | "source": "from sklearn.model_selection import train_test_split",
175 | "metadata": {},
176 | "outputs": [],
177 | "execution_count": null
178 | },
179 | {
180 | "cell_type": "markdown",
181 | "source": "Now train_test_split will return 4 different parameters. We will name them:
\nX_trainset, X_testset, y_trainset, y_testset
\nThe train_test_split will need the parameters:
\nX, y, test_size=0.3, and random_state=3.
\nThe X and y are the arrays required before the split, the test_size represents the ratio of the testing dataset, and the random_state ensures that we obtain the same splits.\n",
182 | "metadata": {}
183 | },
184 | {
185 | "cell_type": "code",
186 | "source": "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)",
187 | "metadata": {},
188 | "outputs": [],
189 | "execution_count": null
190 | },
191 | {
192 | "cell_type": "markdown",
193 | "source": "Practice
\nPrint the shape of X_trainset and y_trainset. Ensure that the dimensions match.\n",
194 | "metadata": {}
195 | },
196 | {
197 | "cell_type": "code",
198 | "source": "# your code\n\n",
199 | "metadata": {},
200 | "outputs": [],
201 | "execution_count": null
202 | },
203 | {
204 | "cell_type": "markdown",
205 | "source": "Click here for the solution
\n\n```python\nprint('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n\n```\n\nClick here for the solution
\n\n```python\nprint('Shape of X training set {}'.format(X_testset.shape),'&',' Size of Y training set {}'.format(y_testset.shape))\n\n```\n\n
\n\n\n
Modeling
\n We will first create an instance of the DecisionTreeClassifier called drugTree.
\n Inside of the classifier, specify criterion=\"entropy\" so we can see the information gain of each node.\n\n",
228 | "metadata": {}
229 | },
230 | {
231 | "cell_type": "code",
232 | "source": "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\ndrugTree # it shows the default parameters",
233 | "metadata": {},
234 | "outputs": [],
235 | "execution_count": null
236 | },
237 | {
238 | "cell_type": "markdown",
239 | "source": "Next, we will fit the data with the training feature matrix X_trainset and training response vector y_trainset \n",
240 | "metadata": {}
241 | },
242 | {
243 | "cell_type": "code",
244 | "source": "drugTree.fit(X_trainset,y_trainset)",
245 | "metadata": {},
246 | "outputs": [],
247 | "execution_count": null
248 | },
249 | {
250 | "cell_type": "markdown",
251 | "source": "
\n\n\n
Prediction
\n Let's make some predictions on the testing dataset and store it into a variable called predTree.\n\n",
252 | "metadata": {}
253 | },
254 | {
255 | "cell_type": "code",
256 | "source": "predTree = drugTree.predict(X_testset)",
257 | "metadata": {},
258 | "outputs": [],
259 | "execution_count": null
260 | },
261 | {
262 | "cell_type": "markdown",
263 | "source": "You can print out predTree and y_testset if you want to visually compare the predictions to the actual values.\n",
264 | "metadata": {}
265 | },
266 | {
267 | "cell_type": "code",
268 | "source": "print (predTree [0:5])\nprint (y_testset [0:5])\n",
269 | "metadata": {},
270 | "outputs": [],
271 | "execution_count": null
272 | },
273 | {
274 | "cell_type": "markdown",
275 | "source": "
\n\n\n
Evaluation
\n Next, let's import metrics from sklearn and check the accuracy of our model.\n\n",
276 | "metadata": {}
277 | },
278 | {
279 | "cell_type": "code",
280 | "source": "from sklearn import metrics\nimport matplotlib.pyplot as plt\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))",
281 | "metadata": {},
282 | "outputs": [],
283 | "execution_count": null
284 | },
285 | {
286 | "cell_type": "markdown",
287 | "source": "__Accuracy classification score__ computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. \n\nIn multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly matches with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.\n",
288 | "metadata": {}
289 | },
290 | {
291 | "cell_type": "markdown",
292 | "source": "
\n\n\n
Visualization
\n \n \nLet's visualize the tree\n\n",
293 | "metadata": {}
294 | },
295 | {
296 | "cell_type": "code",
297 | "source": "# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before\n#!conda install -c conda-forge pydotplus -y\n#!conda install -c conda-forge python-graphviz -y",
298 | "metadata": {},
299 | "outputs": [],
300 | "execution_count": null
301 | },
302 | {
303 | "cell_type": "code",
304 | "source": "tree.plot_tree(drugTree)\nplt.show()",
305 | "metadata": {},
306 | "outputs": [],
307 | "execution_count": null
308 | },
309 | {
310 | "cell_type": "markdown",
311 | "source": "Want to learn more?
\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: SPSS Modeler\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at Watson Studio\n\n",
312 | "metadata": {}
313 | },
314 | {
315 | "cell_type": "markdown",
316 | "source": "### Thank you for completing this lab!\n\n\n## Author\n\nSaeed Aghabozorgi\n\n\n### Other Contributors\n\nJoseph Santarcangelo\n\n\n\n\n## Change Log\n\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n|---|---|---|---|\n| 2023-04-05 | 2.3 | Anita Verma | Changed pandas.get_dummies() to LabelEncoder|\n| 2020-11-20 | 2.2 | Lakshmi | Changed import statement of StringIO|\n| 2020-11-03 | 2.1 | Lakshmi | Changed URL of the csv |\n| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n| | | | |\n| | | | |\n\n\n## © IBM Corporation 2020. All rights reserved. \n",
317 | "metadata": {}
318 | }
319 | ]
320 | }
--------------------------------------------------------------------------------
/Classification/Module 3_ML0101EN-Clas-K-Nearest-neighbors-CustCat.jupyterlite.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "metadata": {
3 | "kernelspec": {
4 | "name": "python",
5 | "display_name": "Pyolite",
6 | "language": "python"
7 | },
8 | "language_info": {
9 | "codemirror_mode": {
10 | "name": "python",
11 | "version": 3
12 | },
13 | "file_extension": ".py",
14 | "mimetype": "text/x-python",
15 | "name": "python",
16 | "nbconvert_exporter": "python",
17 | "pygments_lexer": "ipython3",
18 | "version": "3.8"
19 | }
20 | },
21 | "nbformat_minor": 4,
22 | "nbformat": 4,
23 | "cells": [
24 | {
25 | "cell_type": "markdown",
26 | "source": [
27 | "
\n",
28 | " \n",
29 | " \n",
30 | " \n",
31 | "
\n",
32 | "\n",
33 | "# K-Nearest Neighbors\n",
34 | "\n",
35 | "Estimated time needed: **25** minutes\n",
36 | "\n",
37 | "## Objectives\n",
38 | "\n",
39 | "After completing this lab you will be able to:\n",
40 | "\n",
41 | "* Use K Nearest neighbors to classify data\n"
42 | ],
43 | "metadata": {
44 | "button": false,
45 | "new_sheet": false,
46 | "run_control": {
47 | "read_only": false
48 | }
49 | }
50 | },
51 | {
52 | "cell_type": "markdown",
53 | "source": [
54 | "In this Lab you will load a customer dataset, fit the data, and use K-Nearest Neighbors to predict a data point. But what is **K-Nearest Neighbors**?\n"
55 | ],
56 | "metadata": {
57 | "button": false,
58 | "new_sheet": false,
59 | "run_control": {
60 | "read_only": false
61 | }
62 | }
63 | },
64 | {
65 | "cell_type": "markdown",
66 | "source": [
67 | "**K-Nearest Neighbors** is a supervised learning algorithm. Where the data is 'trained' with data points corresponding to their classification. To predict the class of a given data point, it takes into account the classes of the 'K' nearest data points and chooses the class in which the majority of the 'K' nearest data points belong to as the predicted class.\n"
68 | ],
69 | "metadata": {
70 | "button": false,
71 | "new_sheet": false,
72 | "run_control": {
73 | "read_only": false
74 | }
75 | }
76 | },
77 | {
78 | "cell_type": "markdown",
79 | "source": [
80 | "### Here's an visualization of the K-Nearest Neighbors algorithm.\n",
81 | "\n",
82 | "![](\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/images/KNN_Diagram.png\")
\n"
83 | ],
84 | "metadata": {
85 | "button": false,
86 | "new_sheet": false,
87 | "run_control": {
88 | "read_only": false
89 | }
90 | }
91 | },
92 | {
93 | "cell_type": "markdown",
94 | "source": [
95 | "In this case, we have data points of Class A and B. We want to predict what the star (test data point) is. If we consider a k value of 3 (3 nearest data points), we will obtain a prediction of Class B. Yet if we consider a k value of 6, we will obtain a prediction of Class A.\n"
96 | ],
97 | "metadata": {
98 | "button": false,
99 | "new_sheet": false,
100 | "run_control": {
101 | "read_only": false
102 | }
103 | }
104 | },
105 | {
106 | "cell_type": "markdown",
107 | "source": [
108 | "In this sense, it is important to consider the value of k. Hopefully from this diagram, you should get a sense of what the K-Nearest Neighbors algorithm is. It considers the 'K' Nearest Neighbors (data points) when it predicts the classification of the test point.\n"
109 | ],
110 | "metadata": {
111 | "button": false,
112 | "new_sheet": false,
113 | "run_control": {
114 | "read_only": false
115 | }
116 | }
117 | },
118 | {
119 | "cell_type": "markdown",
120 | "source": [
121 | "Table of contents
\n",
122 | "\n",
123 | "\n",
130 | "
\n",
131 | "
\n"
132 | ],
133 | "metadata": {}
134 | },
135 | {
136 | "cell_type": "code",
137 | "source": "#!pip install scikit-learn==0.23.1",
138 | "metadata": {
139 | "trusted": true
140 | },
141 | "execution_count": null,
142 | "outputs": []
143 | },
144 | {
145 | "cell_type": "code",
146 | "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])\nawait piplite.install(['scipy'])\n\n",
147 | "metadata": {
148 | "trusted": true
149 | },
150 | "execution_count": null,
151 | "outputs": []
152 | },
153 | {
154 | "cell_type": "markdown",
155 | "source": [
156 | "Let's load required libraries\n"
157 | ],
158 | "metadata": {
159 | "button": false,
160 | "new_sheet": false,
161 | "run_control": {
162 | "read_only": false
163 | }
164 | }
165 | },
166 | {
167 | "cell_type": "code",
168 | "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nfrom sklearn import preprocessing\n%matplotlib inline",
169 | "metadata": {
170 | "button": false,
171 | "new_sheet": false,
172 | "run_control": {
173 | "read_only": false
174 | },
