ML basics & experimentations, using Python and plotly.

# ColinLeverger/machine-learning-python

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# Machine Learning with Python

Machine learning project, use of Python, Plotly and Scikit-learn library; we learned how to build, use and benchmark machine learning algorithms/mathematical models.

# Quick overview

## Goal of this project

The goal of this project was to have an overview of machine learning.

We had to build & test algorithms, going from DQR to complex Data Analytic. Because we are only computer scientist yet (and not statistician), we played a lot with functions and benchmarked algorithms, by using our feelings. That is why some of the algorithms used seems to be inappropriate considering the given datasets (Linear Lasso Regression for instance). But we understood better after coding the mathematical solutions and implications behind them.

## Arborecence of the project

### Data Quality Report (first dataset)

• "./data": data used to generate the DQR,
• "./outputs": outputs of algorithms:
• plotly HTML files with bars/graphs/hists...
• DQR in .csv format.
• "./screenshots": two screenshots of the plotly HTML graphs generated,
• "./DQR-V1.py" and "./DQR-V2.py": code used to generate DQR, two versions:
• V1: with standard scikit library,
• V2: coded from scratch!

For this first part, I worked with Valerian SALIOU (founder at https://crisp.im).

### Modelling (second dataset)

• "./code": functions and code used in "./main.py"; generate models, tests, benchmarks, clean data...
• "./data": datasets used,
• "./dqr_outputs": outputs and graphs for DQR,
• "./main.py": code that will orchestrate the benchmarks of different algorithms.

For this second part, I worked with Anais GALISSON.

# Focus on the project & methods used

## First part: generate the Data Quality Report

The DQR-generation step is an important part of the Data analytic process; with a clean report, it is possible to have insights about the data, to detect problems (missing values, strange values, outliers, etc.) and to make decision about these problems.

For this first assignment, we worked with a first dataset; the goal was to predict the salary of a person, considering his age, the job of his parents, his level of education, etc.

### DQR for Categorical Features

DQR for Categorical Features

Feature Count % Miss. Card. Mode Mode Count Mode % 2nd Mode 2nd Mode Count 2nd Mode %
0 workclass 29205 5.607627666451198 8 Private 21576 73.87776065742167 Self-emp-not-inc 2406 8.238315356959426
1 education 30940 0.0 16 HS-grad 9976 32.24305106658048 Some-college 6938 22.424046541693603
2 marital-status 30940 0.0 7 Married-civ-spouse 14201 45.898513251454425 Never-married 10167 32.86037491919845
3 occupation 29198 5.630252100840338 14 Prof-specialty 3932 13.466675799712311 Craft-repair 3887 13.312555654496883
4 relationship 30940 0.0 6 Husband 12496 40.387847446670975 Not-in-family 7904 25.546218487394956
5 race 30940 0.0 5 White 26442 85.46218487394958 Black 2965 9.583063994828702
6 sex 30940 0.0 2 Male 20705 66.91984486102133 Female 10235 33.08015513897867
7 native-country 30386 1.7905623787976777 41 United-States 27719 91.22293161324293 Mexico 607 1.9976304877246098
8 target 30940 0.0 2 <=50K 23506 75.97285067873302 >50K 7434 24.02714932126697

### DQR for Continuous Features

DQR for Continuous Features

Count % Miss. Card. Min 1st Qrt. Mean Median 3rd Qrt. Max Std. Dev.
age 30940.0 0.0 72 17.0 28.0 38.56076276664512 37.0 48.0 90.0 13.639403068811818
fnlwgt 30940.0 0.0 20880 12285.0 117849.0 189786.4014221073 178384.0 237318.0 1484705.0 105406.39438610482
education-num 30940.0 0.0 16 1.0 9.0 10.08125404007757 10.0 12.0 16.0 2.5699668370230184
capital-gain 30940.0 0.0 119 0.0 0.0 1081.8129928894634 0.0 0.0 99999.0 7443.773041287165
capital-loss 30940.0 0.0 91 0.0 0.0 86.56997414350356 0.0 0.0 4356.0 401.7060231857947
hours-per-week 30940.0 0.0 93 1.0 40.0 40.408920491273435 40.0 45.0 99.0 12.336944975043277

### Note

For this first task we didn’t build any models; the goal was just to create & manipulate a DQR.

## Second part: modelling & predictions

For this second part, we had to work on the models themselves; we had to choose three models and we had to test them with the data, to choose the most appropriate (and try to explain why it was the most appropriate).

### The steps followed for this part were:

1. First, use of the DQR code of first part to generate the DQR for the given dataset, and take decisions about problems detected in the set. In our case, the dataset was totally clean (no misses, no outliers, ... perfect!) and it was OK to use it as it was.
2. Implement models,
3. Analyse confusion matrixes and accuracy results to choose the best algorithm,
4. Conclude: why this model was better than this other one?

