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Sunday, March 6, 2022
Weka classification experiment on Iris dataset
Tags: Technology,Machine Learning,Classification,1: Weka Explorer: Preprocess Tab: Iris dataset
2: Weka Experiment Environment: New experiment
3: Weka Experiment Environment: Selecting Naive Bayes' Classifier
4: Weka Experiment Environment: Selecting kNN for comparison with Naive Bayes' Classifier
5: Weka Experiment Environment: Go to 'Run' tab and click on 'start'
6: Weka Experiment Environment: Go to 'Analyze' tab and compare three algorithms
7: Weka Experiment Environment: Analyze tab: Perform test
Thursday, March 3, 2022
Saving Model, Loading Model and Making Predictions for Linear Regression (in Weka)
1: Weka Explorer. Preprocess Tab.
2: Weka Explorer. Classify Tab.
3: Weka Explorer. Visualize Tab
4: Weka Experiment. Setup Tab. Advanced Configuration.
5: Weka Experiment Environment. Analyze Tab
6: Weka Experiment Environment. Comparing ZeroR with Linear Regression.
7: Saving and Loading models from SimpleCLI (documentation)
8: Use of TAB key in Weka SimpleCLI (Doc)
9: Use of Tab key in Weka SimpleCLI (Demo)
10: Training and Saving Linear Regression model from SimpleCLI.
Weka by default, picks up the last column as the target. So, Weka on reading from our file considered 'Day Count' as dependent variable and 'Close Price' as independent variable for 'COALINDIA' ticker data.
11: Dataset Corrected For Column Ordering
12: Training and Saving Linear Regression model after Correction in Dataset
13: Error during prediction for having only one col instead of two
14: Correction in test.csv
15: Error during prediction (string is not numeric)
16: Load Previously Saved Model in The Weka Explorer: Classify Tab
17: Select test data and select output predictions format
18: Select our previously saved model
19: View our saved model (Linear Regression) configuration
20: Re-evaluate model on current test set
21: View Classifier Output with saved model and extrapolated test data
Tags: Technology,Machine Learning,FOSSPandas DataFrame Filtering Using eval()
import pandas as pd
df = pd.DataFrame({
"col1": [1, 2, 3, 1, 2, 3],
"col2": ["A", "B", "A", "B", "A", "B"]
})
c = r"(df['col1'] == 1) | (df['col2'] == 'A')"
df[eval(c)]
df[df['col1'] == 1]
d = "df['col1'] == 1"
df[eval(d)]
Tags: Technology,Machine Learning,Data Visualization,
Wednesday, March 2, 2022
Demo of Linear Regression on Boston Housing Data Using Weka
Tags: Technology,FOSS,Machine Learning,1 - Boston Housing ARFF file
2 - Weka Home Screen
3 - Weka Explorer
4 - Weka Explorer - Preprocess Tab - Boston Relation - MEDV Attribute
5 - Weka Explorer - Classify Tab
6 - Weka Explorer - Classify Tab - Linear Reg Equation
7 - Weka Explorer - Classify Tab - Select Linear Regression
8 - Weka Experiment Environment
9 - Weka Experiment Environment - Add New Algorithm
10 - Weka Experiment Environment - Add New Algo - Linear Regression
11 - Save Weka Experiment
12 - Weka SimpleCLI
13 - Weka KnowledgeFlow Environment
14 - Weka JSON KnowledgeFlow Configuration
15 - Weka Workbench - Classify Tab - Linear Regression
Monday, February 28, 2022
Twinkle, twinkle, litte star! (Full Version)
Twinkle, twinkle, little star! How I wonder what you are! Up above the world so high, Like a diamond in the sky. When the blazing sun is gone, When he nothing shines upon, Then you show your little light, Twinkle, twinkle, all the night. Then the traveller in the dark, Thanks you for your tiny spark, He could not see which way to go, If you did not twinkle so. In the dark blue sky you keep, And often through my curtains peep, For you never shut your eye, Till the sun is in the sky. It's your bright and tiny spark, Lights the traveller in the dark, Though I know not what you are, Twinkle, twinkle, little star.Tags: Nursery Rhymes,English Lessons,
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