175 | "trusted": true
176 | },
177 | "execution_count": null,
178 | "outputs": []
179 | },
180 | {
181 | "cell_type": "markdown",
182 | "source": [
183 | "\n",
184 | "
About the dataset
\n",
185 | "\n"
186 | ],
187 | "metadata": {
188 | "button": false,
189 | "new_sheet": false,
190 | "run_control": {
191 | "read_only": false
192 | }
193 | }
194 | },
195 | {
196 | "cell_type": "markdown",
197 | "source": [
198 | "Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset, with predefined labels, we need to build a model to be used to predict class of a new or unknown case.\n",
199 | "\n",
200 | "The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns.\n",
201 | "\n",
202 | "The target field, called **custcat**, has four possible values that correspond to the four customer groups, as follows:\n",
203 | "1- Basic Service\n",
204 | "2- E-Service\n",
205 | "3- Plus Service\n",
206 | "4- Total Service\n",
207 | "\n",
208 | "Our objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.\n"
209 | ],
210 | "metadata": {
211 | "button": false,
212 | "new_sheet": false,
213 | "run_control": {
214 | "read_only": false
215 | }
216 | }
217 | },
218 | {
219 | "cell_type": "markdown",
220 | "source": [
221 | "Let's download the dataset. To download the data, we will use !wget to download it from IBM Object Storage.\n"
222 | ],
223 | "metadata": {
224 | "button": false,
225 | "new_sheet": false,
226 | "run_control": {
227 | "read_only": false
228 | }
229 | }
230 | },
231 | {
232 | "cell_type": "code",
233 | "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n response = await pyfetch(url)\n if response.status == 200:\n with open(filename, \"wb\") as f:\n f.write(await response.bytes())\n",
234 | "metadata": {
235 | "trusted": true
236 | },
237 | "execution_count": null,
238 | "outputs": []
239 | },
240 | {
241 | "cell_type": "code",
242 | "source": "path=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/teleCust1000t.csv\"",
243 | "metadata": {
244 | "trusted": true
245 | },
246 | "execution_count": null,
247 | "outputs": []
248 | },
249 | {
250 | "cell_type": "markdown",
251 | "source": [
252 | "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
253 | ],
254 | "metadata": {}
255 | },
256 | {
257 | "cell_type": "markdown",
258 | "source": [
259 | "### Load Data From CSV File\n"
260 | ],
261 | "metadata": {
262 | "button": false,
263 | "new_sheet": false,
264 | "run_control": {
265 | "read_only": false
266 | }
267 | }
268 | },
269 | {
270 | "cell_type": "code",
271 | "source": "await download(path, 'teleCust1000t.csv')\n ",
272 | "metadata": {
273 | "trusted": true
274 | },
275 | "execution_count": null,
276 | "outputs": []
277 | },
278 | {
279 | "cell_type": "code",
280 | "source": "df = pd.read_csv('teleCust1000t.csv')\ndf.head()",
281 | "metadata": {
282 | "button": false,
283 | "new_sheet": false,
284 | "run_control": {
285 | "read_only": false
286 | },
287 | "trusted": true
288 | },
289 | "execution_count": null,
290 | "outputs": []
291 | },
292 | {
293 | "cell_type": "markdown",
294 | "source": [
295 | "\n",
296 | "
Data Visualization and Analysis
\n",
297 | "\n"
298 | ],
299 | "metadata": {
300 | "button": false,
301 | "new_sheet": false,
302 | "run_control": {
303 | "read_only": false
304 | }
305 | }
306 | },
307 | {
308 | "cell_type": "markdown",
309 | "source": [
310 | "#### Let’s see how many of each class is in our data set\n"
311 | ],
312 | "metadata": {
313 | "button": false,
314 | "new_sheet": false,
315 | "run_control": {
316 | "read_only": false
317 | }
318 | }
319 | },
320 | {
321 | "cell_type": "code",
322 | "source": "df['custcat'].value_counts()",
323 | "metadata": {
324 | "button": false,
325 | "new_sheet": false,
326 | "run_control": {
327 | "read_only": false
328 | },
329 | "trusted": true
330 | },
331 | "execution_count": null,
332 | "outputs": []
333 | },
334 | {
335 | "cell_type": "markdown",
336 | "source": [
337 | "#### 281 Plus Service, 266 Basic-service, 236 Total Service, and 217 E-Service customers\n"
338 | ],
339 | "metadata": {
340 | "button": false,
341 | "new_sheet": false,
342 | "run_control": {
343 | "read_only": false
344 | }
345 | }
346 | },
347 | {
348 | "cell_type": "markdown",
349 | "source": [
350 | "You can easily explore your data using visualization techniques:\n"
351 | ],
352 | "metadata": {}
353 | },
354 | {
355 | "cell_type": "code",
356 | "source": "df.hist(column='income', bins=50)",
357 | "metadata": {
358 | "trusted": true
359 | },
360 | "execution_count": null,
361 | "outputs": []
362 | },
363 | {
364 | "cell_type": "markdown",
365 | "source": [
366 | "### Feature set\n"
367 | ],
368 | "metadata": {
369 | "button": false,
370 | "new_sheet": false,
371 | "run_control": {
372 | "read_only": false
373 | }
374 | }
375 | },
376 | {
377 | "cell_type": "markdown",
378 | "source": [
379 | "Let's define feature sets, X:\n"
380 | ],
381 | "metadata": {
382 | "button": false,
383 | "new_sheet": false,
384 | "run_control": {
385 | "read_only": false
386 | }
387 | }
388 | },
389 | {
390 | "cell_type": "code",
391 | "source": "df.columns",
392 | "metadata": {
393 | "trusted": true
394 | },
395 | "execution_count": null,
396 | "outputs": []
397 | },
398 | {
399 | "cell_type": "markdown",
400 | "source": [
401 | "To use scikit-learn library, we have to convert the Pandas data frame to a Numpy array:\n"
402 | ],
403 | "metadata": {}
404 | },
405 | {
406 | "cell_type": "code",
407 | "source": "X = df[['region', 'tenure','age', 'marital', 'address', 'income', 'ed', 'employ','retire', 'gender', 'reside']] .values #.astype(float)\nX[0:5]\n",
408 | "metadata": {
409 | "button": false,
410 | "new_sheet": false,
411 | "run_control": {
412 | "read_only": false
413 | },
414 | "trusted": true
415 | },
416 | "execution_count": null,
417 | "outputs": []
418 | },
419 | {
420 | "cell_type": "markdown",
421 | "source": [
422 | "What are our labels?\n"
423 | ],
424 | "metadata": {
425 | "button": false,
426 | "new_sheet": false,
427 | "run_control": {
428 | "read_only": false
429 | }
430 | }
431 | },
432 | {
433 | "cell_type": "code",
434 | "source": "y = df['custcat'].values\ny[0:5]",
435 | "metadata": {
436 | "button": false,
437 | "new_sheet": false,
438 | "run_control": {
439 | "read_only": false
440 | },
441 | "trusted": true
442 | },
443 | "execution_count": null,
444 | "outputs": []
445 | },
446 | {
447 | "cell_type": "markdown",
448 | "source": [
449 | "## Normalize Data\n"
450 | ],
451 | "metadata": {
452 | "button": false,
453 | "new_sheet": false,
454 | "run_control": {
455 | "read_only": false
456 | }
457 | }
458 | },
459 | {
460 | "cell_type": "markdown",
461 | "source": [
462 | "Data Standardization gives the data zero mean and unit variance, it is good practice, especially for algorithms such as KNN which is based on the distance of data points:\n"
463 | ],
464 | "metadata": {
465 | "button": false,
466 | "new_sheet": false,
467 | "run_control": {
468 | "read_only": false
469 | }
470 | }
471 | },
472 | {
473 | "cell_type": "code",
474 | "source": "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\nX[0:5]",
475 | "metadata": {
476 | "button": false,
477 | "new_sheet": false,
478 | "run_control": {
479 | "read_only": false
480 | },
481 | "trusted": true
482 | },
483 | "execution_count": null,
484 | "outputs": []
485 | },
486 | {
487 | "cell_type": "markdown",
488 | "source": [
489 | "### Train Test Split\n",
490 | "\n",
491 | "Out of Sample Accuracy is the percentage of correct predictions that the model makes on data that the model has NOT been trained on. Doing a train and test on the same dataset will most likely have low out-of-sample accuracy, due to the likelihood of our model overfitting.\n",
492 | "\n",
493 | "It is important that our models have a high, out-of-sample accuracy, because the purpose of any model, of course, is to make correct predictions on unknown data. So how can we improve out-of-sample accuracy? One way is to use an evaluation approach called Train/Test Split.\n",
494 | "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.\n",
495 | "\n",
496 | "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that has been used to train the model. It is more realistic for the real world problems.\n"
497 | ],
498 | "metadata": {
499 | "button": false,
500 | "new_sheet": false,
501 | "run_control": {
502 | "read_only": false
503 | }
504 | }
505 | },
506 | {
507 | "cell_type": "code",
508 | "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape, y_train.shape)\nprint ('Test set:', X_test.shape, y_test.shape)",
509 | "metadata": {
510 | "button": false,
511 | "new_sheet": false,
512 | "run_control": {
513 | "read_only": false
514 | },
515 | "trusted": true
516 | },
517 | "execution_count": null,
518 | "outputs": []
519 | },
520 | {
521 | "cell_type": "markdown",
522 | "source": [
523 | "\n",
524 | "
Classification
\n",
525 | "\n"
526 | ],
527 | "metadata": {
528 | "button": false,
529 | "new_sheet": false,
530 | "run_control": {
531 | "read_only": false
532 | }
533 | }
534 | },
535 | {
536 | "cell_type": "markdown",
537 | "source": [
538 | "K nearest neighbor (KNN)
\n"
539 | ],
540 | "metadata": {
541 | "button": false,
542 | "new_sheet": false,
543 | "run_control": {
544 | "read_only": false
545 | }
546 | }
547 | },
548 | {
549 | "cell_type": "markdown",
550 | "source": [
551 | "#### Import library\n"
552 | ],
553 | "metadata": {
554 | "button": false,
555 | "new_sheet": false,
556 | "run_control": {
557 | "read_only": false
558 | }
559 | }
560 | },
561 | {
562 | "cell_type": "markdown",
563 | "source": [
564 | "Classifier implementing the k-nearest neighbors vote.\n"
565 | ],
566 | "metadata": {
567 | "button": false,
568 | "new_sheet": false,
569 | "run_control": {
570 | "read_only": false
571 | }
572 | }
573 | },
574 | {
575 | "cell_type": "code",
576 | "source": "from sklearn.neighbors import KNeighborsClassifier",
577 | "metadata": {
578 | "button": false,
579 | "new_sheet": false,
580 | "run_control": {
581 | "read_only": false
582 | },
583 | "trusted": true
584 | },
585 | "execution_count": null,
