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1 | | -## A Comprehensive Machine Learning Workflow with Python |
2 | | -There are plenty of courses and tutorials that can help you learn machine learning from scratch but here in Kaggle, I want to solve a simple machine learning problem as a comprehensive workflow with python packages.Then |
| 1 | +## <div style="text-align: center">A Comprehensive Machine Learning Workflow with Python </div> |
3 | 2 |
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4 | | -After reading, you can use this workflow to solve other real problems and use it as a template to deal with machine learning problems. |
| 3 | +<div style="text-align: center">There are plenty of <b>courses and tutorials</b> that can help you learn machine learning from scratch but here in <b>Kaggle</b>, I want to predict <b>House prices</b>(in the next version) a popular machine learning Dataset as a comprehensive workflow with python packages. |
| 4 | +After reading, you can use this workflow to solve other real problems and use it as a template to deal with <b>machine learning</b> problems.</div> |
| 5 | +<div style="text-align:center">last update: <b>10/15/2018</b></div> |
5 | 6 |
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6 | 7 | you can follow me on: |
7 | 8 | > ###### [ GitHub](https://github.com/mjbahmani) |
8 | 9 | > ###### [LinkedIn](https://www.linkedin.com/in/bahmani/) |
9 | 10 | > ###### [Kaggle](https://www.kaggle.com/mjbahmani/) |
10 | 11 | ## Notebook Content |
11 | | -* 1- Introduction |
12 | | -* 2- Machine learning workflow |
13 | | -* 3- Problem Definition |
14 | | -* 3-1 Problem feature |
15 | | -* 3-2 Aim |
16 | | -* 3-3 Variables |
17 | | -* 4- Inputs & Outputs |
18 | | -* 4-1 Inputs |
19 | | -* 4-2 Outputs |
20 | | -* 5- Installation |
21 | | -* 5-1 jupyter notebook |
22 | | -* 5-2 kaggle kernel |
23 | | -* 5-3 Colab notebook |
24 | | -* 5-4 install python & packages |
25 | | -* 5-5 Loading Packages |
26 | | -* 6- Exploratory data analysis |
27 | | -* 6-1 Data Collection |
28 | | -* 6-2 Visualization |
29 | | -* 6-2-1 Scatter plot |
30 | | -* 6-2-2 Box |
31 | | -* 6-2-3 Histogram |
32 | | -* 6-2-4 Multivariate Plots |
33 | | -* 6-2-5 Violinplots |
34 | | -* 6-2-6 Pair plot |
35 | | -* 6-2-7 Kde plot |
36 | | -* 6-2-8 Joint plot |
37 | | -* 6-2-9 Andrews curves |
38 | | -* 6-2-10 Heatmap |
39 | | -* 6-2-11 Radviz |
40 | | -* 6-3 Data Preprocessing |
41 | | -* 6-4 Data Cleaning |
42 | | -* 7- Model Deployment |
43 | | -* 7-1 KNN |
44 | | -* 7-2 Radius Neighbors Classifier |
45 | | -* 7-3 Logistic Regression |
46 | | -* 7-4 Passive Aggressive Classifier |
47 | | -* 7-5 Naive Bayes |
48 | | -* 7-6 MultinomialNB |
49 | | -* 7-7 BernoulliNB |
50 | | -* 7-8 SVM |
51 | | -* 7-9 Nu-Support Vector Classification |
52 | | -* 7-10Linear Support Vector Classification |
53 | | -* 7-11 Decision Tree |
54 | | -* 7-12 ExtraTreeClassifier |
55 | | -* 7-13 Neural network |
56 | | -* 7-14 RandomForest |
57 | | -* 7-15 Bagging classifier |
58 | | -* 7-16 AdaBoost classifier |
59 | | -* 7-17 Gradient Boosting Classifier |
60 | | -* 7-18 Linear Discriminant Analysis |
61 | | -* 7-19 Quadratic Discriminant Analysis |
62 | | -* 7-20 Kmeans |
63 | | -* 8- Conclusion |
64 | | -* 9- References |
| 12 | +* 1- [Introduction](#1) |
| 13 | +* 2- [Machine learning workflow](#2) |
| 14 | +* 2-1 [Real world Application Vs Competitions](#2) |
65 | 15 |
|
66 | | -# Introduction |
67 | | -This is a comprehensive ML techniques for IRIS data set, that I have spent for more than two months to complete it. |
| 16 | +* 3- [Problem Definition](#3) |
| 17 | +* 3-1 [Problem feature](#4) |
| 18 | +* 3-2 [Aim](#5) |
| 19 | +* 3-3 [Variables](#6) |
