#PythonPractice

[businessadd]

Python Code Practice for Beginners and Beyond

Consistent Python code practice is the best way to improve your skills. Start with simple exercises like string manipulation, loops, and basic math operations. As you progress, try working with more complex concepts like dictionaries, file handling, or algorithms. Use platforms like LeetCode and Codecademy to practice and build confidence.

[winstonmhangoblog]

Converting a Python console calculator to flask web application. This is the third part of our part flask web development series.In this part,we create our calculator functions in the flask view and connect to the web templates form and display the functionality. I have used tailwindcss for creating the templates.Tailwind css makes it easier and have written no single css but it's a smart web page. This part brings the whole web development circle of frontend and back end to one place. Enjoy the reading. #flask #python #tailwindcss #frontenddevelopment #backenddevelooment #flaskwebapps #pythonflaskcalculator #taiwindcssdesign #pythonprojects #pythonpractice #pythonprogramming (at Lilongwe, Malawi) https://www.instagram.com/p/CI3Y4fCh7dN/?igshid=qwnejietgxmi

[pyadda]

🔰Write the Python script for above task and post it in below comment section. ➖➖➖➖➖➖➖➖➖➖➖ ✳️ Follow us ✳️ @pyadda @pyadda @pyadda 🔰Turn on post notification. . . . . . . #programmer #programmerlife #pythonpractice #pythonprogrammers #programmerslife #programming #programminglife #computer #pythonprojects #python3 #pythoncode #datascience #algorithms #machinelearning #artificialintelligence #pythonprogramming #pythonprogrammer #pythondeveloper #ai #ml #lockdown #wfm #internship #covid19 (at Mumbai - City of Dreams) https://www.instagram.com/p/CAsq2qxARmp/?igshid=1a8skfogjvs3f

[vishisegal]

Predicting whether a person is a Regular Smoker or not using the best Decision Tree from the generated Random Forest

As a part of Assignment for Course: Machine Learning Tools

Steps Involved:

Get the Research Data

Identify the Explanatory Variables: Both Categorical and Quantitative

Load the Dataset

Clean the dataset (remove NAs)

Split dataset in 60:40 ratio, 60% for training model and 40% for testing model

Check Accuracy of the Tree with a defined node.

Check whether other trees are required or not?

Define number of Random Trees in Forest = 25

Plot accuracy of each of them

If the accuracy of other trees is similar to the one we got initially, other trees are not really helpful.

Results Explanation:

2745 records were used for training the model

1830 records were used for testing the model

Importance of Explanatory Variables as per the Prediction Model:

Working Code:

import matplotlib.pylab as plt import numpy as np import pandas as pd import sklearn.metrics # Feature Importance from sklearn.ensemble import ExtraTreesClassifier from sklearn.model_selection import train_test_split # Load the dataset AH_data = pd.read_csv('C:\\Users\\M1049673\\Documents\\PythonPractice\\Coursera\\ML\\Datatree_addhealth.csv') AH_data.replace('?', np.nan, inplace=True) data_clean = AH_data.dropna() print('Datatype Information:\n',data_clean.dtypes) print('\nBasic Data related information:\n',data_clean.describe()) # Split into training and testing sets predictors = data_clean[['BIO_SEX','HISPANIC','WHITE','BLACK','NAMERICAN','ASIAN','age', 'ALCEVR1','ALCPROBS1','marever1','cocever1','inhever1','cigavail','DEP1','ESTEEM1','VIOL1', 'PASSIST','DEVIANT1','SCHCONN1','GPA1','EXPEL1','FAMCONCT','PARACTV','PARPRES']] targets = data_clean.TREG1 pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4) print('\nShape of Prediction Train:',pred_train.shape) print('Shape of Prediction Test:',pred_test.shape) print('Shape of Target Train:',tar_train.shape) print('Shape of Target Test:',tar_test.shape) # Build model on training data from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(n_estimators=25) classifier = classifier.fit(pred_train, tar_train) predictions = classifier.predict(pred_test) print('\nConfusion Matrix:\n',sklearn.metrics.confusion_matrix(tar_test, predictions)) print('Accuracy Score:',sklearn.metrics.accuracy_score(tar_test, predictions)) # fit an Extra Trees model to the data model = ExtraTreesClassifier() model.fit(pred_train, tar_train) # display the relative importance of each attribute print('\nFeatures Importance:',model.feature_importances_) """ Running a different number of trees and see the effect of that on the accuracy of the prediction """ trees = range(25) accuracy = np.zeros(25) for idx in range(len(trees)):    classifier = RandomForestClassifier(n_estimators=idx + 1)    classifier = classifier.fit(pred_train, tar_train)    predictions = classifier.predict(pred_test)    accuracy[idx] = sklearn.metrics.accuracy_score(tar_test, predictions) plt.cla() plt.plot(trees, accuracy) plt.show()

Output:

Datatype Information: BIO_SEX      float64 HISPANIC     float64 WHITE        float64 BLACK        float64 NAMERICAN    float64 ASIAN        float64 age          float64 TREG1        float64 ALCEVR1      float64 ALCPROBS1      int64 marever1       int64 cocever1       int64 inhever1       int64 cigavail     float64 DEP1         float64 ESTEEM1      float64 VIOL1        float64 PASSIST        int64 DEVIANT1     float64 SCHCONN1     float64 GPA1         float64 EXPEL1       float64 FAMCONCT     float64 PARACTV      float64 PARPRES      float64 dtype: object

Basic Data related information:            BIO_SEX     HISPANIC  ...      PARACTV      PARPRES count  4575.000000  4575.000000  ...  4575.000000  4575.000000 mean      1.521093     0.111038  ...     6.290710    13.398033 std       0.499609     0.314214  ...     3.360219     2.085837 min       1.000000     0.000000  ...     0.000000     3.000000 25%       1.000000     0.000000  ...     4.000000    12.000000 50%       2.000000     0.000000  ...     6.000000    14.000000 75%       2.000000     0.000000  ...     9.000000    15.000000 max       2.000000     1.000000  ...    18.000000    15.000000

[8 rows x 25 columns]

Shape of Prediction Train: (2745, 24) Shape of Prediction Test: (1830, 24) Shape of Target Train: (2745,) Shape of Target Test: (1830,)

Confusion Matrix: [[1441   78] [ 190  121]] Accuracy Score: 0.853551912568306

Features Importance: [0.02578917 0.0154114  0.02351627 0.01829161 0.00837805 0.00488314 0.06128191 0.05400669 0.04710931 0.13473079 0.01644478 0.01515276 0.02609708 0.05890997 0.05520846 0.05011535 0.0173519  0.06768484 0.05846291 0.07203345 0.00908774 0.05818886 0.05527677 0.04658681]

[pyadda]

🔰Write the Python script for above task and post it in below comment section. ➖➖➖➖➖➖➖➖➖➖➖ ✳️ Follow us ✳️ @pyadda @pyadda @pyadda 🔰Turn on post notification. . . . . . . #programmer #programmerlife #pythonpractice #pythonprogrammers #programmerslife #programming #programminglife #computer #pythonprojects #python3 #pythoncode #datascience #algorithms #machinelearning #artificialintelligence #pythonprogramming #pythonprogrammer #pythondeveloper #ai #ml #lockdown #wfm #internship #covid19 (at Bangalore, India) https://www.instagram.com/p/CAqDMJhApVP/?igshid=1h802tsz6vnpq