586 | "outputs": []
587 | },
588 | {
589 | "cell_type": "markdown",
590 | "source": [
591 | "### Training\n",
592 | "\n",
593 | "Let's start the algorithm with k=4 for now:\n"
594 | ],
595 | "metadata": {
596 | "button": false,
597 | "new_sheet": false,
598 | "run_control": {
599 | "read_only": false
600 | }
601 | }
602 | },
603 | {
604 | "cell_type": "code",
605 | "source": "k = 4\n#Train Model and Predict \nneigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\nneigh",
606 | "metadata": {
607 | "button": false,
608 | "new_sheet": false,
609 | "run_control": {
610 | "read_only": false
611 | },
612 | "trusted": true
613 | },
614 | "execution_count": null,
615 | "outputs": []
616 | },
617 | {
618 | "cell_type": "markdown",
619 | "source": [
620 | "### Predicting\n",
621 | "\n",
622 | "We can use the model to make predictions on the test set:\n"
623 | ],
624 | "metadata": {
625 | "button": false,
626 | "new_sheet": false,
627 | "run_control": {
628 | "read_only": false
629 | }
630 | }
631 | },
632 | {
633 | "cell_type": "code",
634 | "source": "yhat = neigh.predict(X_test)\nyhat[0:5]",
635 | "metadata": {
636 | "button": false,
637 | "new_sheet": false,
638 | "run_control": {
639 | "read_only": false
640 | },
641 | "trusted": true
642 | },
643 | "execution_count": null,
644 | "outputs": []
645 | },
646 | {
647 | "cell_type": "markdown",
648 | "source": [
649 | "### Accuracy evaluation\n",
650 | "\n",
651 | "In multilabel classification, **accuracy classification score** is a function that computes subset accuracy. This function is equal to the jaccard_score function. Essentially, it calculates how closely the actual labels and predicted labels are matched in the test set.\n"
652 | ],
653 | "metadata": {
654 | "button": false,
655 | "new_sheet": false,
656 | "run_control": {
657 | "read_only": false
658 | }
659 | }
660 | },
661 | {
662 | "cell_type": "code",
663 | "source": "from sklearn import metrics\nprint(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))",
664 | "metadata": {
665 | "trusted": true
666 | },
667 | "execution_count": null,
668 | "outputs": []
669 | },
670 | {
671 | "cell_type": "markdown",
672 | "source": [
673 | "## Practice\n",
674 | "\n",
675 | "Can you build the model again, but this time with k=6?\n"
676 | ],
677 | "metadata": {}
678 | },
679 | {
680 | "cell_type": "code",
681 | "source": "# write your code here\n\n",
682 | "metadata": {
683 | "trusted": true
684 | },
685 | "execution_count": null,
686 | "outputs": []
687 | },
688 | {
689 | "cell_type": "markdown",
690 | "source": [
691 | "Click here for the solution
\n",
692 | "\n",
693 | "```python\n",
694 | "k = 6\n",
695 | "neigh6 = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
696 | "yhat6 = neigh6.predict(X_test)\n",
697 | "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh6.predict(X_train)))\n",
698 | "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat6))\n",
699 | "\n",
700 | "```\n",
701 | "\n",
702 | "Want to learn more?
\n",
783 | "\n",
784 | "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: SPSS Modeler\n",
785 | "\n",
786 | "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at Watson Studio\n"
787 | ],
788 | "metadata": {
789 | "button": false,
790 | "new_sheet": false,
791 | "run_control": {
792 | "read_only": false
793 | }
794 | }
795 | },
796 | {
797 | "cell_type": "markdown",
798 | "source": [
799 | "### Thank you for completing this lab!\n",
800 | "\n",
801 | "## Author\n",
802 | "\n",
803 | "Saeed Aghabozorgi\n",
804 | "\n",
805 | "### Other Contributors\n",
806 | "\n",
807 | "Joseph Santarcangelo\n",
808 | "\n",
809 | "## Change Log\n",
810 | "\n",
811 | "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
812 | "| ----------------- | ------- | ---------- | ---------------------------------- |\n",
813 | "| 2021-01-21 | 2.4 | Lakshmi | Updated sklearn library |\n",
814 | "| 2020-11-20 | 2.3 | Lakshmi | Removed unused imports |\n",
815 | "| 2020-11-17 | 2.2 | Lakshmi | Changed plot function of KNN |\n",
816 | "| 2020-11-03 | 2.1 | Lakshmi | Changed URL of csv |\n",
817 | "| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
818 | "| | | | |\n",
819 | "| | | | |\n",
820 | "\n",
821 | "## © IBM Corporation 2020. All rights reserved. \n"
822 | ],
823 | "metadata": {}
824 | }
825 | ]
826 | }
--------------------------------------------------------------------------------
/Classification/classification_tree_svm.ipynb:
--------------------------------------------------------------------------------
1 | {"cells":[{"cell_type":"markdown","id":"ef3f373c-e39a-4b5f-9bb8-a920d161f1b6","metadata":{},"outputs":[],"source":["\u003ccenter\u003e\n"," \u003cimg src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"\u003e\n","\u003c/center\u003e\n"]},{"cell_type":"markdown","id":"8dbdecec-cd11-4a57-9df4-82c8a80a78eb","metadata":{},"outputs":[],"source":["# **Credit Card Fraud Detection using Scikit-Learn and Snap ML**\n"]},{"cell_type":"markdown","id":"ec071c2d-d1d4-4614-aa2a-189fc7c98f94","metadata":{},"outputs":[],"source":["Estimated time needed: **30** minutes\n"]},{"cell_type":"markdown","id":"0b1690b3-6524-4109-bf72-1a4daf11b4c7","metadata":{},"outputs":[],"source":["In this exercise session you will consolidate your machine learning (ML) modeling skills by using two popular classification models to recognize fraudulent credit card transactions. These models are: Decision Tree and Support Vector Machine. You will use a real dataset to train each of these models. The dataset includes information about \n","transactions made by credit cards in September 2013 by European cardholders. You will use the trained model to assess if a credit card transaction is legitimate or not.\n","\n","In the current exercise session, you will practice not only the Scikit-Learn Python interface, but also the Python API offered by the Snap Machine Learning (Snap ML) library. Snap ML is a high-performance IBM library for ML modeling. It provides highly-efficient CPU/GPU implementations of linear models and tree-based models. Snap ML not only accelerates ML algorithms through system awareness, but it also offers novel ML algorithms with best-in-class accuracy. For more information, please visit [snapml](https://ibm.biz/BdPfxy) information page.\n"]},{"cell_type":"markdown","id":"6f509f1e-49cf-4411-a4b8-b06a9e61ad6d","metadata":{},"outputs":[],"source":["## Objectives\n"]},{"cell_type":"markdown","id":"676d912b-2c5d-47d8-9cf8-2d15db8d8503","metadata":{},"outputs":[],"source":["After completing this lab you will be able to:\n"]},{"cell_type":"markdown","id":"c1b4e289-2670-476a-8746-9f4a1db8dadd","metadata":{},"outputs":[],"source":["* Perform basic data preprocessing in Python\n","* Model a classification task using the Scikit-Learn and Snap ML Python APIs\n","* Train Suppport Vector Machine and Decision Tree models using Scikit-Learn and Snap ML\n","* Run inference and assess the quality of the trained models\n"]},{"cell_type":"markdown","id":"27a382a6-b52f-4adb-be30-5754e81775b1","metadata":{},"outputs":[],"source":["## Table of Contents\n"]},{"cell_type":"markdown","id":"cf840df9-6d1a-490a-b8c8-e2d413329748","metadata":{},"outputs":[],"source":["\u003cdiv class=\"alert alert-block alert-info\" style=\"margin-top: 10px\"\u003e\n"," \u003col\u003e\n"," \u003cli\u003e\u003ca href=\"#introduction\"\u003eIntroduction\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#import_libraries\"\u003eImport Libraries\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#dataset_analysis\"\u003eDataset Analysis\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#dataset_preprocessing\"\u003eDataset Preprocessing\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#dataset_split\"\u003eDataset Train/Test Split\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#dt_sklearn\"\u003eBuild a Decision Tree Classifier model with Scikit-Learn\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#dt_snap\"\u003eBuild a Decision Tree Classifier model with Snap ML\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#dt_sklearn_snap\"\u003eEvaluate the Scikit-Learn and Snap ML Decision Tree Classifiers\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#svm_sklearn\"\u003eBuild a Support Vector Machine model with Scikit-Learn\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#svm_snap\"\u003eBuild a Support Vector Machine model with Snap ML\u003c/a\u003e\u003c/li\u003e\n"," \u003cli\u003e\u003ca href=\"#svm_sklearn_snap\"\u003eEvaluate the Scikit-Learn and Snap ML Support Vector Machine Models\u003c/a\u003e\u003c/li\u003e\n"," \u003c/ol\u003e\n","\u003c/div\u003e\n","\u003cbr\u003e\n","\u003chr\u003e\n"]},{"cell_type":"markdown","id":"d4427c75-08bc-41f9-97d3-7354e7ab6001","metadata":{},"outputs":[],"source":["\u003cdiv id=\"Introduction\"\u003e\n"," \u003ch2\u003eIntroduction\u003c/h2\u003e\n"," \u003cbr\u003eImagine that you work for a financial institution and part of your job is to build a model that predicts if a credit card transaction is fraudulent or not. You can model the problem as a binary classification problem. A transaction belongs to the positive class (1) if it is a fraud, otherwise it belongs to the negative class (0).\n"," \u003cbr\u003e\n"," \u003cbr\u003eYou have access to transactions that occured over a certain period of time. The majority of the transactions are normally legitimate and only a small fraction are non-legitimate. Thus, typically you have access to a dataset that is highly unbalanced. This is also the case of the current dataset: only 492 transactions out of 284,807 are fraudulent (the positive class - the frauds - accounts for 0.172% of all transactions).\n"," \u003cbr\u003e\n"," \u003cbr\u003eTo train the model you can use part of the input dataset and the remaining data can be used to assess the quality of the trained model. First, let's download the dataset.