| 20 | +* 4-[ Inputs & Outputs](#7) |
| 21 | +* 4-1 [Inputs ](#8) |
| 22 | +* 4-2 [Outputs](#9) |
| 23 | +* 5- [Installation](#10) |
| 24 | +* 5-1 [ jupyter notebook](#11) |
| 25 | +* 5-2[ kaggle kernel](#12) |
| 26 | +* 5-3 [Colab notebook](#13) |
| 27 | +* 5-4 [install python & packages](#14) |
| 28 | +* 5-5 [Loading Packages](#15) |
| 29 | +* 6- [Exploratory data analysis](#16) |
| 30 | +* 6-1 [Data Collection](#17) |
| 31 | +* 6-2 [Visualization](#18) |
| 32 | +* 6-2-1 [Scatter plot](#19) |
| 33 | +* 6-2-2 [Box](#20) |
| 34 | +* 6-2-3 [Histogram](#21) |
| 35 | +* 6-2-4 [Multivariate Plots](#22) |
| 36 | +* 6-2-5 [Violinplots](#23) |
| 37 | +* 6-2-6 [Pair plot](#24) |
| 38 | +* 6-2-7 [Kde plot](#25) |
| 39 | +* 6-2-8 [Joint plot](#26) |
| 40 | +* 6-2-9 [Andrews curves](#27) |
| 41 | +* 6-2-10 [Heatmap](#28) |
| 42 | +* 6-2-11 [Radviz](#29) |
| 43 | +* 6-3 [Data Preprocessing](#30) |
| 44 | +* 6-4 [Data Cleaning](#31) |
| 45 | +* 7- [Model Deployment](#32) |
| 46 | +* 7-1[ KNN](#33) |
| 47 | +* 7-2 [Radius Neighbors Classifier](#34) |
| 48 | +* 7-3 [Logistic Regression](#35) |
| 49 | +* 7-4 [Passive Aggressive Classifier](#36) |
| 50 | +* 7-5 [Naive Bayes](#37) |
| 51 | +* 7-6 [MultinomialNB](#38) |
| 52 | +* 7-7 [BernoulliNB](#39) |
| 53 | +* 7-8 [SVM](#40) |
| 54 | +* 7-9 [Nu-Support Vector Classification](#41) |
| 55 | +* 7-10 [Linear Support Vector Classification](#42) |
| 56 | +* 7-11 [Decision Tree](#43) |
| 57 | +* 7-12 [ExtraTreeClassifier](#44) |
| 58 | +* 7-13 [Neural network](#45) |
| 59 | +* 7-13-1 [What is a Perceptron?](#45) |
| 60 | +* 7-14 [RandomForest](#46) |
| 61 | +* 7-15 [Bagging classifier ](#47) |
| 62 | +* 7-16 [AdaBoost classifier](#48) |
| 63 | +* 7-17 [Gradient Boosting Classifier](#49) |
| 64 | +* 7-18 [Linear Discriminant Analysis](#50) |
| 65 | +* 7-19 [Quadratic Discriminant Analysis](#51) |
| 66 | +* 7-20 [Kmeans](#52) |
| 67 | +* 7-21 [Backpropagation](#53) |
| 68 | +* 9- [Conclusion](#54) |
| 69 | +* 10- [References](#55) |
68 | 70 |
|
69 | | -it is clear that everyone in this community is familiar with IRIS dataset but if you need to review your information about the dataset please visit this link. |
| 71 | + <a id="1"></a> <br> |
| 72 | +## 1- Introduction |
| 73 | +This is a **comprehensive ML techniques with python** , that I have spent for more than two months to complete it. |
70 | 74 |
|
71 | | -I have tried to help beginners in Kaggle how to face machine learning problems. and I think it is a great opportunity for who want to learn machine learning workflow with python completely. I have covered most of the methods that are implemented for iris until 2018, you can start to learn and review your knowledge about ML with a simple dataset and try to learn and memorize the workflow for your journey in Data science world. |
| 75 | +it is clear that everyone in this community is familiar with IRIS dataset but if you need to review your information about the dataset please visit this [link](https://archive.ics.uci.edu/ml/datasets/iris). |
72 | 76 |
|
73 | | -I am open to getting your feedback for improving this kernel |
| 77 | +I have tried to help **beginners** in Kaggle how to face machine learning problems. and I think it is a great opportunity for who want to learn machine learning workflow with python completely. |
| 78 | +I have covered most of the methods that are implemented for iris until **2018**, you can start to learn and review your knowledge about ML with a simple dataset and try to learn and memorize the workflow for your journey in Data science world. |
| 79 | + |
| 80 | +I am open to getting your feedback for improving this **kernel** |
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