Kalim Amzad KalimAmzad

## roc_aoc.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve, auc
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression

# Generate a synthetic dataset for classification
X, y = make_classification(n_samples=1000, n_classes=2, n_features=20, random_state=42)

## knn.py
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from collections import Counter

def euclidean_distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2)**2))

def manhattan_distance(x1, x2):
    return np.sum(np.abs(x1 - x2))

## Dimensionality Reduction.ipynb

      
        
          
            
              
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                KalimAmzad
                / Dimensionality Reduction.ipynb
            
            
              Last active
              January 13, 2023 05:43
            
              
                Dimensionality Reduction
              
          
        
      
        
  
    
    

          
    
      
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	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.model_selection import train_test_split
	from sklearn.metrics import roc_curve, auc
	from sklearn.datasets import make_classification
	from sklearn.linear_model import LogisticRegression

	# Generate a synthetic dataset for classification
	X, y = make_classification(n_samples=1000, n_classes=2, n_features=20, random_state=42)
	import numpy as np
	import pandas as pd
	from sklearn.model_selection import KFold
	from collections import Counter

	def euclidean_distance(x1, x2):
	return np.sqrt(np.sum((x1 - x2)**2))

	def manhattan_distance(x1, x2):
	return np.sum(np.abs(x1 - x2))