\n"," \u003cbr\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"b63e0611-cc91-414d-97db-c24950151d52","metadata":{},"outputs":[],"source":["# install the opendatasets package\n!pip install opendatasets\n\nimport opendatasets as od\n\n# download the dataset (this is a Kaggle dataset)\n# during download you will be required to input your Kaggle username and password\nod.download(\"https://www.kaggle.com/mlg-ulb/creditcardfraud\")"]},{"cell_type":"markdown","id":"92cf3cd1-1413-4e75-a88e-2b6413de6c58","metadata":{},"outputs":[],"source":["__Did you know?__ When it comes to Machine Learning, you will most likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](https://ibm.biz/BdPfxf)\n"]},{"cell_type":"markdown","id":"c44bc0d4-2b4c-4a91-883a-35fe50a7c070","metadata":{},"outputs":[],"source":["\u003cdiv id=\"import_libraries\"\u003e\n"," \u003ch2\u003eImport Libraries\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"9e5df4a7-505b-4921-9f45-cad5c0fffd79","metadata":{},"outputs":[],"source":["# Snap ML is available on PyPI. To install it simply run the pip command below.\n!pip install snapml"]},{"cell_type":"code","id":"f0b103cc-6ffd-4bb2-8baf-55d246bfdd66","metadata":{},"outputs":[],"source":["# Import the libraries we need to use in this lab\nfrom __future__ import print_function\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import normalize, StandardScaler\nfrom sklearn.utils.class_weight import compute_sample_weight\nfrom sklearn.metrics import roc_auc_score\nimport time\nimport warnings\nwarnings.filterwarnings('ignore')"]},{"cell_type":"markdown","id":"cfd4973c-1def-4a5b-aeb1-dffd117a515f","metadata":{},"outputs":[],"source":["\u003cdiv id=\"dataset_analysis\"\u003e\n"," \u003ch2\u003eDataset Analysis\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"markdown","id":"3cc91238-9566-4282-a711-a2dc01ef2752","metadata":{},"outputs":[],"source":["In this section you will read the dataset in a Pandas dataframe and visualize its content. You will also look at some data statistics. \n","\n","Note: A Pandas dataframe is a two-dimensional, size-mutable, potentially heterogeneous tabular data structure. For more information: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html. \n"]},{"cell_type":"code","id":"d81a695f-54e6-469a-8af1-80a682c98003","metadata":{},"outputs":[],"source":["# read the input data\nraw_data = pd.read_csv('creditcardfraud/creditcard.csv')\nprint(\"There are \" + str(len(raw_data)) + \" observations in the credit card fraud dataset.\")\nprint(\"There are \" + str(len(raw_data.columns)) + \" variables in the dataset.\")\n\n# display the first rows in the dataset\nraw_data.head()"]},{"cell_type":"code","id":"269cfea0-24a8-49b7-8932-656bd4b95547","metadata":{},"outputs":[],"source":["#Uncomment the following lines if you are unable to download the dataset using the Kaggle website.\n\n#url= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/creditcard.csv\"\n#raw_data=pd.read_csv(url)\n#print(\"There are \" + str(len(raw_data)) + \" observations in the credit card fraud dataset.\")\n#print(\"There are \" + str(len(raw_data.columns)) + \" variables in the dataset.\")\n#raw_data.head()"]},{"cell_type":"markdown","id":"0e7a1db2-d2a1-4948-b47a-ef55ac8c249c","metadata":{},"outputs":[],"source":["In practice, a financial institution may have access to a much larger dataset of transactions. To simulate such a case, we will inflate the original one 10 times.\n"]},{"cell_type":"code","id":"b856cd22-57e4-4774-9bb9-ff9d2e1ae5ec","metadata":{},"outputs":[],"source":["n_replicas = 10\n\n# inflate the original dataset\nbig_raw_data = pd.DataFrame(np.repeat(raw_data.values, n_replicas, axis=0), columns=raw_data.columns)\n\nprint(\"There are \" + str(len(big_raw_data)) + \" observations in the inflated credit card fraud dataset.\")\nprint(\"There are \" + str(len(big_raw_data.columns)) + \" variables in the dataset.\")\n\n# display first rows in the new dataset\nbig_raw_data.head()"]},{"cell_type":"markdown","id":"02909172-1b5b-4118-83d1-79df94fb32b3","metadata":{},"outputs":[],"source":["Each row in the dataset represents a credit card transaction. As shown above, each row has 31 variables. One variable (the last variable in the table above) is called Class and represents the target variable. Your objective will be to train a model that uses the other variables to predict the value of the Class variable. Let's first retrieve basic statistics about the target variable.\n","\n","Note: For confidentiality reasons, the original names of most features are anonymized V1, V2 .. V28. The values of these features are the result of a PCA transformation and are numerical. The feature 'Class' is the target variable and it takes two values: 1 in case of fraud and 0 otherwise. For more information about the dataset please visit this webpage: https://www.kaggle.com/mlg-ulb/creditcardfraud.\n"]},{"cell_type":"code","id":"b6eea1c2-9a0f-488a-b06f-2f19d1c11c51","metadata":{},"outputs":[],"source":["# get the set of distinct classes\nlabels = big_raw_data.Class.unique()\n\n# get the count of each class\nsizes = big_raw_data.Class.value_counts().values\n\n# plot the class value counts\nfig, ax = plt.subplots()\nax.pie(sizes, labels=labels, autopct='%1.3f%%')\nax.set_title('Target Variable Value Counts')\nplt.show()"]},{"cell_type":"markdown","id":"0345ac2b-7b3e-4379-be48-5db5df114308","metadata":{},"outputs":[],"source":["As shown above, the Class variable has two values: 0 (the credit card transaction is legitimate) and 1 (the credit card transaction is fraudulent). Thus, you need to model a binary classification problem. Moreover, the dataset is highly unbalanced, the target variable classes are not represented equally. This case requires special attention when training or when evaluating the quality of a model. One way of handing this case at train time is to bias the model to pay more attention to the samples in the minority class. The models under the current study will be configured to take into account the class weights of the samples at train/fit time.\n"]},{"cell_type":"markdown","id":"76a77289-16aa-4861-a36e-0d5df963bb35","metadata":{},"outputs":[],"source":["### Practice\n"]},{"cell_type":"markdown","id":"7a580e4a-13b9-4033-944a-022f5871dc3c","metadata":{},"outputs":[],"source":["The credit card transactions have different amounts. Could you plot a histogram that shows the distribution of these amounts? What is the range of these amounts (min/max)? Could you print the 90th percentile of the amount values?\n"]},{"cell_type":"code","id":"e8f64dfb-12a5-423b-be7f-4d1c018b12a5","metadata":{},"outputs":[],"source":["# your code here"]},{"cell_type":"code","id":"915a7737-d86d-4f47-99bb-6ed85aa316d1","metadata":{},"outputs":[],"source":["# we provide our solution here\nplt.hist(big_raw_data.Amount.values, 6, histtype='bar', facecolor='g')\nplt.show()\n\nprint(\"Minimum amount value is \", np.min(big_raw_data.Amount.values))\nprint(\"Maximum amount value is \", np.max(big_raw_data.Amount.values))\nprint(\"90% of the transactions have an amount less or equal than \", np.percentile(raw_data.Amount.values, 90))"]},{"cell_type":"markdown","id":"bc974abe-8deb-4921-a72d-dfdcf6317c7e","metadata":{},"outputs":[],"source":["\u003cdiv id=\"dataset_preprocessing\"\u003e\n"," \u003ch2\u003eDataset Preprocessing\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"markdown","id":"3ab66dad-f870-45bf-aecf-a45f2231d684","metadata":{},"outputs":[],"source":["In this subsection you will prepare the data for training. \n"]},{"cell_type":"code","id":"fafe2ed1-84a3-4593-a278-9bf2d259fa4b","metadata":{},"outputs":[],"source":["# data preprocessing such as scaling/normalization is typically useful for \n# linear models to accelerate the training convergence\n\n# standardize features by removing the mean and scaling to unit variance\nbig_raw_data.iloc[:, 1:30] = StandardScaler().fit_transform(big_raw_data.iloc[:, 1:30])\ndata_matrix = big_raw_data.values\n\n# X: feature matrix (for this analysis, we exclude the Time variable from the dataset)\nX = data_matrix[:, 1:30]\n\n# y: labels vector\ny = data_matrix[:, 30]\n\n# data normalization\nX = normalize(X, norm=\"l1\")\n\n# print the shape of the features matrix and the labels vector\nprint('X.shape=', X.shape, 'y.shape=', y.shape)"]},{"cell_type":"markdown","id":"578fedad-8101-4bd1-9427-12a075ee4256","metadata":{},"outputs":[],"source":["\u003cdiv id=\"dataset_split\"\u003e\n"," \u003ch2\u003eDataset Train/Test Split\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"markdown","id":"cf03ffcf-efc6-42ad-87e6-190c00c1d657","metadata":{},"outputs":[],"source":["Now that the dataset is ready for building the classification models, you need to first divide the pre-processed dataset into a subset to be used for training the model (the train set) and a subset to be used for evaluating the quality of the model (the test set).