We worked with a second dataset, which concerned the alcohol and the grades in Highschool. The goal was to predict the grade of a student, considering his weekly alcohol consumption.

### Confusion matrixes

To observe the accuracy of an execution, we have used confusion matrixes (see below)

### Code output example

``````###########################
### Model Decision Tree ###
###########################
For criterion entropy the scores are [ 0.57575758  0.55384615  0.609375    0.67213115  0.68333333]
For criterion gini the scores are [ 0.54545455  0.69230769  0.609375    0.68852459  0.66666667]
The best hyper parameter in this case for the Model Decision Tree was gini

# Accuracy #
Accuracy = 0.582278481013

# Confusion Matrix #

###########################
### Model Random Forest ###
###########################
For criterion entropy the scores are [ 0.68181818  0.63076923  0.671875    0.67213115  0.68333333]
For criterion gini the scores are [ 0.72727273  0.67692308  0.640625    0.70491803  0.66666667]
The best hyper parameter in this case for the Model Random Forest was gini

# Accuracy #
Accuracy = 0.670886075949

# Confusion Matrix #

###############################
### Model Nearest Neighbors ###
###############################
For n 1 the scores are [ 0.57575758  0.56923077  0.5625      0.57377049  0.53333333]
For n 2 the scores are [ 0.48484848  0.53846154  0.5625      0.59016393  0.63333333]
...
For n 5 the scores are [ 0.60606061  0.53846154  0.640625    0.59016393  0.6       ]
For n 6 the scores are [ 0.65151515  0.56923077  0.625       0.60655738  0.56666667]
For n 7 the scores are [ 0.66666667  0.6         0.640625    0.60655738  0.56666667]
...
For n 46 the scores are [ 0.59090909  0.50769231  0.640625    0.57377049  0.66666667]
For n 47 the scores are [ 0.57575758  0.52307692  0.625       0.59016393  0.65      ]
For n 48 the scores are [ 0.57575758  0.47692308  0.578125    0.59016393  0.66666667]
For n 49 the scores are [ 0.57575758  0.47692308  0.578125    0.59016393  0.66666667]
The best number of neighbors for this dataset is 15

# Accuracy #
Accuracy = 0.632911392405

# Confusion Matrix #

###############################
### Model Linear Regression ###
###############################
Coefficients for LinearRegression:
[ -1.12056428e-01   8.60336080e-03   9.44102565e-02  -8.56978851e-02
-5.61255131e-02   1.58404580e-03   1.35585020e-02  -3.55624437e-02
-1.31684645e-03   1.27718032e-01   2.47913987e-01  -9.13592399e-03
-3.95554345e+10  -3.95554345e+10  -3.95554345e+10  -3.95554345e+10
-3.95554345e+10   6.00245012e-02  -9.01743471e+09  -9.01743471e+09
-9.01743471e+09  -9.01743471e+09  -9.01743471e+09  -7.36754287e+10
-7.36754287e+10  -2.68648177e+11  -2.68648177e+11   1.16301526e+11
1.16301526e+11   4.46974197e+10   4.46974197e+10   2.03854421e+11
2.03854421e+11   5.02692427e+11   5.02692427e+11   5.02692427e+11
7.89833795e+10   7.89833795e+10  -9.51988693e+09  -9.51988693e+09
1.73048537e+10   1.73048537e+10  -8.01148755e+08  -8.01148755e+08
-4.34514687e+10  -4.34514687e+10  -4.34514687e+10  -4.34514687e+10
2.87836634e+11   2.87836634e+11  -3.19201118e+11  -3.19201118e+11
-2.23312010e+11  -2.23312010e+11   1.83929953e+10   1.83929953e+10
1.76526496e-01   8.54051430e-02]
Residual sum of squares for LinearRegression: 0.80
Coefficients for Lasso:
[-0.         -0.          0.         -0.         -0.         -0.         -0.
-0.         -0.          0.05923985  0.24852654  0.         -0.          0.
-0.          0.         -0.          0.         -0.          0.         -0.
-0.         -0.         -0.          0.         -0.          0.         -0.
0.          0.         -0.          0.         -0.         -0.          0.
-0.         -0.          0.         -0.          0.          0.         -0.
0.         -0.         -0.         -0.         -0.          0.          0.
-0.          0.         -0.          0.         -0.         -0.          0.
0.         -0.        ]
Residual sum of squares for Lasso: 1.02

Variance score for LinearRegression: 0.71
R2 score for LinearRegression: 0.76
Variance score for Lasso: 0.64
R2 score for Lasso: 0.80

Process finished with exit code 0
``````

# Going further

If you are interested in my work, please read the reports we wrote about it, which are available in the repo: DQR report, Modelling report.

ML basics & experimentations, using Python and plotly.

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