\n"]},{"cell_type":"code","id":"ebf3c04a-5eee-4000-ab41-8628a18cbf4f","metadata":{},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y) \nprint('X_train.shape=', X_train.shape, 'Y_train.shape=', y_train.shape)\nprint('X_test.shape=', X_test.shape, 'Y_test.shape=', y_test.shape)"]},{"cell_type":"markdown","id":"d21e2582-33b9-46c9-9b3a-7fbba2c98750","metadata":{},"outputs":[],"source":["\u003cdiv id=\"dt_sklearn\"\u003e\n"," \u003ch2\u003eBuild a Decision Tree Classifier model with Scikit-Learn\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"f47fb031-dd33-40f8-93a4-198d9e12e095","metadata":{},"outputs":[],"source":["# compute the sample weights to be used as input to the train routine so that \n# it takes into account the class imbalance present in this dataset\nw_train = compute_sample_weight('balanced', y_train)\n\n# import the Decision Tree Classifier Model from scikit-learn\nfrom sklearn.tree import DecisionTreeClassifier\n\n# for reproducible output across multiple function calls, set random_state to a given integer value\nsklearn_dt = DecisionTreeClassifier(max_depth=4, random_state=35)\n\n# train a Decision Tree Classifier using scikit-learn\nt0 = time.time()\nsklearn_dt.fit(X_train, y_train, sample_weight=w_train)\nsklearn_time = time.time()-t0\nprint(\"[Scikit-Learn] Training time (s): {0:.5f}\".format(sklearn_time))"]},{"cell_type":"markdown","id":"bfaf66ee-a51c-43a2-8c33-5e38fa16e6c1","metadata":{},"outputs":[],"source":["\u003cdiv id=\"dt_snapml\"\u003e\n"," \u003ch2\u003eBuild a Decision Tree Classifier model with Snap ML\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"52f544f8-fdf0-4414-b5b1-551298a47235","metadata":{},"outputs":[],"source":["# if not already computed, \n# compute the sample weights to be used as input to the train routine so that \n# it takes into account the class imbalance present in this dataset\n# w_train = compute_sample_weight('balanced', y_train)\n\n# import the Decision Tree Classifier Model from Snap ML\nfrom snapml import DecisionTreeClassifier\n\n# Snap ML offers multi-threaded CPU/GPU training of decision trees, unlike scikit-learn\n# to use the GPU, set the use_gpu parameter to True\n# snapml_dt = DecisionTreeClassifier(max_depth=4, random_state=45, use_gpu=True)\n\n# to set the number of CPU threads used at training time, set the n_jobs parameter\n# for reproducible output across multiple function calls, set random_state to a given integer value\nsnapml_dt = DecisionTreeClassifier(max_depth=4, random_state=45, n_jobs=4)\n\n# train a Decision Tree Classifier model using Snap ML\nt0 = time.time()\nsnapml_dt.fit(X_train, y_train, sample_weight=w_train)\nsnapml_time = time.time()-t0\nprint(\"[Snap ML] Training time (s): {0:.5f}\".format(snapml_time))"]},{"cell_type":"markdown","id":"863dac88-0c61-4ed5-b145-0b886cdaf59d","metadata":{},"outputs":[],"source":["\u003cdiv id=\"dt_sklearn_snapml\"\u003e\n"," \u003ch2\u003eEvaluate the Scikit-Learn and Snap ML Decision Tree Classifier Models\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"854e529e-6fd6-4643-bf5d-638328aa9c66","metadata":{},"outputs":[],"source":["# Snap ML vs Scikit-Learn training speedup\ntraining_speedup = sklearn_time/snapml_time\nprint('[Decision Tree Classifier] Snap ML vs. Scikit-Learn speedup : {0:.2f}x '.format(training_speedup))\n\n# run inference and compute the probabilities of the test samples \n# to belong to the class of fraudulent transactions\nsklearn_pred = sklearn_dt.predict_proba(X_test)[:,1]\n\n# evaluate the Compute Area Under the Receiver Operating Characteristic \n# Curve (ROC-AUC) score from the predictions\nsklearn_roc_auc = roc_auc_score(y_test, sklearn_pred)\nprint('[Scikit-Learn] ROC-AUC score : {0:.3f}'.format(sklearn_roc_auc))\n\n# run inference and compute the probabilities of the test samples\n# to belong to the class of fraudulent transactions\nsnapml_pred = snapml_dt.predict_proba(X_test)[:,1]\n\n# evaluate the Compute Area Under the Receiver Operating Characteristic\n# Curve (ROC-AUC) score from the prediction scores\nsnapml_roc_auc = roc_auc_score(y_test, snapml_pred) \nprint('[Snap ML] ROC-AUC score : {0:.3f}'.format(snapml_roc_auc))"]},{"cell_type":"markdown","id":"bdfd6017-e6b3-4ae5-8622-d6042a00c703","metadata":{},"outputs":[],"source":["As shown above both decision tree models provide the same score on the test dataset. However Snap ML runs the training routine 12x faster than Scikit-Learn. This is one of the advantages of using Snap ML: acceleration of training of classical machine learning models, such as linear and tree-based models. For more Snap ML examples, please visit [snapml-examples](https://ibm.biz/BdPfxP).\n"]},{"cell_type":"markdown","id":"93ba40ba-1bb0-4fee-be5d-30393685744e","metadata":{},"outputs":[],"source":["\u003cdiv id=\"svm_sklearn\"\u003e\n"," \u003ch2\u003eBuild a Support Vector Machine model with Scikit-Learn\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"0aaa921a-6e17-4aa9-b501-d8c4eac4d746","metadata":{},"outputs":[],"source":["# import the linear Support Vector Machine (SVM) model from Scikit-Learn\nfrom sklearn.svm import LinearSVC\n\n# instatiate a scikit-learn SVM model\n# to indicate the class imbalance at fit time, set class_weight='balanced'\n# for reproducible output across multiple function calls, set random_state to a given integer value\nsklearn_svm = LinearSVC(class_weight='balanced', random_state=31, loss=\"hinge\", fit_intercept=False)\n\n# train a linear Support Vector Machine model using Scikit-Learn\nt0 = time.time()\nsklearn_svm.fit(X_train, y_train)\nsklearn_time = time.time() - t0\nprint(\"[Scikit-Learn] Training time (s): {0:.2f}\".format(sklearn_time))"]},{"cell_type":"markdown","id":"f2eb1ece-234e-4490-9d71-10c534b9eec0","metadata":{},"outputs":[],"source":["\u003cdiv id=\"svm_snap\"\u003e\n"," \u003ch2\u003eBuild a Support Vector Machine model with Snap ML\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"2078a824-1839-4c36-9a0d-2c19c39a01d4","metadata":{},"outputs":[],"source":["# import the Support Vector Machine model (SVM) from Snap ML\nfrom snapml import SupportVectorMachine\n\n# in contrast to scikit-learn's LinearSVC, Snap ML offers multi-threaded CPU/GPU training of SVMs\n# to use the GPU, set the use_gpu parameter to True\n# snapml_svm = SupportVectorMachine(class_weight='balanced', random_state=25, use_gpu=True, fit_intercept=False)\n\n# to set the number of threads used at training time, one needs to set the n_jobs parameter\nsnapml_svm = SupportVectorMachine(class_weight='balanced', random_state=25, n_jobs=4, fit_intercept=False)\n# print(snapml_svm.get_params())\n\n# train an SVM model using Snap ML\nt0 = time.time()\nmodel = snapml_svm.fit(X_train, y_train)\nsnapml_time = time.time() - t0\nprint(\"[Snap ML] Training time (s): {0:.2f}\".format(snapml_time))"]},{"cell_type":"markdown","id":"fd2825c3-05d2-4177-956d-7c2723c13449","metadata":{},"outputs":[],"source":["\u003cdiv id=\"svm_sklearn_snap\"\u003e\n"," \u003ch2\u003eEvaluate the Scikit-Learn and Snap ML Support Vector Machine Models\u003c/h2\u003e\n","\u003c/div\u003e\n"]},{"cell_type":"code","id":"13d141f3-fc01-4fff-9225-7ee5b8cdb13d","metadata":{},"outputs":[],"source":["# compute the Snap ML vs Scikit-Learn training speedup\ntraining_speedup = sklearn_time/snapml_time\nprint('[Support Vector Machine] Snap ML vs. Scikit-Learn training speedup : {0:.2f}x '.format(training_speedup))\n\n# run inference using the Scikit-Learn model\n# get the confidence scores for the test samples\nsklearn_pred = sklearn_svm.decision_function(X_test)\n\n# evaluate accuracy on test set\nacc_sklearn = roc_auc_score(y_test, sklearn_pred)\nprint(\"[Scikit-Learn] ROC-AUC score: {0:.3f}\".format(acc_sklearn))\n\n# run inference using the Snap ML model\n# get the confidence scores for the test samples\nsnapml_pred = snapml_svm.decision_function(X_test)\n\n# evaluate accuracy on test set\nacc_snapml = roc_auc_score(y_test, snapml_pred)\nprint(\"[Snap ML] ROC-AUC score: {0:.3f}\".format(acc_snapml))"]},{"cell_type":"markdown","id":"d9c9c383-8488-4c4e-9ec9-2c87b60332a4","metadata":{},"outputs":[],"source":["As shown above both SVM models provide the same score on the test dataset. However, as in the case of decision trees, Snap ML runs the training routine faster than Scikit-Learn. For more Snap ML examples, please visit [snapml-examples](https://ibm.biz/BdPfxP). Moreover, as shown above, not only is Snap ML seemlessly accelerating scikit-learn applications, but the library's Python API is also compatible with scikit-learn metrics and data preprocessors.\n"]},{"cell_type":"markdown","id":"bb0ef68e-9160-4472-8072-aace3e649ecf","metadata":{},"outputs":[],"source":["### Practice\n"]},{"cell_type":"markdown","id":"6ec6d773-355a-45df-834a-497678a83797","metadata":{},"outputs":[],"source":["In this section you will evaluate the quality of the SVM models trained above using the hinge loss metric (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.hinge_loss.html). Run inference on the test set using both Scikit-Learn and Snap ML models. Compute the hinge loss metric for both sets of predictions. Print the hinge losses of Scikit-Learn and Snap ML.\n"]},{"cell_type":"code","id":"2b4cdadb-0dc7-46df-a96a-c03a70023704","metadata":{},"outputs":[],"source":["# your code goes here"]},{"cell_type":"code","id":"255c8bf9-979b-46dd-8ab1-80c6026e26c0","metadata":{},"outputs":[],"source":["# get the confidence scores for the test samples\nsklearn_pred = sklearn_svm.decision_function(X_test)\nsnapml_pred = snapml_svm.decision_function(X_test)\n\n# import the hinge_loss metric from scikit-learn\nfrom sklearn.metrics import hinge_loss\n\n# evaluate the hinge loss from the predictions\nloss_snapml = hinge_loss(y_test, snapml_pred)\nprint(\"[Snap ML] Hinge loss: {0:.3f}\".format(loss_snapml))\n\n# evaluate the hinge loss metric from the predictions\nloss_sklearn = hinge_loss(y_test, sklearn_pred)\nprint(\"[Scikit-Learn] Hinge loss: {0:.3f}\".format(loss_snapml))\n\n# the two models should give the same Hinge loss"]},{"cell_type":"markdown","id":"ac1c96f3-aa3e-4994-badd-135ba2f262ea","metadata":{},"outputs":[],"source":["## Authors\n"]},{"cell_type":"markdown","id":"48d1f5f9-a1f1-4237-99b2-ec5365924ce8","metadata":{},"outputs":[],"source":["Andreea Anghel\n"]},{"cell_type":"markdown","id":"ee595903-8c72-458b-83ac-4f97d7f0d474","metadata":{},"outputs":[],"source":["### Other Contributors\n"]},{"cell_type":"markdown","id":"d04171cd-fad2-4bf4-a519-2e6821d5a5e3","metadata":{},"outputs":[],"source":["Joseph Santarcangelo\n"]},{"cell_type":"markdown","id":"7f3815b6-3b71-4310-aac3-28cd8277a85f","metadata":{},"outputs":[],"source":["## Change Log\n"]},{"cell_type":"markdown","id":"f64915d1-9b98-4de7-840e-b9018b59ee1d","metadata":{},"outputs":[],"source":["| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n","|---|---|---|---|\n","| 2021-08-31 | 0.1 | AAN | Created Lab Content |\n"]},{"cell_type":"markdown","id":"54920996-2b56-4130-b1d4-14262bc459e0","metadata":{},"outputs":[],"source":[" Copyright \u0026copy; 2021 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/).\n"]}],"metadata":{"kernelspec":{"display_name":"Python","language":"python","name":"conda-env-python-py"},"language_info":{"name":""}},"nbformat":4,"nbformat_minor":4}
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343 | 7. Additional Terms.
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345 | "Additional permissions" are terms that supplement the terms of this
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414 |
415 | However, if you cease all violation of this License, then your
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433 | material under section 10.
434 |
435 | 9. Acceptance Not Required for Having Copies.
436 |
437 | You are not required to accept this License in order to receive or
438 | run a copy of the Program. Ancillary propagation of a covered work
439 | occurring solely as a consequence of using peer-to-peer transmission
440 | to receive a copy likewise does not require acceptance. However,
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444 | covered work, you indicate your acceptance of this License to do so.
445 |
446 | 10. Automatic Licensing of Downstream Recipients.
447 |
448 | Each time you convey a covered work, the recipient automatically
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450 | propagate that work, subject to this License. You are not responsible
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452 |
453 | An "entity transaction" is a transaction transferring control of an
454 | organization, or substantially all assets of one, or subdividing an
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468 | any patent claim is infringed by making, using, selling, offering for
469 | sale, or importing the Program or any portion of it.
470 |
471 | 11. Patents.
472 |
473 | A "contributor" is a copyright holder who authorizes use under this
474 | License of the Program or a work on which the Program is based. The
475 | work thus licensed is called the contributor's "contributor version".
476 |
477 | A contributor's "essential patent claims" are all patent claims
478 | owned or controlled by the contributor, whether already acquired or
479 | hereafter acquired, that would be infringed by some manner, permitted
480 | by this License, of making, using, or selling its contributor version,
481 | but do not include claims that would be infringed only as a
482 | consequence of further modification of the contributor version. For
483 | purposes of this definition, "control" includes the right to grant
484 | patent sublicenses in a manner consistent with the requirements of
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486 |
487 | Each contributor grants you a non-exclusive, worldwide, royalty-free
488 | patent license under the contributor's essential patent claims, to
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490 | propagate the contents of its contributor version.
491 |
492 | In the following three paragraphs, a "patent license" is any express
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494 | (such as an express permission to practice a patent or covenant not to
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498 |
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505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
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509 | covered work in a country, or your recipient's use of the covered work
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520 |
521 | A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License. You may not convey a covered
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526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
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533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 |
536 | Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 |
540 | 12. No Surrender of Others' Freedom.
541 |
542 | If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License. If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all. For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 |
552 | 13. Use with the GNU Affero General Public License.
553 |
554 | Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work. The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 |
563 | 14. Revised Versions of this License.
564 |
565 | The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time. Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 |
570 | Each version is given a distinguishing version number. If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 |
584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 |
600 | 16. Limitation of Liability.
601 |
602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 |
612 | 17. Interpretation of Sections 15 and 16.
613 |
614 | If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 |
621 | END OF TERMS AND CONDITIONS
622 |
623 | How to Apply These Terms to Your New Programs
624 |
625 | If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 |
629 | To do so, attach the following notices to the program. It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 |
634 |
635 | Copyright (C)
636 |
637 | This program is free software: you can redistribute it and/or modify
638 | it under the terms of the GNU General Public License as published by
639 | the Free Software Foundation, either version 3 of the License, or
640 | (at your option) any later version.
641 |
642 | This program is distributed in the hope that it will be useful,
643 | but WITHOUT ANY WARRANTY; without even the implied warranty of
644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
645 | GNU General Public License for more details.
646 |
647 | You should have received a copy of the GNU General Public License
648 | along with this program. If not, see .
649 |
650 | Also add information on how to contact you by electronic and paper mail.
651 |
652 | If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 |
655 | Copyright (C)
656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 | This is free software, and you are welcome to redistribute it
658 | under certain conditions; type `show c' for details.
659 |
660 | The hypothetical commands `show w' and `show c' should show the appropriate
661 | parts of the General Public License. Of course, your program's commands
662 | might be different; for a GUI interface, you would use an "about box".
663 |
664 | You should also get your employer (if you work as a programmer) or school,
665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | .
668 |
669 | The GNU General Public License does not permit incorporating your program
670 | into proprietary programs. If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library. If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License. But first, please read
674 | .
675 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Coursera-IBM-AI-Engineering
2 | I have saved all the program which i did during my coursera course
3 |
--------------------------------------------------------------------------------
/Regression/Module 2_ML0101EN-Reg-Mulitple-Linear-Regression-Co2.jupyterlite.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "metadata": {
3 | "kernelspec": {
4 | "name": "python",
5 | "display_name": "Pyolite",
6 | "language": "python"
7 | },
8 | "language_info": {
9 | "codemirror_mode": {
10 | "name": "python",
11 | "version": 3
12 | },
13 | "file_extension": ".py",
14 | "mimetype": "text/x-python",
15 | "name": "python",
16 | "nbconvert_exporter": "python",
17 | "pygments_lexer": "ipython3",
18 | "version": "3.8"
19 | },
20 | "widgets": {
21 | "state": {},
22 | "version": "1.1.2"
23 | }
24 | },
25 | "nbformat_minor": 4,
26 | "nbformat": 4,
27 | "cells": [
28 | {
29 | "cell_type": "markdown",
30 | "source": [
31 | "\n",
32 | " \n",
33 | " \n",
34 | " \n",
35 | "
\n",
36 | "\n",
37 | "# Multiple Linear Regression\n",
38 | "\n",
39 | "Estimated time needed: **15** minutes\n",
40 | "\n",
41 | "## Objectives\n",
42 | "\n",
43 | "After completing this lab you will be able to:\n",
44 | "\n",
45 | "* Use scikit-learn to implement Multiple Linear Regression\n",
46 | "* Create a model, train it, test it and use the model\n"
47 | ],
48 | "metadata": {
49 | "button": false,
50 | "new_sheet": false,
51 | "run_control": {
52 | "read_only": false
53 | }
54 | }
55 | },
56 | {
57 | "cell_type": "markdown",
58 | "source": [
59 | "Table of contents
\n",
60 | "\n",
61 | "\n",
70 | "
\n",
71 | "
\n"
72 | ],
73 | "metadata": {}
74 | },
75 | {
76 | "cell_type": "markdown",
77 | "source": [
78 | "### Importing Needed packages\n"
79 | ],
80 | "metadata": {
81 | "button": false,
82 | "new_sheet": false,
83 | "run_control": {
84 | "read_only": false
85 | }
86 | }
87 | },
88 | {
89 | "cell_type": "code",
90 | "source": "",
91 | "metadata": {
92 | "trusted": true
93 | },
94 | "execution_count": null,
95 | "outputs": []
96 | },
97 | {
98 | "cell_type": "code",
99 | "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])\n",
100 | "metadata": {
101 | "trusted": true
102 | },
103 | "execution_count": null,
104 | "outputs": []
105 | },
106 | {
107 | "cell_type": "code",
108 | "source": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport pylab as pl\nimport numpy as np\n%matplotlib inline",
109 | "metadata": {
110 | "button": false,
111 | "new_sheet": false,
112 | "run_control": {
113 | "read_only": false
114 | },
115 | "trusted": true
116 | },
117 | "execution_count": null,
118 | "outputs": []
119 | },
120 | {
121 | "cell_type": "markdown",
122 | "source": [
123 | "### Downloading Data\n",
124 | "\n",
125 | "we will use the link, we will use !wget to download it from IBM Object Storage.\n"
126 | ],
127 | "metadata": {
128 | "button": false,
129 | "new_sheet": false,
130 | "run_control": {
131 | "read_only": false
132 | }
133 | }
134 | },
135 | {
136 | "cell_type": "code",
137 | "source": "path='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv'",
138 | "metadata": {
139 | "button": false,
140 | "new_sheet": false,
141 | "run_control": {
142 | "read_only": false
143 | },
144 | "trusted": true
145 | },
146 | "execution_count": null,
147 | "outputs": []
148 | },
149 | {
150 | "cell_type": "code",
151 | "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n response = await pyfetch(url)\n if response.status == 200:\n with open(filename, \"wb\") as f:\n f.write(await response.bytes())",
152 | "metadata": {
153 | "trusted": true
154 | },
155 | "execution_count": null,
156 | "outputs": []
157 | },
158 | {
159 | "cell_type": "markdown",
160 | "source": [
161 | "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
162 | ],
163 | "metadata": {}
164 | },
165 | {
166 | "cell_type": "markdown",
167 | "source": [
168 | "Understanding the Data
\n",
169 | "\n",
170 | "### `FuelConsumption.csv`:\n",
171 | "\n",
172 | "We have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n",
173 | "\n",
174 | "* **MODELYEAR** e.g. 2014\n",
175 | "* **MAKE** e.g. Acura\n",
176 | "* **MODEL** e.g. ILX\n",
177 | "* **VEHICLE CLASS** e.g. SUV\n",
178 | "* **ENGINE SIZE** e.g. 4.7\n",
179 | "* **CYLINDERS** e.g 6\n",
180 | "* **TRANSMISSION** e.g. A6\n",
181 | "* **FUELTYPE** e.g. z\n",
182 | "* **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n",
183 | "* **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n",
184 | "* **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n",
185 | "* **CO2 EMISSIONS (g/km)** e.g. 182 --> low --> 0\n"
186 | ],
187 | "metadata": {
188 | "button": false,
189 | "new_sheet": false,
190 | "run_control": {
191 | "read_only": false
192 | }
193 | }
194 | },
195 | {
196 | "cell_type": "markdown",
197 | "source": [
198 | "Reading the data in
\n"
199 | ],
200 | "metadata": {
201 | "button": false,
202 | "new_sheet": false,
203 | "run_control": {
204 | "read_only": false
205 | }
206 | }
207 | },
208 | {
209 | "cell_type": "code",
210 | "source": "await download(path, \"FuelConsumption.csv\")\npath=\"FuelConsumption.csv\"",
211 | "metadata": {
212 | "trusted": true
213 | },
214 | "execution_count": null,
215 | "outputs": []
216 | },
217 | {
218 | "cell_type": "code",
219 | "source": "df = pd.read_csv(path)\n\n# take a look at the dataset\ndf.head()",
220 | "metadata": {
221 | "button": false,
222 | "new_sheet": false,
223 | "run_control": {
224 | "read_only": false
225 | },
226 | "trusted": true
227 | },
228 | "execution_count": null,
229 | "outputs": []
230 | },
231 | {
232 | "cell_type": "markdown",
233 | "source": [
234 | "Let's select some features that we want to use for regression.\n"
235 | ],
236 | "metadata": {}
237 | },
238 | {
239 | "cell_type": "code",
240 | "source": "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\ncdf.head(9)",
241 | "metadata": {
242 | "button": false,
243 | "new_sheet": false,
244 | "run_control": {
245 | "read_only": false
246 | },
247 | "trusted": true
248 | },
249 | "execution_count": null,
250 | "outputs": []
251 | },
252 | {
253 | "cell_type": "markdown",
254 | "source": [
255 | "Let's plot Emission values with respect to Engine size:\n"
256 | ],
257 | "metadata": {}
258 | },
259 | {
260 | "cell_type": "code",
261 | "source": "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
262 | "metadata": {
263 | "button": false,
264 | "new_sheet": false,
265 | "run_control": {
266 | "read_only": false
267 | },
268 | "scrolled": true,
269 | "trusted": true
270 | },
271 | "execution_count": null,
272 | "outputs": []
273 | },
274 | {
275 | "cell_type": "markdown",
276 | "source": [
277 | "#### Creating train and test dataset\n",
278 | "\n",
279 | "Train/Test Split involves splitting the dataset into training and testing sets respectively, which are mutually exclusive. After which, you train with the training set and test with the testing set.\n",
280 | "This will provide a more accurate evaluation on out-of-sample accuracy because the testing dataset is not part of the dataset that have been used to train the model. Therefore, it gives us a better understanding of how well our model generalizes on new data.\n",
281 | "\n",
282 | "We know the outcome of each data point in the testing dataset, making it great to test with! Since this data has not been used to train the model, the model has no knowledge of the outcome of these data points. So, in essence, it is truly an out-of-sample testing.\n",
283 | "\n",
284 | "Let's split our dataset into train and test sets. Around 80% of the entire dataset will be used for training and 20% for testing. We create a mask to select random rows using the **np.random.rand()** function:\n"
285 | ],
286 | "metadata": {
287 | "button": false,
288 | "new_sheet": false,
289 | "run_control": {
290 | "read_only": false
291 | }
292 | }
293 | },
294 | {
295 | "cell_type": "code",
296 | "source": "msk = np.random.rand(len(df)) < 0.8\ntrain = cdf[msk]\ntest = cdf[~msk]",
297 | "metadata": {
298 | "button": false,
299 | "new_sheet": false,
300 | "run_control": {
301 | "read_only": false
302 | },
303 | "trusted": true
304 | },
305 | "execution_count": null,
306 | "outputs": []
307 | },
308 | {
309 | "cell_type": "markdown",
310 | "source": [
311 | "#### Train data distribution\n"
312 | ],
313 | "metadata": {
314 | "button": false,
315 | "new_sheet": false,
316 | "run_control": {
317 | "read_only": false
318 | }
319 | }
320 | },
321 | {
322 | "cell_type": "code",
323 | "source": "plt.scatter(train.ENGINESIZE, train.CO2EMISSIONS, color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
324 | "metadata": {
325 | "button": false,
326 | "new_sheet": false,
327 | "run_control": {
328 | "read_only": false
329 | },
330 | "trusted": true
331 | },
332 | "execution_count": null,
333 | "outputs": []
334 | },
335 | {
336 | "cell_type": "markdown",
337 | "source": [
338 | "Multiple Regression Model
\n"
339 | ],
340 | "metadata": {
341 | "button": false,
342 | "new_sheet": false,
343 | "run_control": {
344 | "read_only": false
345 | }
346 | }
347 | },
348 | {
349 | "cell_type": "markdown",
350 | "source": [
351 | "In reality, there are multiple variables that impact the co2emission. When more than one independent variable is present, the process is called multiple linear regression. An example of multiple linear regression is predicting co2emission using the features FUELCONSUMPTION_COMB, EngineSize and Cylinders of cars. The good thing here is that multiple linear regression model is the extension of the simple linear regression model.\n"
352 | ],
353 | "metadata": {}
354 | },
355 | {
356 | "cell_type": "code",
357 | "source": "from sklearn import linear_model\nregr = linear_model.LinearRegression()\nx = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\ny = np.asanyarray(train[['CO2EMISSIONS']])\nregr.fit (x, y)\n# The coefficients\nprint ('Coefficients: ', regr.coef_)",
358 | "metadata": {
359 | "button": false,
360 | "new_sheet": false,
361 | "run_control": {
362 | "read_only": false
363 | },
364 | "trusted": true
365 | },
366 | "execution_count": null,
367 | "outputs": []
368 | },
369 | {
370 | "cell_type": "markdown",
371 | "source": [
372 | "As mentioned before, **Coefficient** and **Intercept** are the parameters of the fitted line.\n",
373 | "Given that it is a multiple linear regression model with 3 parameters and that the parameters are the intercept and coefficients of the hyperplane, sklearn can estimate them from our data. Scikit-learn uses plain Ordinary Least Squares method to solve this problem.\n",
374 | "\n",
375 | "#### Ordinary Least Squares (OLS)\n",
376 | "\n",
377 | "OLS is a method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by minimizing the sum of the squares of the differences between the target dependent variable and those predicted by the linear function. In other words, it tries to minimizes the sum of squared errors (SSE) or mean squared error (MSE) between the target variable (y) and our predicted output ($\\hat{y}$) over all samples in the dataset.\n",
378 | "\n",
379 | "OLS can find the best parameters using of the following methods:\n",
380 | "\n",
381 | "* Solving the model parameters analytically using closed-form equations\n",
382 | "* Using an optimization algorithm (Gradient Descent, Stochastic Gradient Descent, Newton’s Method, etc.)\n"
383 | ],
384 | "metadata": {}
385 | },
386 | {
387 | "cell_type": "markdown",
388 | "source": [
389 | "Prediction
\n"
390 | ],
391 | "metadata": {}
392 | },
393 | {
394 | "cell_type": "code",
395 | "source": "y_hat= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\nx = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB']])\ny = np.asanyarray(test[['CO2EMISSIONS']])\nprint(\"Residual sum of squares: %.2f\"\n % np.mean((y_hat - y) ** 2))\n\n# Explained variance score: 1 is perfect prediction\nprint('Variance score: %.2f' % regr.score(x, y))",
396 | "metadata": {
397 | "button": false,
398 | "new_sheet": false,
399 | "run_control": {
400 | "read_only": false
401 | },
402 | "trusted": true
403 | },
404 | "execution_count": null,
405 | "outputs": []
406 | },
407 | {
408 | "cell_type": "markdown",
409 | "source": [
410 | "**Explained variance regression score:**\\\n",
411 | "Let $\\hat{y}$ be the estimated target output, y the corresponding (correct) target output, and Var be the Variance (the square of the standard deviation). Then the explained variance is estimated as follows:\n",
412 | "\n",
413 | "$\\texttt{explainedVariance}(y, \\hat{y}) = 1 - \\frac{Var{ y - \\hat{y}}}{Var{y}}$\\\n",
414 | "The best possible score is 1.0, the lower values are worse.\n"
415 | ],
416 | "metadata": {}
417 | },
418 | {
419 | "cell_type": "markdown",
420 | "source": [
421 | "Practice
\n",
422 | "Try to use a multiple linear regression with the same dataset, but this time use FUELCONSUMPTION_CITY and FUELCONSUMPTION_HWY instead of FUELCONSUMPTION_COMB. Does it result in better accuracy?\n"
423 | ],
424 | "metadata": {}
425 | },
426 | {
427 | "cell_type": "code",
428 | "source": "# write your code here\n\n",
429 | "metadata": {
430 | "trusted": true
431 | },
432 | "execution_count": null,
433 | "outputs": []
434 | },
435 | {
436 | "cell_type": "markdown",
437 | "source": [
438 | "Click here for the solution
\n",
439 | "\n",
440 | "```python\n",
441 | "regr = linear_model.LinearRegression()\n",
442 | "x = np.asanyarray(train[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
443 | "y = np.asanyarray(train[['CO2EMISSIONS']])\n",
444 | "regr.fit (x, y)\n",
445 | "print ('Coefficients: ', regr.coef_)\n",
446 | "y_= regr.predict(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
447 | "x = np.asanyarray(test[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_CITY','FUELCONSUMPTION_HWY']])\n",
448 | "y = np.asanyarray(test[['CO2EMISSIONS']])\n",
449 | "print(\"Residual sum of squares: %.2f\"% np.mean((y_ - y) ** 2))\n",
450 | "print('Variance score: %.2f' % regr.score(x, y))\n",
451 | "\n",
452 | "```\n",
453 | "\n",
454 | "Want to learn more?
\n",
462 | "\n",
463 | "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: SPSS Modeler\n",
464 | "\n",
465 | "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at Watson Studio\n"
466 | ],
467 | "metadata": {
468 | "button": false,
469 | "new_sheet": false,
470 | "run_control": {
471 | "read_only": false
472 | }
473 | }
474 | },
475 | {
476 | "cell_type": "markdown",
477 | "source": [
478 | "### Thank you for completing this lab!\n",
479 | "\n",
480 | "## Author\n",
481 | "\n",
482 | "Saeed Aghabozorgi\n",
483 | "\n",
484 | "### Other Contributors\n",
485 | "\n",
486 | "Joseph Santarcangelo\n",
487 | "\n",
488 | "## Change Log\n",
489 | "\n",
490 | "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
491 | "| ----------------- | ------- | ---------- | ---------------------------------- |\n",
492 | "| 2020-11-03 | 2.1 | Lakshmi | Made changes in URL |\n",
493 | "| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
494 | "| | | | |\n",
495 | "| | | | |\n",
496 | "\n",
497 | "## © IBM Corporation 2020. All rights reserved. \n"
498 | ],
499 | "metadata": {}
500 | }
501 | ]
502 | }
--------------------------------------------------------------------------------
/Regression/Module 2_ML0101EN-Reg-Simple-Linear-Regression-Co2.jupyterlite.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "metadata": {
3 | "kernelspec": {
4 | "display_name": "Pyolite",
5 | "language": "python",
6 | "name": "python"
7 | },
8 | "language_info": {
9 | "codemirror_mode": {
10 | "name": "python",
11 | "version": 3
12 | },
13 | "file_extension": ".py",
14 | "mimetype": "text/x-python",
15 | "name": "python",
16 | "nbconvert_exporter": "python",
17 | "pygments_lexer": "ipython3",
18 | "version": "3.8"
19 | },
20 | "widgets": {
21 | "state": {},
22 | "version": "1.1.2"
23 | }
24 | },
25 | "nbformat_minor": 4,
26 | "nbformat": 4,
27 | "cells": [
28 | {
29 | "cell_type": "markdown",
30 | "source": "
\n \n \n \n
\n\n# Simple Linear Regression\n\nEstimated time needed: **15** minutes\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n* Use scikit-learn to implement simple Linear Regression\n* Create a model, train it, test it and use the model\n",
31 | "metadata": {
32 | "button": false,
33 | "new_sheet": false,
34 | "run_control": {
35 | "read_only": false
36 | }
37 | }
38 | },
39 | {
40 | "cell_type": "markdown",
41 | "source": "### Importing Needed packages\n",
42 | "metadata": {
43 | "button": false,
44 | "new_sheet": false,
45 | "run_control": {
46 | "read_only": false
47 | }
48 | }
49 | },
50 | {
51 | "cell_type": "code",
52 | "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])\n\n",
53 | "metadata": {
54 | "trusted": true
55 | },
56 | "execution_count": null,
57 | "outputs": []
58 | },
59 | {
60 | "cell_type": "code",
61 | "source": "import matplotlib.pyplot as plt\nimport pandas as pd\nimport pylab as pl\nimport numpy as np\n%matplotlib inline",
62 | "metadata": {
63 | "button": false,
64 | "new_sheet": false,
65 | "run_control": {
66 | "read_only": false
67 | },
68 | "trusted": true
69 | },
70 | "execution_count": null,
71 | "outputs": []
72 | },
73 | {
74 | "cell_type": "markdown",
75 | "source": "### Downloading Data\n\nTo download the data, we will use !wget to download it from IBM Object Storage.\n",
76 | "metadata": {
77 | "button": false,
78 | "new_sheet": false,
79 | "run_control": {
80 | "read_only": false
81 | }
82 | }
83 | },
84 | {
85 | "cell_type": "code",
86 | "source": "path= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%202/data/FuelConsumptionCo2.csv\"",
87 | "metadata": {
88 | "button": false,
89 | "new_sheet": false,
90 | "run_control": {
91 | "read_only": false
92 | },
93 | "trusted": true
94 | },
95 | "execution_count": null,
96 | "outputs": []
97 | },
98 | {
99 | "cell_type": "code",
100 | "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n response = await pyfetch(url)\n if response.status == 200:\n with open(filename, \"wb\") as f:\n f.write(await response.bytes())\n\n",
101 | "metadata": {
102 | "trusted": true
103 | },
104 | "execution_count": null,
105 | "outputs": []
106 | },
107 | {
108 | "cell_type": "markdown",
109 | "source": "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n",
110 | "metadata": {}
111 | },
112 | {
113 | "cell_type": "markdown",
114 | "source": "## Understanding the Data\n\n### `FuelConsumption.csv`:\n\nWe have downloaded a fuel consumption dataset, **`FuelConsumption.csv`**, which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada. [Dataset source](http://open.canada.ca/data/en/dataset/98f1a129-f628-4ce4-b24d-6f16bf24dd64?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01)\n\n* **MODELYEAR** e.g. 2014\n* **MAKE** e.g. Acura\n* **MODEL** e.g. ILX\n* **VEHICLE CLASS** e.g. SUV\n* **ENGINE SIZE** e.g. 4.7\n* **CYLINDERS** e.g 6\n* **TRANSMISSION** e.g. A6\n* **FUEL CONSUMPTION in CITY(L/100 km)** e.g. 9.9\n* **FUEL CONSUMPTION in HWY (L/100 km)** e.g. 8.9\n* **FUEL CONSUMPTION COMB (L/100 km)** e.g. 9.2\n* **CO2 EMISSIONS (g/km)** e.g. 182 --> low --> 0\n",
115 | "metadata": {
116 | "button": false,
117 | "new_sheet": false,
118 | "run_control": {
119 | "read_only": false
120 | }
121 | }
122 | },
123 | {
124 | "cell_type": "markdown",
125 | "source": "## Reading the data in\n",
126 | "metadata": {
127 | "button": false,
128 | "new_sheet": false,
129 | "run_control": {
130 | "read_only": false
131 | }
132 | }
133 | },
134 | {
135 | "cell_type": "code",
136 | "source": "",
137 | "metadata": {},
138 | "execution_count": null,
139 | "outputs": []
140 | },
141 | {
142 | "cell_type": "code",
143 | "source": "await download(path, \"FuelConsumption.csv\")\npath=\"FuelConsumption.csv\"",
144 | "metadata": {
145 | "trusted": true
146 | },
147 | "execution_count": null,
148 | "outputs": []
149 | },
150 | {
151 | "cell_type": "code",
152 | "source": "df = pd.read_csv(\"FuelConsumption.csv\")\n\n# take a look at the dataset\ndf.head()\n\n",
153 | "metadata": {
154 | "button": false,
155 | "new_sheet": false,
156 | "run_control": {
157 | "read_only": false
158 | },
159 | "trusted": true
160 | },
161 | "execution_count": null,
162 | "outputs": []
163 | },
164 | {
165 | "cell_type": "markdown",
166 | "source": "### Data Exploration\n\nLet's first have a descriptive exploration on our data.\n",
167 | "metadata": {
168 | "button": false,
169 | "new_sheet": false,
170 | "run_control": {
171 | "read_only": false
172 | }
173 | }
174 | },
175 | {
176 | "cell_type": "code",
177 | "source": "# summarize the data\ndf.describe()",
178 | "metadata": {
179 | "button": false,
180 | "new_sheet": false,
181 | "run_control": {
182 | "read_only": false
183 | },
184 | "trusted": true
185 | },
186 | "execution_count": null,
187 | "outputs": []
188 | },
189 | {
190 | "cell_type": "markdown",
191 | "source": "Let's select some features to explore more.\n",
192 | "metadata": {}
193 | },
194 | {
195 | "cell_type": "code",
196 | "source": "cdf = df[['ENGINESIZE','CYLINDERS','FUELCONSUMPTION_COMB','CO2EMISSIONS']]\ncdf.head(9)",
197 | "metadata": {
198 | "button": false,
199 | "new_sheet": false,
200 | "run_control": {
201 | "read_only": false
202 | },
203 | "trusted": true
204 | },
205 | "execution_count": null,
206 | "outputs": []
207 | },
208 | {
209 | "cell_type": "markdown",
210 | "source": "We can plot each of these features:\n",
211 | "metadata": {}
212 | },
213 | {
214 | "cell_type": "code",
215 | "source": "viz = cdf[['CYLINDERS','ENGINESIZE','CO2EMISSIONS','FUELCONSUMPTION_COMB']]\nviz.hist()\nplt.show()",
216 | "metadata": {
217 | "button": false,
218 | "new_sheet": false,
219 | "run_control": {
220 | "read_only": false
221 | },
222 | "trusted": true
223 | },
224 | "execution_count": null,
225 | "outputs": []
226 | },
227 | {
228 | "cell_type": "markdown",
229 | "source": "Now, let's plot each of these features against the Emission, to see how linear their relationship is:\n",
230 | "metadata": {}
231 | },
232 | {
233 | "cell_type": "code",
234 | "source": "plt.scatter(cdf.FUELCONSUMPTION_COMB, cdf.CO2EMISSIONS, color='blue')\nplt.xlabel(\"FUELCONSUMPTION_COMB\")\nplt.ylabel(\"Emission\")\nplt.show()",
235 | "metadata": {
236 | "button": false,
237 | "new_sheet": false,
238 | "run_control": {
239 | "read_only": false
240 | },
241 | "trusted": true
242 | },
243 | "execution_count": null,
244 | "outputs": []
245 | },
246 | {
247 | "cell_type": "code",
248 | "source": "plt.scatter(cdf.ENGINESIZE, cdf.CO2EMISSIONS, color='blue')\nplt.xlabel(\"Engine size\")\nplt.ylabel(\"Emission\")\nplt.show()",
249 | "metadata": {
250 | "button": false,
251 | "new_sheet": false,
252 | "run_control": {
253 | "read_only": false
254 | },
255 | "scrolled": true,
256 | "trusted": true
257 | },
258 | "execution_count": null,
259 | "outputs": []
260 | },
261 | {
262 | "cell_type": "markdown",
263 | "source": "## Practice\n\nPlot **CYLINDER** vs the Emission, to see how linear is their relationship is:\n",
264 | "metadata": {}
265 | },
266 | {
267 | "cell_type": "code",
268 | "source": "# write your code here\n\n\n",
269 | "metadata": {
270 | "button": false,
271 | "new_sheet": false,
272 | "run_control": {
273 | "read_only": false
274 | },
275 | "trusted": true
276 | },
277 | "execution_count": null,
278 | "outputs": []
279 | },
280 | {
281 | "cell_type": "markdown",
282 | "source": "Click here for the solution
\n\n```python\nplt.scatter(cdf.CYLINDERS, cdf.CO2EMISSIONS, color='blue')\nplt.xlabel(\"Cylinders\")\nplt.ylabel(\"Emission\")\nplt.show()\n\n```\n\nClick here for the solution
\n\n```python\ntrain_x = train[[\"FUELCONSUMPTION_COMB\"]]\n\ntest_x = test[[\"FUELCONSUMPTION_COMB\"]]\n\n```\n\nClick here for the solution
\n\n```python\nregr = linear_model.LinearRegression()\n\nregr.fit(train_x, train_y)\n\n```\n\nClick here for the solution
\n\n```python\npredictions = regr.predict(test_x)\n\n```\n\nClick here for the solution
\n\n```python\nprint(\"Mean Absolute Error: %.2f\" % np.mean(np.absolute(predictions - test_y)))\n\n```\n\nWant to learn more?
\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: SPSS Modeler\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at Watson Studio\n",
517 | "metadata": {
518 | "button": false,
519 | "new_sheet": false,
520 | "run_control": {
521 | "read_only": false
522 | }
523 | }
524 | },
525 | {
526 | "cell_type": "markdown",
527 | "source": "### Thank you for completing this lab!\n\n## Author\n\nSaeed Aghabozorgi\n\n### Other Contributors\n\nJoseph Santarcangelo\n\nAzim Hirjani\n\n## Change Log\n\n| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n| ----------------- | ------- | ------------- | ---------------------------------- |\n| 2020-11-03 | 2.1 | Lakshmi Holla | Changed URL of the csv |\n| 2020-08-27 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n| | | | |\n| | | | |\n\n## © IBM Corporation 2020. All rights reserved. \n",
528 | "metadata": {}
529 | }
530 | ]
531 | }
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